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Annaratone and R. Ruhl. “Balancing interprocessor communication and computation on torus-connected multicomputers running compiler-parallelized code,” in Proceedings SHPCC 92, pp. 358-365, March 1992.","https:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs10440-022-00541-7",{"doi":514},"10.1007\u002Fs10440-022-00541-7",{"id":510,"text":516,"url":512,"identifiers":517},"V. Balasunderam, G. Fox, K. Kennedy, and U. Kremer. “A static performance estimator to guide data partitioning decisions.” SIGPLAN Notices, vol. 27(7) pp. 212-233, July 1991.",{"doi":514},{"id":510,"text":519,"url":512,"identifiers":520},"K. Chanchio and X. H. Sun. “Mppvm: A software system for non-dedicated heterogeneous computing, ” in Proceedings of the International Conference on Parallel Processing, 1996.",{"doi":514},{"id":510,"text":522,"url":512,"identifiers":523},"M. J. Clement and M. J. Quinn. “Analytical performance prediction on multicomputers, ” in Proceedings of Supercomputing '93, pp. 886-905, November 1993.",{"doi":514},{"id":510,"text":525,"url":512,"identifiers":526},"N. Crockett, X.-H. Tu, J. K. Flanagan, and F. Sorenson. “A stochastic disk i\u002Fo simulation technique, ” in Proceedings of the Winter Simulation Conference, 1997.",{"doi":514},{"id":528,"createTime":529,"updateTime":530,"relativeEntities":531,"slug":532,"properties":533,"entityType":237,"verifyStatus":378,"verifyTime":544,"verifyNote":380,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":545,"fullTextUrl":20,"authors":546,"publicationType":298,"publisherRelationship":641,"citationCount":20,"citationInfo":20,"publishDate":701,"publishYear":702,"citationAnalyzeStatus":703,"lastCitationAnalyze":704,"indexDatabases":705,"openAccess":20,"references":20,"isForceReanalyzing":362},"d1b2371f-114c-4fa5-8172-dbdfeeba67b5","2024-01-20T11:29:20.182+00:00","2026-08-16T11:23:36.433+00:00",[],"Thermal-aware-virtual-machine-placement-based-on-multi-objective-optimization",{"abstract":534,"title":536,"gsPaper":538,"references":540,"doi":542},{"EN":535},"VMP (Virtual Machine Placement) is a crucial technology for energy consumption optimization of the cloud data center. Existing works mainly focus on virtual machine consolidation to increase resource utilization and reduce computing energy consumption. However, existing studies usually ignore the thermal effect that an intensive workload on IT (Information Technology) equipment can raise energy consumption by cooling systems and generate hotspots. In addition, an excessive number of virtual machine migrations increases migration costs and risks violating the SLA (Service Level Agreement) signed with users. In this paper, we present a comprehensive system model and formulate the problem as a constrained multi-objective optimization. We propose a novel thermal-aware VMP strategy to solve the problem by jointly considering virtual machines’ migration cost, energy consumption, and heat recirculation around server racks. Our strategy makes placement decisions using MOPFGA (Multi-objective algorithm based on Pathfinder Algorithm and Genetic Algorithm) that combines classic MOPFA and GA enhanced by OBL (Opposition Based Learning) for fast convergence and avoidance of local optimum. Extensive experiments based on CloudSim using real data center workload data from PlanetLab show that our algorithm overcomes the defects of the MOPFA (multi-objective pathfinder algorithm) and GA (genetic algorithm) and significantly improves the overall efficiency of a data center. Compared with several state-of-the-art algorithms, MOPFGA on average reduces virtual machine migrations by 77.52%, increases CRAC (Computer Room Air Conditioner) supply temperature by 1.24%, and reduces cooling energy consumption by 24.78% and computational energy consumption by 23.62%.",{"EN":537},"Thermal-aware virtual machine placement based on multi-objective optimization",{"VOID":539},"[]",{"VOID":541},"Lin W, Shi F, Wu W et al (2020) A taxonomy and survey of power models and power modeling for cloud servers[J]. ACM Comput Surv (CSUR) 53(5):1–41\nKamiyama N (2019) Virtual machine trading in public clouds[J]. IEEE Trans Netw Serv Manag 17(1):403–415\nTeng F, Yu L, Li T, Deng D, Magoule's F (2017) Energy efficiency of VM consolidation in IAAS clouds. J Supercomput 73(2):782–809\nGuo Z, Yao W, Wang D (2017) A virtual machine migration algorithm based on group selection in cloud data center[C]. In: IFIP International Conference on Network and Parallel Computing. Springer, Cham, pp 24-36\nBuyya R, Beloglazov A, Abawajy J (2010) Energy-efficient management of data center resources for cloud computing: a vision, architectural elements, and open challenges[J]. arXiv preprint arXiv:1006.0308\nMapetu JPB, Kong L, Chen Z (2021) A dynamic VM consolidation approach based on load balancing using Pearson correlation in cloud computing[J]. J Supercomput 77(6):5840–5881\nZhang Q, Meng Z, Hong X et al (2021) A survey on data center cooling systems: technology, power consumption modeling and control strategy optimization. J Syst Archit. 119:102253\nTang Q, Gupta SKS, Varsamopoulos G (2008) Energy-efficient thermal-aware task scheduling for homogeneous high-performance computing data centers: a cyber-physical approach. IEEE Trans Parallel Distrib Syst 19(11):1458–1472\nLi J, Deng Y, Zhou Y et al (2022) Towards thermal-aware workload distribution in cloud data centers based on failure models[J]. IEEE Trans Comput. https:\u002F\u002Fdoi.org\u002F10.1109\u002FTC.2022.3158476\nFeng H, Deng Y, Zhou Y et al (2021) Towards heat-recirculation-aware virtual machine placement in data centers[J]. IEEE Trans Netw Serv Manag 19:256–270\nYapici H, Cetinkaya N (2019) A new meta-heuristic optimizer: pathfinder algorithm[J]. Appl Soft Comput 78:545–568\nMirjalili S (2019) Genetic algorithm[M]. Evolutionary algorithms and neural networks. Springer, Cham, pp 43–55\nMahdavi S, Rahnamayan S, Deb K (2018) Opposition based learning: a literature review[J]. Swarm Evol Comput 39:1–23\nTao F, Li C, Liao TW et al (2015) BGM-BLA: a new algorithm for dynamic migration of virtual machines in cloud computing[J]. IEEE Trans Serv Comput 9(6):910–925\nMann ZÁ (2016) Multicore-aware virtual machine placement in cloud data centers[J]. IEEE Trans Comput 65(11):3357–3369\nRegaieg R, Koubàa M, Osei-Opoku E et al. (2018) Multi-objective mixed integer linear programming model for vm placement to minimize resource wastage in a heterogeneous cloud provider data center[C]. In: 2018 10th International Conference on Ubiquitous and Future Networks (ICUFN). IEEE, pp 401-406\nXu H, Liu Y, Wei W et al (2019) Migration cost and energy-aware virtual machine consolidation under cloud environments considering remaining runtime[J]. Int J Parallel Program 47(3):481–501\nHariharan B, Siva R, Kaliraj S et al (2021) ABSO: an energy-efficient multi-objective VM consolidation using adaptive beetle swarm optimization on cloud environment[J]. J Ambient Intell Humaniz Comput. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12652-020-02740-2\nDing W, Luo F, Han L et al (2020) Adaptive virtual machine consolidation framework based on performance-to-power ratio in cloud data centers[J]. Future Gener Comput Syst 111:254–270\nSayadnavard MH, Haghighat AT, Rahmani AM (2022) A multi-objective approach for energy-efficient and reliable dynamic VM consolidation in cloud data centers[J]. Eng Sci Technol Int J 26:100995\nWang J, Gu H, Yu J et al (2022) Research on virtual machine consolidation strategy based on combined prediction and energy-aware in cloud computing platform[J]. J Cloud Comput 11(1):1–18\nShuja J, Gani A, Shamshirband S et al (2016) Sustainable cloud data centers: a survey of enabling techniques and technologies. Renew Sustain Energy Rev 62:195–214\nSun H, Stolf P, Pierson JM (2017) Spatio-temporal thermal-aware scheduling for homogeneous high-performance computing datacenters. Future Gener Comput Syst 71:157–170\nLi X, Jiang X, Garraghan P et al (2018) Holistic energy and failure aware workload scheduling in cloud datacenters[J]. 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Clust Comput. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10586-021-03476-0\nLi J, Deng Y, Zhou Y et al (2022) Towards thermal-aware workload distribution in cloud data centers based on failure models[J]. IEEE Trans Comput 72:586–599\nMoore JD, Chase JS, Ranganathan P et al. (2005) Making scheduling\" cool\": temperature-aware workload placement in data centers[C]. In: USENIX Annual Technical Conference, General Track, pp 61-75\nHumane P, Varshapriya JN (2015) Simulation of cloud infrastructure using CloudSim simulator: a practical approach for researchers. In: International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM), Controls, Energyand Materials, IEEE, pp 207-211\nASHRAE (2018) American society of heating, refrigerating and air-conditioning engineers. http:\u002F\u002Ftc0909.ashraetcs.org\u002F\nPark KS, Pai VS (2006) CoMon: a mostly-scalable monitoring system for planet lab. 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ACM Sigmetr Perform Eval Rev 40(1):163–174",{"VOID":543},"10.1007\u002Fs11227-023-05136-z","2024-09-04T18:54:39.814+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11227-023-05136-z",[547,562,575,599,612,625],{"id":548,"sortIndex":21,"researcher":20,"roles":549,"affiliations":550,"properties":559,"displayName":561,"givenName":20,"familyName":20},"2d4ea11b-ebbd-4b50-9876-f8d99898e1f0",[245],[551],{"id":552,"sortIndex":21,"affiliation":553,"properties":20},"23f6c138-946d-4119-96c4-8e8982363172",{"id":552,"createTime":20,"updateTime":20,"relativeEntities":554,"slug":20,"properties":555,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":558,"statistic":20},[],{"title":556},{"VI":557},"School of Computer Science, South China Normal University, Guangzhou, China",[],{"title":560},{"VI":561},"Bo 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Li",{"url":545,"publisher":642,"properties":696},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":643,"slug":10,"properties":644,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":648,"manageAffiliations":665,"indexDatabases":676,"url":20,"thumbnailPath":20,"statistic":691,"gsStatistic":20,"type":20,"analyzePriority":20},[],{"issn":645,"title":646,"eissn":647},{"VOID":13},{"EN":15},{"VOID":17},[649,653,657,661],{"id":24,"createTime":20,"updateTime":20,"relativeEntities":650,"label":651,"description":652,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":27},{},{"id":30,"createTime":20,"updateTime":20,"relativeEntities":654,"label":655,"description":656,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":33},{},{"id":36,"createTime":20,"updateTime":20,"relativeEntities":658,"label":659,"description":660,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":39},{},{"id":42,"createTime":20,"updateTime":20,"relativeEntities":662,"label":663,"description":664,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":45},{},[666,671],{"id":49,"createTime":20,"updateTime":20,"relativeEntities":667,"slug":20,"properties":668,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":670,"statistic":20},[],{"title":669},{"EN":53},[55],{"id":57,"createTime":20,"updateTime":20,"relativeEntities":672,"slug":20,"properties":673,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":675,"statistic":20},[],{"title":674},{"EN":61},[],[677,684],{"id":65,"indexDatabase":678,"url":76,"indexYears":77,"academicFieldIds":683,"indexDatabaseRanking":83},{"id":67,"createTime":20,"updateTime":20,"relativeEntities":679,"label":680,"description":681,"key":73,"publicationTags":682,"standard":20},[],{"EN":70,"VI":70},{"EN":70,"VI":72},[75],[79,80,81,82],{"id":85,"indexDatabase":685,"url":98,"indexYears":20,"academicFieldIds":690,"indexDatabaseRanking":20},{"id":87,"createTime":20,"updateTime":20,"relativeEntities":686,"label":687,"description":688,"key":94,"publicationTags":689,"standard":20},[],{"EN":90,"VI":90},{"EN":92,"VI":93},[96,97],[100,101],{"impactFactor":21,"impactFactorByYear":692,"i10Index":117,"i10IndexLast5Year":118,"totalPublication":119,"totalPublicationByYear":693,"totalCitation":154,"totalCitationByYear":694,"totalCitationPerPublication":186,"totalCitationPerPublicationByYear":695,"hindexLast5Year":161,"hindex":161},{"2000":104,"2001":105,"2012":106,"2013":107,"2014":108,"2015":109,"2016":110,"2017":111,"2018":112,"2019":113,"2020":108,"2021":114,"2022":115,"2023":116},{"1987":121,"1988":122,"1989":123,"1990":124,"1991":125,"1992":126,"1993":127,"1994":121,"1995":123,"1996":128,"1997":129,"1998":130,"1999":131,"2000":132,"2001":133,"2002":132,"2003":134,"2004":135,"2005":136,"2006":137,"2007":138,"2008":135,"2009":139,"2010":140,"2011":141,"2012":142,"2013":143,"2014":144,"2015":145,"2016":146,"2017":147,"2018":148,"2019":149,"2020":150,"2021":151,"2022":152,"2023":153,"2024":145},{"1987":156,"1988":121,"1989":157,"1990":158,"1991":159,"1992":126,"1993":160,"1994":161,"1995":162,"1996":163,"1999":164,"2004":165,"2005":166,"2006":167,"2007":168,"2008":169,"2009":170,"2010":171,"2011":172,"2012":173,"2013":174,"2014":175,"2015":176,"2016":177,"2017":178,"2018":179,"2019":180,"2020":181,"2021":182,"2022":183,"2023":184,"2024":185},{"1987":188,"1988":189,"1989":190,"1990":191,"1991":192,"1992":193,"1993":194,"1994":195,"1995":196,"1996":197,"1999":198,"2004":199,"2005":190,"2006":200,"2007":201,"2008":202,"2009":111,"2010":203,"2011":204,"2012":205,"2013":206,"2014":207,"2015":208,"2016":209,"2017":210,"2018":211,"2019":212,"2020":213,"2021":214,"2022":215,"2023":216,"2024":105},{"pages":697,"volume":699},{"VOID":698},"12563-12590",{"VOID":700},"79","2023-03-15",2023,"ERROR_IN_GET_PLATFORM_ID","2026-08-16T11:23:36.432+00:00",[83,96],{"id":707,"createTime":708,"updateTime":709,"relativeEntities":710,"slug":711,"properties":712,"entityType":237,"verifyStatus":378,"verifyTime":723,"verifyNote":380,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":724,"fullTextUrl":20,"authors":725,"publicationType":298,"publisherRelationship":799,"citationCount":21,"citationInfo":858,"publishDate":861,"publishYear":859,"citationAnalyzeStatus":505,"lastCitationAnalyze":862,"indexDatabases":863,"openAccess":20,"references":20,"isForceReanalyzing":362},"c8ca888d-5701-4c47-8105-99eb1354bd20","2023-12-23T14:07:38.148+00:00","2026-07-30T23:02:48.189+00:00",[],"Prioritized-scheduling-technique-for-healthcare-tasks-in-cloud-computing",{"abstract":713,"title":715,"gsPaper":717,"references":719,"doi":721},{"EN":714},"The Internet-of-things (IoT) plays a significant role in healthcare monitoring, where the IoT Cloud integration introduces many new opportunities for real-time remote monitoring of the patient. Task scheduling is one of the major challenges in cloud environment. Solving that problem reduces delay, missed tasks, and failure rate, and increases the guarantee ratio. This paper proposes a new task scheduling and allocation technique: Prioritized Sorted Task-Based Allocation (PSTBA) for healthcare monitoring implemented in IoT cloud-based architecture. The proposed technique selects the best virtual machine to execute the health task considering multiple factors such as; the wait time of the VM and the Expected processing time (EPT) of the task as well as its criticality. An extensive simulation study is conducted using the CloudSim simulator to evaluate the performance of the proposed technique. The proposed technique is compared to the Sorted Task-Based Allocation (STBA) and FCFS techniques and it reduces the delay by 13.7% and 80.2%, the failure rate by 21% and 37.5%, and increases the guarantee ratio by 2.2% and 4.5% compared to STBA and FCFS, respectively. In analyzing the critical health tasks, the proposed PSTBA has also reduced the critical health tasks missed ratio by 15.7% and 50.9% compared to STBA and FCFS, respectively. The simulation results demonstrate that PSTBA is more effective than the STBA and FCFS techniques in terms of delay, missed critical tasks, guarantee ratio, and failure rate.",{"EN":716},"Prioritized scheduling technique for healthcare tasks in cloud computing",{"VOID":718},"[\"10859469407080674338\"]",{"VOID":720},"Kraemer FA, Braten AE, Tamkittikhun N, Palma D (2017) Fog computing in healthcare-a review and discussion. IEEE Access 5(May):9206–9222. https:\u002F\u002Fdoi.org\u002F10.1109\u002FACCESS.2017.2704100\nVishnu S, Jino Ramson SR, Jegan R (2020) Internet of medical things (IoMT)-an overview. In: ICDCS 2020–2020 5th International Conference on Devices, Circuits Systems, no. March, pp. 101–104. https:\u002F\u002Fdoi.org\u002F10.1109\u002FICDCS48716.2020.243558.\nBhuiyan MN, Rahman MM, Billah MM, Saha D (2021) Internet of things (IoT): a review of its enabling technologies in healthcare applications, standards protocols, security, and market opportunities. IEEE Internet Things J 8(13):10474–10498. https:\u002F\u002Fdoi.org\u002F10.1109\u002FJIOT.2021.3062630\nHuang J, Wu X, Huang W, Wu X, Wang S (2021) Internet of things in health management systems: A review. Int J Commun Syst. https:\u002F\u002Fdoi.org\u002F10.1002\u002Fdac.4683\nJino Ramson SR, Vishnu S, Shanmugam M (2020) Applications of internet of things (IoT)-an overview. In: ICDCS 2020–2020 5th international conference devices, circuits system, no. March, pp. 92–95, 2020. https:\u002F\u002Fdoi.org\u002F10.1109\u002FICDCS48716.2020.243556.\nJavaid M, Khan IH (2021) Internet of things (IoT) enabled healthcare helps to take the challenges of COVID-19 Pandemic. J Oral Biol Craniofacial Res 11(2):209–214. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jobcr.2021.01.015\nM. Rockwood, V. Joshi, K. Sullivan, and R. Goubran (2014) Using a real-time operating system for multitasking in remote patient monitoring. In: 2014 IEEE International Symposium on Medical Measurement and Applications and Proceedings. https:\u002F\u002Fdoi.org\u002F10.1109\u002FMeMeA.2014.6860109\nIslam K, Alam F, Zahid AI, Khan MM, Inamabbasi M (2022) Internet of things- ( IoT- ) based real-time vital physiological parameter monitoring system for remote asthma patients. wireless commun. and mobile computing, 22 pages. https:\u002F\u002Fdoi.org\u002F10.1155\u002F2022\u002F1191434.\nIslam M, Rahaman A, Islam R (2020) Development of smart healthcare monitoring system in IoT environment. SN Comp Sci 1:185. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs42979-020-00195-y\nSingh AK, Firoz N, Tripathi A, Singh KK, Choudhary P, Vashist PC (2020). Internet of Things From Hype to Reality. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fb978-0-12-821326-1.00007-3\nSurantha N, Atmaja P, David, Wicaksono M (2021) A review of wearable internet-of-things device for healthcare. Procedia Comput. Sci. 179(2020):939–943. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.procs.2021.01.083\nRahaman A, Islam M, Islam R, Sadi MS, Nooruddin S (2020) Developing IoT based smart health monitoring systems : a review. Revue d ’ Intell Artif 33(6):435–440\nKalid N, Zaidan AA, Zaidan BB, Salman OH, Hashim M, Muzammil H (2018) Based real time remote health monitoring systems a review on patients prioritization and related ‘big data’ using body sensors information and communication technology. J Med Syst. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10916-017-0883-4\nBabu SM, Lakshmi AJ, Rao BT (2015) A study on cloud based internet of things: cloudIoT. Proceedings of 2015 Global Conference on Communication Technologies (GCCT 2015).\nBotta A, De Donato W, Persico V, Pescapé A (2016) Integration of cloud computing and Internet of Things: A survey”. Futur Gener Comput Syst 56:684–700. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.future.2015.09.021\n“N I S T SP 500‐291 C l o u d C o m p u t i n g S t a n d a r d s R o a d m a p”.\nNarkhede BE, Raut RD, Narwane VS, Gardas BB (2020) Cloud computing in healthcare – a vision. Challenges Future Directions. 34(1):1–39\nDang LM, Piran J, .Han D, Min K, Moon H (2019) A survey on internet of things and cloud computing for healthcare. pp. 1–49. doi: https:\u002F\u002Fdoi.org\u002F10.3390\u002Felectronics8070768.\nDarwish A, Hassanien AE, Elhoseny M, Sangaiah AK, Muhammad K (2019) The impact of the hybrid platform of internet of things and cloud computing on healthcare systems: opportunities, challenges, and open problems. J Ambient Intell Humaniz Comput 10(10):4151–4166. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12652-017-0659-1\nThar MA, Carl B, Asim M, Kolivand H, Fahim M, Waraich A (2019) Remote health monitoring of elderly through wearable sensors. pp. 24681–24706.\nElhoseny M, Abdelaziz A, Salama AS, Riad AM, Muhammad K, Sangaiah AK (2018) A hybrid model of Internet of Things and cloud computing to manage big data in health services applications. Futur Gener Comput Syst 86:1383–1394. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.future.2018.03.005\nNagarajan SM, Deverajan GG, Chatterjee P, Alnumay W, Ghosh U (2021) Effective task scheduling algorithm with deep learning for Internet of Health Things (IoHT) in sustainable smart cities. Sustain. Cities Soc. 71(1):102945. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.scs.2021.102945\nIqbal N, Imran S, Ahmad RA, Kim DH (2021) A scheduling mechanism based on optimization using IOT-tasks orchestration for efficient patient health monitoring. Sensors 21(16):1–29. https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs21165430\nArivazhagan N et al (2022) Cloud-internet of health things (IOHT) task scheduling using hybrid moth flame optimization with deep neural network algorithm for e healthcare systems. Sci Program. https:\u002F\u002Fdoi.org\u002F10.1155\u002F2022\u002F4100352\nAburukba RO, Alikarrar M, Landolsi T, El-fakih K (2019) Scheduling Internet of Things requests to minimize latency in hybrid fog-cloud computing. Futur Gener Comput Syst. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.future.2019.09.039\nMathew T, Sekaran KC, Jose J (2014) Study and analysis of various task scheduling algorithms in the cloud computing environment. Proc. 2014 Int. Conf. Adv. Comput. Commun Informatics, ICACCI 2014:658–664. https:\u002F\u002Fdoi.org\u002F10.1109\u002FICACCI.2014.6968517\nArunarani AR, Manjula D, Sugumaran V (2019) Task scheduling techniques in cloud computing: A literature survey. Futur Gener Comput Syst 91:407–415. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.future.2018.09.014\nHosseinioun P, Kheirabadi M, Kamel Tabbakh SR, Ghaemi R (2022) aTask scheduling approaches in fog computing: A survey. Trans Emerg Telecommun Technol 33(3):1–11. https:\u002F\u002Fdoi.org\u002F10.1002\u002Fett.3792\nHoussein EH, Gad AG, Wazery YM, Suganthan PN (2021) Task scheduling in cloud computing based on meta-heuristics: review, taxonomy, open challenges, and future trends. Swarm Evol. Comput. 62(October 2020):100841. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.swevo.2021.100841\nAbdulhammed OY (2022) Load balancing of IoT tasks in the cloud computing by using sparrow search algorithm. J Supercomput 78(3):3266–3287. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11227-021-03989-w\nLavanya M, Shanthi B, Saravanan S (2020) Multi objective task scheduling algorithm based on SLA and processing time suitable for cloud environment. Comput. Commun. 151(May 2019):183–195. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.comcom.2019.12.050\nGeng X, Yu L, Bao J, Fu G (2019) A task scheduling algorithm based on priority list and task duplication in cloud computing environment. Web Intell 17(2):121–129. https:\u002F\u002Fdoi.org\u002F10.3233\u002FWEB-190406\nR. Joshua Samuel Raj, et al (2022) Evolutionary algorithm based task scheduling in iot enabled cloud environment. Comput Mater Contin 71(1):1095–1109. https:\u002F\u002Fdoi.org\u002F10.32604\u002Fcmc.2022.021859\nShi Y, Suo K, Hodge J, Mohandoss DP, Kemp S (2021) Towards optimizing task scheduling process in cloud environment. In: 2021 IEEE 11th Annual Computing and Communication Workshop CCWC 2021, pp. 81–87. https:\u002F\u002Fdoi.org\u002F10.1109\u002FCCWC51732.2021.9376146\nAbdelmoneem RM, Benslimane A, Shaaban E (2020) Mobility-aware task scheduling in cloud-Fog IoT-based healthcare architectures. Comput. Networks 179(June):107348. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.comnet.2020.107348\nChudhary R, Sharma S (2021) Fog-cloud assisted framework for heterogeneous internet of healthcare things. Procedia Comput Sci 184(2019):194–201. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.procs.2021.03.030\nAladwani T (2019) Scheduling IoT Healthcare Tasks in Fog Computing Based on their Importance. Procedia Comput Sci 163:560–569. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.procs.2019.12.138\nBoveiri HR, Khayami R, Elhoseny M, Gunasekaran M (2019) An efficient Swarm-Intelligence approach for task scheduling in cloud-based internet of things applications. J Ambient Intell Humaniz Comput 10(9):3469–3479. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12652-018-1071-1\nAbdelaziz A, Elhoseny M, Salama AS, Riad AM (2018) A machine learning model for improving healthcare services on cloud computing environment. 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Exper. pp 23–50. https:\u002F\u002Fdoi.org\u002F10.1002\u002Fspe.995.\nAli SA, Alam M (2016) A relative study of task scheduling algorithms in cloud computing environment. In: 2016 2nd International Conference on Contemporary Computing and Informatics (ic3i) pp. 105–111.\nAlworafi MA, Dhari A, Al-hashmi A (2017) Cost-aware task scheduling in cloud computing environment. no. May. https:\u002F\u002Fdoi.org\u002F10.5815\u002Fijcnis.2017.05.07.\nAbdelmoneem RM, Benslimane A, Shaaban E, Abdelhamid S, Ghoneim S (2019) A cloud-fog based architecture for IoT applications dedicated to healthcare. In: IEEE International Confrence on Communication., vol. 2019-May. doi:https:\u002F\u002Fdoi.org\u002F10.1109\u002FICC.2019.8761092.",{"VOID":722},"10.1007\u002Fs11227-022-04823-7","2024-05-16T10:17:56.020+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11227-022-04823-7",[726,741,758,773,786],{"id":727,"sortIndex":21,"researcher":20,"roles":728,"affiliations":729,"properties":738,"displayName":740,"givenName":20,"familyName":20},"2027044a-3b11-4fe2-bf17-9a9b08c12bbe",[245],[730],{"id":731,"sortIndex":21,"affiliation":732,"properties":20},"678a351c-c1ec-4fbc-b5a2-4345bf56a0c6",{"id":731,"createTime":20,"updateTime":20,"relativeEntities":733,"slug":20,"properties":734,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":737,"statistic":20},[],{"title":735},{"VI":736},"Faculty of Women for Arts, Sciences, and Education, Ain Shams University, Cairo, Egypt",[],{"title":739},{"VI":740},"Eman M. Elshahed",{"id":742,"sortIndex":193,"researcher":20,"roles":743,"affiliations":744,"properties":753,"displayName":755,"givenName":20,"familyName":20},"13b0f51c-e100-4f5b-beec-e18bb26f4dd2",[245],[745],{"id":746,"sortIndex":21,"affiliation":747,"properties":20},"c155a980-f39c-4886-afa0-8832bc51c0c5",{"id":746,"createTime":20,"updateTime":20,"relativeEntities":748,"slug":20,"properties":749,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":752,"statistic":20},[],{"title":750},{"VI":751},"Faculty of Computer Sciences, Ain Shams University, Cairo, Egypt",[],{"title":754,"gsAuthor":756},{"VI":755},"Randa M. 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query in location-based service allows users to request and receive nearest point of interest (POI) without revealing their location or object received. However, since the service is customized, it requires user-specific information. Problems arise when a user due to privacy or security concerns is unwilling to disclose this information. Previous solutions to hide them have been found to be deficient and sometimes inefficient. In this paper, we propose a novel idea that will partition objects into neighborhoods supported by database design that allows a user to retrieve the exact nearest POI without revealing its location, or the object retrieved. The paper is organized into two parts. In the first part, we adopted the concept of topological space to generalize object space. To help limit information disclosed and minimize transmission cost, we create disjointed neighborhoods such that each neighborhood contains no more than one object. We organize the database matrix to align with object location in the area. For optimization, we introduce the concept of kernel in graphical processing unit (GPU), and we then develop parallel implementation of our algorithm by utilizing the computing power of the streaming multiprocessors of GPU and the parallel computing platform and programming model of Compute Unified Device Architecture (CUDA). In the second part, we study serial implementation of our algorithm with respect to execution time and complexity. Our experiment shows a scalable design that is suitable for any population size with minimal impact to user experience. We also study GPU–CUDA parallel implementation and compared the performance with CPU serial processing. The results show 23.9\n                  \n                    \n                  \n                  $$\\times $$\n                  \n                    \n                  \n                 improvement of GPU over CPU. To help determine the optimal size for the parameters in our design or similar scalable algorithm, we provide analysis and model for predicting GPU execution time based on the size of the chosen parameter.",{"EN":874},"Direct private query in location-based services with GPU run time 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In: National Fire Academy Executive Fire officer Program, Branson, MO",{},{"id":510,"text":981,"url":512,"identifiers":982},"Xu J, Tang X, Hu H, DU J (2010) Privacy-conscious location-based queries in mobile environments. Proc. In: Proceedings of the IEEE Transactions On Parallel and Distributed Systems ’10, vol 21, pp 313–326, March 2010",{"doi":514},{"id":20,"text":984,"url":20,"identifiers":985},"Sweeney L (2002) K-anonymity: a model for protecting privacy. Int J Uncertain Fuzziness Knowl Based Syst 10:557–570",{},{"id":510,"text":987,"url":512,"identifiers":988},"Samarati P (2002) Protecting respondents identities in microdata release. In: IEEE Transaction on Knowledge and Data Engineering, vol i3, pp 1010–1027, August 2002",{"doi":514},{"id":510,"text":990,"url":512,"identifiers":991},"Kalnis P, Ghinita G, Mouratidis K, Papadias D (2007) Preventing location-based identity inference in anonymous spatial queries. 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In: IEEE High Performance Computer Architecture (HPCA), 2011 IEEE 17th International Symposium on, San Antonio, TX, pp 382–393, February 12–16",{"doi":514},{"id":1078,"createTime":1079,"updateTime":1080,"relativeEntities":1081,"slug":1082,"properties":1083,"entityType":237,"verifyStatus":378,"verifyTime":1094,"verifyNote":380,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":1095,"fullTextUrl":20,"authors":1096,"publicationType":298,"publisherRelationship":1127,"citationCount":1186,"citationInfo":1187,"publishDate":1189,"publishYear":702,"citationAnalyzeStatus":1190,"lastCitationAnalyze":1191,"indexDatabases":1192,"openAccess":20,"references":20,"isForceReanalyzing":362},"d4b502ba-9396-4a6b-80bb-320579320a5f","2024-01-10T18:52:27.297+00:00","2026-07-28T14:46:21.070+00:00",[],"Node-position-estimation-based-on-optimal-clustering-and-detection-of-coverage-hole-in-wireless-sensor-networks-using-hybrid-deep-reinforcement-learning",{"abstract":1084,"title":1086,"gsPaper":1088,"references":1090,"doi":1092},{"EN":1085},"Sensor nodes, typically small and low-power devices, are components of wireless sensor networks (WSNs). Each node monitors its surroundings for relevant environmental changes and sends all detected events to the data collector for analysis. If the sensor nodes are not placed correctly, there may be areas that are not within the detection zone of any sensor node. Coverage holes in WSNs are usually caused by random deployment and node failure. Energy holes and dead nodes are the main problems caused by detection and recovery of coverage holes in WSNs. The size of coverage holes increases the time complexity and power of recent protocols. However, there is a high computational complexity associated with distributed methods proposed in recent years to solve the coverage hole detection problem. In this paper, we propose optimal cluster-based node position estimation and coverage hole detection in WSNs using a hybrid deep learning approach. First, a modified Lyapunov optimization (MLO) algorithm to compute the node position is presented, which ensures edge nodes in the network. Next, we design optimal clustering technique by using improved sand cat swarm optimization (ISCSO) algorithm to formulate efficient balanced clusters which computes coverage hole area in the network. Afterward, we develop a hybrid deep reinforcement learning (Hyb-DRL) technique for hole shape detection and hole size judgment within clusters, among clusters and along edges. The results show that the proposed approach achieves significant improvements compared to existing benchmark approaches. Specifically, the average energy consumption of CG-DCHD approach is 43.835%, 32.674% and 26.164% lower for node density, hole density and simulation rounds, respectively. The hole detection time is 18.4%, 16.802% and 15.462% lower, while the coverage is 16.885%, 14.977% and 12.219% higher for node density, hole density and simulation rounds, respectively. Additionally, the network lifetime of CG-DCHD approach is 15.58%, 17.702% and 20.492% higher, while the control packet overhead is 0.83%, 1.907% and 1.466% lower for node density, hole density and simulation rounds, respectively.",{"EN":1087},"Node position estimation based on optimal clustering and detection of coverage hole in wireless sensor networks using hybrid deep reinforcement learning",{"VOID":1089},"[\"14603465390976537708\"]",{"VOID":1091},"Wang F, Hu H (2021) Coverage hole detection method of wireless sensor network based on clustering algorithm. Measurement 179:109449\nKhedr AM, Osamy W, Salim A (2018) Distributed coverage hole detection and recovery scheme for heterogeneous wireless sensor networks. Comput Commun 124:61–75\nLi W, Zhang W (2015) Coverage hole and boundary nodes detection in wireless sensor networks. J Netw Comput Appl 48:35–43\nGou P, Mao G, Zhang F, Jia X (2020) Reconstruction of coverage hole model and cooperative repair optimization algorithm in heterogeneous wireless sensor networks. Comput Commun 153:614–625\nZygowski C, Jaekel A (2020) Optimal path planning strategies for monitoring coverage holes in Wireless Sensor Networks. Ad Hoc Netw 96:101990\nChowdhury A, De D (2021) Energy-efficient coverage optimization in wireless sensor networks based on Voronoi–Glowworm swarm optimization-K-means algorithm. Ad Hoc Netw 122:102660\nDeng X, Xu M, Yang LT, Lin M, Yi L, Wang M (2018) Energy balanced dispatch of mobile edge nodes for confident information coverage hole repairing in IoT. IEEE Internet Things J 6(3):4782–4790\nDas S, Debbarma MK (2020) CHPT: an improved coverage-hole patching technique based on tree-center in wireless sensor networks. J Ambient Intell Human Comput 14:5873–5884\nPriyadarshi R, Gupta B (2020) Coverage area enhancement in wireless sensor network. Microsyst Technol 26(5):1417–1426\nNilsazDezfouli N, Barati H (2020) A distributed energy-efficient approach for hole repair in wireless sensor networks. Wirel Netw 26(3):1839–1855\nDeng X, Jiang Y, Yang LT, Lin M, Yi L, Wang M (2019) Data fusion based coverage optimization in heterogeneous sensor networks: a survey. Inf Fusion 52:90–105\nMharsi N, Hadji M (2019) A mathematical programming approach for full coverage hole optimization in Cloud Radio Access Networks. Comput Netw 150:117–126\nKoriem SM, Bayoumi MA (2020) Detecting and measuring holes in wireless sensor network. J King Saud Univ Comput Inf Sci 32(8):909–916\nYi L, Deng X, Zou Z, Ding D, Yang LT (2018) Confident information coverage hole detection in sensor networks for uranium tailing monitoring. J Parallel Distri Comput 118:57–66\nBoukerche A, Sun P (2018) Connectivity and coverage based protocols for wireless sensor networks. Ad Hoc Netw 80:54–69\nFang W, Song X, Wu X, Sun J, Hu M (2018) Novel efficient deployment schemes for sensor coverage in mobile wireless sensor networks. Inf Fusion 41:25–36\nWang Y, Wu S, Chen Z, Gao X, Chen G (2017) Coverage problem with uncertain properties in wireless sensor networks: a survey. Comput Netw 123:200–232\nPhoemphon S, So-In C, Leelathakul N (2020) A hybrid localization model using node segmentation and improved particle swarm optimization with obstacle-awareness for wireless sensor networks. Expert Syst Appl 143:113044\nJia Y, He P, Huo L (2020) Wireless sensor network monitoring algorithm for partial discharge in smart grid. Electr Power Syst Res 189:106592\nXu H (2020) Semi-supervised manifold learning based on polynomial mapping for localization in wireless sensor networks. Signal Process 172:107570\nKhalifa B, Al Aghbari Z, Khedr AM (2021) A distributed self-healing coverage hole detection and repair scheme for mobile wireless sensor networks. Sustain Comput: Inform Syst 30:100428\nVishnupriya G, Ramachandran R (2021) Rabin-Karp algorithm based malevolent node detection and energy-efficient data gathering approach in wireless sensor network. Microprocess Microsyst 82:103829\nRoy S, Mazumdar N, Pamula R (2021) An energy optimized and QoS concerned data gathering protocol for wireless sensor network using variable dimensional PSO. Ad Hoc Netw 123:102669\nDas S, KantiDebBarma M (2018) Computational geometry based coverage hole-detection and hole-area estimation in wireless sensor network. J High Speed Netw 24(4):281–296\nChristopher VB, Jasper J (2021) Jellyfish dynamic routing protocol with mobile sink for location privacy and congestion avoidance in wireless sensor networks. J Syst Architect 112:101840\nMa HC, Sahoo PK, Chen YW (2011) Computational geometry based distributed coverage hole detection protocol for the wireless sensor networks. J Netw Comput Appl 34(5):1743–1756\nWatfa MK, Commuri S (2006) Power conservation approaches to the border coverage problem in wireless sensor networks. In: ICWN, 2006 June. pp 143–152\nCorke P, Peterson R, Rus D (2007) Finding holes in sensor networks. In: Proceedings of the workshop on omniscient space: robot control architecture geared toward adapting to dynamic environments at ICRA April 2007\nKumar Sahoo P, Chiang MJ, Wu SL (2016) An efficient distributed coverage hole detection protocol for wireless sensor networks. Sensors 16(3):386",{"VOID":1093},"10.1007\u002Fs11227-023-05494-8","2024-05-13T17:16:35.799+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11227-023-05494-8",[1097,1114],{"id":1098,"sortIndex":21,"researcher":20,"roles":1099,"affiliations":1100,"properties":1109,"displayName":1111,"givenName":20,"familyName":20},"76f79d3c-8060-4bd6-94de-af7d151d095b",[245],[1101],{"id":1102,"sortIndex":21,"affiliation":1103,"properties":20},"3911fe0f-d65d-44d0-8288-aac16df99fbd",{"id":1102,"createTime":20,"updateTime":20,"relativeEntities":1104,"slug":20,"properties":1105,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1108,"statistic":20},[],{"title":1106},{"VI":1107},"NIT Agartala, Agartala, India",[],{"title":1110,"gsAuthor":1112},{"VI":1111},"Rajib Chowdhuri",{"VOID":1113},"[\"8WuKaq4AAAAJ\"]",{"id":1115,"sortIndex":193,"researcher":20,"roles":1116,"affiliations":1117,"properties":1124,"displayName":1126,"givenName":20,"familyName":20},"7e6f3e60-80f2-49d0-8c0b-eb04b16e240d",[245],[1118],{"id":1102,"sortIndex":21,"affiliation":1119,"properties":20},{"id":1102,"createTime":20,"updateTime":20,"relativeEntities":1120,"slug":20,"properties":1121,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1123,"statistic":20},[],{"title":1122},{"VI":1107},[],{"title":1125},{"VI":1126},"Mrinal Kanti Deb 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act":1200,"title":1202,"gsPaper":1204,"references":1205,"doi":1207},{"EN":1201},"The Johnson–Lindenstrauss (JL) lemma has led to the development of tools for dealing with datasets in high dimensions. The lemma asserts that a set of high-dimensional points can be projected into lower dimensions, while approximately preserving the pairwise distance structure. Significant improvements of the JL lemma since its inception are summarized. Particular focus is placed on reproving Matoušek’s versions of the lemma (Random Struct Algorithms 33(2):142–156, 2008) first using subgaussian projection coefficients and then using sparse projection coefficients. The results of the lemma are illustrated using simulated data. The simulation suggests a projection that is more effective in terms of dimensionality reduction than is borne out by the theory. This more effective projection was applied to a very large natural, rather than simulated, dataset thus further strengthening empirical evidence of the existence of a better than the proven optimal lower bound on the embedding dimension. Additionally, we provide comparisons with other commonly used data reduction and simplification techniques.",{"EN":1203},"Dimensionality reduction via the Johnson–Lindenstrauss Lemma: theoretical and empirical bounds on embedding dimension",{"VOID":539},{"VOID":1206},"Achlioptas D (2003) Database-friendly random projections: Johnson–Lindenstrauss with binary coins. J. Comput. Syst. Sci. 66(4):671–687\nAilon N, Chazelle B (2006) Approximate nearest neighbors and the fast Johnson–Lindenstrauss transform. In: Proceedings of the Thirty-Eighth Annual ACM Symposium on Theory of Computing. ACM, pp 557–563\nBamberger S, Krahmer F (2017) Optimal fast Johnson–Lindenstrauss embeddings for large data sets. arXiv:1712.01774 [cs.DS]\nCannings TI, Samworth RJ (2015) Random-projection ensemble classification. arXiv:1504.04595v2\nCohen MB, Jayram TS, Nelson J (2018) simple analyses of the sparse Johnson–Lindenstrauss transform. In: 1st Symposium on Simplicity in Algorithms (SOSA 2018), vol 61(15), pp 1–9\nCunningham JP, Yu BM (2014) Dimensionality reduction for large-scale neural recordings. Nat Neurosci 17:1500\nDasgupta S, Gupta A (1999) An elementary proof of the Johnson–Lindenstrauss lemma. Technical Report 99-006, UC Berkeley\nDasgupta S (2013) Experiments with random projection. arXiv:1301.3849v1\nDonoho D (2000) Aide-memoire. high dimensional data analysis: the curses and blessings of dimensionality. http:\u002F\u002Fstatweb.stanford.edu\u002F~donoho\u002FLectures\u002FAMS2000\u002FCurses.pdf\nFedoruk J (2016) Dimensionality reduction via the Johnson and Lindenstrauss lemma: mathematical and computational improvements. Master thesis University of Alberta\nFedoruk J , Schmuland B, Johnson J, Heo G (2016) Dimensionality reduction via the Johnson–Lindenstrauss lemma. In: Proceedings International Conference on Advances in Big Data Analytics (ABDA’16), Las Vegas, Nevada. CSREA Press, pp 134–139\nFern X, Brodley CE (2003) Random projection for high dimensional data clustering: a cluster ensemble approach. In: Proceedings of the 20th International Conference on Machine Learning (ICML-03), pp 186–193\nFrankl P, Maehara H (1988) The Johnson–Lindenstrauss lemma and the sphericity of some graphs. J Combin Theory Ser B 44(3):355–362\nFreksen CB, Larsen KG On using Toeplitz and Circulant matrices for Johnson–Lindenstrauss transforms. arXiv:1706.10110 [math.FA]\nGyllensten AC, Sahlgren M (2015) Navigating the semantic horizon using relative neighborhood graphs. CoRR, arXiv:abs\u002F1501.02670\nHastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning. Springer Science+Business Media, New York\nIndyk P, Motwani R (1998) Approximate nearest neighbors: towards removing the curse of dimensionality. In: 30th Annual ACM Symposium on Theory of Computing, Dallas, TX. ACM, New York, pp 604–613\nJaques N (2018) Fast Johnson–Lindenstrauss transform for classification of high dimensional data.http:\u002F\u002Fwww.cs.ubc.ca\u002F~jaquesn\u002FMachineLearningTheory.pdf. Accessed 14 May 2018\nKane DM, Nelson J (2014) Sparser Johnson–Lindenstrauss transforms. J ACM (JACM) 61(1):4\nRoweis S, Saul L (2018) LLE Algorithm Pseudocode. https:\u002F\u002Fcs.nyu.edu\u002F~roweis\u002Flle\u002Falgorithm.html. Accessed April 2018\nLarsen CG, Nelson J (2017) Optimality of the Johnson–Lindenstrauss lemma. arXiv:1609.02094v2 [cs.IT]\nLarsen CG, Nelson J (2014) The Johnson–Lindenstrauss lemma is optimal for linear dimensionality reduction. arXiv:1411.2404v1\nJohnson WB, Lindenstrauss J (1984) Extensions of Lipschitz mappings into a Hilbert space. In: Conference in Modern Analysis and Probability, New Haven, CI, 1982. American Mathematical Society, Providence, RI, pp 189–206\nMatoušek J (2008) On variants of the Johnson–Lindenstrauss lemma. Random Struct Algorithms 33(2):142–156\nPierson E, Yau C (2015) ZIFA: dimensionality reduction for zero-inflated single-cell gene expression analysis. Genome Biol 16:241\nRojo J, Nguye TS (2010) Improving the Johnson–Lindenstrauss lemma. arXiv:1005.1440v1\nRoweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323–2326\nBellec P, Chu C, Chouinard-Decorte F, Benhajali Y, Margulies DS, Craddock RC (2017) The Neuro Bureau ADHD-200 Preprocessed repository. NeuroImage 144(Part B):275–286. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.neuroimage.2016.06.034\nWang J (2011) Classical multidimensional scaling. Geometric structure of high dimensional data and dimensionality reduction. Springer, Heidelberg, pp 115–129\nYou L, Knoll F, Mao Y, Gao S (2017) Practical Johnson–Lindenstrauss transforms via algebraic geometry codes. In: International Conference on Control, Artificial Intelligence, Robotics Optimization (ICCAIRO), pp 171–176",{"VOID":1208},"10.1007\u002Fs11227-018-2401-y","2024-06-25T01:38:30.853+00:00","http:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs11227-018-2401-y",[1212,1227,1242,1257],{"id":1213,"sortIndex":21,"researcher":20,"roles":1214,"affiliations":1215,"properties":1224,"displayName":1226,"givenName":20,"familyName":20},"94f0d654-9b73-45ac-b22d-dbea808d8afc",[245],[1216],{"id":1217,"sortIndex":21,"affiliation":1218,"properties":20},"577de200-834f-4844-a210-01c65dd63565",{"id":1217,"createTime":20,"updateTime":20,"relativeEntities":1219,"slug":20,"properties":1220,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1223,"statistic":20},[],{"title":1221},{"VI":1222},"Department of Mathematical Sciences, MacEwan University, Edmonton, Canada",[],{"title":1225},{"VI":1226},"John Fedoruk",{"id":1228,"sortIndex":193,"researcher":20,"roles":1229,"affiliations":1230,"properties":1239,"displayName":1241,"givenName":20,"familyName":20},"e48cee21-6c74-4dff-bf9f-290d7e73f4a8",[245],[1231],{"id":1232,"sortIndex":21,"affiliation":1233,"properties":20},"198a011c-3476-43ff-bc2a-c674a8a90205",{"id":1232,"createTime":20,"updateTime":20,"relativeEntities":1234,"slug":20,"properties":1235,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1238,"statistic":20},[],{"title":1236},{"VI":1237},"Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Canada",[],{"title":1240},{"VI":1241},"Byron 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FPGAs feature high performance and flexibility. Thus, they have found many applications in modern high-performance computing (HPC) systems. These systems suffer from the limitation of the computing resources problem for running HPC applications. Therefore, multi-FPGA systems have been emerged to alleviate such resource limitations. In this regard, efficient scheduling strategies are required to dynamically steer the execution of applications—represented as task graphs—on a set of connected FPGAs. In this paper, a heuristic-based dynamic critical path-aware scheduling technique named CPA is presented to schedule task graphs on multi-FPGA systems. The proposed technique, by considering the computation and communication capabilities of FPGAs, dynamically assigns priority to tasks in different steps in order to achieve better makespans. The proposed technique has been evaluated by conducting several experiments on real-world and three different shapes of random task graphs with different number of tasks, and its efficiency has been compared with that of three task graph scheduling approaches. The obtained results demonstrate that the proposed CPA technique outperforms well-known heuristic scheduling strategies and improves their makespan by 13.47% on average. In addition, the experiments show that the proposed technique generates the schedules in the order of milliseconds and the average of its yielded makespans is 12.05% longer than that of an optimum schedule.",{"EN":1346},"Dynamic scheduling of task graphs in multi-FPGA systems using critical path",{"VOID":1348},"[\"14944547368954715998\"]",{"VOID":1350},"Ghavidel A, Sedaghat Y, Naghibzadeh M (2019) Hybrid scheduling to enhance reliability of real-time tasks running on reconfigurable devices. J Supercomput. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11227-019-02976-6\nShan J, Casu MR, Cortadella J, Lavagno L, Lazarescu MT (2019) Exact and heuristic allocation of multi-kernel applications to multi-FPGA platforms. In: Proceedings of the 56th Annual Design Automation Conference 2019. ACM, p 3\nRamezani R, Clemente JA, Sedaghat Y, Mecha H (2016) Estimation of hardware task reliability on partially reconfigurable FPGAs. In: 16th European Conference on Radiation and Its Effects on Components and Systems (RADECS). IEEE, pp 1–4\nNjiki M, Elouardi A, Bouaziz S, Casula O, Roy O (2019) A multi-FPGA architecture-based real-time TFM ultrasound imaging. J Real Time Image Proc 16(2):505–521\nSanaullah A, Yang C, Alexeev Y, Yoshii K, Herbordt MC (2018) Real-time data analysis for medical diagnosis using FPGA-accelerated neural networks. BMC Bioinform 19(18):490\nMahmud N, El-Araby E (2018) Towards higher scalability of quantum hardware emulation using efficient resource scheduling. In: 2018 IEEE International Conference on Rebooting Computing (ICRC). IEEE, pp 1–10\nLentaris G, Stratakos I, Stamoulias I, Soudris D, Lourakis M, Zabulis X (2019) High-performance vision-based navigation on SoC FPGA for spacecraft proximity operations. In: IEEE Transactions on Circuits and Systems for Video Technology\nRamezani R (2020) A prefetch-aware scheduling for FPGA-based multi-task graph systems. J Supercomput. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11227-020-03153-w\nClemente JA, Resano J, González C, Mozos D (2011) A hardware implementation of a run-time scheduler for reconfigurable systems. IEEE Trans Very Large Scale Integr VLSI Syst 19(7):1263–1276\nOwaida M, Alonso G (2018) Application partitioning on FPGA clusters: inference over decision tree ensembles. In: 2018 28th International Conference on Field Programmable Logic and Applications (FPL). IEEE, pp 295–2955\nGeng T et al (2018) FPDeep: acceleration and load balancing of CNN training on FPGA clusters. In: 2018 IEEE 26th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM). IEEE, pp 81–84\nRamezani R, Sedaghat Y, Naghibzadeh M, Clemente JA (2017) Reliability and makespan optimization of hardware task graphs in partially reconfigurable platforms. IEEE Trans Aerosp Electron Syst 53(2):983–994\nDai G, Huang T, Chi Y, Xu N, Wang Y, Yang H (2017) Foregraph: exploring large-scale graph processing on multi-fpga architecture. In: Proceedings of the 2017 ACM\u002FSIGDA international symposium on field-programmable gate arrays. ACM, pp 217–226\nFarooq U, Mehrez H, Bhatti MK (2018) Inter-FPGA interconnect topologies exploration for multi-FPGA systems. Des Autom Embed Syst 22(1–2):117–140\nKao C-C (2020) Resource and performance tradeoff for task scheduling of parallel reconfigurable architectures. J Circuits Syst Comput. https:\u002F\u002Fdoi.org\u002F10.1142\u002FS0218126620500292\nJing C, Zhu Y, Li M (2013) Energy-efficient scheduling on multi-FPGA reconfigurable systems. Microprocess Microsyst 37(6–7):590–600\nRamezani R, Sedaghat Y (2014) Scheduling periodic real-time hardware tasks on dynamic partial reconfigurable devices subject to fault tolerance. In: 4th International eConference on Computer and Knowledge Engineering (ICCKE). IEEE, pp 1–6\nCharitopoulos G, Koidis I, Papadimitriou K, Pnevmatikatos D (2017) Run-time management of systems with partially reconfigurable FPGAs. Integration 57:34–44\nRamezani R, Sedaghat Y, Clemente JA (2017) Reliability improvement of hardware task graphs via configuration early fetch. IEEE Trans Very Large Scale Integr VLSI Syst 25(4):1408–1420\nKao C-C (2015) Performance-oriented partitioning for task scheduling of parallel reconfigurable architectures. IEEE Trans Parallel Distrib Syst 26(3):858–867\nLiang H, Sinha S, Zhang W (2018) Parallelizing hardware tasks on multicontext FPGA with efficient placement and scheduling algorithms. IEEE Trans Comput Aided Des Integr Circuits Syst 37(2):350–363\nKoraei M, Jahre M, Fatemi SO (2017) DTP: enabling exhaustive exploration of FPGA temporal partitions for streaming HPC applications. In: Proceedings of the 8th International Symposium on Highly Efficient Accelerators and Reconfigurable Technologies. ACM, p 7\nRamezani R, Sedaghat Y, Naghibzadeh M, Clemente JA (2018) A decomposition-based reliability and makespan optimization technique for hardware task graphs. Reliab Eng Syst Saf 180:13–24\nWieczorek M, Prodan R, Fahringer T (2005) Scheduling of scientific workflows in the ASKALON grid environment. Acm Sigmod Record 34(3):56–62\nEtminani K, Naghibzadeh M (2007) A min–min max–min selective algorithm for grid task scheduling. In: 2007 3rd IEEE\u002FIFIP International Conference in Central Asia on Internet. IEEE, pp 1–7\nTopcuoglu H, Hariri S, Wu M-Y (2002) Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans Parallel Distrib Syst 13(3):260–274\nYu T, Feng B, Stillwell M, Guo L, Ma Y, Thomson J (2018) Lattice-based scheduling for multi-FPGA systems. In: 2018 International Conference on Field-Programmable Technology (FPT). IEEE, pp 318–321\nAbdallah F, Tanougast C, Kacem I, Diou C, Singer D (2019) Genetic algorithms for scheduling in a CPU\u002FFPGA architecture with heterogeneous communication delays. Comput Ind Eng 137:106006\nEl Cadi AA, Souissi O, Atitallah RB, Belanger N, Artiba A (2018) Mathematical programming models for scheduling in a CPU\u002FFPGA architecture with heterogeneous communication delays. J Intell Manuf 29(3):629–640\nIturbe X (2013) Design and implementation of a reliable reconfigurable real-time operating system (R3TOS). PhD Thesis, University of Edinburgh\nAgne A et al (2014) ReconOS: an operating system approach for reconfigurable computing. Micro IEEE 34(1):60–71\nAl-Sharaeh S, Wells BE (1996) A comparison of heuristics for list schedules using the Box-method and P-method for random digraph generation. In: 28th Southeastern Symposium on System Theory. IEEE, pp 467–471\nXilinxCorporation (2012) Virtex-5 FPGA configuration user guide UG191 (v 3.11). www.xilinx.com\u002Fsupport\u002Fdocumentation\u002Fuser_guides\u002Fug191.pdf\nSingh V, Gupta I, Jana PK (2018) A novel cost-efficient approach for deadline-constrained workflow scheduling by dynamic provisioning of resources. Future Gener Comput Syst 79:95–110",{"VOID":1352},"10.1007\u002Fs11227-020-03281-3","2024-06-26T23:56:20.861+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11227-020-03281-3",[1356],{"id":1357,"sortIndex":21,"researcher":20,"roles":1358,"affiliations":1359,"properties":1368,"displayName":1370,"givenName":20,"familyName":20},"60d970b7-7ac3-4e31-8e14-5ad84a693c54",[245],[1360],{"id":1361,"sortIndex":21,"affiliation":1362,"properties":20},"2577f1fa-8bfd-46d4-b923-e5f071b5196a",{"id":1361,"createTime":20,"updateTime":20,"relativeEntities":1363,"slug":20,"properties":1364,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1367,"statistic":20},[],{"title":1365},{"VI":1366},"Department of Software Engineering, Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran",[],{"title":1369,"gsAuthor":1371},{"VI":1370},"Reza Ramezani",{"VOID":1372},"[\"QDa3wGgAAAAJ\"]",{"url":1354,"publisher":1374,"properties":1428},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1375,"slug":10,"properties":1376,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":1380,"manageAffiliations":1397,"indexDatabases":1408,"url":20,"thumbnailPath":20,"statistic":1423,"gsStatistic":20,"type":20,"analyzePriority":20},[],{"issn":1377,"title":1378,"eissn":1379},{"VOID":13},{"EN":15},{"VOID":17},[1381,1385,1389,1393],{"id":24,"createTime":20,"updateTime":20,"relativeEntities":1382,"label":1383,"description":1384,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":27},{},{"id":30,"createTime":20,"updateTime":20,"relativeEntities":1386,"label":1387,"description":1388,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":33},{},{"id":36,"createTime":20,"updateTime":20,"relativeEntities":1390,"label":1391,"description":1392,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":39},{},{"id":42,"createTime":20,"updateTime":20,"relativeEntities":1394,"label":1395,"description":1396,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":45},{},[1398,1403],{"id":49,"createTime":20,"updateTime":20,"relativeEntities":1399,"slug":20,"properties":1400,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1402,"statistic":20},[],{"title":1401},{"EN":53},[55],{"id":57,"createTime":20,"updateTime":20,"relativeEntities":1404,"slug":20,"properties":1405,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1407,"statistic":20},[],{"title":1406},{"EN":61},[],[1409,1416],{"id":65,"indexDatabase":1410,"url":76,"indexYears":77,"academicFieldIds":1415,"indexDatabaseRanking":83},{"id":67,"createTime":20,"updateTime":20,"relativeEntities":1411,"label":1412,"description":1413,"key":73,"publicationTags":1414,"standard":20},[],{"EN":70,"VI":70},{"EN":70,"VI":72},[75],[79,80,81,82],{"id":85,"indexDatabase":1417,"url":98,"indexYears":20,"academicFieldIds":1422,"indexDatabaseRanking":20},{"id":87,"createTime":20,"updateTime":20,"relativeEntities":1418,"label":1419,"description":1420,"key":94,"publicationTags":1421,"standard":20},[],{"EN":90,"VI":90},{"EN":92,"VI":93},[96,97],[100,101],{"impactFactor":21,"impactFactorByYear":1424,"i10Index":117,"i10IndexLast5Year":118,"totalPublication":119,"totalPublicationByYear":1425,"totalCitation":154,"totalCitationByYear":1426,"totalCitationPerPublication":186,"totalCitationPerPublicationByYear":1427,"hindexLast5Year":161,"hindex":161},{"2000":104,"2001":105,"2012":106,"2013":107,"2014":108,"2015":109,"2016":110,"2017":111,"2018":112,"2019":113,"2020":108,"2021":114,"2022":115,"2023":116},{"1987":121,"1988":122,"1989":123,"1990":124,"1991":125,"1992":126,"1993":127,"1994":121,"1995":123,"1996":128,"1997":129,"1998":130,"1999":131,"2000":132,"2001":133,"2002":132,"2003":134,"2004":135,"2005":136,"2006":137,"2007":138,"2008":135,"2009":139,"2010":140,"2011":141,"2012":142,"2013":143,"2014":144,"2015":145,"2016":146,"2017":147,"2018":148,"2019":149,"2020":150,"2021":151,"2022":152,"2023":153,"2024":145},{"1987":156,"1988":121,"1989":157,"1990":158,"1991":159,"1992":126,"1993":160,"1994":161,"1995":162,"1996":163,"1999":164,"2004":165,"2005":166,"2006":167,"2007":168,"2008":169,"2009":170,"2010":171,"2011":172,"2012":173,"2013":174,"2014":175,"2015":176,"2016":177,"2017":178,"2018":179,"2019":180,"2020":181,"2021":182,"2022":183,"2023":184,"2024":185},{"1987":188,"1988":189,"1989":190,"1990":191,"1991":192,"1992":193,"1993":194,"1994":195,"1995":196,"1996":197,"1999":198,"2004":199,"2005":190,"2006":200,"2007":201,"2008":202,"2009":111,"2010":203,"2011":204,"2012":205,"2013":206,"2014":207,"2015":208,"2016":209,"2017":210,"2018":211,"2019":212,"2020":213,"2021":214,"2022":215,"2023":216,"2024":105},{"pages":1429,"volume":1431},{"VOID":1430},"597-618",{"VOID":1432},"77",{"total":21,"publishYear":1434,"statisticByYear":1435},2020,{},"2020-04-22","2026-07-27T05:37:51.392+00:00",[83,96],{"id":1440,"createTime":1441,"updateTime":1442,"relativeEntities":1443,"slug":1444,"properties":1445,"entityType":237,"verifyStatus":378,"verifyTime":1456,"verifyNote":380,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":1457,"fullTextUrl":20,"authors":1458,"publicationType":298,"publisherRelationship":1491,"citationCount":21,"citationInfo":1551,"publishDate":1554,"publishYear":1552,"citationAnalyzeStatus":1190,"lastCitationAnalyze":1555,"indexDatabases":1556,"openAccess":20,"references":20,"isForceReanalyzing":362},"0162c5da-dd79-4805-8111-85acbd8587c0","2024-01-30T18:15:43.377+00:00","2026-07-26T10:04:59.569+00:00",[],"Transforming-powerlist-based-divide-and-conquer-programs-for-an-improved-execution-model",{"abstract":1446,"title":1448,"gsPaper":1450,"references":1452,"doi":1454},{"EN":1447},"Powerlists are data structures that can be successfully used for defining parallel programs based on divide-and-conquer paradigm. These parallel recursive data structures and their algebraic theories offer both a methodology to design parallel algorithms and parallel programming abstractions to ease the development of parallel applications. The paper presents a technique for speeding up the parallel recursive programs defined based on powerlists. The improvements are achieved by applying transformation rules that introduce tuple functions and prefix operators, for which a more efficient execution model is defined. Together with the execution model, a cost model is also defined in order to allow a proper evaluation. The treated examples emphasise the fact that the transformation leads to important improvements of the programs. The speeding up is achieved by reducing the number of recursive calls, and also by enable the fusion of splitting\u002Fcombining operations on different data structures. In addition, enhancing the function that has to be computed to other useful functions using a tuple, could improved the cost reduction even more.",{"EN":1449},"Transforming powerlist-based divide-and-conquer programs for an improved execution model",{"VOID":1451},"[\"7595073394939615244\"]",{"VOID":1453},"Achatz K, Schulte W (1995) Architecture independent massive parallelization of divide-and-conquer algorithms. Fakultaet fuer Informatik, Universitaet Ulm, Ulm\nBertot Y, Casteran P (2004) Interactive theorem proving and program development. Springer, Berlin\nBird R (1987) An introduction to the theory of lists. In: Broy M (ed) Logic of programming and calculi of discrete design. Springer, Berlin, pp 5–42\nChin W (1992) Safe fusion of functional expressions. In: Proc. Conference on Lisp and Functional Programming, San Francisco, California\nChin W (1993) Towards an automated tupling strategy. In: Proc Conference on Partial Evaluation and Program Manipulation. ACM Press, Copenhagen, pp 119–132\nCole M (1989) Algorithmic skeletons: structured management of parallel computation. MIT Press, Cambridge\nCooley JW, Tukey JW (1965) An algorithm for the machine calculation of complex Fourier series. Math Comput 19:297–301\nCousineau G, Mauny M (1998) The functional approach to programming. Cambridge University Press, Cambridge\nGesbert L, Gava F, Loulergue F, Dabrowski F (2010) Bulk synchronous parallel ML with exceptions. Future Gener Comput Syst 26:486–490\nGorlatch S (2003) SAT: a programming methodology with skeletons and collective operations. In: Rabhi FA, Gorlatch S (eds) Patterns and skeletons for parallel and distributed computing. Springer, Berlin, pp 29–64\nHu Z, Iwasaki H, Takeichi M (1996) Construction of list homomorphisms by tupling and fusion. In: Penczek W, Szalas A (eds) Mathematical Foundations of Computer Science (Lecture Notes in Computer Science), vol 1113. Springer, Berlin, pp 407–418\nKornerup J (1997) Data structures for parallel recursion. In: Ph.D. dissertation, University of Texas\nLoulergue F, Niculescu V, Tesson J (2014) Implementing powerlists with bulk synchronous parallel ML. In: 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC2014), Timisoara, Romania, 22–25 Sept. IEEE Computer Society 2014, pp 325–332\nLoulergue F, Niculescu V, Robillard S. (2013) Powerlists in Coq: programming and reasoning. In: First International Symposium on Computing and Networking (CANDAR 2013) Matsuyama, Japan, Dec. 4–6, 2013, pp 57–65. IEEE Computer Society\nMisra J (1994) Powerlist: a structure for parallel recursion. ACM Trans Program Lang Syst 16(6):1737–1767\nNiculescu V (2007) Data-distributions in powerlist theory. In: Jones CB, Liu Z, Woodcock J (eds) Theoretical aspects of computing (ICTAC) (Ser LNCS) vol. 4711. Springer, pp 396–409\nNiculescu V (2011) PARES—a model for parallel recursive programs. Rom J Inf Sci Technol 14(2):159–182\nNiculescu V, Loulergue F, Bufnea D, Sterca A (2017) A java framework for high level parallel programming using powerlists. In: 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) 18–20, pp 255–262\nValiant LG (1990) A bridging model for parallel computation. Commun ACM 33(8):103–111\nWadler P (1990) Deforestation: transforming programs to eliminate trees. 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6,"gsPaper":1568,"references":1570,"doi":1572},{"EN":1565},"Cloud computing is a suitable platform for workflows that work with massive data and big data. Through virtualization, cloud computing converts physical infrastructures to virtual machines (VMs). Virtual machines can meet fluctuating and dynamic requests through simpler management. Workflow scheduling in cloud computing is important, concerning the fact that proper scheduling can enhance the efficiency of the cloud and good scheduling can cause energy consumption reduction. As energy efficiency is one of the most important issues in cloud computing, in this paper a new statistical analysis-based algorithm is suggested for defining similarities of input workflows. The proposed algorithm, which is called massive data similarity statistics analysis algorithm (MSSA), classifies virtual machines into virtual clusters and it executes scheduling by reforming the virtual clusters. Furthermore, MSSA investigates the similarities of message passing in two different periods; it decides for the next period, and finally, carries out the load balancing by a new method for transferring the machines in virtual clusters. The results of simulation with CloudSim show that the proposed algorithm is more energy efficient in comparison with traditional methods, like FIFO, and heuristic methods such as BlindPick, and relatively new method, named eOO as well as makespan. The main parameter for comparing is makespan and energy consumption. The results showed that the proposed method is more energy efficient compared with similar algorithms and it reduced the makespan significantly.",{"EN":1567},"An energy-aware scheduling of dynamic workflows using big data similarity statistical analysis in cloud computing",{"VOID":1569},"[\"17498715145191461220\"]",{"VOID":1571},"Schomm F, Stahl F, Vossen G (2013) Marketplaces for data: an initial survey. 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In: 2015 IEEE International Conference on Big Data (big data). IEEE, pp 2862–2864\nZhang F, Cao J, Hwang K, Li K, Khan SU (2014) Adaptive workflow scheduling on cloud computing platforms with iterativeordinal optimization. IEEE Trans Cloud Comput 3(2):156–168\nXiao P, Hu Z-G, Zhang Y-P (2013) An energy-aware heuristic scheduling for data-intensive workflows in virtualized datacenters. J Comput Sci Technol 28(6):948–961\nZhang F, Cao J, Tan W, Khan SU, Li K, Zomaya AY (2014) Evolutionary scheduling of dynamic multitasking workloads for big-data analytics in elastic cloud. IEEE Trans Emerg Top Comput 2(3):338–351\nMadni SHH, AbdLatiff MS, Coulibaly Y (2016) Resource scheduling for infrastructure as a service (IAAS) in cloud computing: challenges and opportunities. J Netw Comput Appl 68:173–200\nSmanchat S, Viriyapant K (2015) Taxonomies of workflow scheduling problem and techniques in the cloud. Futur Gener Comput Syst 52:1–12\nAlkhanak EN, Lee SP, Khan SUR (2015) Cost-aware challenges for workflow scheduling approaches in cloud computing environments: taxonomy and opportunities. Futur Gener Comput Syst 50:3–21\nMansouri N, Dastghaibyfard GH, Mansouri E (2013) Combination of data replication and scheduling algorithm for improving data availability in data grids. J Netw Comput Appl 36(2):711–722\nZhang F, Cao J, Li K, Khan SU, Hwang K (2014) Multi-objective scheduling of many tasks in cloud platforms. Futur Gener Comput Syst 37:309–320\nHanani A, Rahmani AM, Sahafi A (2017) A multi-parameter scheduling method of dynamic workloads for big data calculation in cloud computing. J Supercomput 73(11):4796–4822\nNavimipour NJ (2015) Task scheduling in the cloud environments based on an artificial bee colony algorithm. In: International Conference on Image Processing, pp 38–44\nQin P, Dai B, Huang B, Xu G (2015) Bandwidth-aware scheduling with SDN in Hadoop: a new trend for big data. IEEE Syst J 11(4):2337–2344\nMashayekhy L, Nejad MM, Grosu D, Zhang Q, Shi W (2014) Energy-aware scheduling of mapreduce jobs for big data applications. IEEE Trans Parallel Distrib Syst 26(10):2720–2733\nBodík P, Menache I, Naor J, Yaniv J (2014) Deadline-aware scheduling of big-data processing jobs. In: Proceedings of the 26th ACM symposium on parallelism in algorithms and architectures, pp 211–213\nAbouelela M, El-Darieby M (2016) Scheduling big data applications within advance reservation framework in optical grids. Appl Soft Comput 38:1049–1059\nLi X, Song J, Huang B (2016) A scientific workflow management system architecture and its scheduling based on cloud service platform for manufacturing big data analytics. Int J Adv Manuf Technol 84(1–4):119–131\nGautam JV, Prajapati HB, Dabhi VK, Chaudhary S (2015) A survey on job scheduling algorithms in big data processing. In: 2015 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT). IEEE, pp 1–11\nWang K, Raicu I (2014) Scheduling data-intensive many-task computing applications in the cloud. In: NSFCloud workshop\nBardhan S, Menascé DA (2014) A contention aware hybrid evaluator for schedulers of big data applications in computer clusters. In: 2014 IEEE International Conference on Big Data (big data). IEEE, pp 11–19\nZhao Y, Fei X, Raicu I, Lu S (2011) Opportunities and challenges in running scientific workflows on the cloud. In: 2011 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery. IEEE, pp 455–462\nDashti SE, Rahmani AM (2016) Dynamic VMs placement for energy efficiency by PSO in cloud computing. J Exp Theor Artif Intell 28(1–2):97–112\nLorch JR, Smith AJ (2001) Improving dynamic voltage scaling algorithms with PACE. ACM SIGMETRICS Perform Evaluat Rev 29(1):50–61\nLee YC, Zomaya AY (2010) Energy conscious scheduling for distributed computing systems under different operating conditions. 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