Adaptive golden eagle optimization based multi-objective scientific workflow scheduling on multi-cloud environment

S. Immaculate Shyla1, T. Beula Bell2, C. Jaspin Jeba Sheela3
1Department of Computer Science, Holy Cross College (Autonomous), Nagercoil, India
2Department of Computer Applications, Nesamony Memorial Christian College, Marthandam, India
3Department of PG Computer Science, Nesamony Memorial Christian College, Marthandam, India

Tóm tắt

An exemplary for emerging knowledges and the capacity to provide reliable cloud services, cloud computing. Giving consumers on-demand access to “unlimited” computer resources is one of the key components of cloud computing. Single cloud-holding resources, however, are typically constrained and might not be able to handle the unexpected spike in user demands. In order to support resource sharing amongst clouds, the multi-cloud concept is thus established. These days, offering resources and administrations across numerous clouds is unquestionably amazing. The goal of conventional research on cloud scheduling is to reduce costs or increase speed. However, the major indicator of QoS and a vital problem is the dependability of work process scheduling. As a result, multi-objective scheduling for a logical work process in a multi-cloud environment is suggested in this research with the goal of controlling the work process while also balancing cost and timeliness while satisfying the criterion of reliability. The adaptive golden eagle optimisation (AGEO) algorithm is created to realise this idea. The solution encoding, fitness analysis, and updating functions are used in the proposed algorithm’s validation. Different workflow models are employed for the experimental study, and performance is assessed using various indicators. The projected approach attained 1920 utilization. Similarly, the PSO and GA achieved 1901 and 1900 utilization.

Tài liệu tham khảo

Awad AI, El-Hefnawy NA, Abdel_kader HM (2015) Enhanced particle swarm optimization for task scheduling in cloud computing environments. Procedia Comput Sci 65:920–929 Abd Elaziz M, ShengwuXiong KPN, Jayasena, Li L (2019) Task scheduling in cloud computing based on hybrid moth search algorithm and differential evolution. Knowl Based Syst 169:39–52 Tian W, Xu M, Chen A, Li G, Wang X (2015) Open-source simulators for cloud computing: comparative study and challenging issues. Simul Model Pract Theory 58:239–254 Chaudhary D, Kumar B (2019) Cost optimized hybrid genetic-gravitational search algorithm for load scheduling in cloud computing. Appl Soft Comput 83:105627 Ismayilov G (2020) Neural network based multi-objective evolutionary algorithm for dynamic workflow scheduling in cloud computing. Futur Gener Comput Syst 102:307–322 Nayak SC, Tripathy C (2018) Deadline sensitive lease scheduling in cloud computing environment using AHP. J King Saud Univ-Computer Inform Sci 30(2):152–163 Babu LDD, Venkata Krishna P (2013) Honey bee behavior inspired load balancing of tasks in cloud computing environments. Appl Soft Comput 13(5):2292–2303 Abujassar RS, Jazzar M (2017) Enhancing the cloud computing performance by labeling the free node services as ready-to-execute tasks. J Electr Comput Eng 2017:1–9 Sanaj MS, Joe Prathap PM (2020) Nature inspired chaotic squirrel search algorithm (CSSA) for multi objective task scheduling in an IAAS cloud computing atmosphere. Eng Sci Technol Int J 23(4):891–902 Abujassar RS (2016) Mitigation fault of node mobility for the MANET networks by constructing a backup path with loop free: enhance the recovery mechanism for pro-active MANET protocol. Wireless Netw 22(1):119–133 Jena RK (2015) Multi objective task scheduling in cloud environment using nested PSO framework. Procedia Comput Sci 57:1219–1227 Al-Maytami BA, Fan P, Hussain A, Baker T, Liatsis P (2019) A task scheduling algorithm with improved makespan based on prediction of tasks computation time algorithm for cloud computing. IEEE Access 7:160916–160926 Priya V, Sathiya Kumar C, Kannan R (2019) Resource scheduling algorithm with load balancing for cloud service provisioning. Appl Soft Comput 76:416–424 Seema A, Alsaidy AD, Abbood Mouayad A, Sahib (2020) Heuristic initialization of PSO task scheduling algorithm in cloud computing. J King Saud Univ-Computer Inform Sci 4(6):-13 Ebadifard F, SeyedMortezaBabamir (2018) A PSO-based task scheduling algorithm improved using a load‐balancing technique for the cloud computing environment. Concurr Comput: Pract Exp 30(12):e4368 Khorsand R, Ramezanpour M (2020) An energy-efficient task-scheduling algorithm based on a multi-criteria decision-making method in cloud computing. Int J Commun Syst 33(9):e4379 Doostali S, Babamir SM, Eini M (2021) CP-PGWO: multi-objective workflow scheduling for cloud computing using critical path. Cluster Comput 24(4):3607–3627 Haiyang H, Li Z, Hu H, Chen J, Ge J, Li C, Chang V (2018) Multi-objective scheduling for scientific workflow in multicloud environment. J Netw Comput Appl 114:108–122 Joshi SS (2021) A Novel Golden Eagle Optimizer Based Trusted Ad Hoc On-Demand Distance Vector (GEO-TAODV) Routing Protocol. Int J Comput Networks Appl 8(5):538–548 Mokeddem D (2021) Parameter extraction of solar photovoltaic models using enhanced levy flight based grasshopper optimization algorithm. J Electr Eng Technol 16(1):171–179 Duan H, Chen C, Min G (2017) Energy-aware scheduling of virtual machines in heterogeneous cloud computing systems. Futur Gener Comput Syst 74:142–150 Mohammadi-Balani A, DehghanNayeri M, Azar A, Taghizadeh-Yazdi M (2021) Golden eagle optimizer: a nature-inspired metaheuristic algorithm. Comput Ind Eng 152:107050