Springer Science and Business Media LLC

This paper presents a parallel system for searching a digital score of classical music in a personal library. The application scenario of the system is for a musician who wants to search for a specific score in its own device by playing an excerpt of a few seconds of the composition. We propose a solution, based on audio-to-score alignment, which allows to identify the correct score in a database of musical pieces in real time. This is a challenging task because we focus on a real-time system targeted for handheld devices characterized by both mobility and low power consumption. Experimental results show that it is possible to achieve real-time execution in the tested scenarios using parallel computing techniques with ARM processors.

In this paper, a new cache placement scheme is proposed to achieve higher hit ratios with respect to the two conventional schemes namely set-associative and direct mapping. Similar to set-associative, in this scheme, cache space is divided into sets of different sizes. Hence, the length of tag fields associated to each set is also variable and depends on the partition it is in. The proposed mapping function has been simulated with some standard trace files and statistics are gathered and analyzed for different cache configurations. The results reveal that the proposed scheme exhibits a higher hit ratio compared to the two well-known mapping schemes, namely set-associative and direct mapping, using LRU replacement policy.

Cloud schedulers that allocate resources exclusively to single workflows are not work-conserving as they may be forced to leave gaps in their schedules because of the precedence constraints in the workflows. Thus, they may lead to a waste of financial resources. This problem can be mitigated by multiple-workflow schedulers that share the leased cloud resources among multiple workflows or users by filling the gaps left by one workflow with the tasks of other workflows. This solution may even work when users have different performance objectives for their workflows, such as budgets and deadlines. As an additional requirement, we want the scheduler to be fair to all workflows regardless of their performance objectives. In this paper, we propose a multiple-workflow scheduler that is able to target different quality of service goals for different workflows and that considers fairness among different users. To this aim, we propose an unfairness metric and four workflow selection policies. We prove that the resource selection that decides based on a task’s sub-budget, sub-deadline, finish time, and cost on different resources is selecting the best resource based on the given information, while using the smallest number of calculations. Simulations show that there is a trade-off between overall cost, makespan, and fairness. We conclude that the best workflow selection policy to reduce unfairness is the direct policy, which explicitly selects the workflow that minimizes the value of the proposed unfairness metric in each round.

Cloud computing has long been recognized as the best way to execute and manage high-performance applications across a variety of domains. Yet, cloud computing service providers are offering to their users computing resources with various combinations of configurations and prices. Selecting a proper configuration setting for the optimization of both cost and makespan while running high-performance computing applications in a cloud computing environment remains a complex problem. This complexity comes from a variety of reasons, including the configurations and pricing of computer resources specified by providers, as well as the characteristics of user applications. To tackle this problem, we proposed a three-variant algorithm to assist users in scheduling their workflow applications on clouds to reduce the makespan and monetary costs. Extensive simulation tests with various experimental settings are used to assess the suggested algorithm.

The growing data have brought tremendous pressure for query processing and storage, so there are many studies that focus on using GPU to accelerate join operation, which is one of the most important operations in modern database systems. However, existing GPU acceleration join operation researches are not very suitable for the join operation on big data. Based on this, this paper speeds up nested loop join, hash join and theta join, combining Hadoop with GPU, which is also the first to use GPU to accelerate theta join. At the same time, after the data pre-filtering and pre-processing, using MapReduce and HDFS in Hadoop proposed in this paper, the larger data table can be handled, compared to existing GPU acceleration methods. Also with MapReduce in Hadoop, the algorithm proposed in this paper can estimate the number of results more accurately and allocate the appropriate storage space without unnecessary costs, making it more efficient. Experimental results show that comparing with GPU-based approach without Hadoop, our approach increases the speed by 1.5–2 times, and comparing with the Hadoop-based approaches without GPU, our approach increases the speed by 1.3–2 times.

Cloud computing has become a new computing paradigm that has huge potentials in enterprise and business. Green cloud computing is also becoming increasingly important in a world with limited energy resources and an ever-rising demand for more computational power. To maximize utilization and minimize total cost of the cloud computing infrastructure and running applications, resources need to be managed properly and virtual machines shall allocate proper host nodes to perform the computation. In this paper, we propose performance analysis based resource allocation scheme for the efficient allocation of virtual machines on the cloud infrastructure. We experimented the proposed resource allocation algorithm using CloudSim and its performance is compared with two other existing models.

An unmanned aerial vehicle (UAV) is a promising carriage for data gathering in wireless sensor networks since it has sufficient as well as efficient resources both in terms of time and energy due to its direct communication between the UAV and sensor nodes. On the other hand, to realize the data gathering system with UAV in wireless sensor networks, there are still some challenging issues remain such that the highly affected problem by the speed of UAVs and network density, also the heavy conflicts if a lot of sensor nodes concurrently send its own data to the UAV. To solve those problems, we propose a new data gathering algorithm, leveraging both the UAV and mobile agents (MAs) to autonomously collect and process data in wireless sensor networks. Specifically, the UAV dispatches MAs to the network and every MA is responsible for collecting and processing the data from sensor nodes in an area of the network by traveling around that area. The UAV gets desired information via MAs with aggregated sensory data. In this paper, we design a itinerary of MA migration with considering the network density. Simulation results demonstrate that our proposed method is time- and energy-efficient for any density of the network.