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Usually, a large number of concurrent bag-of-tasks (BoTs) application execution requests are submitted to cloud data centers (CDCs), which needs to be optimally scheduled on the physical cloud resources to obtain maximal performance. In the current paper, NP-Hard cloud task scheduling (CTS) problem for scheduling concurrent BoT applications is modeled as an optimization problem involving minimization of makespan and energy consumption. Whale optimization algorithm (WOA) has been found effective in solving a wide range of optimization problems. However, standard WOA has certain deficiencies such as inadequate exploration ability, slow convergence, high computation complexity, and insufficient exploration–exploitation phase trade-off. These deficiencies eventually result in unacceptable results when the original WOA is applied over task scheduling optimization problems. To address these limitations, a multi-objective scheduling algorithm called OWPSO is suggested, which incorporates opposition-based learning (OBL) and particle swarm optimization (PSO) mechanisms into the standard WOA method. Firstly, the OBL method is applied to produce an optimal initial population to enhance the exploration and convergence speed of the proposed OWPSO approach in the successive generations. Secondly, PSO and OBL methods are incorporated in the exploration phase of the standard WOA approach to enhance exploration ability further. Thirdly, a fitness-based switching mechanism is added to provide an adequate exploration–exploitation phase trade-off. Finally, a discrete resource allocation heuristic is incorporated in the OWPSO to provide an efficient resource allocation. Simulation experiments over the CloudSim simulator reveal that OWPSO approach results in makespan reduction in the range of 1.68−18.38% (for CEA-Curie workloads), 2.10−24.32% (for HPC2N workloads), and energy consumption reduction in the range of 0.93−14.70% (for CEA-Curie workloads), and 0.73−25.94% (for HPC2N workloads) over other well-known meta-heuristics. Statistical tests and box plots further revealed the robustness of proposed OWPSO algorithm. [ABSTRACT FROM AUTHOR]

Most modern parallel programs are written with the moldable property. However, most existing parallel computing systems treat such parallel programs as rigid jobs for scheduling, resulting in two drawbacks. The first is inflexibility and inefficiency in processor allocation, leading to resource fragmentation and thus poor performance. The second is about usage inconvenience, requiring users to figure out the best number of processors for executing a job. As HPC as a service emerges, moldable job scheduling has become an important research issue for achieving both high performance and user convenience. This paper presents our research work on developing new processor allocation approaches for moldable job scheduling based on two-level resource utilization calculation, preemptive job execution, and dual-criteria iterative improvement. A series of simulation experiments have been conducted to evaluate the proposed approaches and compare them to previous methods. The experimental results demonstrate significant performance improvement in terms of average turnaround time. [ABSTRACT FROM AUTHOR]

Cloud computing is an emerging technology which relies on virtualization techniques to achieve the elasticity of shared resources for providing on-demand services. When the service demand increases, more resources are required to satisfy the service demand. Single cloud generally cannot provide unlimited services with limited physical resources; therefore, the federation of multiple clouds may be one possible solution. In such environment, different cloud providers may own different pricing and resource allocating strategies. Thus, how to select the most appropriate provider to host applications becomes an important issue for clients. However, as the requests of accessing distributed resources increase, the occurrences of competing the same resource may also increase. In this study, a Distributed Resource Allocation (DRA) approach is proposed to solve resource competition in the federated cloud environment. Each job is supposed to consist of one or more tasks, and the communication behavior between tasks could be profiled. The proposed approach groups tasks according to communication behavior to minimize communication overhead, and tries to allocate grouped tasks to achieve equilibrium when resource competition occurs. Experimental results show that the cloud provider could obtain more profits by outsourcing resources in the federated cloud with enough resources. [ABSTRACT FROM AUTHOR]

Workflow scheduling has long been an important research topic in the field of parallel computing. Clustering-based methods are one of the major types of workflow scheduling approaches which have been shown superior to other kinds of methods in many cases due to their advantage of minimizing inter-task communication costs. Most previous research dealt with single workflow scheduling and focused on how to cluster the tasks within a workflow into a set of task groups. Recent research showed that utilizing idle time gaps between scheduled tasks is a promising direction for efficient multiple workflow scheduling. Since executing multiple workflows simultaneously is an inevitable need in modern shared parallel computing platforms, efficient task group allocation becomes a critical issue. In this paper, we studied such issue and proposed an innovative dual-criteria task group allocation method which considers both task group's finish time and potential resource utilization to effectively improve overall multi-workflow execution performance. In addition, an adaptive task group rearrangement mechanism was adopted to further improve performance. The proposed method has been evaluated with a series of simulation experiments and compared to previous approaches. The experimental results show that our method outperforms previous approaches across different workload conditions and workflow properties in terms of average makespan. The performance improvement ranges from 5 to 29 % for different conditions, achieving the largest performance improvement for workflows of smaller CCR. [ABSTRACT FROM AUTHOR]

Grid performance are usually measured by the average turnaround time of all jobs in the system. A job's turnaround time consists of two parts: queue waiting time and actual execution time, which in a heterogeneous grid environment, are severely affected by the resource fragmentation and speed heterogeneity factors. Most existing processor allocation methods focus on one of these two factors only. This paper proposes processor allocation methods, which consider both resource fragmentation and speed heterogeneity, to improve system performance of heterogeneous grids. Extensive simulation studies have been conducted to show that the proposed methods can effectively deliver better performance under most resource and workload conditions. [ABSTRACT FROM AUTHOR]