A Tabu search based heuristic for optimized joint resource allocation and task scheduling in Grid/Clouds

Peng, Yi; Ding, Hui; Ramamurthy, Byrav

doi:10.1109/ants.2013.6802891

Cited by 11 publications

(5 citation statements)

References 5 publications

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“…Enhanced Ant Colony optimization algorithms were used to ensure the VM load balance [36] and enhance the performance of the task scheduler [37]. The application of the stochastic Tabu search algorithm was examined in [38] as a job scheduling policy to solve the resource allocation and task scheduling problem in Grid/Cloud networks. A hybrid job scheduling algorithm merged Tabu and Harmony search algorithms [39] and achieved satisfactory results in terms of makespan and cost.…”

Section: Related Workmentioning

confidence: 99%

Scalable Management of Heterogeneous Cloud Resources Based on Evolution Strategies Algorithm

Lončar

Loncar

2022

IEEE Access

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The emergence and rapid development of Cloud computing have intensively changed the Information Technology paradigm. Cloud computing found its wide application in science and engineering. Cloud-based systems provide new solutions for scientific experiments and access to data distributed across data centers. Optimization of Cloud resource management is becoming a significant area of research. This research focuses on scheduling tasks and processing scientific data in a heterogeneous Cloud environment where most jobs require a large number of resources and computing power. Our approach proposes a strategy for optimizing task scheduling across different virtual machines and data centers based on the metaheuristic Evolution Strategies algorithm. The Evolution Strategies algorithm was tested, which has not been used in this domain. As an essential property of the system, scalability has played an important role in selecting the algorithm. We created a model and added a Longest Job First broker policy. Compared to the standard Genetic Algorithm, our approach has shown improvements in measured metrics. After testing under different loads, the proposed strategy gave promising results and achieved a better makespan, larger average resource utilization, better throughput, less average execution time, a smaller degree of imbalance, and scalability.

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Section: Related Workmentioning

confidence: 99%

Scalable Management of Heterogeneous Cloud Resources Based on Evolution Strategies Algorithm

Lončar

Loncar

2022

IEEE Access

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“…Tellez et al [35] presented the Tabu search technique for optimal load balancing between cloud and fog nodes. To solve the joint resource allocation task scheduling problem, Pan et al [36] proposed a Tabu search-based heuristic approach for cloud computing. Hamza et al [37] discussed a hybrid approach using Tabu search and simulated annealing for load balancing in cloud computing.…”

Section:  Pe-dca Vs Benchmark Broker Policymentioning

confidence: 99%

PE-DCA: Penalty elimination based data center allocation technique using guided local search for IaaS cloud

Parida

Pati

Chandan³

et al. 2022

COMSIS J

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In Cloud computing the user requests are passaged to data centers (DCs) to accommodate resources. It is essential to select the suitable DCs as per the user requests so that other requests should not be penalized in terms of time and cost. The searching strategies consider the execution time rather than the related penalties while searching DCs. In this work, we discuss Penalty Elimination-based DC Allocation (PE-DCA) using Guided Local Search (GLS) mechanism to locate suitable DCs with reduced cost, response time, and processing time. The PE-DCA addresses, computes, and eliminates the penalties involved in the cost and time through iterative technique using the defined objective and guide functions. The PE-DCA is implemented using CloudAnalyst with various configurations of user requests and DCs. We examine the PE-DCA and the execution after-effects of various costs and time parameters to eliminate the penalties and observe that the proposed mechanism performs best.

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“…Metaheuristic algorithms include Hill Climbing (HC) [9], Simulated Annealing (SA) [10], Tabu Search (TS) [11], Ant Colony Optimization (ACO) [12], Cuckoo Search (CS) [13] and Particle Swarm Optimization (PSO) [14]. These algorithms are based on swarm intelligence, and they are used to find an approximated solution.…”

Section: Introductionmentioning

confidence: 99%

IPSO Task Scheduling Algorithm for Large Scale Data in Cloud Computing Environment

et al. 2019

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Task scheduling is one of the essential techniques in the cloud computing environment. It is required for allocating tasks to the proper resources and optimizing the overall system performance. Particle swarm optimization (PSO) algorithm is one of the most popular scheduling algorithms, which is used to maximize resource utilization. However, the performance of the PSO scheduling algorithm decreases when the number of tasks is significant. In this paper, the improved PSO (IPSO) algorithm is proposed to provide the optimal allocation for a large number of tasks. This is achieved by splitting the submitted tasks into batches in a dynamic way. The resources utilization state is considered in each creation of batches. After getting a sub-optimal solution for each batch, the algorithm appends all the sub-optimal solutions for batches into a final allocation map. Finally, IPSO tries to balance the loads over the final allocation map. The proposed algorithm is compared with different scheduling algorithms, namely, honey bee, ant colony, and round-robin algorithms. The results of experiments show the efficiency of the proposed algorithm in terms of makespan, standard deviation of load, and degree of imbalance.INDEX TERMS Cloud computing, task scheduling, load balancing, particle swarm optimization.

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A Tabu search based heuristic for optimized joint resource allocation and task scheduling in Grid/Clouds

Cited by 11 publications

References 5 publications

Scalable Management of Heterogeneous Cloud Resources Based on Evolution Strategies Algorithm

Scalable Management of Heterogeneous Cloud Resources Based on Evolution Strategies Algorithm

PE-DCA: Penalty elimination based data center allocation technique using guided local search for IaaS cloud

IPSO Task Scheduling Algorithm for Large Scale Data in Cloud Computing Environment

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