Cost and makespan scheduling of workflows in clouds using list multiobjective optimization technique

Han, Pengcheng; Du, Chenglie; Chen, Jinchao; Ling, Fuyuan; Du, Xixun

doi:10.1016/j.sysarc.2020.101837

Cited by 43 publications

(14 citation statements)

References 46 publications

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“…It uses a recently introduced spider monkey optimization algorithm with a budget and deadline constraints approach. A two‐phase list‐based approach was investigated in Reference 17 to address various QoS parameters but it is a simple heuristic to trapped into local optima. Another multiobjective algorithm 18 for makespan and cost used an improved PSO.…”

Section: Related Workmentioning

confidence: 99%

Energy and cost aware workflow scheduling in clouds with deadline constraint

Medara

Singh

Sompalli

2022

Concurrency and Computation

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Cloud computing is a promising platform for executing scientific workflow applications.Commonly, the scientific workflow applications are complex in size and computation intensive. Task scheduling in clouds is a familiar NP-complete problem. Efficient workflow application scheduling is critical for meeting multi-objectives in cloud environments. By reason of its pivotal role, this problem has been widely studied, and many approaches have been developed. Most of the algorithms focus on schedule makespan and execution cost. The cloud data centers consume a large amount of energy while running workflow applications due to a lack of efficient scheduling algorithms to perform the task to a virtual machine (VM) mapping. Hence, there is a significant need to address energy utilization in the cloud data center (CDC) for two reasons such as data center operational cost optimization and to improve the environment. This article presents an energy and cost-aware scheduling (ECWS) approach with the objectives of decreasing energy utilization, execution cost, and maximizing utilization of the resources. The ECWS algorithm is a heterogeneous earliest finish time (HEFT) based energy-efficient heuristic for cloud scheduler. The ECWS algorithm includes three sub-algorithms like RE calculation, RE Threshold selection, and slack algorithm.The effectiveness of the proposed algorithm is evaluated on the WorkflowSim tool using distinct workloads from various scientific areas. The experimental outcome exhibit that the proposed algorithm achieved significant energy conservation, maximized resource utilization, and cost-saving when compared with related well-known algorithms.

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Section: Related Workmentioning

confidence: 99%

Energy and cost aware workflow scheduling in clouds with deadline constraint

Medara

Singh

Sompalli

2022

Concurrency and Computation

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“…Pengcheng et al [28] considered the scheduling mechanism as a multi-objective optimization problem and presented the mechanism to optimize the cost and makespan. It eliminates useless resources to narrow down the search space.…”

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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“…TTij is zero if ti and tj are assigned to the same VM [31]. The data transfer time is calculated as follows [32]:…”

Section: Evaluation Metricmentioning

confidence: 99%

Optimized scheduling of scientific workflows based on iterated local search

Jihad

Al-Janabi

Yassen

2022

IJEECS

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Recent years have witnessed a great interest in scientific applications with large data and processing-intensive, so cloud computing is used which provides the resources needed to implement and run these applications. One of the challenges in the management of scientific workflow applications is scheduling them to solve many combinatorial optimization problems, including reducing execution time, cost, resource utilization, and energy consumption. Due to the fact that the iterated local search algorithm (ILS) has been successfully applied to solve many combinatorial optimization problems, this paper investigates the performance of ILS in solving the scientific workflow scheduling problem which is a highly constrained problem. The main components that are different from one problem to others are the ILS parameters, local search, and perturbation, which must be carefully designed. The performance of the standard ILS has been examined and compared with the latest technology. The experimental results show that the proposed algorithm (ILS) obtained good results compared to the best-known results in the literature. This is due to the ILS being an adaptable metaheuristic, which can be simply adapted to diﬀerent search situations and instances.

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Cost and makespan scheduling of workflows in clouds using list multiobjective optimization technique

Cited by 43 publications

References 46 publications

Energy and cost aware workflow scheduling in clouds with deadline constraint

Energy and cost aware workflow scheduling in clouds with deadline constraint

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

Optimized scheduling of scientific workflows based on iterated local search

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