Renumber Coevolutionary Multiswarm Particle Swarm Optimization for Multi-objective Workflow Scheduling on Cloud Computing Environment

Li, Hai-Hao; Chen, Zong-Gan; Zhan, Zhi-Hui; Du, Ke-Lin; Zhang, Mengjie

doi:10.1145/2739482.2764632

Cited by 21 publications

(8 citation statements)

References 8 publications

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“…We created five problem instances of different sizes from 100 to 500, which are numbered sequentially from C1 to C5. In every problem instance, VMs are generated by discrete uniform distribution of [1,8] for CPU and [1,32] for memory. The memory is the bottleneck resource.…”

Section: Test C: Hard Heterogeneous Environmentmentioning

confidence: 99%

“…This way, the physical resources are virtualized as uniform resources and therefore are efficient for parallel and distributed computing [5], [6]. Virtual machines (VMs) are created according to the type of operating system and the amount of required resources such as CPU, memory, and storage, specified by the customers and then run on a physical server to host application to meet requirements of customers [7], [8]. On the other hand, virtualization allows multiple VMs to be executed on the same physical server and share hardware resources.…”

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confidence: 99%

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An Energy Efficient Ant Colony System for Virtual Machine Placement in Cloud Computing

Liu

Zhan

Deng

et al. 2018

IEEE Trans. Evol. Computat.

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358

212

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Virtual machine placement (VMP) and energy efficiency are significant topics in cloud computing research. In this paper, evolutionary computing is applied to VMP to minimize the number of active physical servers, so as to schedule underutilized servers to save energy. Inspired by the promising performance of the ant colony system (ACS) algorithm for combinatorial problems, an ACS-based approach is developed to achieve the VMP goal. Coupled with order exchange and migration (OEM) local search techniques, the resultant algorithm is termed an OEMACS. It effectively minimizes the number of active servers used for the assignment of virtual machines (VMs) from a global optimization perspective through a novel strategy for pheromone deposition which guides the artificial ants toward promising solutions that group candidate VMs together. The OEMACS is applied to a variety of VMP problems with differing VM sizes in cloud environments of homogenous and heterogeneous servers. The results show that the OEMACS generally outperforms conventional heuristic and other evolutionary-based approaches, especially on VMP with bottleneck resource characteristics, and offers significant savings of energy and more efficient use of different resources. Index Terms-Ant colony system (ACS), cloud computing, virtual machine placement (VMP). I. INTRODUCTION C LOUD computing is a large-scale distributed computing paradigm, driven by an increasing demand for various

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Section: Test C: Hard Heterogeneous Environmentmentioning

confidence: 99%

mentioning

confidence: 99%

An Energy Efficient Ant Colony System for Virtual Machine Placement in Cloud Computing

Liu

Zhan

Deng

et al. 2018

IEEE Trans. Evol. Computat.

Self Cite

358

212

View full text Add to dashboard Cite

show abstract

“…Authors in References 43‐50 considered the cost minimization as performance metric for scheduling the single workflow without any constraints. Authors in References 51‐53 optimized the makespan of the workflow application.…”

Section: Application Of Gtlbo In Workflow Scheduling Problem On Cloudmentioning

confidence: 99%

A variant of teaching‐learning‐based optimization and its application for minimizing the cost of Workflow Execution in the Cloud Computing

Ram

Srivastava

Mishra

2021

Concurrency and Computation

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Teaching learning‐based optimization (TLBO) was proposed by Rao to solve optimization problems. It is based on the theory of teaching‐learning mechanism. Although it performs well in unimodal problems yet its performance is not good in multimodal problems. To further improve this algorithm's performance and make it suitable for both unimodal problems and multimodal problems, we made some major changes in the theory and the algorithm's operators. The proposed algorithm is able to capture diverse optimal solutions in less number of iterations and is very good for solving multimodal problems. This newly created variant of TLBO is named generalized TLBO (GTLBO). The performance of GTLBO is tested on CEC−06, 2019 benchmark functions and other 15 classical benchmark functions, and it is found that the proposed algorithm is performing better comparatively. Then it is simulated for solving the workflow scheduling problem in CloudSim. Standard scientific workflow applications as Montage, Epigenomics, Sipht, and a sample workflow are used as dataset to test algorithms' performance in cloud environments. Our proposed approach, GTLBO, provides the proper distribution of workloads and offers minimal execution‐cost for the workflow applications. Results reflect the supremacy of the proposed algorithm GTLBO comparatively.

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“…This way, the learning among particles via resource index becomes much more clear and reasonable. Later, Li et al [2015b] further extended their work to a multiobjective scheduling model and proposed using coevolutionary multiswarm PSO [Zhan et al 2013] to solve the problem.…”

Section: Scheduling For User Qosmentioning

confidence: 99%

Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches

et al. 2015

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A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org.

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Renumber Coevolutionary Multiswarm Particle Swarm Optimization for Multi-objective Workflow Scheduling on Cloud Computing Environment

Cited by 21 publications

References 8 publications

An Energy Efficient Ant Colony System for Virtual Machine Placement in Cloud Computing

An Energy Efficient Ant Colony System for Virtual Machine Placement in Cloud Computing

A variant of teaching‐learning‐based optimization and its application for minimizing the cost of Workflow Execution in the Cloud Computing

Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches

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