Renumber strategy enhanced particle swarm optimization for cloud computing resource scheduling

Li, Hai-Hao; Fu, Yuwen; Zhan, Zhi-Hui; Li, Jingjing

doi:10.1109/cec.2015.7256982

Cited by 32 publications

(17 citation statements)

References 20 publications

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“…EXPERIMENTS AND COMPARISONS In our experiment, for every type of resource r j , we define its processing capability (cap j ) as Random (1,10) and its cost per unit time as Normal(cap j ,0.1). Where Normal(a, b) represents the random value generated by the normal (Gaussian) distribution with mean a and standard deviation b.…”

Section: E Flowchart Of the Acsmentioning

confidence: 99%

“…Where Normal(a, b) represents the random value generated by the normal (Gaussian) distribution with mean a and standard deviation b. For every task t i , we define its size t i _ as Random (10,30) In cloud computing, the tasks are assumed to have a complex topological structure. We design an algorithm shown in Fig.…”

Section: E Flowchart Of the Acsmentioning

confidence: 99%

“…Moreover, the PSO approach does not perform well in the situation of tight deadline for large scale workflow. Later, Li et al [10] proposed to use renumber strategy to enhance the PSO and also extended the scheduling to multiobjective optimization [11].…”

Section: Introductionmentioning

confidence: 99%

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Deadline Constrained Cloud Computing Resources Scheduling through an Ant Colony System Approach

Chen

Zhan

Li³

et al. 2015

2015 International Conference on Cloud Computing Research and Innovation (ICCCRI)

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Abstract-Cloud computing resources scheduling is essential for executing workflows in the cloud platform because it relates to both the execution time and execution costs. In solving the problem of optimizing the execution costs while meeting deadline constraints, we developed an efficient approach based on ant colony system (ACS). For scheduling T tasks on R resources, an ant in ACS represents a solution with T dimensions, with each dimension being a task and the value of each dimension being an integer ranges in [1, R] to indicate scheduling the task on which resource. With such solution encoding, the ant in ACS constructs a solution in T steps, with each step optimally selecting one resource from the R resources, according to both the pheromone and heuristic information. Therefore, the solution encoding is very simple and straight to reflect the mapping relation of tasks and resources. Moreover, the solution construct process is very natural to find optimal solution based on the encoding scheme. We have conducted extensive experiments based on workflows with various scales and various cloud resources. We compare the results with those of particle swarm optimization (PSO) and dynamic objective genetic algorithm (DOGA) approaches. The experimental results show that ACS is able to find better solutions with a lower cost than both PSO and DOGA do on various scheduling scales and deadline conditions.

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Section: E Flowchart Of the Acsmentioning

confidence: 99%

Section: E Flowchart Of the Acsmentioning

confidence: 99%

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Deadline Constrained Cloud Computing Resources Scheduling through an Ant Colony System Approach

Chen

Zhan

Li³

et al. 2015

2015 International Conference on Cloud Computing Research and Innovation (ICCCRI)

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“…Then the task t 5 can be executed only after both t 2 and t 3 have finished and t 3 has transferred the data to t 5 , with data transfer time being 2. The cloud workflow scheduling model has been defined in [3], [6], and [7], which is also briefly described as follows. A schedule is defined as S = (T, R, M, TEC, TET) where T represents a set of tasks, R = {r 1 , r 2 , … , r n } is a set of resources, M represents the tasks to resources mappings, TEC is short for 'total execution cost', and TET is short for 'total execution time'.…”

Section: Introductionmentioning

confidence: 99%

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

Chen

Zhan

et al. 2015

Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation

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Resources scheduling is a significant research topic in cloud computing, which is often modeled as a cost-minimization and deadline-constrained workflow scheduling model. This is a constrained single objective problem that to minimize the overall workflow execution cost while meeting deadline constraints. In this paper, we offer a new horizon to convert this single-objective problem to a multi-objective problem and present coevolutionary multiswarm particle swarm optimization (CMPSO) to find the non-dominated solutions with different execute cost and time. Meanwhile, the renumber strategy is adopted in CMPSO to make the learning efficient. CMPSO is compared with a renumber PSO (RNPSO) by setting the execute time in the CMPSO's nondominated solutions as the deadline constraint of RNPSO. Results show that CMPSO not only offers many non-dominated solutions with different prices and execute time, but also obtains better solution than RNPSO under a same deadline.

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“…Therefore, learning from the resources index might make the particles fly randomly. In order to make the guidance more efficient, Li et al [2015a] proposed a renumber strategy to use the metric of price per unit time to reorder the resources. This way, the learning among particles via resource index becomes much more clear and reasonable.…”

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 strategy enhanced particle swarm optimization for cloud computing resource scheduling

Cited by 32 publications

References 20 publications

Deadline Constrained Cloud Computing Resources Scheduling through an Ant Colony System Approach

Deadline Constrained Cloud Computing Resources Scheduling through an Ant Colony System Approach

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

Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches

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