Study on cloud resource allocation strategy based on particle swarm ant colony optimization algorithm

Yang, Zhengqiu; Liu, Meiling; Xiu, Jiapeng; Liu, Chen

doi:10.1109/ccis.2012.6664453

Cited by 14 publications

(6 citation statements)

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“…The meta-heuristic ant colony algorithm is considered to resolve these types of problematic issues, but the algorithm has slow convergence speed and parameter selection problems. To resolve this problematic issue, Yang et al [82] propose an optimize ant colony algorithm based on particle swarm algorithm for resolving resources allocation problem in IaaS cloud. Hence, Xu and Yu [83] investigate the issue of resource allocation in cloud computing.…”

Section: Efficiency Aware Resource Allocationmentioning

confidence: 99%

“…Further, Villegas et al [90] propose an extensive and empirical performance of cost analysis for resource allocation and scheduling policies for IaaS Cloud. Firstly, this study presents the taxonomy of mutual types of policies, based on the information type used [82] Ant colony optimization algorithm based on particle swarm algorithm Efficient resources allocation…”

Section: Load Balancing Aware Resource Allocationmentioning

confidence: 99%

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Recent advancements in resource allocation techniques for cloud computing environment: a systematic review

et al. 2016

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There are two actors in cloud computing environment cloud providers and cloud users. On one hand cloud providers hold enormous computing resources in the cloud large data centers that rent the resources out to the cloud users on a pay-per-use basis to maximize the profit by achieving high resource utilization. On the other hand cloud users who have applications with loads variation and lease the resources from the providers they run their applications within minimum expenses. One of the most critical issues of cloud computing is resource management in infrastructure as a service (IaaS). Resource management related problems include resource allocation, resource adaptation, resource brokering, resource discovery, resource mapping, resource modeling, resource provisioning and resource scheduling. In this review we investigated resource allocation schemes and algorithms used by different researchers and categorized these approaches according to the problems addressed schemes and the parameters used in evaluating different approaches. Based on different studies considered, it is observed that different schemes did not consider some important parameters and enhancement is required to improve the performance of the existing schemes. This review contributes to the existing body of research and will help the researchers to gain more insight into resource allocation techniques for IaaS in cloud computing in the future.

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Section: Efficiency Aware Resource Allocationmentioning

confidence: 99%

Section: Load Balancing Aware Resource Allocationmentioning

confidence: 99%

Recent advancements in resource allocation techniques for cloud computing environment: a systematic review

et al. 2016

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show abstract

“…It has parameter selection problem and slow convergence speed. To solve the cloud computing resource allocation problem, the authors discussed in [12], proposes an optimized ant colony algorithm which is based on particle swarm. The new algorithm can solve many issues of heuristic ant colony algorithm with the help of cloud computing resource allocation framework.…”

Section: Resource Discovery and Monitoring And 4) Resource Selectionmentioning

confidence: 99%

“…We have used the above defined configuration for closest data center policy in first and second phase but in third phase we have used reconfigure dynamically with load balancing policy and optimize response time policy. The detailed displayed result for response time, data center request processing time and estimated cost of first phase has shown in Figures [6, 7, 8], the detailed displayed result for response time, data center request processing time and estimated cost of second phase has shown in Figures [9,10] and the detailed displayed result for response time, data center request processing time and estimated cost of third phase has shown in Figures [11,12,13,14,15,16]. The final output of our proposed algorithm for an efficient resource allocation (ERA) is the summation of the results of first phase and second phase.…”

Section: Outputmentioning

confidence: 99%

An Efficient Architecture and Algorithm for Resource Provisioning in Fog Computing

Agarwal¹,

Yadav²,

Yadav³

2016

IJIEEB

122

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Cloud computing is a model of sharing computing resources over any communication network by using virtualization. Virtualization allows a server to be sliced in virtual machines. Each virtual machine has its own operating system/applications that rapidly adjust resource allocation. Cloud computing offers many benefits, one of them is elastic resource allocation. To fulfill the requirements of clients, cloud environment should be flexible in nature and can be achieve by efficient resource allocation. Resource allocation is the process of assigning available resources to clients over the internet and plays vital role in Infrastructure-as-a-Service (IaaS) model of cloud computing. Elastic resource allocation is required to optimize the allocation of resources, minimizing the response time and maximizing the throughput to improve the performance of cloud computing. Sufficient solutions have been proposed for cloud computing to improve the performance but for fog computing still efficient solution have to be found. Fog computing is the virtualized intermediate layer between clients and cloud. It is a highly virtualized technology which is similar to cloud and provide data, computation, storage, and networking services between end users and cloud servers. This paper presents an efficient architecture and algorithm for resources provisioning in fog computing environment by using virtualization technique.

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“…But it also suffers from premature convergence and an excessive computational time. In [10], a cloud resource allocation strategy based on optimized ACO was presented. The overall performance of resource allocation in the cloud environment is enhanced by the algorithm, but the algorithm lacks the validation of simulation experiments.…”

Section: Introductionmentioning

confidence: 99%

Modified Elite Chaotic Immune Clonal Selection Algorithm for sever resource allocation in cloud computing systems

Zhou

Dutkiewicz

Liu

et al. 2014

2014 International Symposium on Wireless Personal Multimedia Communications (WPMC)

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Cloud computing is a promising technology to improve computational efficiency for both IT enterprise and individuals. Resource allocation in cloud computing is very challenging as both server computing power and network bandwidth are limited. The computational efficiency of cloud computing system can be significantly improved if the resources are allocated in a balanced fashion. However, resource allocation in cloud computing is a multi-constrained nonlinear optimization problem. The computational complexity for an exhaustive search over all combinations of resource allocations is too high for practical implementation. In this paper, we develop a Modified Elite Chaotic Immune Clonal Selection Algorithm to increase the overall efficiency of the system. An elite strategy and chaotic approaches are designed to improve population diversity and escape from local optima. Performance comparisons are made with simulated annealing algorithm (SA) and three other heuristic algorithms. Simulation results show that the Modified Elite Chaotic Immune Clonal Selection Algorithm solves the resource allocation problem with higher system resource efficiency than all other heuristic algorithms.

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Study on cloud resource allocation strategy based on particle swarm ant colony optimization algorithm

Cited by 14 publications

References 0 publications

Recent advancements in resource allocation techniques for cloud computing environment: a systematic review

Recent advancements in resource allocation techniques for cloud computing environment: a systematic review

An Efficient Architecture and Algorithm for Resource Provisioning in Fog Computing

Modified Elite Chaotic Immune Clonal Selection Algorithm for sever resource allocation in cloud computing systems

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