A multi-objective ant colony system algorithm for virtual machine placement in cloud computing

Gao, Yongqiang; Guan, Haibing; Qi, Zhengwei; Hou, Yang; Liu, Liang

doi:10.1016/j.jcss.2013.02.004

Cited by 591 publications

(373 citation statements)

References 33 publications

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“…However, the evaluation is shown by varying only the number of cores demanded by VMs while keeping other resource demands unchanged and as a result the evaluation is simplified to one-dimensional resource. Another recent work [16] proposed a multi-objective ACO algorithm to reduce resource wastage and power consumption in cloud data centers. This work considers two types of resources (i.e.…”

Section: Related Workmentioning

confidence: 99%

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Virtual Machine Consolidation in Cloud Data Centers Using ACO Metaheuristic

Ferdaus

Murshed

Calheiros

et al. 2014

Lecture Notes in Computer Science

151

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Abstract. In this paper, we propose the AVVMC VM consolidation scheme that focuses on balanced resource utilization of servers across different computing resources (CPU, memory, and network I/O) with the goal of minimizing power consumption and resource wastage. Since the VM consolidation problem is strictly NP-hard and computationally infeasible for large data centers, we propose adaptation and integration of the Ant Colony Optimization (ACO) metaheuristic with balanced usage of computing resources based on vector algebra. Our simulation results show that AVVMC outperforms existing methods and achieves improvement in both energy consumption and resource wastage reduction.

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Section: Related Workmentioning

confidence: 99%

“…16) using a probabilistic decision rule (Eq. 15) [line [11][12][13][14][15][16][17][18][19][20][21][22]. If the current PM is fully utilized or there are no feasible VMs left to assign to the PM, a new empty PM is taken to fill in [line [14][15][16].…”

Section: Avvmc Algorithmmentioning

confidence: 99%

Virtual Machine Consolidation in Cloud Data Centers Using ACO Metaheuristic

Ferdaus

Murshed

Calheiros

et al. 2014

Lecture Notes in Computer Science

151

View full text Add to dashboard Cite

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“…The performance of the proposed MEGSA-VMM algorithm is compared with the other existing methods like the ACO [13], EGSA-1, EGSA-2 and GSA [20] regarding load, QoS, migration cost and energy. ACO is an optimization algorithm, which performs load balancing using the ant-colony concept.…”

Section: Methods Taken For Comparisonmentioning

confidence: 99%

“…Load balancing can be performed in two ways namely, distributed and nondistributed load balancing. In distributed balancing, load from the overloaded virtual machine gets distributed to the other virtual machines, whereas in the non-distributed load balancing, only one virtual machine is involved in load balancing [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Multiple Objective–Based Modified Exponential Gravitational Search Algorithm to VMM Strategy for Load Balancing in Cloud

Polepally¹,

Chatrapati²

2017

IJIES

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Cloud computing is a trending topic in the field of science and technology since the internet dependent services have been growing rapidly. In this environment, there are a lot of immense infrastructures and resources to satisfy the internet users. When a large number of service requests reach at a particular time, load balancing becomes a necessity. Load balancing involves the effective migration of the resources from the loaded physical machine to the other physical machine. For the effective migration, a method named Modified Exponential Gravitational Search Algorithm based on Virtual Machine Migration strategy (MEGSA-VMM) has been proposed that uses the gravitational concepts for performing the frequency-based velocity computations. MEGSA algorithm is the integration of the gravitational search algorithm and exponential weighted moving average theory. Also, the qualityof-Service (QoS) constraints considered for VM migration are migration cost, migration time, resource usage and energy. Simulation of the proposed method and the comparison of the results obtained, with the traditional methods like Ant-Colony Optimization (ACO), Gravitational Search Algorithm (GSA) and Exponential Gravitational Search Algorithm (EGSA) is performed. The proposed method is found to achieve an optimum migration with a minimum energy at a rate of 0.26 and minimum migration cost at a rate of 0.015.

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“…Following our argumentation, we further investigated the listed publications with respect to the evaluation of QoS aspects and summarize the results in Table 1. [21] dynamic no spare capacity Rolia2005 [22] dynamic yes spare capacity Bichler2006 [23] dynamic no not explicitly considered Cherkasova2006 [41] dynamic yes spare capacity Stillwell2010 [19] static no not explicitly considered Xu2010 [66] static no not explicitly considered Speitkamp2010 [14] dynamic no quantiles of historical service demands Feller2011 [43] dynamic no not explicitly considered Gao2013 [67] static no not explicitly considered At first, Table 1 distinguishes between static and dynamic workload. If the optimization problem implicates a time dimension for service capacity demands, we consider the approach to be dynamic, following the classification by [43].…”

Section: Service and Component Capacity Managementmentioning

confidence: 99%

Capacity Management as a Service for Enterprise Standard Software

Müller

Bosse

Turowski

2018

J. Complex Syst. Inform. Model. Q.

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Abstract. Capacity management approaches optimize component utilization from a strong technical perspective. In fact, the quality of involved services is considered implicitly by linking it to resource capacity values. This practice hinders to evaluate design alternatives with respect to given service levels that are expressed in user-centric metrics such as the mean response time for a business transaction. We argue that utilized historical workload traces often contain a variety of performance-related information that allows for the integration of performance prediction techniques through machine learning. Since enterprise applications excessively make use of standard software that is shipped by large software vendors to a wide range of customers, standardized prediction models can be trained and provisioned as part of a capacity management service which we propose in this article. Therefore, we integrate knowledge discovery activities into well-known capacity planning steps, which we adapt to the special characteristics of enterprise applications. Using a realworld example, we demonstrate how prediction models that were trained on a large scale of monitoring data enable cost-efficient measurement-based prediction techniques to be used in early design and redesign phases of planned or running applications. Finally, based on the trained model, we demonstrate how to simulate and analyze future workload scenarios. Using a Pareto approach, we were able to identify cost-effective design alternatives for an enterprise application whose capacity is being managed.

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A multi-objective ant colony system algorithm for virtual machine placement in cloud computing

Cited by 591 publications

References 33 publications

Virtual Machine Consolidation in Cloud Data Centers Using ACO Metaheuristic

Virtual Machine Consolidation in Cloud Data Centers Using ACO Metaheuristic

Multiple Objective–Based Modified Exponential Gravitational Search Algorithm to VMM Strategy for Load Balancing in Cloud

Capacity Management as a Service for Enterprise Standard Software

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