Energy-Aware Ant Colony Based Workload Placement in Clouds

Feller, Eugen; Rilling, Louis; Morin, Christine

doi:10.1109/grid.2011.13

Cited by 282 publications

(188 citation statements)

References 14 publications

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“…Later, Brugger et al [14] used a later version of the ACO metaheuristic that demonstrated superior performance over genetic algorithm for large problem instances. Feller et al [15] used another version of ACO to address VM consolidation and has shown better results than FFD. 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.…”

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%

“…Because of the lack of access to large scale testbeds or real cloud infrastructures and ease of reproducibility, we resorted to simulation-based evaluation to compare the performance of the proposed AVVMC to the following existing works in literature: 1) an adapted version of Max-Min Ant System (MMAS) metaheuristic for VM consolidation (MMVMC) [15], 2) a greedy algorithm (VectorGreedy) [6] for solving consolidation that uses vector algebra for mean estimation of multi-dimensional resources, 3) a modified version of the FFD algorithm (FFD-Volume) [7] that uses volume-based mean estimator, and 4) another modified FFD algorithm (FFD-L1Norm) based on L 1 norm mean estimator. The simulated data center consists of a cluster of homogeneous PMs and VM resource demand for each resource type is expressed in percentage of total resource capacity of PM.…”

mentioning

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%

Section: Avvmc Algorithmmentioning

confidence: 99%

mentioning

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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show abstract

“…[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]. In contrast, static approaches usually take the peak demand of a service and optimize allocations with respect to this single value, thus, practically wasting optimization potential.…”

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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“…Genetic Algorithm (GA) method is employed to avoid SLA (Service level agreement) violation, reduce number of real nodes in use and reduce virtual machine migrations. Feller et al [14] proposed Energy-Aware ACO-based Workload Consolidation algorithm minimizing the number of physical machines required. In [15], a multi-objective ant colony system algorithm for virtual machine placement in cloud computing is proposed to minimize resource wastage and power consumption.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective Virtual Machine Placement for Load Balancing

Feng

2017

ITM Web Conf.

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Abstract. The virtual machine placement is closely related to the efficient and balanced utilization of physical resources. In this paper, the influence of two scenarios about resource utilization on load balancing is analyzed. A multi-objective ant colony optimization algorithm is proposed to solve the virtual machine placement problem, which balances the load among physical machines and the internal load of physical machine simultaneously. The proposed algorithm is compared with two single objective ant colony optimization algorithms, first fit algorithm and greedy algorithm under some instances. The results show that the proposed algorithm can search and find solutions that exhibit good balance among objectives while others cannot. This demonstrates the proposed algorithm can balance the load in the process of mapping virtual machines to physical machines.

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Energy-Aware Ant Colony Based Workload Placement in Clouds

Cited by 282 publications

References 14 publications

Virtual Machine Consolidation in Cloud Data Centers Using ACO Metaheuristic

Virtual Machine Consolidation in Cloud Data Centers Using ACO Metaheuristic

Capacity Management as a Service for Enterprise Standard Software

Multi-objective Virtual Machine Placement for Load Balancing

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