Sandpiper: Black-box and gray-box resource management for virtual machines

Wood, Timothy; Shenoy, Prashant; Venkataramani, Arun; Yousif, Mazin

doi:10.1016/j.comnet.2009.04.014

Cited by 307 publications

(208 citation statements)

References 10 publications

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“…Consequently, a good amount of research works have been done in this area and depending on the modeling techniques used, different problem solving techniques are proposed. Most of the works that apply greedy heuristics primarily model VM consolidation as variants of the bin packing problem and propose extensions of simple greedy algorithms such as First Fit Decreasing (FFD) [7], Best Fit [6], Best Fit Decreasing [8], and so on [9,10]. However, as VM consolidation is a NP-hard problem, greedy approaches are not guaranteed to generate near optimal solutions.…”

Section: Related Workmentioning

confidence: 99%

“…Pheromone levels are implemented using a n×m matrix τ . Each ant starts with an empty solution, a set of PMs, and a randomly shuffled set of VMs [line [6][7][8][9][10][11][12]. Inside the while loop, an ant is chosen at random and is allowed to choose a VM to assign next to its current PM among all the feasible VMs (Eq.…”

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%

See 1 more Smart Citation

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

show abstract

“…For example, Wood et al [16] propose an approach called Sandpiper for overload detection and live migration of VMs from overloaded hosts to Similarly, Khanna et al [17] propose an approach for dynamic consolidation of VMs based on live migration. Their approach for host overload detection is also based on resource usage exceeding a threshold value.…”

Section: Related Workmentioning

confidence: 99%

Tackling uncertainty in long-term predictions for host overload and underload detection in cloud computing

2017

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Dynamic workloads in cloud computing can be managed through live migration of virtual machines from overloaded or underloaded hosts to other hosts to save energy and/or mitigate performance-related Service Level Agreement (SLA) violations. The challenging issue is how to detect when a host is overloaded to initiate live migration actions in time. In this paper, a new approach to make long-term predictions of resource demands of virtual machines for host overload detection is presented. To take into account the uncertainty of long-term predictions, a probability distribution model of the prediction error is built. Based on the probability distribution of the prediction error, a decision-theoretic approach is proposed to make live migration decision that take into account live migration overheads. Experimental results using the CloudSim simulator and PlanetLab workloads show that the proposed approach achieves better performance and higher stability compared to other approaches that do not take into account the uncertainty of long-term predictions and the live migration overhead.

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“…They emphasized their model for SLA optimization. Later, bin packing problem was considered in [10][11][12] for efficient VM consolidation. Majority of existing approaches have used Best Fit Decrease (BFD) based consolidation [2].…”

Section: Related Workmentioning

confidence: 99%