Energy Efficient VM Scheduling for Cloud Data Centers: Exact Allocation and Migration Algorithms

Ghribi, Chaima; Hadji, Makhlouf; Zeghlache, Djamal

doi:10.1109/ccgrid.2013.89

Cited by 174 publications

(69 citation statements)

References 6 publications

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“…This is even more important as most of related work requires precise information on resource demand as input to their optimisation models, see [1,11,15,17,20]. However, having exact knowledge on e.g.…”

Section: Uncertainty On the Resource Demands For Vmsmentioning

confidence: 99%

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Optimising for energy or robustness? Trade-offs for VM consolidation in virtualized datacenters under uncertainty

Zola

Kassler

2016

Optim Lett

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Reducing the energy consumption of virtualized datacenters and the Cloud is very important in order to lower CO 2 footprint and operational cost of a Cloud operator. However, there is a trade-off between energy consumption and perceived application performance. In order to save energy, Cloud operators want to consolidate as many Virtual Machines (VM) on the fewest possible physical servers, possibly involving overbooking of resources. However, that may involve SLA violations when many VMs run on peak load. Such consolidation is typically done using VM migration techniques, which stress the network. As a consequence, it is important to find the right balance between the energy consumption and the number of migrations to perform. Unfortunately, the resources that a VM requires are not precisely known in advance, which makes it very difficult to optimise the VM migration schedule. In this paper, we therefore propose a novel approach based on the theory of Robust Optimisation (RO). We model the VM consolidation problem as a robust Mixed Integer Linear Program and allow to specify bounds for e.g. resource requirements of the VMs. We show that, by using our model, Cloud operators can effectively trade-off uncertainty of resource requirements with total energy consumption. Also, our model allows us to quantify the price of the robustness in terms of energy saving against resource requirement violations.

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Section: Uncertainty On the Resource Demands For Vmsmentioning

confidence: 99%

“…However, finding an optimal allocation of VMs to support their resource demands on the given set of physical servers in order to minimise e.g. energy consumption is a very hard computational problem and has led to a number of interesting mathematical modelling approaches in recent years [1,11,14,15,17,19,20].…”

mentioning

confidence: 99%

Optimising for energy or robustness? Trade-offs for VM consolidation in virtualized datacenters under uncertainty

Zola

Kassler

2016

Optim Lett

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“…Therefore, optimal solution can only be found for small problem instances (Fischer et al, 2013b). Consequently, several heuristic algorithms have been proposed to find a near optimal solution Sun et al, 2013;Ghribi et al, 2013;Beloglazov & Buyya, 2012;Nogueira et al, 2011). Some algorithms have been proposed to find optimal VNE solutions to be used as optimal bound for the heuristic based VNE solutions (Cheng et al, 2011;Botero et al, 2012).…”

Section: Related Workmentioning

confidence: 99%

Memetic Elitist Pareto Evolutionary Algorithm for Virtual Network Embedding

Shahin¹

2015

CIS

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Assigning virtual network resources to physical network components, called Virtual Network Embedding, is a major challenge in cloud computing platforms. In this paper, we propose a memetic elitist pareto evolutionary algorithm for virtual network embedding problem, which is called MEPE-VNE. MEPE-VNE applies a non-dominated sortingbased multi-objective evolutionary algorithm, called NSGA-II, to reduce computational complexity of constructing a hierarchy of non-dominated Pareto fronts and assign a rank value to each virtual network embedding solution based on its dominance level and crowding distance value. Local search is applied to enhance virtual network embedding solutions and speed up convergence of the proposed algorithm. To reduce loss of good solutions, MEPE-VNE ensures elitism by passing virtual network embedding solutions with best fitness values to next generation. Performance of the proposed algorithm is evaluated and compared with existing algorithms using extensive simulations, which show that the proposed algorithm improves virtual network embedding by increasing acceptance ratio and revenue while decreasing the cost incurred by substrate network.

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“…Chaima Ghribi et al used B-matching algorithms to minimize the numbers of servers [5] . Chaima Ghribi et al proposed the allocation and migration algorithms to minimize overall energy consumption [7] . Changming Zhao et al proposed a novel algorithm named Segmentation Iteration Correlation Combination (SICC), which integrates the methods of statistic regression modeling in order to reduce the difference of peak-mean value of VM resource utilization [8] .…”

Section: Related Workmentioning

confidence: 99%

Multi-Objective Biogeography-Based Method to Optimize Virtual Machine Consolidation

Shi

Luo³

et al. 2016

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-Virtual machine consolidation (VMC) is an important issue in cloud computing, which can be used to reduce power consumption and achieve reasonable resource allocation. In this paper, an IMBBO algorithm is proposed to solve the multi-objective optimization problem of VMC through improving the classical Biogeography-Based Optimization (BBO). An improved Cosine migration model and an improved mutation model are presented to increase the efficiency of achieving the optimal solution. Meanwhile, three optimization objectives for server power consumption, load balancing and migration resource overhead are mainly addressed. Finally, several experiments are done to evaluate the performance of IMBBO by comparing with Gravitational Search Algorithm (GSA) based on the synthetic and real VM running data. The results show that the IMBBO optimizes VM consolidation with higher efficiency.

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Energy Efficient VM Scheduling for Cloud Data Centers: Exact Allocation and Migration Algorithms

Cited by 174 publications

References 6 publications

Optimising for energy or robustness? Trade-offs for VM consolidation in virtualized datacenters under uncertainty

Optimising for energy or robustness? Trade-offs for VM consolidation in virtualized datacenters under uncertainty

Memetic Elitist Pareto Evolutionary Algorithm for Virtual Network Embedding

Multi-Objective Biogeography-Based Method to Optimize Virtual Machine Consolidation

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