Energy Efficient Resource Allocation Strategy for Cloud Data Centres

Quân, Đặng Minh; Basmadjian, Robert; Meer, Hermann de; Lent, Ricardo; Mahmoodi, Toktam; Sannelli, Domenico; Mezza, Federico; Telesca, Luigi; Dupont, Corenten

doi:10.1007/978-1-4471-2155-8_16

Cited by 25 publications

(19 citation statements)

References 8 publications

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“…In addition, the problem of optimally mapping virtual machines (VMs) to servers can be reduced to the bin packing problem [10], [11], and [12]. This problem is known to be NP-hard, therefore heuristic approaches can only lead to sub-optimal solutions.…”

Section: Related Workmentioning

confidence: 99%

“…Live migration of VMs between servers is adopted by the VMware Distributed Power Management system, using lower and upper utilization thresholds to enact migration procedures [17]. The heuristic approaches presented in [11] and in [12] use techniques derived from the Best Fit Decreasing and the First Fit Decreasing algorithms, respectively. In both cases, the goal is to place each migrating VM on the server that minimizes the overall power consumption of the data center.…”

Section: Related Workmentioning

confidence: 99%

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A multi-dimensional job scheduling

Sheikhalishahi

Wallace

Grandinetti

et al. 2016

Future Generation Computer Systems

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A multi-dimensional job scheduling

Sheikhalishahi

Wallace

Grandinetti

et al. 2016

Future Generation Computer Systems

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“…In the heuristics proposed in [15], for each VM to be moved we find the appropriate server that leads to minimize the current overall power consumption of a data centre. This is similar to the First Fit Decreasing (FFD) algorithm which has been used in previous works [17][18] [19], with the addition of powerawareness for choosing the server.…”

Section: Search Heuristicsmentioning

confidence: 99%

“…The problem of consolidating and rearranging the allocation of virtual machines in a datacenter in an energy efficient manner is described in [15]. It is known to be a NP-hard problem [16] with a large solution space.…”

Section: Search Heuristicsmentioning

confidence: 99%

An energy aware framework for virtual machine placement in cloud federated data centres

Dupont

Schulze

Giuliani

et al. 2012

Proceedings of the 3rd International Conference on Future Energy Systems: Where Energy, Computing and Communication Meet

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International audienceData centres are powerful ICT facilities which constantly evolve in size, complexity, and power consumption. At the same time users' and operators' requirements become more and more complex. However, existing data centre frameworks do not typically take energy consumption into account as a key parameter of the data centre's configuration. To lower the power consumption while fulfilling performance requirements we propose a flexible and energy-aware framework for the (re)allocation of virtual machines in a data centre. The framework, being independent from the data centre management system, computes and enacts the best possible placement of virtual machines based on constraints expressed through service level agreements. The framework's flexibility is achieved by decoupling the expressed constraints from the algorithms using the Constraint Programming (CP) paradigm and programming language, basing ourselves on a cluster management library called Entropy. Finally, the experimental and simulation results demonstrate the effectiveness of this approach in achieving the pursued energy optimization goals

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“…The algorithm proposed in [6] is based on first fit approximation approach for the bin packing problem. The heuristic approach proposed in [7] is derived from First fit decreasing algorithm. The framework presented in [8] solves the VM placement problem using the constraint programming paradigm.…”

Section: IImentioning

confidence: 99%

A PSO Based VM Resource Scheduling Model for Cloud Computing

Kumar

Raza

2015

2015 IEEE International Conference on Computational Intelligence &Amp; Communication Technology

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Cloud computing, a new paradigm for utility computing, has brought a revolutionary change in the IT industry by enabling the elastic on demand resource provisioning of computing resources. As cloud infrastructure heavily relies on virtualization technology, there is a need for an efficient and effective virtual machine scheduling strategy. Virtual machine scheduling problem can be defined as an allocation of a set of virtual machines (VMs) to a set of physical machines (PMs). The proposed work focuses on PSO based VM scheduling strategy for VM placement in cloud infrastructure. The strategy focuses on efficient VM allocation to physical servers in order to minimize the total resource wastage and the number of servers used. Simulation experiments were conducted to observe the allocation of VMs to the servers and to evaluate the proposed algorithm with respect to performance and scalability. The results are compared with Best-Fit, First-Fit and Worst-Fit placement strategies. Simulation study conducted to evaluate the performance reveals the effectiveness of the model.

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Energy Efficient Resource Allocation Strategy for Cloud Data Centres

Cited by 25 publications

References 8 publications

A multi-dimensional job scheduling

A multi-dimensional job scheduling

An energy aware framework for virtual machine placement in cloud federated data centres

A PSO Based VM Resource Scheduling Model for Cloud Computing

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