Energy-efficient Virtual Machine Provision Algorithms for Cloud Systems

Lin, Ching-Chi; Liu, Pangfeng; Wu, Jan‐Jan

doi:10.1109/ucc.2011.21

Cited by 29 publications

(8 citation statements)

References 8 publications

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“…Let N be the optimal number of bins used in a bin packing problem. Dósa and György [14] show that First Fit Decreasing method uses only 11 9 N + 6 9 bins and this bound is tight. Xia and Tan [15] prove that First Fit method uses 17 10 N + 7 10 bins, and the absolute performance ratio of First Fit is at most 12 7 .…”

Section: Related Workmentioning

confidence: 89%

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Workload characteristics-aware virtual machine consolidation algorithms

Yang

Liu

2012

4th IEEE International Conference on Cloud Computing Technology and Science Proceedings

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An energy conservation strategy must address two issues -placement of virtual machine images and workload characteristics of virtual machines. For performance reason most cloud systems copy a prototype image into the local disk of a physical machine before starting a virtual machine. If the physical machine that stores the image of a virtual machine is off-line, then we cannot run this virtual machine. The workload characteristics of virtual machines determine whether it is data-intensive or CPU-intensive. We assume that the system has a distributed file system therefore a physical machine can run any virtual machine even if it does not have the image. However, we observe that the performance of a data-intensive virtual machine running on a physical machine without its image could result in 60% performance loss compared with running the same virtual machine on a physical machine that has the virtual machine image. On the other hand, the performance of a CPU-intensive virtual machine is almost independent of whether the physical machine has the image or not. As a result, an energy conservation algorithm must consider the workload characteristic of a virtual machine when finding a physical machine to run it, especially for data-intensive virtual machines. This paper proposes a workload characteristics-aware virtual machine consolidation algorithms. We propose an approximation algorithm and two dynamic programmings to consolidate virtual machines and reduce the number of physical machines. We conduct experiments and compare the numbers of physical machines used by our approximation algorithm with the optimal number of physical machines found by our dynamic programming. The experiment results indicate that our approximation algorithm finds good solutions much faster than the dynamic programming.

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Section: Related Workmentioning

confidence: 89%

“…Lin et al [11] propose two algorithms, Dynamic RoundRobin (DRR) and Hybrid, which combines DRR and First Fit, for energy-aware virtual machine scheduling and consolidation. DRR is an extension from the original round-robin method and follows two rules to help consolidate virtual machines.…”

Section: Related Workmentioning

confidence: 99%

Workload characteristics-aware virtual machine consolidation algorithms

Yang

Liu

2012

4th IEEE International Conference on Cloud Computing Technology and Science Proceedings

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“…Since the CPU contribution is dominant among these factors in MEC host, it is the main focus in the literature [36]. Two models are widely used for the energy consumption: one model is based on the Dynamic voltage and frequency scaling (DVFS) [37] technique while the other model is based on an observation in [38] [39] that the serverenergy consumption is linear to the CPU utilization ratio, which depends on the computation load. We modelled the energy consumption of the server at the MEC host based on CPU utilization as in (1), assuming a fixed running frequency, where is the energy consumption for a fully utilized server, is the fraction of the idle energy consumption and denotes the CPU utilization ratio.…”

Section: B Energy Modelingmentioning

confidence: 99%

Mobility-Aware Energy Optimization in Hosts Selection for Computation Offloading in Multi-Access Edge Computing

Thananjeyan

Chan

Wong

et al. 2020

IEEE Open J. Commun. Soc.

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Multi-access edge computing (MEC) has been proposed as an approach capable of addressing latency and bandwidth issues in application computation offloading to extend the capabilities beyond the computational and storage limitations of mobile devices. However, there is a critical challenge in MEC to maintain the service continuity between the offloaded user application that is running on the MEC host and the mobile device when a user is moving from radio node to radio node. Furthermore, energy consumption of application computation offloading is an important consideration for MEC service providers in terms of operational costs. Therefore, we formulate the MEC host selection and user application migration problem as a shortest path problem of network energy minimization. We simulate the problem in a hierarchical MEC network deployment environment. We also propose the metric, computational intensity (CI), that can be used by MEC service providers to address the MEC host selection problem. Our results show that with the increment of CI, the selection of MEC hosts tends to move toward level 3 (central deployment) due to energy efficiency and then return to the deployment at level 1 (radio node level) due to latency constraint of the user application. We show that with high accuracy in predicting the user mobility and the available resources in the MEC network, latency-and mobility-aware MEC host selection and user application migration can be pre-calculated to improve response time and energy efficiency.

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“…In the same context, power consumption modeling has been used for power-aware VM allocation using genetic algorithms [17] or through heuristic algorithms [18]. The definition of energy efficiency in cloud computing has been playing an important role also for the application of VM consolidation strategies [19] [20].…”

Section: State Of the Art And Related Workmentioning

confidence: 99%

Estimation and forecasting of ecological efficiency of virtual machines

Katsaros

Stichler

Subirats

et al. 2016

Future Generation Computer Systems

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The massive development of the cloud marketplace is leading to an increase in the number of the Data Centers (DCs) globally and eventually to an increase of the CO 2 related footprint. The calculation of the impact of Virtual Machines (VMs) on the environment is a challenging task, not only due to the technical difficulties but also due to the lack of information from the energy providers. The ecological efficiency of a system captures the relationship between the performance of the system with its environmental footprint. In this paper we present a methodology for the estimation and prediction of the ecological efficiency of VMs in private cloud infrastructures. We specifically focus on the information management starting from the energy resources in a region, the energy consumption and the performance of the resources and finally the calculation of ecological efficiency of a VM. To this end, we have designed and implemented a framework through which the ecological efficiency of a running VM can be assessed and the ecological efficiency of a VM to be deployed can be forecasted. The presented framework is being evaluated through several private cloud scenarios with VM deployments in hosts located in Germany.

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Energy-efficient Virtual Machine Provision Algorithms for Cloud Systems

Cited by 29 publications

References 8 publications

Workload characteristics-aware virtual machine consolidation algorithms

Workload characteristics-aware virtual machine consolidation algorithms

Mobility-Aware Energy Optimization in Hosts Selection for Computation Offloading in Multi-Access Edge Computing

Estimation and forecasting of ecological efficiency of virtual machines

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