Energy Conservation Using Dynamic Voltage Frequency Scaling for Computational Cloud

Florence, A. Paulin; Shanthi, V.; Simon, C. B. Sunil

doi:10.1155/2016/9328070

Cited by 11 publications

(6 citation statements)

References 10 publications

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“…Constraint (9) shows all the available frequency options are bigger than the critical speed. Constraint (10) shows that every node within the system is assigned with a frequency. Combining Constraint (9) and Constraint (10), nodes are always active within the frequency selection process to avoid unacceptable migration cost and unavailability of the system.…”

Section: Discussionmentioning

confidence: 99%

“…Chihoub et al [9] introduced another idea to improve the energy efficiency of cloud database systems, in which they analyzed the tradeoff between energy and consistency of the system by means of Dynamic Voltage and Frequency Scaling (DVFS). DVFS [10] is an efficient technology to control power consumption of processors. Power control policies can be made with DVFS technique.…”

Section: Introductionmentioning

confidence: 99%

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Frequency Selection Approach for Energy Aware Cloud Database

et al. 2019

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A lot of cloud systems are adopted in industry and academia to face the explosion of the data volume and the arrival of the big data era. Meanwhile, energy efficiency and energy saving become major concerns for data centers where massive cloud systems are deployed. However, energy waste is quite common due to resource provisioning. In this paper, using dynamic voltage and frequency scaling (DVFS), a frequency selection approach is introduced to improve the energy efficiency of the cloud systems in terms of resource provisioning. In the approach, two algorithms, genetic algorithm (GA) and Monte Carlo tree search algorithm (MCTS), are proposed. A cloud database system is taken as an example to evaluate the approach. The results of the experiments show that both algorithms have its advantages. The algorithms have great scalability, in which the GA can be applied to thousands of nodes and the MCTS can be applied to hundreds of nodes. Both algorithms have high accuracy compared with optimal solutions (up to 99.9% and 99.6% for GA and MCTS, respectively). According to an optimality bound analysis, 26% of energy can be saved at most using our frequency selection approach. INDEX TERMS Cloud database system, dynamic voltage and frequency scaling, energy efficiency, frequency selection, optimization.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Frequency Selection Approach for Energy Aware Cloud Database

et al. 2019

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“…So as to have an estimation of the energy consumed by the cloud client application, researchers in [8] have performed a power model under various DVFS policies. In [9], the flow pattern of the cloud tasks is studied and according to the results obtained, the researchers attempt to adjust the incoming VM tasks with in-demand frequency using DVFS.…”

Section: Related Work On Green Cloud Computingmentioning

confidence: 99%

Green Cloud Computing: Efficient Energy-Aware and Dynamic Resources Management in Data Centers

Diouani¹,

Medromi²

2018

ijacsa

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Abstract-The users of Cloud computing over the last years are constantly increasing since it has become a very important technology in the computing landscape. It provides client decentralized services and a pay-as-you-go model for consuming resources. The growing need for the cloud services oblige the providers to adopt an enlarged sized data center infrastructure which runs thousands of hosts and servers to store and process data. As a result, these large servers engender a lot of heat with visual carbon emission in the air, as well as important energy consumption and higher operating cost. This is why researches in energy economics continue to progress including energy saving techniques in servers, in the network, cooling, and renewable energies, etc. In this paper, we tackled the existing energy efficient methods in the green cloud computing fields and we put forward our green cloud solution for data center dynamic resource management. Our proposed approach aims to reduce the infrastructure energy consumption and maintain the required performances.

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“…DVFS (Florence et al, 2016;Xu et al, 2012;Moreno and De Niz, 2012;Chen et al, 2013) reduces the processor energy usage by reducing processor frequency. This will slow down the processing speed and stretch the execution time as much as possible.…”

Section: Introductionmentioning

confidence: 99%

Energy performance evaluation for dynamic frequency scaling on rate monotonic and earliest deadline first scheduling algorithm

Sabri¹

2018

Int. j. adv. appl. sci.

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Dynamic Voltage and Frequency Scaling (DVFS) algorithm is widely used to reduce processor's energy consumption in mixed-criticality systems. Existing works show that there are more DVFS algorithms were developed based on Earliest Deadline First (EDF) scheduling scheme compared to Rate Monotonic (RM) scheduling scheme. To understand the reason why EDF is prevalent for DVFS algorithms, simulation can be done to get the power consumption of both scheduling schemes. However, there is no simulation tools available dedicated for this purpose. This research aims to investigate power consumption of both scheduling schemes under different processor speed levels using SimSo simulator. The processor's utilization and schedulability are determined using a set of task in SimSo. Power consumption for Least Common Multiple of tasks period is then calculated based on the proposed power model. The result shows that EDF performs better than RM and able to reduce 30% of energy at processor speed of 0.7. In addition, the capability of SimSo to be used in DVFS algorithm creation also had been observed. It is found that SimSo can be used for further research with additional codes.

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Energy Conservation Using Dynamic Voltage Frequency Scaling for Computational Cloud

Cited by 11 publications

References 10 publications

Frequency Selection Approach for Energy Aware Cloud Database

Frequency Selection Approach for Energy Aware Cloud Database

Green Cloud Computing: Efficient Energy-Aware and Dynamic Resources Management in Data Centers

Energy performance evaluation for dynamic frequency scaling on rate monotonic and earliest deadline first scheduling algorithm

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