Energy Consumption Optimization in Cloud Data Centers

Kliazovich, Dzmitry; Bouvry, Pascal; Granelli, Fabrizio; Fonseca, Nelson Luís Saldanha da

doi:10.1002/9781119042655.ch8

Cited by 21 publications

(12 citation statements)

References 34 publications

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“…Finally, application of cognitive techniques [40] will be explored to address the impact of energy efficiency in cloud computing data centers [41]. Finally, application of cognitive techniques [40] will be explored to address the impact of energy efficiency in cloud computing data centers [41].…”

Section: Discussionmentioning

confidence: 99%

Energy efficiency of TCP: An analytical model and its application to reduce energy consumption of the most diffused transport protocol

Usman

Kliazovich

Granelli

et al. 2015

Int J Communication

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Energy efficient communications become a challenge for both industries and researchers. Incorporating energy efficiency into the design of network protocols and architectures represents a relevant issue in networking research. Currently, very few works address energy efficiency as a fundamental feature of network protocols. This paper benchmarks energy efficiency of TCP to understand the parameters and operational mechanics that determine and contribute to energy consumption. We propose an analytical model with energy consumption to protocol operation cycles and novel optimization techniques for reducing energy consumption of TCP. The evaluation results, obtained from NS2 simulations, demonstrate that even minor modifications of the protocol behavior can bring significant savings of energy.

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Section: Discussionmentioning

confidence: 99%

Energy efficiency of TCP: An analytical model and its application to reduce energy consumption of the most diffused transport protocol

Usman

Kliazovich

Granelli

et al. 2015

Int J Communication

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“…DVFS [2] is a key strategy for energy optimization in Cloud Computing, as shown by the current emergence of many energy optimization strategies that are being developed considering DVFS-enabled networked data centers (e.g., jobs scheduling [10–13]). In essence, DVFS is a technique that, based on the type of governor, adjusts frequency levels of processing elements according to the amount of workload to be executed.…”

Section: Methodsmentioning

confidence: 99%

“…Data centers are the backend computing infrastructures that make up Cloud systems whose performance have a significant economic and environmental impact. It is estimated that data centers consume around 1.5% of the world´s electricity and they are responsible for the emission of about 2% of the CO 2 worldwide with energy costs in an average data center doubling every five years [2, 3]. Designing faster and more powerful data centers has been a priority in the information technology (IT) industry in the recent years.…”

Section: Introductionmentioning

confidence: 99%

“…Improving data centers through IT involves using more adequate servers, networks and data storage resources as well as managing these servers, networks and storage systems more efficiently. Particularly, job scheduling is at the core of the successful power management in Cloud Computing and there is a main strategy for reducing energy consumption: Dynamic Voltage and Frequency Scaling (DVFS), which allows to dynamically adapt the machines performance to the changing conditions of the workload [2]. DVFS technique cannot only be applied for intra-host energy saving but for inter-host energy saving, such as in the recent and successfull DVFS-based scheduling mechanims described in [10–13].…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Voltage Frequency Scaling Simulator for Real Workflows Energy-Aware Management in Green Cloud Computing

et al. 2017

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Nowadays, the growing computational capabilities of Cloud systems rely on the reduction of the consumed power of their data centers to make them sustainable and economically profitable. The efficient management of computing resources is at the heart of any energy-aware data center and of special relevance is the adaptation of its performance to workload. Intensive computing applications in diverse areas of science generate complex workload called workflows, whose successful management in terms of energy saving is still at its beginning. WorkflowSim is currently one of the most advanced simulators for research on workflows processing, offering advanced features such as task clustering and failure policies. In this work, an expected power-aware extension of WorkflowSim is presented. This new tool integrates a power model based on a computing-plus-communication design to allow the optimization of new management strategies in energy saving considering computing, reconfiguration and networks costs as well as quality of service, and it incorporates the preeminent strategy for on host energy saving: Dynamic Voltage Frequency Scaling (DVFS). The simulator is designed to be consistent in different real scenarios and to include a wide repertory of DVFS governors. Results showing the validity of the simulator in terms of resources utilization, frequency and voltage scaling, power, energy and time saving are presented. Also, results achieved by the intra-host DVFS strategy with different governors are compared to those of the data center using a recent and successful DVFS-based inter-host scheduling strategy as overlapped mechanism to the DVFS intra-host technique.

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“…They indeed are unable to differentiate between individual IT subsystems. For example, using existing metrics, it is not possible to distinguish the efficiency of the data center communication system from the efficiency of the computing servers, as both remain considered under the common umbrella of IT equipment [7].…”

Section: Introductionmentioning

confidence: 99%

Performance Metrics for Data Center Communication Systems

Fiandrino

Kliazovich

Bouvry

et al. 2015

2015 IEEE 8th International Conference on Cloud Computing

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Abstract-Cloud computing has become a de facto approach for service provisioning over the Internet. It operates relying on a pool of shared computing resources available on demand and usually hosted in data centers. Assessing performance and energy efficiency of data centers becomes fundamental. Industries use a number of metrics to assess efficiency and energy consumption of cloud computing systems, focusing mainly on the efficiency of IT equipment, cooling and power distribution systems. However, none of the existing metrics is precise enough to distinguish and analyze the performance of data center communication systems from IT equipment. This paper proposes a framework of new metrics able to assess performance and energy efficiency of cloud computing communication systems, processes and protocols. The proposed metrics have been evaluated for the most common data center architectures including fat-tree three-tier, BCube and DCell.

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Energy Consumption Optimization in Cloud Data Centers

Cited by 21 publications

References 34 publications

Energy efficiency of TCP: An analytical model and its application to reduce energy consumption of the most diffused transport protocol

Energy efficiency of TCP: An analytical model and its application to reduce energy consumption of the most diffused transport protocol

Dynamic Voltage Frequency Scaling Simulator for Real Workflows Energy-Aware Management in Green Cloud Computing

Performance Metrics for Data Center Communication Systems

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