DENS: Data Center Energy-Efficient Network-Aware Scheduling

Kliazovich, Dzmitry; Bouvry, Pascal; Khan, Samee U.

doi:10.1109/greencom-cpscom.2010.31

Cited by 160 publications

(138 citation statements)

References 21 publications

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“…The obtained bell-shaped function favors selection of servers with the load level above average located in racks with the minimum or no congestion. Reference [37] provides more details about DENS metrics and its performance in different operation scenarios. …”

Section: The Dens Methodologymentioning

confidence: 99%

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Simulation and Performance Analysis of Data Intensive and Workload Intensive Cloud Computing Data Centers

2012

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Data centers are becoming increasingly popular for the provisioning of computing resources. The cost and operational expenses of data centers have skyrocketed with the increase in computing capacity [1]. Energy consumption is a growing concern for data center operators. It is becoming one of the main entries on a data center operational expenses (OPEX) bill [2,3]. The Gartner Group estimates energy consumptions to account for up to 10% of the current OPEX, and this estimate is projected to rise to 50% in the next few years [4]. However, computing-based energy consumption is not the only power-related portion of the OPEX bill. High power consumption generates heat and requires an accompanying cooling system that costs in a range of $2-$5 million per year for classical data centers [5]. Failure to keep data center temperatures within operational ranges drastically decreases hardware reliability and may potentially violate the service level agreement (SLA) with the customers.From the perspective of energy efficiency, a cloud computing data center can be defined as a pool of computing and communication resources organized in the way to transform the received power into computing or data transfer work to satisfy user demands. The first power saving solutions focused on making the data center hardware components power efficient. Technologies, such as dynamic voltage and frequency scaling (DVFS), and dynamic power management (DPM) [6], were

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Section: The Dens Methodologymentioning

confidence: 99%

“…The details of the selection process are presented in [37]. The factor related to the choice of computing servers combines the server load L s (l) and its communication potential Q r (q) that corresponds to the fair share of the uplink resources on the ToR switch.…”

Section: The Dens Methodologymentioning

confidence: 99%

Simulation and Performance Analysis of Data Intensive and Workload Intensive Cloud Computing Data Centers

2012

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“…The increase in computing capacity caused a rapid and sudden increase in the expenses of operating data centers. It has been shown by [1] that energy consumption by data centers forms a major part of the operational expenses bill.…”

Section: Introductionmentioning

confidence: 99%

“…Two technologies were widely used and deployed, namely: Dynamic Voltage and Frequency Scaling (DVFS), and Dynamic Power Management (DPM) [1]. The project of DPM explores some technologies that are concerned with improving the issue of conserving power capabilities in the platforms that are based on software that is open source.…”

Section: Introductionmentioning

confidence: 99%

Scalability of the DVFS Power Management Technique as Applied to 3-Tier Data Center Architecture in Cloud Computing

Bani‐Ahmad¹,

Sa’adeh²

2017

JCC

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The increase in computing capacity caused a rapid and sudden increase in the Operational Expenses (OPEX) of data centers. OPEX reduction is a big concern and a key target in modern data centers. In this study, the scalability of the Dynamic Voltage and Frequency Scaling (DVFS) power management technique is studied under multiple different workloads. The environment of this study is a 3-Tier data center. We conducted multiple experiments to find the impact of using DVFS on energy reduction under two scheduling techniques, namely: Round Robin and Green. We observed that the amount of energy reduction varies according to data center load. When the data center load increases, the energy reduction decreases. Experiments using Green scheduler showed around 83% decrease in power consumption when DVFS is enabled and DC is lightly loaded. In case the DC is fully loaded, in which case the servers' CPUs are constantly busy with no idle time, the effect of DVFS decreases and stabilizes to less than 10%. Experiments using Round Robin scheduler showed less energy saving by DVFS, specifically, around 25% in light DC load and less than 5% in heavy DC load. In order to find the effect of task weight on energy consumption, a set of experiments were conducted through applying thin and fat tasks. A thin task has much less instructions compared to fat tasks. We observed, through the simulation, that the difference in power reduction between both types of tasks when using DVFS is less than 1%.

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“…in parameter sweep applications [2,3], the scheduling problems in Computational Grids (CGs) and in Data Grids (DGs) is dealing with in a separated way. Much of the current efforts are focused on scheduling workloads in a data center or schedule movement of data and data placement [42] for efficient resource/storage utilization or energy-effective scheduling in largescale data centers [41], [8], [33], [18], [48], [51], [57], [7], [10], [16], [17]. A recent example is that of GridBatch [44] for large scale data-intensive problems on cloud infrastructures.…”

Section: Introductionmentioning

confidence: 99%

Data Scheduling in Data Grids and Data Centers: A Short Taxonomy of Problems and Intelligent Resolution Techniques

Kołodziej

Khan

2013

Transactions on Computational Collective Intelligence X

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Abstract. Data-aware scheduling in today's large-scale heterogeneous environments has become a major research issue. Data Grids (DGs) and Data Centers arise quite naturally to support needs of scientific communities to share, access, process, and manage large data collections geographically distributed. Data scheduling, although similar in nature with grid scheduling, is given rise to the definition of a new family of optimization problems. New requirements such as data transmission, decoupling of data from processing, data replication, data access and security are to be added to the scheduling problem are the basis for the definition of a whole taxonomy of data scheduling problems. In this paper we briefly survey the state-of-the-art in the domain. We exemplify the model and methodology for the case of data-aware independent job scheduling in computational grid and present several heuristic resolution methods for the problem.

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DENS: Data Center Energy-Efficient Network-Aware Scheduling

Cited by 160 publications

References 21 publications

Simulation and Performance Analysis of Data Intensive and Workload Intensive Cloud Computing Data Centers

Simulation and Performance Analysis of Data Intensive and Workload Intensive Cloud Computing Data Centers

Scalability of the DVFS Power Management Technique as Applied to 3-Tier Data Center Architecture in Cloud Computing

Data Scheduling in Data Grids and Data Centers: A Short Taxonomy of Problems and Intelligent Resolution Techniques

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