Game based traffic exchange for green data center networks

Ghassemi, A.; Goudarzi, Pejman; Mirsarraf, Mohammad Reza; Gulliver, T. Aaron

doi:10.1109/jcn.2018.000008

Cited by 6 publications

(9 citation statements)

References 14 publications

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“…In [34] and [35], the authors propose a non-cooperative gametheoretic workload management technique to minimize data center energy costs, while considering TOU electricity pricing information.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…Similar to [34] and [35], our work uses information about TOU electricity pricing and develops a non-cooperative game-theoretic workload management technique to minimize the cloud operating cost. Our framework also uses information about renewable power and net metering policies at various data center locations like [34]. However, both [34] and [35] do not consider detailed models for data center compute and cooling power, co-location interference, and peak demand pricing distribution.…”

Section: Related Workmentioning

confidence: 99%

“…Our framework also uses information about renewable power and net metering policies at various data center locations like [34]. However, both [34] and [35] do not consider detailed models for data center compute and cooling power, co-location interference, and peak demand pricing distribution. Moreover, these efforts do not consider heterogeneity in workload (applications/task types executing in the data center) and heterogeneity within the data center (types of servers, server rack arrangements, etc.).…”

Section: Related Workmentioning

confidence: 99%

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Energy and Network Aware Workload Management for Geographically Distributed Data Centers

Hogade

Pasricha

Siegel

2022

IEEE Trans. Sustain. Comput.

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Cloud service providers are distributing data centers geographically to minimize energy costs through intelligent workload distribution. With increasing data volumes in emerging cloud workloads, it is critical to factor in the network costs for transferring workloads across data centers. For geo-distributed data centers, many researchers have been exploring strategies for energy cost minimization and intelligent inter-data-center workload distribution separately. However, prior work does not comprehensively and simultaneously consider data center energy costs, data transfer costs, and data center queueing delay. In this paper, we propose a novel game theory-based workload management framework that takes a holistic approach to the cloud operating cost minimization problem by making intelligent scheduling decisions aware of data transfer costs and the data center queueing delay. Our framework performs intelligent workload management that considers heterogeneity in data center compute capability, cooling power, interference effects from task co-location in servers, time-of-use electricity pricing, renewable energy, net metering, peak demand pricing distribution, and network pricing. Our simulations show that the proposed game-theoretic technique can minimize the cloud operating cost more effectively than existing approaches.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Energy and Network Aware Workload Management for Geographically Distributed Data Centers

Hogade

Pasricha

Siegel

2022

IEEE Trans. Sustain. Comput.

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show abstract

“…In the last decade, energy cost reduction of cloud data centres has been studied from different viewpoints [12][13][14][15][16][17][18][19][20][21][22]. One typical perspective is to design various techniques to reduce the energy consumption of data centres, for example, increasing the energy efficiency, right-sizing, and thermal management techniques [13][14][15].…”

Section: Related Workmentioning

confidence: 99%

Cloud data center participation in smart demand response programs for energy cost minimisation

et al. 2022

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Demand Response Programs (DRPs) in Smart Grid (SG) are designed to encourage consumers to shift their loads to regions and hours with less load stress by alternating the price of electricity. On the other hand, with the emergence of cloud computing, the demand for cloud data centres increases resulting in high energy costs. In this paper, we address the problem of cloud data centre demand response in an SG area in presence of utility companies which compete to attract more customers. We formulate two optimization problems for cloud and data centres to minimise their costs by changing their energy demand. These problems are shown to be convex and can be readily solved by standard convex optimization techniques. We also propose an algorithm for cloud and data centres to participate in DRPs by concurrently performing regional and temporal workload management. The uncertainty of renewable energy generation in data centres is treated by training a multilayer perceptron to predict the generated energy. Numerical results show that the proposed algorithm outperforms other existing algorithms in terms of the energy cost. In addition, our algorithm flattens the energy demand profile of utility companies and balances the electric load across different locations.

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