On Data Center Demand Response: A Cloud Federation Approach

Moghaddam, Monireh Mohebbi; Manshaei, Mohammad Hossein; Saad, Walid; Goudarzi, Maziar

doi:10.1109/access.2019.2928552

Cited by 21 publications

(7 citation statements)

References 48 publications

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“…The approach uses a game-theoretic formulation that allows for appropriate loss of QoS while maximizing the cloud service provider's profits. In [33], the authors consider a cloud data center and smart grid utility demand-response program. The work proposes a cooperative game theory-based technique to migrate workloads between data centers and better utilize the benefits provided by demand response schemes over multiple data center locations.…”

Section: Related Workmentioning

confidence: 99%

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%

Energy and Network Aware Workload Management for Geographically Distributed Data Centers

Hogade

Pasricha

Siegel

2022

IEEE Trans. Sustain. Comput.

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“…Beyond these categories, [30], [31] and [32] are three relevant studies which addressed DR of cooperative CPs. The authors in [30] presented the aggregated participation of the CPs in a smart grid's power reduction program.…”

Section: B Energy Management Of Cloud Federationmentioning

confidence: 99%

“…In this work, DCs aggregate their loads to mitigate their power demand uncertainty in the market. Leveraging the cooperative game theory, [32] proposed a cloud federation formation algorithm in the presence of a location-dependent DR program and analyzed the effect of this cooperation on the SG performance. In contrast to the works in [30] and [31], we consider the cooperative workload management of geo-distributed cloud providers.…”

Section: B Energy Management Of Cloud Federationmentioning

confidence: 99%

“…Besides, unlike these works which consider the aggregation of CPs demands, in our work, VMs are migrated from one CP toward the others to reduce the electricity bill. Compared to [32] in which the SG is unaware of the CPs cooperation, here, we consider the SG as an active agent in our game model, takes strategic decision and model the interaction of the SG with CPs along with the CPs cooperation. The related works in this category are summarized in Table IV.…”

Section: B Energy Management Of Cloud Federationmentioning

confidence: 99%

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On Coordination of Smart Grid and Cooperative Cloud Providers

Moghaddam¹,

Manshaei²,

Soorki³

et al. 2020

Preprint

Self Cite

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Cooperative cloud providers in the form of cloud federations can potentially reduce their energy costs by exploiting electricity price fluctuations across different locations. In this environment, on the one hand, the electricity price has a significant influence on the federations formed, and, thus, on the profit earned by the cloud providers, and on the other hand, the cloud cooperation has an inevitable impact on the performance of the smart grid. In this regard, the interaction between independent cloud providers and the smart grid is modeled as a two-stage Stackelberg game interleaved with a coalitional game in this paper. In this game, in the first stage the smart grid, as a leader chooses a proper electricity pricing mechanism to maximize its own profit. In the second stage, cloud providers cooperatively manage their workload to minimize their electricity costs. Given the dynamic of cloud providers in the federation formation process, an optimization model based on a constrained Markov decision process (CMDP) has been used by the smart grid to achieve the optimal policy. Numerical results show that the proposed solution yields around 28% and 29% profit improvement on average for the smart grid, and the cloud providers, respectively, compared to the noncooperative scheme.

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“…When the data is stored on the cloud, it is crucial to keep confidentiality. Only the authorized tenants/users should have access to the data [8]. Encryption is a conventional way to keep the data confidential, such as identity-based encryption [9].…”

Section: Introductionmentioning

confidence: 99%

Data Placement for Multi-Tenant Data Federation on the Cloud

Ji¹,

Mo²,

Yang³

et al. 2021

Preprint

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Due to privacy concerns of users and law enforcement in data security and privacy, it becomes more and more difficult to share data among organizations. Data federation brings new opportunities to the data-related cooperation among organizations by providing abstract data interfaces. With the development of cloud computing, organizations store data on the cloud to achieve elasticity and scalability for data processing. The existing data placement approaches generally only consider one aspect, which is either execution time or monetary cost, and do not consider data partitioning for hard constraints. In this paper, we propose an approach to enable data processing on the cloud with the data from different organizations. The approach consists of a data federation platform named FedCube and a Lyapunov-based data placement algorithm. FedCube enables data processing on the cloud. We use the data placement algorithm to create a plan in order to partition and store data on the cloud so as to achieve multiple objectives while satisfying the constraints based on a multi-objective cost model. The cost model is composed of two objectives, i.e., reducing monetary cost and execution time. We present an experimental evaluation to show our proposed algorithm significantly reduces the total cost (up to 69.8%) compared with existing approaches.

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On Data Center Demand Response: A Cloud Federation Approach

Cited by 21 publications

References 48 publications

Energy and Network Aware Workload Management for Geographically Distributed Data Centers

Energy and Network Aware Workload Management for Geographically Distributed Data Centers

On Coordination of Smart Grid and Cooperative Cloud Providers

Data Placement for Multi-Tenant Data Federation on the Cloud

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