Data centers power reduction: A two time scale approach for delay tolerant workloads

Yao, Yuan; Huang, Longbo; Sharma, Abhihshek; Golubchik, Leana; Neely, Michael J.

doi:10.1109/infcom.2012.6195508

Cited by 201 publications

(185 citation statements)

References 16 publications

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“…Since this information is not available a priori, we seek online control mechanisms which do not rely on such information. To this end, we model the problem as a Lyapunov optimization problem [6]. The presented solution only relies on the information that is readily available in current cellular networks, e.g., user queue backlogs and average power price.…”

Section: A Motivationmentioning

confidence: 99%

“…There have been several recent works that modeled energy cost reduction in data centers following the framework of twotimescale Lyapunov optimization [6], [10]. In [6], an online algorithm is presented to distribute the load among a set of data centers, activate enough servers in each data center, and scale their speeds so as to satisfy the load and stabilize the system queues.…”

Section: B Related Workmentioning

confidence: 99%

“…In [6], an online algorithm is presented to distribute the load among a set of data centers, activate enough servers in each data center, and scale their speeds so as to satisfy the load and stabilize the system queues. In [10], employing multiple power sources including long-term and real-time power market, renewable green energy, and local power storage for reducing long-term power cost are investigated.…”

Section: B Related Workmentioning

confidence: 99%

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Online algorithms for energy cost minimization in cellular networks

Abbasi

Ghaderi

2014

2014 IEEE 22nd International Symposium of Quality of Service (IWQoS)

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Abstract-Dynamic base station activation and transmission power control are the key mechanisms to reduce energy consumption in cellular networks. In this work, we consider employing these methods for the purpose of minimizing long-term energy cost in cellular networks. Based on the two-timescale Lyapunov optimization technique, we formulate an online control problem to ensure achieving minimal energy cost while stabilizing use queues. While the control problem can be solved in a centralized manner, we limit our attention to distributed solutions which are highly attractive in the design of next generation mobile networks. Due to the combinatorial nature of the problem and the complex relation of achievable rates to interfering signals, the problem is non-convex. Consequently, conventional duality methods cannot be employed to achieve the distributed solution. Thus, we design a distributed solution for the problem based on Gibbs sampling method. The proposed algorithm can be implemented in a fully distributed manner, does not depend on the convexity or continuity of the energy cost functions, and guarantees solution optimality. Numerical results are provided to demonstrate the behavior of the solution in some example network scenarios.

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Section: A Motivationmentioning

confidence: 99%

Section: B Related Workmentioning

confidence: 99%

Section: B Related Workmentioning

confidence: 99%

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Online algorithms for energy cost minimization in cellular networks

Abbasi

Ghaderi

2014

2014 IEEE 22nd International Symposium of Quality of Service (IWQoS)

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“…Existing literature ([1]- [3] and references therein) on resource scheduling in cloud systems focuses mainly on a single cloud that operates alone. A common theme is to minimize the operational costs (mainly consisting of electricity bills) in one or multiple data centers of the cloud, while providing certain performance guarantee for job scheduling, e.g., in terms of average job completion times [1]- [3].…”

Section: A Optimal Scheduling In Cloud Computingmentioning

confidence: 99%

“…A common theme is to minimize the operational costs (mainly consisting of electricity bills) in one or multiple data centers of the cloud, while providing certain performance guarantee for job scheduling, e.g., in terms of average job completion times [1]- [3]. Different from these studies, this work investigates bounded scheduling delay for each job even in the worst cases, and profit maximization for individual selfish clouds in a cloud federation.…”

Section: A Optimal Scheduling In Cloud Computingmentioning

confidence: 99%

Profit-maximizing virtual machine trading in a federation of selfish clouds

et al. 2013

2013 Proceedings IEEE INFOCOM

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Abstract-The emerging federated cloud paradigm advocates sharing of resources among cloud providers, to exploit temporal availability of resources and diversity of operational costs for job serving. While extensive studies exist on enabling interoperability across different cloud platforms, a fundamental question on cloud economics remains unanswered: When and how should a cloud trade VMs with others, such that its net profit is maximized over the long run? In order to answer this question by the federation, a number of important, correlated decisions, including job scheduling, server provisioning and resource pricing, need to be dynamically made, with long-term profit optimality being a goal. In this work, we design efficient algorithms for inter-cloud resource trading and scheduling in a federation of geo-distributed clouds. For VM trading among clouds, we apply a double auctionbased mechanism that is strategyproof, individual rational, and ex-post budget balanced. Coupling with the auction mechanism is an efficient, dynamic resource trading and scheduling algorithm, which carefully decides the true valuations of VMs in the auction, optimally schedules stochastic job arrivals with different SLAs onto the VMs, and judiciously turns on and off servers based on the current electricity prices. Through rigorous analysis, we show that each individual cloud, by carrying out our dynamic algorithm, can achieve a time-averaged profit arbitrarily close to the offline optimum.

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An online profit optimisation framework for diverse requests in distributed datacentres

Wang

Bao

2016

Trans. Emerging Tel. Tech.

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The rapid development of cloud computing attracts increasing amounts of enterprises and institutes to submit their diverse computing requests to the cloud service providers that are usually supported by large‐scale distributed datacentres. The explosion of service requests has drastically increased the operational expenditure of cloud service providers, which makes it imperative to find out a highly efficient method to reduce the cost and maximise the profit. However, the time and spatial differences of costs and the diverse characteristics of requests bring in multiple challenges to tackle this problem. In this work, we jointly take the electricity cost, communication cost and diverse characteristics of requests into consideration and propose a profit optimisation framework that includes three important decisions: service requests acceptance control, requests dispatching and resource provision. An efficient online algorithm is then designed to provide cloud service providers with the advice concerning these decisions to achieve the maximal time‐averaged profit over the long run. The rigorous theoretical analysis demonstrates that the proposed framework is able to approach a time averaged profit that is arbitrarily close to optimum. Extensive trace‐driven experiments further confirm the effectiveness and superiority of our framework. Copyright © 2016 John Wiley & Sons, Ltd.

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Data centers power reduction: A two time scale approach for delay tolerant workloads

Cited by 201 publications

References 16 publications

Online algorithms for energy cost minimization in cellular networks

Online algorithms for energy cost minimization in cellular networks

Profit-maximizing virtual machine trading in a federation of selfish clouds

An online profit optimisation framework for diverse requests in distributed datacentres

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