Provably-Efficient Job Scheduling for Energy and Fairness in Geographically Distributed Data Centers

Ren, Shaolei; He, Yuxiong; Xu, Fei

doi:10.1109/icdcs.2012.77

Cited by 93 publications

(65 citation statements)

References 11 publications

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“…A significant body of work has already focused on cloud resource scheduling from a cloud provider's perspective [19][20][21][22][23]. In this context, the common goals are to reduce the storage/electricity cost and to improve platform utilization.…”

Section: Related Workmentioning

confidence: 99%

Scheduling Jobs in the Cloud Using On-Demand and Reserved Instances

Shen

Deng

Iosup

et al. 2013

Euro-Par 2013 Parallel Processing

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Abstract. Deploying applications in leased cloud infrastructure is increasingly considered by a variety of business and service integrators. However, the challenge of selecting the leasing strategy -larger or faster instances? on-demand or reserved instances? etc.-and to configure the leasing strategy with appropriate scheduling policies is still daunting for the (potential) cloud user. In this work, we investigate leasing strategies and their policies from a broker's perspective. We propose, CoH, a family of Cloud-based, online Hybrid scheduling policies that minimizes rental cost by making use of both on-demand and reserved instances. We formulate the resource provisioning and job allocation policies as Integer Programming problems. As the policies need to be executed online, we limit the time to explore the optimal solution of the integer program, and compare the obtained solution with various heuristics-based policies; then automatically pick the best one. We show, via simulation and using multiple real-world traces, that the hybrid leasing policy can obtain significantly lower cost than typical heuristics-based policies.

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Section: Related Workmentioning

confidence: 99%

Scheduling Jobs in the Cloud Using On-Demand and Reserved Instances

Shen

Deng

Iosup

et al. 2013

Euro-Par 2013 Parallel Processing

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“…In contrast, we manage the uncertain datacenter demand and multi-source energy supply in a systematic control view using two-stage Lyapunov. [29], [30] have used two-stage Lyapunov to design two-timescale algorithm and T -Step Lookahead algorithm, but both of them study how to schedule jobs or distribute requests in solely gridpowered geographical datacenters rather than how to supply complementary multi-source energy in an uncertain datacenter environment with arbitrary demand.…”

Section: Related Workmentioning

confidence: 99%

“…To the contrast, we seek to design an efficient online control strategy for long-term optimal operation of the DPSS under arbitrary power demand and uncertain renewable energy supply in a synergetic manner, without requiring a priori knowledge or stationary distribution of system statistics. Especially, we formulate a stochastic optimization model that minimizes the long-term operational cost of a datacenter under time-varying power demand and renewable energy production, two-timescale pricing schemes from the smart grid and a finite UPS battery capacity, and derive a practical and provably-efficient online DPSS control algorithm, SmartDPSS, based on the two-stage Lyapunov optimization technique [27]- [30]. The basic idea of the algorithm is to decide the amount of energy to purchase from the long-term-ahead grid market in intervals of longer periods of time, to tackle demand dynamics and energy price fluctuations in the future interval, and also to decide the amount of energy to purchase from the real-time grid market, as well as the amount of energy to store into or discharge from the UPS battery, in smaller time scales.…”

Section: Introductionmentioning

confidence: 99%

SmartDPSS: Cost-Minimizing Multi-source Power Supply for Datacenters with Arbitrary Demand

Deng

Liu

Jin

et al. 2013

2013 IEEE 33rd International Conference on Distributed Computing Systems

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Abstract-To tackle soaring power costs, significant carbon emission and unexpected power outage, Cloud Service Providers (CSPs) typically equip their Datacenters with a Power Supply System (DPSS) nurtured by multiple sources: (1) smart grid with time-varying electricity prices, (2) uninterrupted power supply (UPS), and (3) renewable energy with intermittent and uncertain supply. It remains a significant challenge how to operate among multiple power supply sources in a complementary manner, to deliver reliable energy to datacenter users with arbitrary demand over time, while minimizing a CSP's operation cost over the long run. This paper proposes an efficient, online control algorithm for DPSS, SmartDPSS, based on the two-timescale Lyapunov optimization techniques. Without requiring a priori knowledge of system statistics, SmartDPSS allows CSPs to make online decisions on how much power demand, including delay-sensitive demand and delay-tolerant demand, to serve at each time, the amount of power to purchase from the long-term-ahead and realtime grid markets, and charging and discharging of UPS over time, in order to fully leverage the available renewable energy and time-varying prices from the grid markets, for minimum operational cost. We thoroughly analyze the performance of our online control algorithm with rigorous theoretical analysis. We also demonstrate its optimality in terms of operational cost, demand service delay, datacenter availability, system robustness and scalability, using extensive simulations based on one-month worth of traces from live power systems.

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“…Xu Earlier solutions for geo-distributed clouds have focused primarily on achieving global efficiency in resource sharing. Several mechanisms were designed to achieve higher utility and overall global profit [8] [9]. Most geo-distributed resource allocation in the past [10]- [16] have considered a completely co-operative model of a shared pool of geodistributed resources that are allocated to optimize the global resource usage cost.…”

Section: Introductionmentioning

confidence: 99%

Optimized Contract-Based Model for Resource Allocation in Federated Geo-Distributed Clouds

Palanisamy

2021

IEEE Trans. Serv. Comput.

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Abstract-In the era of Big Data, with data growing massively in scale and velocity, cloud computing and its pay-as-you-go model continues to provide significant cost benefits and a seamless service delivery model for cloud consumers. The evolution of small-scale and large-scale geo-distributed datacenters operated and managed by individual Cloud Service Providers (CSPs) raises new challenges in terms of effective global resource sharing and management of autonomously-controlled individual datacenter resources towards a globally efficient resource allocation model. Earlier solutions for geo-distributed clouds have focused primarily on achieving global efficiency in resource sharing, that although tries to maximize the global resource allocation, results in significant inefficiencies in local resource allocation for individual datacenters and individual cloud provi ders leading to unfairness in their revenue and profit earned. In this paper, we propose a new contracts-based resource sharing model for federated geo-distributed clouds that allows CSPs to establish resource sharing contracts with individual datacenters apriori for defined time intervals during a 24 hour time period. Based on the established contracts, individual CSPs employ a contracts cost and duration aware job scheduling and provisioning algorithm that enables jobs to complete and meet their response time requirements while achieving both global resource allocation efficiency and local fairness in the profit earned. The proposed techniques are evaluated through extensive experiments using realistic workloads generated using the SHARCNET cluster trace. The experiments demonstrate the effectiveness, scalability and resource sharing fairness of the proposed model.

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Provably-Efficient Job Scheduling for Energy and Fairness in Geographically Distributed Data Centers

Cited by 93 publications

References 11 publications

Scheduling Jobs in the Cloud Using On-Demand and Reserved Instances

Scheduling Jobs in the Cloud Using On-Demand and Reserved Instances

SmartDPSS: Cost-Minimizing Multi-source Power Supply for Datacenters with Arbitrary Demand

Optimized Contract-Based Model for Resource Allocation in Federated Geo-Distributed Clouds

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