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Decreasing the soaring energy cost is imperative in large data centers. Meanwhile, limited computational resources need to be fairly allocated among different organizations. Latency is another major concern for resource management. Nevertheless, energy cost, resource allocation fairness, and latency are important but often contradicting metrics on scheduling data center workloads. Moreover, with the ever-increasing power density, data center operation must be judiciously optimized to prevent server overheating. In this paper, we explore the benefit of electricity price variations across time and locations. We study the problem of scheduling batch jobs to multiple geographically-distributed data centers. We propose a provably-efficient online scheduling algorithm-GreFar-which optimizes the energy cost and fairness among different organizations subject to queueing delay constraints, while satisfying the maximum server inlet temperature constraints. GreFar does not require any statistical information of workload arrivals or electricity prices. We prove that it can minimize the cost arbitrarily close to that of the optimal offline algorithm with future information. Moreover, we compare the performance of GreFar with ones of a similar algorithm, referred to as T-unaware, that is not able to consider the server inlet temperature in the scheduling process. We prove that GreFar is able to save up to 16 percent of energy-fairness cost with respect to T-unaware

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A detailed survey of approaches reducing energy consumption of core networks is presented in this paper. We consider a multilayer architecture, in which the optical layer can be realized either with a Wavelength Division Multiplexing (WDM) network or an Elastic Optical Network (EON). We focus on the design and operation stages, i.e., deciding which devices to install in the network during the former step, and choosing which devices to put into sleep mode during the latter one. A taxonomy for classifying the surveyed approaches is provided in order to compare the works covering energy efficiency in core networks (in terms of both optimal formulations and heuristic solutions). Moreover, our work provides a global view of the traffic assumptions, the topologies, and the power consumption models in the literature. The need of further investigations in this field clearly emerges. We envision future works targeting: (1) more effective standardization efforts to practically realize sleep modes; (2) the evaluation of the impact of sleep mode on the device lifetime; (3) the extensive adoption of new paradigms like Software Defined Networking (SDN) and EON; and (4) a radical improvement in the testbed implementations

This paper deals with a strategy to save energy in an IP network during low traffic hours allowing a subset of IP router interfaces to be put in sleep mode by means of an Energy Aware Routing (EAR) strategy. The EAR is fully compatible with OSPF and is based on the Shortest Path Tree (SPT) exportation mechanism, consisting in sharing the SPTs among couple of routers. The EAR strategy is able to control the set of links to be put in sleep mode through the concept of move. This approach gives the network operator the possibility to control the network performance and allows a smoothed QoS degradation strategy to be implemented. A formulation of the EAR problem is presented and will be demonstrated that this problem can be traced back to the well-known problem of the maximum clique search in an undirected weighted graph. A heuristics, called Max Compatibility, is presented and, as shown in the performance evaluation study, it allows to save about 30% of network links with a negligible increase of network path lengths and link loads. © 2011 IEEE