Sleeping is an important method to reduce energy consumption in many information and communication systems. In this paper we focus on a typical server under dynamic load, where entering and leaving sleeping mode incurs an energy and a response time penalty. We seek to understand under what kind of system configuration and control method will sleep mode obtain a Pareto Optimal optimal tradeoff between energy saving and average response time. We prove that the optimal "sleeping" policy has a simple hysteretic structure. Simulation results then show that this policy results in significant energy savings, especially for relatively delay insensitive applications and under low traffic load. However, we demonstrate that seeking the maximum energy saving presents another tradeoff: it drives up the peak temperature in the server, with potential reliability consequences.

Abstract-Multimode fibers are characterized by multipath propagation of optical signals and this leads to severe intersymbol interference at the output of the fiber. In this work an approach based on the Rake receiver is proposed to overcome this drawback. An optimization algorithm was developed and appropriate software was employed to apply the proposed methodology on specific multimode fiber. Extensive simulation results were produced and are presented herein. The numerical results have shown that the order of magnitude of the maximum data rate, R, supported at different CDMA gains, in order to achieve a Bit Error Rate value smaller or equal to a convergent point, is related to the length of the multimode fiber, L, by the expression R = dL −1 with d increasing from 10 6 to 10 7 (Kbps. m) when CDMA gain increases from 50 to 500.

No abstract

No abstract

Multi-core architectures have supplanted single core schemes, in part because the maximum clock speed of a single core is limited by its energy consumption. They provide the additional, less exploited benefit of allowing a finer trade-ff between energy consumption and delay by turning off subsets of cores. We investigate how this tradeoff varies with the number of cores, and whether heterogeneity brings additional benefits to outweigh its increased complexity. We study optimal sleep policies in two settings: switching of homogeneous cores on a fast timescale, which models multiple cores in a CPU, and switching of heterogeneous cores on a slow timescale, which models different generation servers in a data centre. In the homogeneous-core case, we show the optimal policy is monotone hysteretic, and that the performance is less sensitive to load estimation errors. By implementing this policy on a real experimental testbed, we show that theory makes a good prediction of the resource pooling benefits, and extend our study to realistic bursty job arrivals and heavy-tailed job sizes. In the heterogeneous-core case, we provide a low complexity algorithm to find a combination of servers that have approximately the minimum power draw while providing a specified minimum processing speed.