Reducing the carbon footprint of data centers is becoming a primary goal of large IT companies. Unlike traditional energy sources, renewable energy sources are usually intermittent and unpredictable. How to better utilize the green energy from these renewable sources in data centers is a challenging problem. In this paper, we exploit the opportunities offered by geographical load balancing, opportunistic scheduling of delay-tolerant workloads, and thermal storage management in data centers to facilitate green energy integration and reduce the cost of brown energy usage. Moreover, bandwidth cost variations between users and data centers are considered. Specifically, this problem is first formulated as a stochastic program, and then, an online control algorithm based on the Lyapunov optimization technique, called Stochastic Cost Minimization Algorithm (SCMA), is proposed to solve it. The algorithm can enable an explicit trade-off between cost saving and workload delay. Numerical results based on real-world traces illustrate the effectiveness of SCMA in practice.

Reducing the carbon footprint of data centers is becoming a primary goal of large IT companies. Due to the intermittency and unpredictability of renewable energy sources such as wind and solar, it is quite challenging to utilize them in data centers. In this paper, we explore the opportunities offered by delay-tolerant workloads and thermal storage to facilitate the renewable energy integration in data centers and meanwhile, reduce the cost of using brown energy (i.e., energy from the utility grid). A stochastic optimization problem is formulated to tackle the stochastic renewable generation and workload arrival processes. Then, an online control algorithm based on the Lyapunov optimization approach is proposed to solve it. Simulation results based on the real-world traces show the effectiveness of the algorithm in practice.

Transmission power control (TPC) has been extensively used not only to save energy, but also to improve the network throughput in wireless ad hoc networks. Among the existing throughput-oriented TPC protocols, many can achieve significant throughput improvement but have to use multiple channels and/or multiple transceivers, and others just require a single channel and a single transceiver but can only have limited throughput enhancement. In this paper, we propose a new adaptive transmission power control protocol, ATPMAC, which can improve the network throughput significantly using a single channel and a single transceiver. Specifically, by controlling the transmission power, ATPMAC can enable several concurrent transmissions without interfering with each other. Moreover, ATPMAC does not introduce any additional signalling overhead. We show by simulations that ATPMAC can improve the network throughput by up to 136% compared to IEEE 802.11 in a random topology.

A Genetic Algorithm, embedded in a simulation-based method, is applied to the identification of Asynchronous Finite State Machines. Two different coding schemes and their associated crossover operations are examined. It is shown that one operator / coding pair outperforms the other in that the scheme reduces noticeably the production of invalid chromosomes thus increasing the efficiency and the convergence rate of the evolution process.