Using genetic algorithm in profile-based assignment of applications to virtual machines for greener data centers

The increase in data center dependent services has made energy optimization of data centers one of the most exigent challenges in today's Information Age. The necessity of green and energy-efficient measures is very high for reducing carbon footprint and exorbitant energy costs. However, inefficient application management of data centers results in high energy consumption and low resource utilization efficiency. Unfortunately, in most cases, deploying an energy-efficient application management solution inevitably degrades the resource utilization efficiency of the data centers. To address this problem, a Penalty-based Genetic Algorithm (GA) is presented in this paper to solve a defined profile-based application assignment problem whilst maintaining a trade-off between the power consumption performance and resource utilization performance. Case studies show that the penalty-based GA is highly scalable and provides 16% to 32% better solutions than a greedy algorithm.

Innovative planning approaches for generator interconnection group study

Today, a large number of wind generator interconnection requests have been queued and are being processed. The generator interconnection group study is a way to reduce the generator interconnection cycle time and increase interconnection certainty. However, it is very challenging to identify the “best” transmission upgrades for a large group of generator interconnections. It is also very important to differentiate the constraints caused by each generator interconnection request and identify their responsibilities for transmission upgrades. This paper outlines some innovative study approaches that can be used in a group study with large numbers of generator interconnection requests in a constrained area. Improved study methods are introduced, and a summary and conclusions are derived from the study.

Shared generation of pseudo-random functions

In Crypto’95, Micali and Sidney proposed a method for shared generation of a pseudo-random function f(·) among n players in such a way that for all the inputs x, any u players can compute f(x) while t or fewer players fail to do so, where 0⩽tgreedy algorithm for generating the secret seeds S in which d is close to the optimum by a factor of at most u ln 2.

Shared generation of pseudo-random functions with cumulative maps

In Crypto’95, Micali and Sidney proposed a method for shared generation of a pseudo-random function f(·) among n players in such a way that for all the inputs x, any u players can compute f(x) while t or fewer players fail to do so, where 0 ≤ t < u ≤ n. The idea behind the Micali-Sidney scheme is to generate and distribute secret seeds S = s1, . . . , sd of a poly-random collection of functions, among the n players, each player gets a subset of S, in such a way that any u players together hold all the secret seeds in S while any t or fewer players will lack at least one element from S. The pseudo-random function is then computed as where f s i (·)’s are poly-random functions. One question raised by Micali and Sidney is how to distribute the secret seeds satisfying the above condition such that the number of seeds, d, is as small as possible. In this paper, we continue the work of Micali and Sidney. We first provide a general framework for shared generation of pseudo-random function using cumulative maps. We demonstrate that the Micali-Sidney scheme is a special case of this general construction.We then derive an upper and a lower bound for d. Finally we give a simple, yet efficient, approximation greedy algorithm for generating the secret seeds S in which d is close to the optimum by a factor of at most u ln 2.