Desktop Grids have emerged as an important methodology to harness the idle cycles of millions of participant desktop PCs over the Internet. However, to effectively utilize the resources of a Desktop Grid, it is necessary to use scheduling policies suitable for such systems. A scheduling policy must be applicable to large-scale systems involving large numbers of machines. Also, the policy must be fault-aware in the sense that it copes with resource volatility. Further adding to the complexity of scheduling for Desktop Grids is the inherent heterogeneity of such systems. Sub-optimal performance would result if the scheduling policy does not take into account information on heterogeneity. In this paper, we suggest and develop several scheduling policies for Desktop Grid systems involving different levels of heterogeneity. In particular, we propose a policy which utilizes the solution to a linear programming problem which maximizes system capacity. We consider parallel applications that consist of independent tasks.
Resource management systems (RMSs) are an important component in heterogeneous computing (HC) systems. One of the jobs of an RMS is the mapping of arriving tasks onto the machines of the HC system. Many different mapping heuristics have been proposed in recent years. However, most of these heuristics suffer from several limitations. One of these limitations is the performance degradation that results from using outdated global information about the status of all machines in the HC system. This paper proposes several heuristics which address this limitation by only requiring partial information in making the mapping decisions. These heuristics utilize the solution to a linear programming (LP) problem which maximizes the system capacity. Simulation results show that our heuristics perform very competitively while requiring dramatically less information.
The software component allocation problem is concerned with mapping a set of software components to the computational units available in a heterogeneous computing system while maximizing a certain objective function. This problem is important in the domain of component-based software engineering, and solving it is not a trivial task. In this paper, we demonstrate a software framework for defining and solving component allocation problem instances. In addition, we implement two metaheuristics for solving the problem. The experiments show that these meta-heuristics achieve good performance. The framework is designed to be extensible and therefore other researchers can conveniently use it to implement new meta-heuristics for solving the software component allocation problem. INDEX TERMS Component allocation, model-driven engineering, embedded systems, heterogeneous systems, genetic algorithms, ant colony optimization.
In the past few years, scheduling for computer clusters has become a hot topic. The main focus has been towards achieving better performance. It is true that this goal has been attained to a certain extent, but on the other hand, it has been at the expense of increased energy consumption and consequent economic and environmental costs. As these clusters are becoming more popular and complex, reducing energy consumption in such systems has become a necessity. Several power-aware scheduling policies have been proposed for homogeneous clusters. In this work, we propose a new policy for heterogeneous clusters. Our simulation experiments show that using our proposed policy results in significant reduction in energy consumption while performing very competitively in heterogeneous clusters.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.