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Generally, a parallel application consists of precedence constrained stochastic tasks, where task processing times and intertask communication times are random variables following certain probability distributions. Scheduling such precedence constrained stochastic tasks with communication times on a heterogeneous cluster system with processors of different computing capabilities to minimize a parallel application's expected completion time is an important but very difficult problem in parallel and distributed computing. In this paper, we present a model of scheduling stochastic parallel applications on heterogeneous cluster systems. We discuss stochastic scheduling attributes and methods to deal with various random variables in scheduling stochastic tasks. We prove that the expected makespan of scheduling stochastic tasks is greater than or equal to the makespan of scheduling deterministic tasks, where all processing times and communication times are replaced by their expected values. To solve the problem of scheduling precedence constrained stochastic tasks efficiently and effectively, we propose a stochastic dynamic level scheduling (SDLS) algorithm, which is based on stochastic bottom levels and stochastic dynamic levels. Our rigorous performance evaluation results clearly demonstrate that the proposed stochastic task scheduling algorithm significantly outperforms existing algorithms in terms of makespan, speedup, and makespan standard deviation.

The thermal profile of multicore systems vary both within an application's execution (intra) and also when the system switches from one application to another (inter). In this paper, we propose an adaptive thermal management approach to improve the lifetime reliability of multicore systems by considering both inter-and intra-application thermal variations. Fundamental to this approach is a reinforcement learning algorithm, which learns the relationship between the mapping of threads to cores, the frequency of a core and its temperature (sampled from on-board thermal sensors). Action is provided by overriding the operating system's mapping decisions using affinity masks and dynamically changing CPU frequency using in-kernel governors. Lifetime improvement is achieved by controlling not only the peak and average temperatures but also thermal cycling, which is an emerging wear-out concern in modern systems. The proposed approach is validated experimentally using an Intel quad-core platform executing a diverse set of multimedia benchmarks. Results demonstrate that the proposed approach minimizes average temperature, peak temperature and thermal cycling, improving the mean-timeto-failure (MTTF) by an average of 2x for intra-application and 3x for inter-application scenarios when compared to existing thermal management techniques. Furthermore, the dynamic and static energy consumption are also reduced by an average 10% and 11% respectively.

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This paper presents a new strategy for load distribution ina single-level tree network equipped with or without front-ends. The load is distributed in more than one installment in an optimal manner to minimize the processing time. This is a deviation and an improvement over earlier studies in which the load distribution is done in only one installment. Recursive equations for the general case, and their closed-form solutions for a special case in which the network has identical processors and identical links, are derived. An asymptotic analysis of the network performance with respect to the number of processors and the number of installments is carried out. Discussions of the results in terms of some practical issues like the tradeoff relationship between the number of processors and the number of installments are also presented.

The problem of obtaining optimal processing time in a distributed computing system consisting of ( N + 1) processors and : V communication links, arranged in a single-level tree architecture, is considered. It is shown that optimality can be achieved through a hierarchy of steps involving optimal load distribution, load sequencing, and processor-link arrangement. Closed-form expressions for optimal processing time is derived for a general case of networks with different processor speeds and different communication link speeds. Using these closedform expressions, this paper analytically proves a number of significant results that in earlier studies were only conjectured from computational results. In addition, it also extends these results to a more general framework. The above analysis is carried out for the cases in which the root processor may or may not be equipped with a front-end processor. Illustrative examples are given for all cases considered. Index Terms-Communication delays, distributed processing, optimal arrangement, optimal load distribution, optimal load sequencing, optimal processing time, single-level tree networks D. Ghose received the B.Sc. (Eng.) degree in electrical engineering from the Regional Engineering College, Rourkela, India, in 1982, and the M.E.

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