In a Distributed Computing Systems (DCS) tasks submitted to it, are usually partitioned into different modules and these modules may be allocated to different processing nodes so as to achieve minimum turn around time of the tasks utilizing the maximum resources of the existing system such as CPU speed, memory capacities etc. The problem lies on how to obtain the optimal allocation of these multiple tasks by keeping in mind that no processing node is overloaded due to this allocation. This paper proposes an algorithm A*RS, using well-known A*, which aims to reduce the search space and time for task allocation. It aims at minimization of turn around time of tasks in the way so that processing nodes do not become overloaded due to this allocation. Our experimental results justify the claims with necessary supports by comparing it with the earlier algorithm for multiple tasks allocation.

A Distributed Computing System (DCS) contributes in proper partitioning of the tasks into modules and allocating them to various nodes so as to enable parallel execution of their modules by individual different processing nodes of the system. The scheduling of various modules on particular processing nodes may be preceded by appropriate allocation of modules of the different tasks to various processing nodes and then only the appropriate execution characteristic can be obtained. A number of algorithms have been proposed for allocation of tasks in a DCS. Most of the solutions proposed had simplifying assumptions. The very first assumption has been: consideration of a single task with their corresponding modules only; second, no consideration of the status of processing nodes in terms of the previously allocated modules of various tasks and third, the capacity and capability of the processing nodes. This work proposes algorithms for a realistic situation wherein multiple tasks with their modules compete for execution on a DCS dynamically considering their architectural capability. In this work, we propose two algorithms based on the two well-known A* and GA for the task allocation models. The paper explains the algorithms elaborately by illustrated examples and presents a comparative performance study among our algorithms and the algorithms for task allocation proposed in the various literatures. The results demonstrate that our GA based task allocation algorithm achieves better performance compared with the other algorithms.

We consider the problem of placing resources in a distributed computing system so that certain performance requirements may be met while minimizing the number of resource copies needed. Resources include special I/O processors, expensive peripheral devices, or such software modules as compilers, library routines, and data files. Due to the delay in accessing each of these resources, system performance degrades as the distance between each processor and its nearest resource copy increases. Thus, every processor must be within a given distance k1 of at least one resource copy, which is called the k-bounded placement problem. The structure of a distributed computing system is represented by a graph. The k-bounded placement problem is first transformed into the problem of finding smallest k-dominating sets in a graph. Searching for smallest k-dominating sets is formulated as a state-space search problem. We derive heuristic information to speed up the search, which is then used to solve the problem with the well-known A* algorithm. An illustrative example and some experimental results are presented to demonstrate the effectiveness of the heuristic search.

A generalized class of consecutive-k-out-of-n:G systems, referred to as Con/k*/n:G systems, is studied. A Con/k*/n:G system has n ordered components and is good if and only if ki good consecutive components that originate at component i are all good, where ki is a function of i. Theorem 1 gives an O(n) time equation to compute the reliability of a linear system and Theorem 2 gives an O(n2) time equation for a circular system. A distributed computing system with a linear (ring) topology is an example of such system. This application is very important, since for other classes of topologies, such as general graphs, planar graphs, series-parallel graphs, tree graphs, and star graphs, this problem has been proven to be NP-hard.

We present an architecture of a VOD system employing proxy servers. The proposed VOD system provides efficient and reliable VOD services and solves the problems caused by traditional VOD systems of centralized, hierarchical or distributed architecture. The proxy servers are placed between video servers and user systems. The proxy server is a small size video server that has not only caching function but also intelligence such as VCR-like video stream control or navigation of other proxy/video servers to search for a selected video program. Using a VOD system of the proposed architecture, the VOD services can be provided to more users because it reduces the workload of video servers and network traffic. We provide the performance model of the system. Service availability is also analyzed. The proposed architecture shows better performance and availability than the traditional VOD architectures.

A distributed computing system consists of processing elements, communication links, memory units, data files, and programs. These resources are interconnected via a communication network and controlled by a distributed operating system. The distributed program reliability (DPR) in a distributed computing system is the probability that a program which runs on multiple processing elements and needs to retrieve data files from other processing elements will be executed successfully. This reliability varies according to 1) the topology of the distributed computing system, 2) the reliability of the communication edges, 3) the data files and programs distribution among processing elements, and 4) the data files required to execute a program. In this paper, we show that computing the distributed program reliability on a star distributed computing system is #P-complete. A polynomially solvable case is developed for computing the distributed program reliability when some additional file distribution is restricted on the star topology. We also propose a polynomial time algorithm for computing the distributed program reliability with approximate solutions when the star topology has no the additional file distribution.