No abstract

No abstract

In many distributed computing environments, collections of applications need to be processed using a set of heterogeneous computing (HC) resources to maximize some performance goal. An important research problem in these environments is how to assign resources to applications (matching) and order the execution of the applications (scheduling) so as to maximize some performance criterion without violating any constraints. This process of matching and scheduling is called mapping.1Howard Jay Siegel holds a joint appointment in the Computer Science Department as well. 2Albert I. Reuther is currently with MIT Lincoln Laboratory, Lexington, MA. CHARACTERIZING RESOURCE ALLOCATION HEURISTICSTo make meaningful comparisons among mapping heuristics, a system designer needs to understand the assumptions made by the heuristics for (1) the model used for the application and communication tasks, (2) the model used for system platforms, and (3) the attributes of the mapping heuristics. This chapter presents a three-part classification scheme (3PCS) for HC systems. The 3PCS is useful for researchers who want to (a) understand a mapper given in the literature, (b) describe their design of a mapper more thoroughly by using a common standard, and (c) select a mapper to match a given real-world environment.

No abstract

ÐProviding up-to-date input to users' applications is an important data management problem for a distributed computing environment, where each data storage location and intermediate node may have specific data available, storage limitations, and communication links available. Sites in the network request data items and each request has an associated deadline and priority. In a military situation, the data staging problem involves positioning data for facilitating a faster access time when it is needed by programs that will aid in decision making. This work concentrates on solving a basic version of the data staging problem in which all parameter values for the communication system and the data request information represent the best known information collected so far and stay fixed throughout the scheduling process. The network is assumed to be oversubscribed and not all requests for data items can be satisfied. A mathematical model for the basic data staging problem is introduced. Then, three multiple-source shortest-path algorithmbased heuristics for finding a near-optimal schedule of the communication steps for staging the data are presented. Each heuristic can be used with each of four cost criteria developed. Thus, 12 implementations are examined. In addition, two different weightings for the relative importance of different priority levels are considered. The performance of the proposed heuristics are evaluated and compared by simulations. The proposed heuristics are shown to perform well with respect to upper and lower bounds. Furthermore, the heuristics and a complex cost criterion allow more highest priority messages to be received than a simple-cost-based heuristic that schedules all highest priority messages first.