Characterizing Resource Allocation Heuristics for Heterogeneous Computing Systems

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.

Statistical measures for quantifying task and machine heterogeneities

“…ETC matrices were previously used with different degrees of heterogeneity (e.g., [2,6,9,10,31,37]). Most of these ETC matrices were generated by the range-based method described in [9] and the CVB method described in [5].…”

“…The assumption of such ETC information is a common practice in resource allocation research (e.g., [6,16,21,26,29,36,39]). An ETC matrix for a given HC system can be obtained from user supplied information, experimental data, or task profiling and analytical benchmarking [2,21,29,39].…”

“…The following variables will be used in the calculation of each of the statistical measures: T is the number of tasks to be mapped, M is the number of machines in the system, and μ (t) i is the mean ETC of task i over all machines, given by μ 2 . The standard deviation of the ETC of all tasks on machine j, σ…”

Dynamically mapping tasks with priorities and multiple deadlines in a heterogeneous environment

“…The estimated time to complete (ETC) values of each task on each machine is assumed to be known based on user-supplied information, experiential data, task profiling and analytical benchmarking, or other techniques (e.g., [1,15,16,21,26,40]). Determination of ETC values is a separate research problem, and the assumption of such ETC information is a common practice in mapping research (e.g., [16,20,21,24,31,39]).…”

Dynamic resource allocation heuristics that manage tradeoff between makespan and robustness

“…The estimated time to compute (ETC) values of each task on each machine are assumed to be known based on user supplied information, experiential data, task profiling and analytical benchmarking, or other techniques (e.g., [1,15,16,19,25,34]). Determination of ETC values is a separate research problem, and the assumption of such ETC information is a common practice in mapping research (e.g., [16,18,19,21,30,33]).…”