A Taxonomy of Network Computing Systems

Rapid advances in networking and microprocessor technologies have led to the emergence of Internet-wide distributed computing systems ranging from simple LAN-based clusters to planetary-scale networks. As these network computing systems evolve by combining the best features of existing systems, differences among NCs are blurring. To address this problem, researchers have proposed formal taxonomies of NC systems. We propose a new taxonomy that is both broad enough to encompass all NC systems and simple enough to be widely used

Measuring Scalability of Resource Management Systems

Scalability refers to the extent of configuration modifications over which a system continues to be economically deployable. Until now, scalability of resource management systems (RMSs) has been examined implicitly by studying different performance measures of the RMS designs for different parameters. However, a framework is yet to be developed for quantitatively evaluating scalability to unambiguously examine the trade-offs among the different RMS designs. In this paper, we present a methodology to study scalability of RMSs based on overhead cost estimation. First, we present a performance model for a managed distributed system (e.g., Grid computing system) that separates the manager and managee. Second, based on the performance model we present a metric used to quantify the scalability of a RMS. Third, simulations are used to apply the proposed scalability metric to selected RMSs from the literature. The results show that the proposed metric is useful in quantifying the scalabilities of the RMSs

Dynamic Mapping of a Class of Independent Tasks onto Heterogeneous Computing Systems

This paper describes and compares eight heuristics that can be used in such an RMS for dynamically assigning independent tasks to machine

Dynamic matching and scheduling of a class of independent tasks onto heterogeneous computing systems

Dynamic mapping (matching and scheduling) heuristics for a class of independent tasks using heterogeneous distributed computing systems are studied. Two types of mapping heuristics are considered: on-line and batch mode heuristics. Three new heuristics, one for batch and two for on-line, are introduced as part of this research. Simulation studies are performed to compare these heuristics with some existing ones. In total, five on-line heuristics and three batch heuristics are examined. The on-line heuristics consider, to varying degrees and in different ways, task affinity for different machines and machine ready times. The batch heuristics consider these factors, as well as aging of tasks waiting to execute. The simulation results reveal that the choice of mapping heuristic depends on parameters such as: (a) the structure of the heterogeneity among tasks and machines, (b) the optimization requirements, and (c) the arrival rate of the tasks. 1

Representing Task and Machine Heterogeneities for Heterogeneous Computing Systems

A distributed heterogeneous computing (HC) system consists of diversely capable machines harnessed together to execute a set of tasks that vary in their computational requirements. Heuristics are needed to map (match and schedule) tasks onto machines in an HC system so as to optimize some figure of merit. An HC system model is needed to simulate different HC environments to allow the study of the relative performance of different mapping heuristics under different circumstances. This paper characterizes a simulated HC environment by using the expected execution times of the tasks that arrive in the system on the different machines present in the system. This information is arranged in an expected time to compute (ETC) matrix as a model of the given HC system, where the entry (i, j) is the expected execution time of task i on machine j. The ETC model is used to express the heterogeneity among the runtimes of the tasks to be executed, and among the machines in the HC system. An existing range-based technique to express heterogeneity in ETC matrices is described. A coefficient-of-variation based technique to express heterogeneity in ETC matrices is proposed, and compared with the range-based technique. The coefficient-of-variation-based ETC generation method provides a greater control over the spread of values (i.e., heterogeneity) in any given row or column of the ETC matrix than the range-based method

Characterizing resource allocation heuristics for heterogeneous computing systems

Includes bibliographical references (pages 122-128).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. To 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