this paper is organized as follows. Section 2 defines the computational environment parameters that were varied in the simulations. Descriptions of the 11 mapping heuristics are found in Section 3. Section 4 examines selected results from the simulation study. A list of implementation parameters and procedures that could be varied for each heuristic is presented in Section 5

Devices]: Modes of Computation---Parallelism and concurrency General Terms: Algorithms, Design, Performance, Theory Additional Key Words and Phrases: Automatic parallelization, DAG, multiprocessors, parallel processing, software tools, static scheduling, task graphs This research was supported by the Hong Kong Research Grants Council under contract numbers HKUST 734/96E, HKUST 6076/97E, and HKU 7124/99E. Authors' addresses: Y.-K. Kwok, Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong; email: ykwok@eee.hku.hk; I. Ahmad, Department of Computer Science, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong. Permission to make digital / hard copy of part or all of this work for personal or classroom use is granted without fee provided that the copies are not made or distributed for profit or commercial advantage, the copyright notice, the title of the publication, and its date appear, and notice is given that copying is by permission of the ACM, Inc. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and / or a fee. 2000 ACM 0360-0300/99/1200--0406 $5.00 ACM Computing Surveys, Vol. 31, No. 4, December 1999 1.

Heterogeneous computing (HC) environments are well suited to meet the computational demands of large, diverse groups of tasks (i.e., a meta-task). The problem of mapping (defined as matching and scheduling) these tasks onto the machines of an HC environment has been shown, in general, to be NP-complete, requiring the development of heuristic techniques. Selecting the best heuristic to use in a given environment, however, remains a difficult problem, because comparisons are often clouded by different underlying assumptions in the original studies of each heuristic. Therefore, a collection of eleven heuristics from the literature has been selected, implemented, and analyzed under one set of common assumptions. The eleven heuristics examined are Opportunistic Load Balancing, User-Directed Assignment, Fast Greedy, Min-min, Max-min, Greedy, Genetic Algorithm, Simulated Annealing, Genetic Simulated Annealing, Tabu, and A*. This study provides one even basis for comparison and insights into c...

It is increasingly common for computer users to have access to several computers on a network, and hence tobe able to execute many of their tasks on any of several computers. The choice of which computers execute which tasks is commonly determined by users based on a knowledge of computer speeds for each task and the current load on each computer. A number of task scheduling systems have been developed that balance the load of the computers on the network, but such systems tend to minimize the idle time of the computers rather than minimize the idle time of the users. This paper focusesonthebene ts that can be achieved when the scheduling system considers both the computer availabilities and the performance of each task on each computer. The SmartNet resource scheduling system is described and compared to two di erent resource allocation strategies: load balancing and user directed assignment. Results are presented where theoperation of hundreds of di erent networks of computers running thousands of di erent mixes of tasks are simulated in a batch environment. These results indicate that, for the computer environments

With the advent of large scale heterogeneous environments, there is a need for matching and scheduling algorithms which can allow multiple DAG-structured applications to share the computational resources of the network. This paper presents a matching and scheduling framework where multiple applications compete for the computational resources on the network. In this environment, each application makes its own scheduling decisions. Thus, no centralized scheduling resource is required. Applications do not need direct knowledge of the other applications. The only knowledge of other applications arrives indirectly through load estimates (like queue lengths). This paper also presents algorithms for each portion of this scheduling framework. One of these algorithms is modification of a static scheduling algorithm, the DLS algorithm, first presented by Sih and Lee [1]. Other algorithms attempt to predict the future task arrivals by modeling the task arrivals as Poisson random processes. A series of simulations are presented to examine the performance of these algorithms in this environment. These simulations also compare the performance of this environment to a more conventional, single user environment.

Vol. 8, pp. 679-690.

Applications based upon the finite element method are well known for their demand for computational resources. An effective method for satisfying this demand is heterogeneous parallel computing. This paper presents the results obtained by applying heterogeneous computing to a large, existing finite element application code: CSTEM. A difficult problem associated with heterogeneous computing is the mapping and scheduling problem---the process of assigning the tasks of a parallel program to the individual processors. A simple assignment heuristic, Levelized Min-Time (LMT), is presented, along with simulated results from applying the LMT algorithm to heterogeneous CSTEM on a variety of different heterogeneous machine clusters.

The design and implementation of a national computing systems Grid has become a reachable goal for both computer and computational scientists. A distributed infrastructure capable of sophisticated computational functions can bring many benefits to scientific work, but poses many challenges to computer scientists, both technical and socio-political. Technical challenges include having basic software tools, functioning and pervasive security, and administration, while socio-political issues include building a user community, adding incentives for sites to be part of a user-centric environment, and educating funding sources about the needs of this community. This paper details the areas relating to Grid research that we feel still need to be addressed to fully leverage the advantages of the Grid. Keywords: Grid computing, parallel distributed computing Introduction Grids are not a new idea. The concept of using multiple distributed resources to cooperatively work on a single a...

Recently, with the widespread use of increasingly powerful commercial off-the-shelf (COTS) products, some real-time distributed system designers have started a shift from custom-made systems to COTS-based systems to get lower costs and more flexible systems. This research investigates the problem of allocating real-time applications to a set of COTS heterogeneous machines connected together by a COTS high-speed network. For the intended distributed real-time system, the work presented in this paper includes characterizing and modeling the applications and the hardware platform, identifying and quantifying the performance goal, and designing and developing heuristics for allocating the applications so as to optimize the performance goal. Each application has certain quality of service (QoS) constraints that must not be violated (e.g., constraints on the end-to-end latency and throughput). Unlike most of the related work in real-time systems, the focus of this work is on finding an initial static allocation of the applications onto the machines to maximize the allowable increase in workload until dynamic reallocation of resources is required to avoid a QoS violation. This paper presents and compares three greedy heuristics to solve the initial mapping problem.

A major challenge in Metacomputing Systems (Computational Grids) is to effectively use their shared resources, such as compute cycles, memory, communication network, and data repositories, to optimize desired global objectives. In this paper we develop a unified framework for resource scheduling in metacomputing systems where tasks with various requirements are submitted from participant sites. Our goal is to minimize the overall execution time of a collection of application tasks. In our model, each application task is represented by a Directed Acyclic Graph (DAG). A task consists of several subtasks and the resource requirements are specified at subtask level. Our framework is general and it accommodates emerging notionsof Qualityof Service (QoS) and advance resource reservations. In this paper, we present several scheduling algorithms which consider compute resources and data repositories that have advance reservations. As shown by our simulationresults, it is advantageous to schedule the system resources in a unified manner rather than scheduling each type of resource separately. Our algorithms have at least 30 % improvement over the separated approach with respect to completion time. 1.