A Comparison Study of Eleven Static Heuristics for Mapping a Class of Independent Tasks onto Ileterogeneous Distributed Computing Systems

ABSTRACT Il\u27lixed-machine heterogeneous computing (HC) environments utilize a distributed suite of different high-performance machines, interconnected with high-speed links to perform different computationally intensive applications that have diverse comput ational requirements. HC environments are well suited to meet thl: computational dell-tands of large, diverse groups of tasks. The problem of mapping (defined as matching and scheduling) these tasks onto the machines of a distributed 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 enviroi~menth, owever, remains a difficult problem, because comparisons are often clouded by different underlying assumptions in the original studies of each heuristic. There~fore; a collection of eleven heuristics from the literature has been selected: a,dapted, in~plementeda, nd anaiyzed under one set of common assumptions. It is assumed that the heuristics derive a, mapping statically (i.e., off-line). It is also assumed that a meta-task (i.e., a set of independent, non-communicating tasks) is being mapped, and that the goal is to minimize the total execution time of the metla-task. The eleven heuristics examined are Opportunistic Load Balancing, Minimum Execution Time, MininLlum Clompletion Time, Min-min, hllax-min, Duplex? Genetic i-Ilgorithm, Simulated Annealing, Genetic Simulat.ed .Annealing, Tabu, and Ax. This study provides one even basis for comparisor] and insights into circumstances where one technique will out perform another. The evaluation procedure is specified, the heuristics are defined, and then comparison results are discussed. It is shown that for the ca.ses studied here, the relat,ively simple Min-min heuristic performs well in comparison to the other techniques

A fast, effective local search for scheduling independent jobs in heterogeneous computing environments

The efficient scheduling of independent computational jobs in a heterogeneous computing (HC) environment is an important problem in domains such as grid computing. Finding optimal schedules for such an environment is (in general) an NP-hard problem, and so heuristic approaches must be used. Work with other NP-hard problems has shown that solutions found by heuristic algorithms can often be improved by applying local search procedures to the solution found. This paper describes a simple but effective local search procedure for scheduling independent jobs in HC environments which, when combined with fast construction heuristics, can find shorter schedules on benchmark problems than other solution techniques found in the literature, and in significantly less time

A hybrid ant algorithm for scheduling independent jobs in heterogeneous computing environments

The efficient scheduling of independent computational jobs in a heterogeneous computing (HC) environment is an important problem in domains such as grid computing. Finding optimal schedules for such an environment is (in general) an NP-hard problem, and so heuristic approaches must be used. In this paper we describe an ant colony optimisation (ACO) algorithm that, when combined with local and tabu search, can find shorter schedules on benchmark problems than other techniques found in the literature

Distributed data mining in grid computing environments

The official published version of this article can be found at the link below.The computing-intensive data mining for inherently Internet-wide distributed data, referred to as Distributed Data Mining (DDM), calls for the support of a powerful Grid with an effective scheduling framework. DDM often shares the computing paradigm of local processing and global synthesizing. It involves every phase of Data Mining (DM) processes, which makes the workflow of DDM very complex and can be modelled only by a Directed Acyclic Graph (DAG) with multiple data entries. Motivated by the need for a practical solution of the Grid scheduling problem for the DDM workflow, this paper proposes a novel two-phase scheduling framework, including External Scheduling and Internal Scheduling, on a two-level Grid architecture (InterGrid, IntraGrid). Currently a DM IntraGrid, named DMGCE (Data Mining Grid Computing Environment), has been developed with a dynamic scheduling framework for competitive DAGs in a heterogeneous computing environment. This system is implemented in an established Multi-Agent System (MAS) environment, in which the reuse of existing DM algorithms is achieved by encapsulating them into agents. Practical classification problems from oil well logging analysis are used to measure the system performance. The detailed experiment procedure and result analysis are also discussed in this paper

Static mapping heuristics for tasks with dependencies, priorities, deadlines, and multiple versions in heterogeneous environments

Includes bibliographical references.Heterogeneous computing (HC) environments composed of interconnected machines with varied computational capabilities are well suited to meet the computational demands of large, diverse groups of tasks. The problem of mapping (defined as matching and scheduling) these tasks onto the machines of a distributed HC environment has been shown, in general, to be NP-complete. Therefore, the development of heuristic techniques to find near-optimal solutions is required. In the HC environment investigated, tasks had deadlines, priorities, multiple versions, and may be composed of communicating subtasks. The best static (off-line) techniques from some previous studies were adapted and applied to this mapping problem: a genetic algorithm (GA), a GENITOR-style algorithm, and a greedy Min-min technique. Simulation studies compared the performance of these heuristics in several overloaded scenarios, i.e., not all tasks executed. The performance measure used was a sum of weighted priorities of tasks that completed before their deadline, adjusted based on the version of the task used. It is shown that for the cases studied here, the GENITOR technique found the best results, but the faster Min-min approach also performed very well.This research was supported in part by the DARPA/ITO Quorum Program under GSA subcontract number GS09K99BH0250 and a Purdue University Dean of Engineering Donnan Scholarship

Robust processor allocation for independent tasks when dollar cost for processors is a constraint

Includes bibliographical references (pages 9-10).In a distributed heterogeneous computing system, the resources have different capabilities and tasks have different requirements. Different classes of machines used in such systems typically vary in dollar cost based on their computing efficiencies. Makespan (defined as the completion time for an entire set of tasks) is often the performance feature that is optimized. Resource allocation is often done based on estimates of the computation time of each task on each class of machines. Hence, it is important that makespan be robust against errors in computation time estimates. The dollar cost to purchase the machines for use can be a constraint such that only a subset of the machines available can be purchased. The goal of this study is to: (1) select a subset of all the machines available so that the cost constraint for the machines is satisfied, and (2) find a static mapping of tasks so that the robustness of the desired system feature, makespan, is maximized against the errors in task execution time estimates. Six heuristic techniques to this problem are presented and evaluated

Mapping of subtasks with multiple versions in a heterogeneous ad hoc grid environment

Includes bibliographical references (pages 7-8).An ad hoc grid is a heterogeneous computing system composed of mobile devices. The problem studied here is to statically assign resources to the subtasks of an application, which has an execution time constraint, when the resources are oversubscribed. Each subtask has a preferred version, and a secondary version that uses fewer resources. The goal is to assign resources so that the application meets its execution time constraint while minimizing the number of secondary versions used. Five resource allocation heuristics to derive near-optimal solutions to this problem are presented and evaluated

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

Includes bibliographical references (pages 166-167).In a distributed heterogeneous computing system, the resources have different capabilities and tasks have different requirements. To maximize the performance of the system, it is essential to assign the resources to tasks (match) and order the execution of tasks on each resource (schedule) to exploit the heterogeneity of the resources and tasks. Dynamic mapping (defined as matching and scheduling) is performed when the arrival of tasks is not known a priori. In the heterogeneous environment considered in this study, tasks arrive randomly, tasks are independent (i.e., no inter-task communication), and tasks have priorities and multiple soft deadlines. The value of a task is calculated based on the priority of the task and the completion time of the task with respect to its deadlines. The goal of a dynamic mapping heuristic in this research is to maximize the value accrued of completed tasks in a given interval of time. This research proposes, evaluates, and compares eight dynamic mapping heuristics. Two static mapping schemes (all arrival information of tasks are known) are designed also for comparison. The performance of the best heuristics is 84% of a calculated upper bound for the scenarios considered