Heterogeneous Grid Systems Research Articles

An enhanced scheduling algorithm for task planners in heterogeneous distributed computing systems

The basics of designing the spatial schedulers are considered which are used in global heterogeneous GRID-systems. Several theorems are proven, which consider the bipartite graphs of task requests and resource relations. These theorems help to reduce the number of decision options by removing the unpromising elements in the adjacency matrix. This reduces the time complexity of the Hungarian algorithm from O(n3) to O(n1,5 log n). This approach is used in the adaptive multianalysis algorithm, which is based on a preliminary analysis and correction of the bipartite graph matrix. Its application to the matrices, which are filled to less than 30% of their volume, the scheduling algorithm has the statistical time complexity, which is close to linear.

An efficient grid scheduling strategy for data parallel applications

Scheduling large-scale application in heterogeneous grid systems is a fundamental NP-complete problem that is critical to obtain good performance and execution cost. To achieve high performance in a grid system it requires effective task partitioning, resource management and load balancing. The heterogeneous and dynamic nature of a grid, as well as the diverse demands of applications running on the grid, makes grid scheduling a major task. Existing schedulers in wide-area heterogeneous systems require a large amount of information about the application and the grid environment to produce reasonable schedules. However, this required information may not be available, may be too expensive to collect, or may increase the runtime overhead of the scheduler such that the scheduler is rendered ineffective. We believe that no one scheduler is appropriate for all grid systems and applications. This is because while data parallel applications in which further data partitioning is possible can be further improved by efficient management of resources, smart selection of resources and load balancing can be possible, in functional/not-dividable-task parallel applications such partitioning is either not possible or difficult or expensive in term of performance. In this paper, we propose a scheduler for data parallel applications (SDPA) which offers an efficient task partitioning and load balancing strategy for data parallel applications in grid environment. The proposed SDPA offers two major features: maintaining job priority even if insufficient number of free resources is available and pre-task assignment to cut the idle time of nodes. The SDPA selects nodes smartly according to the nature of task and the nodes' resources availability. Simulation results conducted reveal that SDPA achieves performance improvement over reported strategies in the reviewed literature in terms of execution time, throughput and waiting time.

A SURVEY: PARTICLE SWARM OPTIMIZATION-BASED ALGORITHMS FOR GRID COMPUTING SCHEDULING SYSTEMS

Bio-inspired heuristics have been promising in solving complex scheduling optimization problems. Several researches have been conducted to tackle the problems of task scheduling for the heterogeneous and dynamic grid systems using different bio-inspired mechanisms such as Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO). PSO has been proven to have a relatively more promissing performance in dealing with most of the task scheduling challenges. However, to achieve optimum performance, new models and techniques for PSO need to be developed. This study surveys PSObased scheduling algorithms for Grid systems and presents a classification for the various approaches adopted. Meta task-based and workflow-based are the main categories explored. Each scheduling algorithm is described and discussed under the suitable category.

Performance evaluation and energy consumption of a real-time heterogeneous grid system using DVS and DPM

Energy consumption of large scale systems has been severely studied due to economic and ecological reasons. This paper studies energy gains that come from the application of two popular energy saving techniques, Dynamic Voltage Scaling (DVS) and Dynamic Power Management (DPM), in a real-time 2-level heterogeneous grid system. While these techniques generally work in a competitive way, we show that under certain circumstances they can work together and achieve greater savings when they are both applied at the processor level. A simulation model is used to evaluate the performance of the system. Experimental results show encouraging energy savings up to 46% and minimum performance degradation when both energy saving techniques are applied.

Parallel multiobjective evolutionary algorithms for batch scheduling in heterogeneous computing and grid systems

This article presents six parallel multiobjective evolutionary algorithms applied to solve the scheduling problem in distributed heterogeneous computing and grid systems. The studied evolutionary algorithms follow an explicit multiobjective approach to tackle the simultaneous optimization of a system-related (i.e. makespan) and a user-related (i.e. flowtime) objectives. Parallel models of the proposed methods are developed in order to efficiently solve the problem. The experimental analysis demonstrates that the proposed evolutionary algorithms are able to efficiently compute accurate results when solving standard and new large problem instances. The best of the proposed methods outperforms both deterministic scheduling heuristics and single-objective evolutionary methods previously applied to the problem.

The Model and Approach to Scheduling Resources in Heterogeneous Grid-systems

The Model and Approach to Scheduling Resources in Heterogeneous Grid-systems

The system of evaluation resources in a dynamic scheduling system of heterogeneous GRID systems

This paper proposed a system of classifying the characteristics of computing resources distributed heterogeneous Grid systems on various parameters. Proposed quantitative evaluation of the normalized performance indicators Grid systems that affect the allocation of tasks to resources in dynamic planning. Proposed an integral factor of degree of fitting application for a resource. The main approaches to the valuation parameters of nodes Grid systems for use in the calculation of integral index of fitting application for a resource

Exploring Job Migration Technique for P2P Grid Systems

P2P Grids can potentially solve large-scale scientific problems by using geographically distributed heterogeneous resources. However, a number of major technical obstacles must be overcome before this potential can be realized. One problem critical to the effective utilization of P2P Grids is the efficient scheduling of jobs. This study addresses the above-mentioned problem by describing and evaluating a P2P communication model, a P2P resource monitoring system and a job migration mechanism. In this study, the authors propose a P2P communication mechanism, which is built to deliver various information across heterogeneous Grid systems. Based on this P2P communication mechanism, they can develop job migration technology and then improve the usage of distributed computing resources.

Hierarchical Scheduling in Heterogeneous Grid Systems

This article proposes hierarchal scheduling schemes for grid systems: A self-discovery scheme for the resource discovery stage and an adaptive child scheduling method for the resource selection stage. In addition, we propose three rescheduling algorithms: (1) The butterfly algorithm, which reschedules jobs when better resources become available, (2) the fallback algorithm, which reschedules jobs that had their resources taken away from the grid, before the actual resource allocation, and (3) the load-balance algorithm, which balances the load among resources. We also propose a hybrid system to combine the proposed hierarchal schemes with the well-known peer-to-peer (P2P) principle. We compare the performance of the proposed schemes against the P2P-based grid systems through simulation with respect to a set of predefined metrics.

Performance effective pre-scheduling strategy for heterogeneous grid systems in the master slave paradigm

It is well known that grid technology has the ability to coordinate shared resources and scheduled tasks. However, the problem of resource management and task scheduling has always been one of the main challenges. In this paper, we present a performance effective pre-scheduling strategy for dispatching tasks onto heterogeneous processors. The main extension of this study is the consideration of heterogeneous communication overheads in grid systems. One significant improvement of our approach is that average turnaround time could be minimized by selecting the processor that has the smallest communication ratio first. The other advantage of the proposed method is that system throughput can be increased by dispersing processor idle time. Our proposed technique can be applied on heterogeneous cluster systems as well as computational grid environments, in which the communication costs vary in different clusters. To evaluate performance of the proposed techniques, we have implemented the proposed algorithms along with previous methods. The experimental results show that our techniques outperform other algorithms in terms of lower average turnaround time, higher average throughput, less processor idle time and higher processors’ utilization.