Tai-Lung Chen

Adaptive Scheduling Based on Quality of Service in Heterogeneous Environments

With the emergence of grid and cloud technologies, the problem of scheduling tasks in heterogeneous distributed systems has been arousing attention. In this paper, we present two scheduling techniques for optimizing overall execution time and minimizing resource usage cost. To evaluate the effectiveness of the proposed techniques, we have implemented both techniques along with the QoS Min-Min scheduling algorithm. The experimental results show that the proposed techniques provide noticeable improvements.

Grid enabled master slave task scheduling for heterogeneous processor paradigm

Efficient task scheduling is an important issue on system performance of computational grid. In this paper, we present an efficient method for dispatching tasks to heterogeneous processors in master slave environment. The main idea of the proposed technique is first to allocate tasks to processors that with lower communication overheads. Improvement of this approach is that average turnaround time can be minimized. The proposed model can also be applied to map tasks to heterogeneous cluster systems in grid environments in which the communication costs are various from clusters.

Message clustering technique towards efficient irregular data redistribution in clusters and grids

Efficient scheduling algorithms are essential to irregular data redistribution in cluster grid Cluster grid is an environment with heterogeneous computing nodes and complex network It is important for schedulers to keep an eye on load balance and low communication cost while distributing different size of data segment on various processors High Performance Fortran Version 2 (HPF2) provides GEN_BLOCK distribution format which facilitates generalized block distributions In this paper, we present a message clustering technique to derive low communication cost when performing such operation in cluster grids The main idea of the proposed technique is to cluster three kinds of messages and normalize the cost The performance evaluation is given and show the proposed method successfully adapts to heterogeneous environment.

A dynamic file maintenance scheme with Bayesian network for data grids

With the large amount of data which is produced by scientific experiments and simulations, the data replication has become an important issue in data grid. In order to shorten the file accessing time, multiple copies of file and stores can be created in the appropriate location. In this paper, we present a dynamical maintenance service of replication to maintain the data in grid environments. Based on the Bayesian Networks (BN), the optimization strategy was proposed in this study; named Implicit Dynamic Maintenance Service with Bayesian Network (IDMSBN).

A compound scheduling strategy for irregular array redistribution in cluster based parallel system

With the advancement of network and techniques of clusters, joining clusters to construct a wide parallel system becomes a trend. Irregular array redistribution employs generalized blocks to help utilize the resource while executing scientific application on such platforms. Research for irregular array redistribution is focused on scheduling heuristics because communication cost could be saved if this operation follows an efficient schedule. In this paper, a two-step communication cost modification (T2CM) and a synchronization delay-aware scheduling heuristic (SDSH) are proposed to normalize the communication cost and reduce transmission delay in algorithm level. The performance evaluations show the contributions of proposed method for irregular array redistribution.

Scheduling for atomic broadcast operation in heterogeneous networks with one port model

With the emergence of the network technologies, heterogeneous computing has become a wide accept paradigm for distributed and network computing. In this paper, we present different algorithms and evaluate their performance on performing atomic one-to-all broadcast in heterogeneous network with one port model. Based on general graph model, two scheduling algorithms, the Nearest Neighbor First and the Maximum Degree Neighbor First are firstly illustrated. The pre-scheduling strategy with constructing message forwarding table for avoiding redundant transmissions is applied as runtime support. By extending graph-based approaches, five tree-based heuris-tic algorithms, the Nearest Neighbor First, the Maximum Degree Neighbor First, the Maximum Height Sub-tree First, the Maximum Sub-Tree First and the Maximum Weighted Sub-tree First, are developed based on different network characteristics. The performance analysis shows that the Maximum Weighted Sub-tree First performs best in high degree heterogeneous environments. Overall speaking, contribution of this study relies on informing significant suggestions for adapting proper broadcasting mechanism in different hetero-geneous platforms.

Performance-Effective and Contention-Free Broadcasts on Irregular Network with Heterogeneous Workstations

With the advance of network and computer techniques, the development of scalable computing becomes a new trend. To integrate and utilize distributed and heterogeneous resources efficiently, message broadcasting is an important and crucial technique for such systems. In this paper, we present a location aware broadcast scheme (LABS) for performing broadcast on irregular heterogeneous network. The LABS introduces a new scheduling scheme that based on heterogeneity of workstation and network topology. Together with a binomial tree optimization technique, LABS can arrange communications in a contention free and shortest routing path manner. To evaluate the performance of LABS, we have implemented the proposed techniques along with other algorithms. The experimental results show that LABS has good performance in different circumstances. Especially, LABS has significant improvements when the environment is with high heterogeneity.

An Efficient Task Dispatching Method in Heterogeneous Networks

Grid technology has been recognized as an efficient solution to coordinate large-scale shared resources and execute complex applications in heterogeneous network environments. In this paper, we present an efficient task scheduling strategy for distributing tasks onto computing nodes in the underlying heterogeneous networks. The contribution of the proposed technique is to minimize average turnaround time by dispatching tasks to processors with smallest communication ratio, the communication to computation cost ratio. System throughput could be also enhanced by dispersing processor idle time. The proposed technique can be applied to heterogeneous cluster systems as well as computational grid environments, in which the communication costs vary in different clusters. Experimental results show that the proposed scheme outperforms other previous algorithms in terms of throughput and turnaround time.