A quantitative comparison of load balancing approaches in distributed object computing systems

Several load balancing schemes are recently proposed for distributed object computing systems, which are widely envisioned to be the desired distributed software development paradigm due to the higher modularity and the capability of handling machine and operating system heterogeneity. However, while the rationales and mechanisms employed are dramatically different, the relative strengths and weaknesses of these approaches are unknown, making it difficult for a practitioner to choose an appropriate approach for the problem at hand. In this paper, we describe in detail three representative approaches, which are all practicable, and present a quantitative comparison using our experimental distributed object computing platform. Among these three approaches, namely, JavaSpaces based, request redirection based, and fuzzy decision based, we find that the fuzzy decision based algorithm outperforms the other two considerably.published_or_final_versio

Implementation of Decentralized Load Sharing in Networked Workstations Using the Condor Package

In recent years a number of load sharing (LS) mechanisms have been proposed or implemented to fully utilize system resources. We have designed and implemented a decentralized real-time LS mechanism based on the Condor package [17, 18]. Two important features of our design are use of region-change broadcasts in the information policy to provide each workstation with timely state information at minimum communication cost, and use of preferred lists in the location policy to avoid task collisions. With these two features, we remove the central manager workstation in Condor, configure its functionalities into each participating workstation, transform Condor into a decentralized LS mechanism, and equip Condor with the capability to tolerate single workstation failures. Also discussed are the experiments on the proposed LS mechanism and the off-the-shelf Condor package and our observations of empirical data. Index Terms : distributed systems, adaptive load sharing, region-change broadcasts, ..

Decentralized load balancing in heterogeneous computational grids

With the rapid development of high-speed wide-area networks and powerful yet low-cost computational resources, grid computing has emerged as an attractive computing paradigm. The space limitations of conventional distributed systems can thus be overcome, to fully exploit the resources of under-utilised computing resources in every region around the world for distributed jobs. Workload and resource management are key grid services at the service level of grid software infrastructure, where issues of load balancing represent a common concern for most grid infrastructure developers. Although these are established research areas in parallel and distributed computing, grid computing environments present a number of new challenges, including large-scale computing resources, heterogeneous computing power, the autonomy of organisations hosting the resources, uneven job-arrival pattern among grid sites, considerable job transfer costs, and considerable communication overhead involved in capturing the load information of sites. This dissertation focuses on designing solutions for load balancing in computational grids that can cater for the unique characteristics of grid computing environments. To explore the solution space, we conducted a survey for load balancing solutions, which enabled discussion and comparison of existing approaches, and the delimiting and exploration of the apportion of solution space. A system model was developed to study the load-balancing problems in computational grid environments. In particular, we developed three decentralised algorithms for job dispatching and load balancing—using only partial information: the desirability-aware load balancing algorithm (DA), the performance-driven desirability-aware load-balancing algorithm (P-DA), and the performance-driven region-based load-balancing algorithm (P-RB). All three are scalable, dynamic, decentralised and sender-initiated. We conducted extensive simulation studies to analyse the performance of our load-balancing algorithms. Simulation results showed that the algorithms significantly outperform preexisting decentralised algorithms that are relevant to this research