One of the today issues in software engineering is to find new effective ways to deal intelligently with the increasing complexity of distributed computing systems. In this context a crucial role is played by the balancing of the work load among all nodes in the system. So far load balancing approaches have been designed for networks with fixed or dynamic topologies. These approaches work well in the case each node knows its similes and is able to contact them to delegate tasks. However, they do not address the needs of more dynamic systems where nodes are able to process different types of jobs and have limited knowledge about their neighbors and the whole system. To address these issue, we are experimenting with the usage of autonomic self-aggregation techniques that rewire such highly dynamic systems in groups of homogeneous nodes that are then able to balance the load among each others. We present our approach and show through simulation that it provides significant advantages under the circumstances described before.

/pdf/applying-self-aggregation-to-load-balancing-experimental-1nvo71bwk0.pdf

Applying self-aggregation to load balancing: experimental results

Nearest neighbor load balancing algorithms, like diffusion, are popular due to their simplicity, flexibility, and robustness. We show that they are also asymptotically very efficient when a random rather than a worst case initial load distribution is considered. We show that diffusion needs Θ ((log n ) 2/ d ) balancing time on a d -dimensional mesh network with n d processors. Furthermore, some but not all of the algorithms known to perform better than diffusion in the worst case also perform better for random loads. We also present new results on worst case performance regarding the maximum load deviation.

Analysis of nearest neighbor load balancing algorithms for random loads

Heterogeneous computing opens up new challenges and opportunities in fields such as parallel processing, design of algorithms for applications, partitioning and mapping of parallel tasks, interconnection network technology and the design of heterogeneous programming environments. Lots of load balancing algorithms have been proposed and experimented with in the past years for homogeneous parallel and distributed systems. The author presents a priority-based decay usage load balancing algorithm for a heterogeneous computing environment. The algorithm determines the task precedence graph of the parallel jobs dynamically at run-time and assigns appropriate priorities to the processes to resolve the dependencies. The heuristic algorithm has been tested on some heterogeneous program models.

A dynamic load balancing algorithm for a heterogeneous computing environment

† Abstract- A parallel computer system is a collection of processing elements that communicate and cooperate to solve large computational problems efficiently. To achieve this, at first the large computational problem is partitioned into several tasks with different work-loads and then are assigned to the different processing elements for computation. Distribution of the work load is known as Load Balancing. An appropriate distribution of work-loads across the various processing elements is very important as disproportional workloads can eliminate the performance benefit of parallelizing the job. Hence, load balancing on parallel systems is a critical and challenging activity. Load balancing algorithms can be broadly categorized as static or dynamic. Static load balancing algorithms distribute the tasks to processing elements at compile time, while dynamic algorithms bind tasks to processing elements at run time. This paper explains only the different dynamic load balancing techniques in brief used in parallel systems and concluding with the comparative performance analysis result of these algorithms.

pdf/an-empirical-study-and-analysis-of-the-dynamic-load-4zdhsom34j.pdf

An Empirical Study and Analysis of the Dynamic Load Balancing Techniques Used in Parallel Computing Systems

Nearest neighbor load balancing algorithms like load diiusion are popular due to their simplicity, exibility and robustness. In this paper we show that they are also asymptotically very eecient when a random rather than a worst case initial load distribution is considered. For processor loads described by independent random variables we show that diiusion needs ? (log n) 2=d time to achieve good load balance on a d-dimensional mesh or torus network with n d processors. We also argue that some but not all of the nearest neighbor algorithms known to perform better than diiusion in the worst case also perform better for random loads. In addition, we use the maximum norm for deening the quality of load balancing which has more direct implications for the execution time of the underlying application than previous deenitions. Previouly known results for worst case instances are adapted to the new quality criterion.

On the Efficiency of Nearest Neighbor Load Balancing for Random Loads.

An Analytical Model for Load Balancing on Symmetrical Multiprocessor Systems

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An Analytical Model for Load Balancing on Symmetrical Multiprocessor Systems