Cluster-Based Load Balancing Algorithms for Grids

E-science applications may require huge amounts of data and high processing power where grid infrastructures are very suitable for meeting these requirements. The load distribution in a grid may vary leading to the bottlenecks and overloaded sites. We describe a hierarchical dynamic load balancing protocol for Grids. The Grid consists of clusters and each cluster is represented by a coordinator. Each coordinator first attempts to balance the load in its cluster and if this fails, communicates with the other coordinators to perform transfer or reception of load. This process is repeated periodically. We analyze the correctness, performance and scalability of the proposed protocol and show from the simulation results that our algorithm balances the load by decreasing the number of high loaded nodes in a grid environment.Comment: 17 pages, 11 figures; International Journal of Computer Networks, volume3, number 5, 201

Safe self-scheduling: A parallel loop scheduling scheme for shared-memory multiprocessors

The article of record as published may be found at https://doi.org/10.1007/BF02577870In this paper we present Safe Self-Scheduling (SSS), a new scheduling scheme that schedules parallel loops with variable length iteration execution times not known at compile time. The scheme assumes a shared memory space. SSS combines static scheduling with dynamic scheduling and draws favorable advantages from each. First, it reduces the dynamic scheduling overhead by statistically scheduling a major portion of loop iterations. Second, the workload is balanced with simple and efficient self-scheduling scheme by applying a new measure, the smallest critical chore size. Experimental results comparing SSS with other scheduling schemes indicate that SSS surpasses other scheduling schemes. In the experiment on Gauss-Jordan, an application that is suitable for static scheduling schemes, SSS is the only self-scheduling scheme that outperforms the static scheduling scheme. This indicates that SSS achieves a balanced workload with a very small amount of overhead.USDO