Institute of Electrical and Electronic Engineers (IEEE)
IEEE Transactions on Control Systems Technology
This brief documents experimental results using a deterministic dynamic nonlinear system for load balancing, previously reported by Tang et al. in a cluster of computer nodes used for parallel computations in the presence of time delays and resource constraints. While previous publications by the authors have provided theoretical analysis of this load-balancing strategy using an idealized model, and have documented experiments using a simulated database, experimental results using a complete database for DNA profiles are documented here. Evaluation of the proposed load-balancing strategy using an actual database was critical because of several characteristics of the database that cannot be accurately captured using either a simulation model or database, including the variation in times required for the database to perform search operations, the time-varying and task-dependent computational load the database imposes upon each node of the parallel computer, and the time-varying network traffic imposed by both the communication of database search requests and results, mixed with the traffic generated by the load-balancing strategy. Although the load-balancing strategy can be represented in a relatively straightforward manner using mathematics, its implementation is by necessity an approximation to its mathematical description. The reported experimental results serve to validate the superiority of using the controller based on the anticipated work loads to a controller based on local work loads, which has been predicted with experiments using a simulated database and documented in prior publications. The experiments demonstrate the efficacy of the load-balancing strategy using an anticipated pattern of work loads and provide support for scalability of the approach.
Tang, Z.; Birdwell, J. Douglas; Chiasson, J.; Abdallah, Chaouki T.; and Hayat, Majeed M., "Resource-Constrained Load Balancing Controller for a Parallel Database" (2008). Electrical and Computer Engineering Faculty Research and Publications. 576.
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