A Hierarchical Load Balancing Strategy Considering Communication Delay Overhead for Large Distributed Computing Systems
Jixiang Yang, Ling Ling, Haibin Liu · Mathematical Problems in Engineering · 2016
Load balancing technology can effectively exploit potential enormous compute power available on distributed systems and achieve scalability. Communication delay overhead on distributed system, which is time-varying and is usually ignored or assumed to be deterministic for traditional load balancing strategies, can greatly degrade the load balancing performance. Considering communication delay overhead and its time-varying feature, a hierarchical load balancing strategy based on generalized neural network (HLBSGNN) is presented for large distributed systems. The novelty of the HLBSGNN is threefold: ( 1 ) the hierarchy with optimized communication is employed to reduce load balancing overhead for large distributed computing systems, ( 2 ) node computation rate and communication delay randomness imposed by the communication medium are considered, and ( 3 ) communication and migration overheads are optimized via forecasting delay. Comparisons with traditional strategies, such as centralized, distributed, and random delay strategies, indicate that the HLBSGNN is more effective and efficient.