Design and performance evaluation of queue-and-rate-adjustment dynamic load balancing policies for distributed networks

In this paper, we classify the dynamic distributed load balancing algorithms for heterogenous distributed computer systems into three policies: Queue Adjustment Policy (QAP), Rate Adjustment Policy ( RAP), and Queue and Rate Adjustment Policy (QRAP). We propose two efficient algorithms, referred to as Rate-based Load Balancing via Virtual Routing (RLBVR) and Queue-based Load Balancing via Virtual Routing (QLBVR), which belong to the above RAP and QRAP policies, respectively. We also consider algorithms Estimated Load Information Scheduling Algorithm ( ELISA) and Perfect Information Algorithm, which were introduced in the literature, to implement QAP policy. Our focus is to analyze and understand the behaviors of these algorithms in terms of their load balancing abilities under varying load conditions ( light, moderate, or high) and the minimization of the mean response time of jobs. We compare the above classes of algorithms by a number of rigorous simulation experiments to elicit their behaviors under some influencing parameters, such as load on the system and status exchange intervals. We also extend our experimental verification to large scale cluster systems such as a Mesh architecture, which is widely used in real-life situations. From these experiments, recommendations are drawn to prescribe the suitability of the algorithms under various situations.