On the Optimization of Load Balancing in Distributed Networks in the Presence of Delay

In large-scale distributed-computing environments, there are a number of inherent time-delay factors that can seriously alter the expected performance of load-balancing (or scheduling) policies that do not account for such delays. This situation arises, for example, in systems for which each computational element (CE) is connected by means of a shared broadband communication medium. The delays, in addition to being large, fluctuate randomly as a result of uncertainties in the network condition and uncertainty in the size of the loads to be transferred between the CEs. The performance of such distributed systems under any load-balancing policy is therefore stochastic in nature and must be assessed in a probabilistic sense. Moreover, the design of load-balancing policies that best suit such delay-infested environments must also be carried out in a statistical framework. Indeed, it has been observed in recent simulation-based and experimental studies that the delay, especially that corresponding to transferring large loads between CEs, can cause the system to fall into a mode whereby CEs unnecessarily exchange loads back and forth. This results in an undesirable situation where loads continue to be in transit. It has been observed that the notion of limiting the number of balancing instants in scheduling algorithms can be used to customize certain load-balancing policies to random-delay environments. But the effectiveness of this strategy depends on the decision as to when and how often such load-balancing cycles are to be executed. If a system starts from a certain unbalanced state, we would expect that there would be an optimal time at which balancing must be executed so as to minimize the overall completion time of a workload. In particular, the load-balancing instant should be large enough to ensure that all CEs have updated knowledge of the state of the system. Yet, the balancing instant should be soon enough so that the system does not remain in an unbalanced state for an excessively prolonged time whereby allowing some of the CEs to be idle. It has also been noticed that an additional strategy for mitigating the effects of delay in load balancing is to reduce the gain, or strength, of the load-balancing policy. That is, for each overloaded CE, only a fraction of the load to be transferred is actually transferred to other CEs. Thus, in view of these unique phenomena that occur in load balancing in delay-infested networks, optimizing the performance over the inter-balancing times and the …