-Deterministic dynamic time-delay systems are developed to model load balancing in a cluster of computer nodes used for parallel computations. A linear model is developed whose stability can be characterized in terms of the delays in the transfer of information between nodes and the gains in the load balancing algorithm. A higher Þdelity nonlinear model is also introduced. These models are then compared with an experimental implementation of the load balancing algorithm on a parallel computer network.
In this paper, experimental evaluation of the load balancing algorithm in real environments is presented. we emphasize on the effects of delays in the exchange of information among nodes, and the constraints these effects impose on the design of a load balancing strategy. two testbeds in two different real environments have been built; The first implementation was over a local area network whereas the second one was over Planet-Lab. The results show the effect of the network delays and variances in the tasks processing time on choosing adequate gain values for the load balancing algorithm.
Summary.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 load-2 Authors Suppressed Due to Excessive Length balancing gains becomes an important problem. In this Chapter, the performance of a single-instant load-balancing strategy on a distributed physical system is studied both theoretically and experimentally. The experiments are performed on an inhouse wireless distributed testbed as well as a Monte-Carlo simulation tool. The theoretical analysis is carried out based on the concept of regeneration in stochastic processes. In particular, a probabilistic analysis of the queu...
In this paper, we present a new dynamic, and adaptive distributed load balancing algorithm. This algorithm is able to handle the loss of some computational nodes, the connectivity of the network, and the variations in tasks and transfer delays. An experimental verification of the algorithm is presented using PlanetLab.
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