work speeds of some LANs have exceeded 100 Mbits per second, thereby reducing the penalty of interprocessor communication. So, dispatching tasks from heavily loaded computers to lightly loaded ones to improve system performance is both logical and feasible. 1 Researchers have proposed many techniques to allocate jobs dynamically in a LAN or multiprocessor system to improve performance. For simplicity, most algorithms ignore practical issues such as the difference in workstation speeds, sudden batch arrivals, and user behaviors. These schedulers might not perform satisfactorily in practice. Moreover, most algorithms have been evaluated by simulations using the steady-state mean response time as the primary performance metric. Not much research has been done on keeping the load balanced in the transient periods, so the scheduling of batches of jobs (for example, by shell scripts) might greatly upset the state of global balance and cause poor performance. Another common simplification is to dispatch jobs immediately upon arrival, irrespective of the overall system loading. Therefore, the sudden arrival of a large number of jobs might saturate the system. An important difference between a LAN environment and a multiprocessor system is that a LAN usually has a user associated with each workstation. These issues are important in contemporary LAN environments, in which workstations have different capabilities and are highly autonomous. We've developed techniques that remedy these simplistic, inadequate system assumptions. These techniques enable schedulers to adapt to more situations (for example, batch arrivals) and to improve job fairness. (Job fairness means that all jobs receive equal treatment if desired.) We also propose a strategy to delay job execution dynamically when the system is fully utilized. In this way, both the executing and delayed jobs receive better service because overcompetition is eliminated. These techniques and strategy are general and can be incorporated into existing schedulers. We've applied them on a wellknown scheduler and compared its performance to other standard schedulers and to the Utopia 2 and LSF 3 load-sharing software packages. (Load Sharing Facility is the commercial version of Utopia.) The results indicate that the proposed strategy adapts well to load fluctuation, improves job fairness, and produces shorter schedules (from 7.8% to 22.2% shorter) than Utopia and LSF do.
There are several publications regarding load balancing and extensive research has been done on static and dynamic strategies and algorithms REF .
Improved Strategies for Dynamic Load Balancing
IEEE Concurr.
IEEE Concurr.
Computer Science