Performance Of Gang Scheduling Research Articles

Gang scheduling in a two-cluster system implementing migrations and periodic feedback

In order to maximize the efficiency of a complex distributed system such as a grid, a proper scheduling algorithm is necessary. The scheduling algorithm is responsible for allocating the available system resources to the existing jobs. In this paper, we consider scheduling gangs in a multi-cluster system in the presence of critical jobs which arrive in a sporadic manner. We examine an adaptive grid scheduler with periodic feedback which allocates the existing jobs to the available clusters. A migration scheme is implemented in order to alleviate the impact of critical sporadic jobs on the performance of gang scheduling. A simulation model is used to provide results on the performance of the system.

The impact of task service time variability on gang scheduling performance in a two-cluster system

Gang scheduling is a common task scheduling policy for parallel and distributed systems which combines elements of space-sharing and time-sharing. In this paper we present a migration strategy which reduces the fragmentation in the schedule caused by gang scheduled jobs. We consider the existence of high priority jobs in the workload. These jobs need to be started immediately and they may interrupt a parallel job’s execution. A distributed system consisting of two homogeneous clusters is simulated to evaluate the performance for various workloads. We study the impact on performance of the variability in service time of the parallel tasks. Our simulation results indicate that the proposed strategy can result in a significant performance gain and that the performance improvement depends on the variability of gang tasks’ service time.

The Impact of Critical Sporadic Jobs on Gang Scheduling Performance in Distributed Systems

A commonly used task scheduling algorithm for parallel and distributed systems is gang scheduling. In this paper, we study gang scheduling performance in the presence of critical sporadic jobs. A simulation model is used to address performance issues associated with gang scheduling on distributed processors for various workloads. Simulated results indicate that the relative performance of the gang scheduling policies depends on the arrival rates of gangs and critical sporadic jobs and also on the variability in the critical sporadic jobs inter-arrival times.

The impact of job memory requirements on gang-scheduling performance

Almost all previous research on gang-scheduling has ignored the impact of real job memory requirements on the performance of the policy. This is despite the fact that on parallel supercomputers, because of the problems associated with demand paging, executing jobs are typically allocated enough memory so that their entire address space is memory-resident. In this paper, we examine the impact of job memory requirements on the performance of gang-scheduling policies. We first present an analysis of the memory-usage characteristics of jobs in the production workload on the Cray T3E at the San Diego Supercomputer Center. We also characterize the memory usage of some of the applications that form part of the workload on the LLNL ASCI supercomputer. Next, we examine the issue of long-term scheduling on MPPs, i.e., we study policies for deciding which jobs among a set of competing jobs should be allocated memory and thus should be allowed to execute on the processors of the system. Using trace-driven simulation, we evaluate the impact of using different long-term scheduling policies on the overall performance of Distributed Hierarchical Control (DHC), a gang-scheduling policy that has been studied extensively in the research literature.

Gang scheduling performance benefits for fine-grain synchronization

Multiprogrammed multiprocessors executing fine-grain parallel programs appear to require new scheduling policies. A promising new idea is gang scheduling, where a set of threads are scheduled to execute simultaneously on a set of processors. This has the intuitive appeal of supplying the threads with an environment that is very similar to a dedicated machine. It allows the threads to interact efficiently by using busy waiting, without the risk of waiting for a thread that currently is not running. Without gang scheduling, threads have to block in order to synchronize, thus suffering the overhead of a context switch. While this is tolerable in coarse-grain computations, and might even lead to performance benefits if the threads are highly unbalanced, it causes severe performance degradation in the fine-grain case. We have developed a model to evaluate the performance of different combinations of synchronization mechanisms and scheduling policies, and validated it by an implementation on the Makbilan multiprocessor. The model leads to the conclusion that gang scheduling is required for efficient fine-grain synchronization on multiprogrammed multiprocessors.