Catálogo de publicaciones - libros

Job scheduling typically focuses on the CPU with little work existing to include I/O or memory. Time-shared execution provides the chance to hide I/O and long-communication latencies though potentially creating a memory conflict. We consider two different cases: standard local CPU scheduling and coscheduling on hyperthreaded CPUs. The latter supports coscheduling without any context switches and provides additional options for CPU-internal resource sharing. We present an approach that includes all possible resources into the schedule optimization and improves utilization by coscheduling two jobs if feasible. Our LOMARC approach partially reorders the queue by lookahead to increase the potential to find good matches. In simulations based on the workload model of [12], we have obtained improvements of about 50% in both response times and relative bounded response times on hyperthreaded CPUs (i.e. cut times by half) and of about 25% on standard CPUs for our LOMARC scheduling approach.

Job scheduling typically focuses on the CPU with little work existing to include I/O or memory. Time-shared execution provides the chance to hide I/O and long-communication latencies though potentially creating a memory conflict. We consider two different cases: standard local CPU scheduling and coscheduling on hyperthreaded CPUs. The latter supports coscheduling without any context switches and provides additional options for CPU-internal resource sharing. We present an approach that includes all possible resources into the schedule optimization and improves utilization by coscheduling two jobs if feasible. Our LOMARC approach partially reorders the queue by lookahead to increase the potential to find good matches. In simulations based on the workload model of [12], we have obtained improvements of about 50% in both response times and relative bounded response times on hyperthreaded CPUs (i.e. cut times by half) and of about 25% on standard CPUs for our LOMARC scheduling approach.

Job scheduling typically focuses on the CPU with little work existing to include I/O or memory. Time-shared execution provides the chance to hide I/O and long-communication latencies though potentially creating a memory conflict. We consider two different cases: standard local CPU scheduling and coscheduling on hyperthreaded CPUs. The latter supports coscheduling without any context switches and provides additional options for CPU-internal resource sharing. We present an approach that includes all possible resources into the schedule optimization and improves utilization by coscheduling two jobs if feasible. Our LOMARC approach partially reorders the queue by lookahead to increase the potential to find good matches. In simulations based on the workload model of [12], we have obtained improvements of about 50% in both response times and relative bounded response times on hyperthreaded CPUs (i.e. cut times by half) and of about 25% on standard CPUs for our LOMARC scheduling approach.

Job scheduling typically focuses on the CPU with little work existing to include I/O or memory. Time-shared execution provides the chance to hide I/O and long-communication latencies though potentially creating a memory conflict. We consider two different cases: standard local CPU scheduling and coscheduling on hyperthreaded CPUs. The latter supports coscheduling without any context switches and provides additional options for CPU-internal resource sharing. We present an approach that includes all possible resources into the schedule optimization and improves utilization by coscheduling two jobs if feasible. Our LOMARC approach partially reorders the queue by lookahead to increase the potential to find good matches. In simulations based on the workload model of [12], we have obtained improvements of about 50% in both response times and relative bounded response times on hyperthreaded CPUs (i.e. cut times by half) and of about 25% on standard CPUs for our LOMARC scheduling approach.

Job scheduling typically focuses on the CPU with little work existing to include I/O or memory. Time-shared execution provides the chance to hide I/O and long-communication latencies though potentially creating a memory conflict. We consider two different cases: standard local CPU scheduling and coscheduling on hyperthreaded CPUs. The latter supports coscheduling without any context switches and provides additional options for CPU-internal resource sharing. We present an approach that includes all possible resources into the schedule optimization and improves utilization by coscheduling two jobs if feasible. Our LOMARC approach partially reorders the queue by lookahead to increase the potential to find good matches. In simulations based on the workload model of [12], we have obtained improvements of about 50% in both response times and relative bounded response times on hyperthreaded CPUs (i.e. cut times by half) and of about 25% on standard CPUs for our LOMARC scheduling approach.

Job scheduling typically focuses on the CPU with little work existing to include I/O or memory. Time-shared execution provides the chance to hide I/O and long-communication latencies though potentially creating a memory conflict. We consider two different cases: standard local CPU scheduling and coscheduling on hyperthreaded CPUs. The latter supports coscheduling without any context switches and provides additional options for CPU-internal resource sharing. We present an approach that includes all possible resources into the schedule optimization and improves utilization by coscheduling two jobs if feasible. Our LOMARC approach partially reorders the queue by lookahead to increase the potential to find good matches. In simulations based on the workload model of [12], we have obtained improvements of about 50% in both response times and relative bounded response times on hyperthreaded CPUs (i.e. cut times by half) and of about 25% on standard CPUs for our LOMARC scheduling approach.