Dynamic load sharing with unknown memory demands in clusters

Abstract

A compute farm is a pool of clustered workstations to provide high performance computing services for CPU-intensive, memory-intensive, and I/O active jobs in a batch mode. Existing load sharing schemes with memory considerations assume jobs' memory demand sizes are known in advance or predictable based on users' hints. This assumption can greatly simplify the designs and implementations of load sharing schemes, but is not desirable in practice. In order to address this concern, we present three new results and contributions in this study. Conducting Linux kernel instrumentation, we have collected different types of workload execution traces to quantitatively characterize job interactions, and modeled page fault behavior as a function of the overloaded memory sizes and the amount of jobs' I/O activities. Based on experimental results and collected dynamic system information, we have built a simulation model which accurately emulates the memory system operations and job migrations with virtual memory considerations. We have proposed a memory-centric load sharing scheme and its variations to effectively process dynamic memory, allocation demands, aiming at minimizing execution time of each individual job by dynamically migrating and remotely submitting jobs to eliminate or reduce page faults and to reduce the queuing time for CPU services. Conducting trace-driven simulations, we have examined these load sharing policies to show their effectiveness.