Improving Distributed Workload Performance by Sharing both CPU and Memory Resources

We develop and examine job migration policies by considering effective usage of global memory in addition to CPU load sharing in distributed systems. When a node is identified for lacking sufficient memory space to serve jobs, one or more jobs of the node will be migrated to remote nodes with low memory allocations. If the memory space is sufficiently large, the jobs will be scheduled by a CPU-based load sharing policy. Following the principle of sharing both CPU and memory resources, we present several load sharing alternatives. Our objective is to reduce the number of page faults caused by unbalanced memory allocations for jobs among distributed nodes, so that overall performance of a distributed system can be significantly improved. We have conducted trace-driven simulations to compare CPUbased load sharing policies with our policies. We show that our load sharing policies not only improve performance of memorybound jobs, but also maintain the same load sharing quality as the C...

• Type: Conference
• Year: 2000
• Where: ICDCS
• Authors: Xiaodong Zhang, Yanxia Qu, Li Xiao
• Keywords: Distributed And Parallel Computing, ICDCS 2000, Load Sharing, Load Sharing Policies, Memory Allocations