DVM

Zhiqiang Ma, Zhonghua Sheng, Lin Gu, Liufei Wen, Gong Zhang
2012 Proceedings of the 8th ACM SIGPLAN/SIGOPS conference on Virtual Execution Environments - VEE '12  
As cloud-based computation becomes increasingly important, providing a general computational interface to support datacenterscale programming has become an imperative research agenda. Many cloud systems use existing virtual machine monitor (VMM) technologies, such as Xen, VMware, and Windows Hypervisor, to multiplex a physical host into multiple virtual hosts and isolate computation on the shared cluster platform. However, traditional multiplexing VMMs do not scale beyond one single physical
more » ... t, and it alone cannot provide the programming interface and clusterwide computation that a datacenter system requires. We design a new instruction set architecture, DISA, to unify myriads of compute nodes to form a big virtual machine called DVM, and present programmers the view of a single computer where thousands of tasks run concurrently in a large, unified, and snapshotted memory space. The DVM provides a simple yet scalable programming model and mitigates the scalability bottleneck of traditional distributed shared memory systems. Along with an efficient execution engine, the capacity of a DVM can scale up to support large clusters. We have implemented and tested DVM on three platforms, and our evaluation shows that DVM has excellent performance in terms of execution time and speedup. On one physical host, the system overhead of DVM is comparable to that of traditional VMMs. On 16 physical hosts, the DVM runs 10 times faster than MapReduce/Hadoop and X10. On 256 EC2 instances, DVM shows linear speedup on a parallelizable workload.
doi:10.1145/2151024.2151032 dblp:conf/vee/MaSGWZ12 fatcat:xykdqu64cvfklco5tt5pycy7b4