Micro-architectural characterization of desktop cloud workloads

Desktop cloud replaces traditional desktop computers with completely virtualized systems from the cloud. It is becoming one of the fastest growing segments in the cloud computing market. However, as far as we know, there is little work done to understand the behavior of desktop cloud. On one hand, desktop cloud workloads are different from conventional data center workloads in that they are rich with interactive operations. Desktop cloud workloads are different from traditional non-virtualized desktop workloads in that they have an extra layer of software stack - hypervisor. On the other hand, desktop cloud servers are mostly built with conventional commodity processors. While such processors are well optimized for traditional desktops and high performance computing workloads, their effectiveness for desktop cloud workloads remains to be studied. As an attempt to shed some lights on the effectiveness of conventional general-purpose processors on desktop cloud workloads, we have studied the behavior of desktop cloud workloads and compared it with that of SPEC CPU2006, TPC-C, PARSEC, and CloudSuite. We evaluate a Xen-based virtualization platform. The performance results reveal that desktop cloud workloads have significantly different characteristics with SPEC CPU2006, TPC-C and PARSEC, but they perform similarly with data center scale-out benchmarks from CloudSuite. In particular, desktop cloud workloads have high instruction cache miss rate (12.7% on average), high percentage of kernel instructions (23%, on average), and low IPC (0.36 on average). And they have much higher TLB miss rates and lower utilization of off-chip memory bandwidth than traditional benchmarks. Our experimental numbers indicate that the effectiveness of existing commodity processors is quite low for desktop cloud workloads. In this paper, we provide some preliminary discussions on some potential architectural and micro-architectural enhancements. We hope that the performance numbers presented in this paper will give some insights to the designers of desktop cloud systems.