Prefetching For Cloud Workloads: An Analysis Based On Address Patterns

Cloud computing is gaining popularity due to its ability to provide infrastructure, platform and software services to clients on a global scale. Using cloud services, clients reduce the cost and complexity of buying and managing the underlying hardware and software layers. Popular services like web search, data analytics and data mining typically work with big data sets that do not fit into top level caches. Thus performance efficiency of last-level caches and the off-chip memory becomes a crucial determinant of cloud application performance. In this paper we use CloudSuite as an example and we study how prefetching schemes affect cloud workloads. We conduct detailed analysis on address patterns to explore the correlation between prefetching performance and intrinsic workload characteristics. Our work focuses particularly on the behavior of memory accesses at the last-level cache and beyond. We observe that cloud workloads in general do not have dominant strides. State-of-the-art prefetching schemes are only able to improve performance for some cloud applications such as web search. Our analysis shows that cloud workloads with long temporal reuse patterns often get negatively impacted by prefetching, especially if their working set is larger than the cache size.