As the global user base for online services continues to expand and new services and features are rapidly developed, data centers from which these online cloud services operate experience constant pressure to achieve higher performance and improve their quality of service. However, supporting the adoption of cloud services in all aspects of people?s daily lives requires expanding data centers to an extreme scale, with hundreds of millions of servers and ecologically unthinkable energy bills. This research develops technologies to improve the performance and efficiency of future data centers, targeting higher performance and lower energy costs from each deployed server. As such, it directly contributes to sustainable growth of data centers and online services, while at the same time training world-class experts specialized in tackling the challenges facing future data centers and clouds.
This research leverages a recently-codified phenomenon called "temporal streams" to solve a number of long-standing micro-architectural performance bottlenecks facing server systems in the cloud. Many of the performance enhancing techniques developed over the course of the past several decades for the desktop, mobile, and super-computer domains provide limited benefits to server systems, because the size and complexity of a typical cloud workload requires significantly greater meta-data storage capacity than currently available to these techniques. This work re-architects the meta-data storage of speculative structures, leveraging temporal streams to expand their effective capacity. Specifically, this work targets instruction prefetchers, branch predictors, and hardware memorization as case studies to demonstrate the ability of temporal streaming to provide sufficient meta-data storage for these mechanisms when executing cloud workloads.
