Rethinking the Architecture of Warehouse-Scale Computers

Abstract

As the worlds computation continues to move into the massive datacenter infrastructures recently coined as "warehouse-scale computers" (WSCs), developing highly efficient systems for these computing platforms is increasingly critical.The architecture of modern WSCs remain in their relative infancy. WSC architects have started with commodity off-the-shelf components including commodity processors and open source system software components, that are then stitched together to design a simple and cost effective WSC. This approach has been effective for producing systems that are functional, and that can scale the delivery of web-services as demand increases. However, efficiency has suffered, as these components have not been designed and refined with the unique characteristics of WSCs in mind. These characteristics may be critical for a highly efficient WSC design, and as such, we must rethink the architecture of modern WSCs.This dissertation argues that one such characteristic has been overlooked: the diversity in execution environments in modern WSCs. We define a given task's execution environment as the coupling of the machine configuration, and the co-running tasks simultaneously executing alongside the given task.At any given time in a WSC, we have a high degree of diversity across these execution environments. This dissertation argues that acknowledging, exploiting, and adapting to, the diversity in execution environments are crit-3 4 ical for the design of a highly efficient WSC. When ignoring this diversity, three critical design problems arise, including 1) the homogeneous assumption, where all machines and cores in a WSC are assumed to be equal and managed accordingly, 2) the rigidness of applications, where application binaries can not adapt to changes across and within execution environments, and 3) the oblivion of interference, where interference between tasks within an execution environment can not be measured or managed.This dissertation addresses each of these three design problems. First, we address the homogeneous assumption at the cluster level by redesigning the task manager in the WSC to learn which execution environments tasks prefer, and map them accordingly. Second, we address the rigidness of applications at the machine level by providing a mechanism to allow applications to adapt to their execution environment, and leverage this mechanism to solve pressing problems in WSCs. Lastly, we address the oblivion of interference at both the cluster and machine levels by providing novel metrics and techniques for measuring and managing interference to improve the utilization of WSCs.By incorporating an awareness of the diversity in execution environments in these three key design areas, we produce a WSC design that is significantly more efficient in both the performance of the applications that live in this domain, and the utilization of compute resources in the WSC. By improving efficiency for these two metrics, we effectively require a smaller WSC from some fixed workload, which has implic...

Simulating Heterogeneous Clouds at Scale

“…The CloudLightning Simulator was designed to simulate clouds relying on the Warehouse Scale Computer (WSC) architecture (Barroso et al 2013). WSC has been adopted by a multitude of companies including Google, Amazon, Yahoo, Microsoft, and Apple, and has been widely used in the design of cloud environments (Mars 2012). In the WSC architecture, interconnected cloud computing nodes are grouped into cells that are centrally managed ( Fig.…”