QoS policies and architecture for cache/memory in CMP platforms

  • Authors:

  • Ravi Iyer;Li Zhao;Fei Guo;Ramesh Illikkal;Srihari Makineni;Don Newell;Yan Solihin;Lisa Hsu;Steve Reinhardt

  • Affiliations:

  • Intel Corporation;Intel Corporation;North Carolina State University;Intel;Intel;Intel;North Carolina State University;University of Michigan;University of Michigan

  • Venue:
  • Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
  • Year:
  • 2007

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Abstract

As we enter the era of CMP platforms with multiple threads/cores on the die, the diversity of the simultaneous workloads running on them is expected to increase. The rapid deployment of virtualization as a means to consolidate workloads on to a single platform is a prime example of this trend. In such scenarios, the quality of service (QoS) that each individual workload gets from the platform can widely vary depending on the behavior of the simultaneously running workloads. While the number of cores assigned to each workload can be controlled, there is no hardware or software support in today's platforms to control allocation of platform resources such as cache space and memory bandwidth to individual workloads. In this paper, we propose a QoS-enabled memory architecture for CMP platforms that addresses this problem. The QoS-enabled memory architecture enables more cache resources (i.e. space) and memory resources (i.e. bandwidth) for high priority applications based on guidance from the operating environment. The architecture also allows dynamic resource reassignment during run-time to further optimize the performance of the high priority application with minimal degradation to low priority. To achieve these goals, we will describe the hardware/software support required in the platform as well as the operating environment (O/S and virtual machine monitor). Our evaluation framework consists of detailed platform simulation models and a QoS-enabled version of Linux. Based on evaluation experiments, we show the effectiveness of a QoS-enabled architecture and summarize key findings/trade-offs.