METE: meeting end-to-end QoS in multicores through system-wide resource management

  • Authors:

  • Akbar Sharifi;Shekhar Srikantaiah;Asit K. Mishra;Mahmut Kandemir;Chita R. Das

  • Affiliations:

  • The Pennsylvania State University, University Park, PA, USA;The Pennsylvania State University, University Park, PA, USA;The Pennsylvania State University, University Park, PA, USA;The Pennsylvania State University, University Park, PA, USA;The Pennsylvania State University, University Park, PA, USA

  • Venue:
  • Proceedings of the ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

Abstract

Management of shared resources in emerging multicores for achieving predictable performance has received considerable attention in recent times. In general, almost all these approaches attempt to guarantee a certain level of performance QoS (weighted IPC, harmonic speedup, etc) by managing a single shared resource or at most a couple of interacting resources. A fundamental shortcoming of these approaches is the lack of coordination between these shared resources to satisfy a system level QoS. This is undesirable because providing end-to-end QoS in future multicores is essential for supporting wide-spread adoption of these architectures in virtualized servers and cloud computing systems. An initial step towards such an end-to-end QoS support in multicores is to ensure that at least the major computational and memory resources on-chip are managed efficiently in a coordinated fashion. In this paper, we propose METE, a platform for end-to-end on-chip resource management in multicore processors. Assuming that each application specifies a performance target/SLA, the main objective of METE is to dynamically provision sufficient on-chip resources to applications for achieving the specified targets. METE employs a feedback based system, designed as a Single-Input, Multiple-Output (SIMO) controller with an Auto-Regressive-Moving-Average (ARMA) model, to capture the behaviors of different applications. We evaluate a specific implementation of METE that manages cores, shared caches and off-chip bandwidth in an integrated manner on 8 and 16 core systems using a detailed full system simulator and workloads derived from the SPECOMP and SPECJBB multithreaded benchmarks. The collected results indicate that our proposed scheme is able to provision shared resources among co-runner applications dynamically over the course of execution, to provide end-to-end QoS and satisfy specified performance targets. Furthermore, the elegance of the control theory based multi-layer resource provisioning is in assuring QoS guarantees.