CoQoS: Coordinating QoS-aware shared resources in NoC-based SoCs

  • Authors:

  • Bin Li;Li Zhao;Ravi Iyer;Li-Shiuan Peh;Michael Leddige;Michael Espig;Seung Eun Lee;Donald Newell

  • Affiliations:

  • Intel Corporation, Hillsboro, OR, USA;Intel Corporation, Hillsboro, OR, USA;Intel Corporation, Hillsboro, OR, USA;MIT, Cambridge, MA, USA;Intel Corporation, Hillsboro, OR, USA;Intel Corporation, Hillsboro, OR, USA;Intel Corporation, Hillsboro, OR, USA;Intel Corporation, Hillsboro, OR, USA

  • Venue:
  • Journal of Parallel and Distributed Computing
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

Abstract

Contention in performance-critical shared resources affects performance and quality-of-service (QoS) significantly. While this issue has been studied recently in CMP architectures, the same problem exists in SoC architectures where the challenge is even more severe due to the contention of shared resources between programmable cores and fixed-function IP blocks. In the SoC environment, efficient resource sharing and a guarantee of a certain level of QoS are highly desirable. Researchers have proposed different techniques to support QoS, but most existing works focus on only one individual resource. Coordinated management of multiple QoS-aware shared resources remains an open problem. In this paper, we propose a class-of-service based QoS architecture (CoQoS), which can jointly manage three performance-critical resources (cache, NoC, and memory) in a NoC-based SoC platform. We evaluate the interaction between the QoS-aware allocation of shared resources in a trace-driven platform simulator consisting of detailed NoC and cache/memory models. Our simulations show that the class-of-service based approach provides a low-cost flexible solution for SoCs. We show that assigning the same class-of-service to multiple resources is not as effective as tuning the class-of-service of each resource while observing the joint interactions. This demonstrates the importance of overall QoS support and the coordination of QoS-aware shared resources.