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

ABSTRACTManagement 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.