PCASA: probabilistic control-adjusted selective allocation for shared caches

Authors:
Konstantinos Aisopos;Jaideep Moses;Ramesh Illikkal;Ravishankar Iyer;Donald Newell
Affiliations:
Princeton University, Princeton, NJ;Intel Labs, Intel Corporation, Hillsboro, OR;Intel Labs, Intel Corporation, Hillsboro, OR;Intel Labs, Intel Corporation, Hillsboro, OR;Server Product Group, AMD, Austin, TX
Venue:
DATE '12 Proceedings of the Conference on Design, Automation and Test in Europe
Year:
2012

Citing 8
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Abstract

Chip Multi-Processors (CMPs) are designed with an increasing number of cores to enable multiple and potentially heterogeneous applications to run simultaneously on the same system. However, this results in increasing pressure on shared resources, such as shared caches. With multiple processor cores sharing the same caches, high-priority applications may end up contending with low-priority applications for cache space and suffer significant performance slow-down, hence affecting the Quality of Service (QoS). In datacenters, Service Level Agreements (SLAs) impose a reserved amount of computing resources and specific cache space per cloud customer. Thus, to meet SLAs, a deterministic capacity management solution is required to control the occupancy of all applications. In this paper, we propose a novel QoS architecture, based on Probabilistic Selective Allocation (PSA), for priority-aware caches. Further, we show that applying a control-theoretic approach (Proportional Integral controller) to dynamically adjust PSA provides accurate and fine-grained capacity management.