NDLI: Heracles:improving resource efficiency at scale

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A case for core-assisted bottleneck acceleration in GPUs

Redundant memory mappings for fast access to large memories

Quantitative comparison of hardware transactional memory for Blue Gene/Q, zEnterprise EC12, Intel Core, and POWER8

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Exploring the potential of heterogeneous von neumann/dataflow execution models

PIM-enabled instructions

Thermal time shifting

A variable warp size architecture

Manycore network interfaces for in-memory rack-scale computing

Towards sustainable in-situ server systems in the big data era

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Profiling a warehouse-scale computer

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LaZy superscalar

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The load slice core microarchitecture

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Data reorganization in memory using 3D-stacked DRAM

Flexible software profiling of GPU architectures

Flexible auto-refresh

Semantic locality and context-based prefetching using reinforcement learning

Architecting to achieve a billion requests per second throughput on a single key-value store server platform

Dynamic thread block launch

Volume 43, Issue 1, March 2015

Heracles:improving resource efficiency at scale

Content Provider	ACM Digital Library
Author	Ranganathan, Parthasarathy Lo, David Kozyrakis, Christos Govindaraju, Rama Cheng, Liqun
Abstract	User-facing, latency-sensitive services, such as websearch, underutilize their computing resources during daily periods of low traffic. Reusing those resources for other tasks is rarely done in production services since the contention for shared resources can cause latency spikes that violate the service-level objectives of latency-sensitive tasks. The resulting under-utilization hurts both the affordability and energy-efficiency of large-scale datacenters. With technology scaling slowing down, it becomes important to address this opportunity. We present Heracles, a feedback-based controller that enables the safe colocation of best-effort tasks alongside a latency-critical service. Heracles dynamically manages multiple hardware and software isolation mechanisms, such as CPU, memory, and network isolation, to ensure that the latency-sensitive job meets latency targets while maximizing the resources given to best-effort tasks. We evaluate Heracles using production latency-critical and batch workloads from Google and demonstrate average server utilizations of 90% without latency violations across all the load and colocation scenarios that we evaluated.
Starting Page	450
Ending Page	462
Page Count	13
File Format	PDF
ISSN	01635964
DOI	10.1145/2872887.2749475
Journal	ACM SIGARCH Computer Architecture News (CARN)
Volume Number	43
Issue Number	3
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	1981-04-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Content Type	Text
Resource Type	Article

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