NDLI: Automated Fine-Grained CPU Provisioning for Virtual Machines

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ACM Transactions on Architecture and Code Optimization (TACO) : Volume 13

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Effective Transactional Memory Execution Management for Improved Concurrency

Efficient Power Gating of SIMD Accelerators Through Dynamic Selective Devectorization in an HW/SW Codesigned Environment

FLARES: An Aging Aware Algorithm to Autonomously Adapt the Error Correction Capability in NAND Flash Memories

Automated Fine-Grained CPU Provisioning for Virtual Machines

An Evaluation of High-Level Mechanistic Core Models

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Topology-Aware and Dependence-Aware Scheduling and Memory Allocation for Task-Parallel Languages

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Exploiting Existing Comparators for Fine-Grained Low-Cost Error Detection

Hardware Fault Recovery for I/O Intensive Applications

Multiprogram Throughput Metrics: A Systematic Approach

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Automated Fine-Grained CPU Provisioning for Virtual Machines

Content Provider	ACM Digital Library
Author	Bartolini, Davide B. Sironi, Filippo Santambrogio, Marco D. Sciuto, Donatella
Copyright Year	2014
Abstract	Ideally, the pay-as-you-go model of Infrastructure as a Service (IaaS) clouds should enable users to rent just enough resources (e.g., CPU or memory bandwidth) to fulfill their service level objectives (SLOs). Achieving this goal is hard on current IaaS offers, which require users to explicitly specify the amount of resources to reserve; this requirement is nontrivial for users, because estimating the amount of resources needed to attain application-level SLOs is often complex, especially when resources are virtualized and the service provider colocates virtual machines (VMs) on host nodes. For this reason, users who deploy VMs subject to SLOs are usually prone to overprovisioning resources, thus resulting in inflated business costs. This article tackles this issue with $\textit{AutoPro}:$ a runtime system that enhances IaaS clouds with automated and fine-grained resource provisioning based on performance SLOs. Our main contribution with $\textit{AutoPro}$ is filling the gap between application-level performance SLOs and allocation of a contended resource, without requiring explicit reservations from users. In this article, we focus on CPU bandwidth allocation to throughput-driven, compute-intensive multithreaded applications colocated on a multicore processor; we show that a theoretically sound, yet simple, control strategy can enable automated fine-grained allocation of this contended resource, without the need for offline profiling. Additionally, $\textit{AutoPro}$ helps service providers optimize infrastructure utilization by provisioning idle resources to best-effort workloads, so as to maximize node-level utilization. Our extensive experimental evaluation confirms that $\textit{AutoPro}$ is able to automatically determine and enforce allocations to meet performance SLOs while maximizing node-level utilization by supporting batch workloads on a best-effort basis.
Starting Page	1
Ending Page	25
Page Count	25
File Format	PDF
ISSN	15443566
e-ISSN	15443973
DOI	10.1145/2637480
Volume Number	11
Issue Number	3
Journal	ACM Transactions on Architecture and Code Optimization (TACO)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-07-31
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Cloud computing Performance management Resource allocation Virtualization
Content Type	Text
Resource Type	Article
Subject	Hardware and Architecture Information Systems Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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