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Prachi N. Thakar

dblp:70/77 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2001
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Cloud and datacenter computing · 75% Energy-efficient computing · 25%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Cloud and datacenter computing › resource management
datacenter resource management
0.012001
Managing Energy and Server Resources in Hosting Centres · SOSP 2001
Cloud and datacenter computing › resource allocation
economic resource allocation
0.012001
Managing Energy and Server Resources in Hosting Centres · SOSP 2001
Cloud and datacenter computing
resource management
0.012001
Managing Energy and Server Resources in Hosting Centres · SOSP 2001
Energy-efficient computing › datacenter energy efficiency
server cluster power management
0.012001
Managing Energy and Server Resources in Hosting Centres · SOSP 2001

Methods — techniques the papers use, named apart from their topics

greedy resource allocation · 0.0SLA-based degradation · 0.0
YearPublicationVenuePosition
2001 Managing Energy and Server Resources in Hosting Centres
abstract
Internet hosting centers serve multiple service sites from a common hardware base. This paper presents the design and implementation of an architecture for resource management in a hosting center operating system, with an emphasis on energy as a driving resource management issue for large server clusters. The goals are to provision server resources for co-hosted services in a way that automatically adapts to offered load, improve the energy efficiency of server clusters by dynamically resizing the active server set, and respond to power supply disruptions or thermal events by degrading service in accordance with negotiated Service Level Agreements (SLAs).Our system is based on an economic approach to managing shared server resources, in which services "bid" for resources as a function of delivered performance. The system continuously monitors load and plans resource allotments by estimating the value of their effects on service performance. A greedy resource allocation algorithm adjusts resource prices to balance supply and demand, allocating resources to their most efficient use. A reconfigurable server switching infrastructure directs request traffic to the servers assigned to each service. Experimental results from a prototype confirm that the system adapts to offered load and resource availability, and can reduce server energy usage by 29% or more for a typical Web workload.
Jeffrey S. Chase, Darrell C. Anderson, Prachi N. Thakar, Amin Vahdat, Ronald P. Doyle
SOSP3