Paper Info
Title
Location, location, location!: modeling data proximity in the cloud
Abstract
Cloud applications have increasingly come to rely on distributed storage systems that hide the complexity of handling network and node failures behind simple, data-centric interfaces (such as PUTs and GETs on key-value pairs). While these interfaces are very easy to use, the application is completely oblivious to the location of its data in the network; as a result, it has no way to optimize the placement of data or computation. In this paper, we propose exposing the network location of data to applications. The primary challenge is that data does not usually exist at a single point in the network; it can be striped, replicated, cached and coded across different locations, in arbitrary ways that vary across storage systems. For example, an item that is synchronously mirrored in both Seattle and London will appear equally far from both locations for writes, but equally close to both locations for reads. Accordingly, we describe Contour, a system that allows applications to query and manipulate the location of data without requiring them to be aware of the physical machines storing the data, the replication protocols used or the underlying network topology.
Year
DOI
Venue
2010
10.1145/1868447.1868462
HotNets
Keywords
Field
DocType
key-value pair,node failure,network location,different location,data-centric interface,storage system,cloud application,physical machine,arbitrary way,underlying network topology,data proximity,location,network topology,cloud computing
Data modeling,Cache,Computer science,Distributed data store,Computer network,Network topology,Cloud computing,Computation,Distributed computing
Conference
Citations 
PageRank 
References 
11
0.64
6
Authors
5
Name
Order
Citations
PageRank
Birjodh Tiwana1110.64
Mahesh Balakrishnan2113257.92
Marcos Kawazoe Aguilera32519153.60
Hitesh Ballani4138663.25
Zhuoqing Morley Mao55719363.11