NDLI: A Distributed Algorithm for Large-Scale Graph Partitioning

Please wait, while we are loading the content...

ACM Transactions on Autonomous and Adaptive Systems (TAAS) : Volume 11

ACM Transactions on Autonomous and Adaptive Systems (TAAS) : Volume 10

Issue 4(Special Section on Best Papers from SEAMS 2014 and Regular Articles), February 2016

Issue 3, October 2015

Issue 2, June 2015

SASO 2013: Selected, Revised, and Extended Best Papers

Static, Dynamic, and Adaptive Heterogeneity in Distributed Smart Camera Networks

A Generic Holonic Control Architecture for Heterogeneous Multiscale and Multiobjective Smart Microgrids

Superdiffusive Dispersion and Mixing of Swarms

Cooperative Resource Allocation in Open Systems of Systems

A Distributed Algorithm for Large-Scale Graph Partitioning

Crowdsourcing through Cognitive Opportunistic Networks

Issue 1, March 2015

ACM Transactions on Autonomous and Adaptive Systems (TAAS) : Volume 9

ACM Transactions on Autonomous and Adaptive Systems (TAAS) : Volume 8

ACM Transactions on Autonomous and Adaptive Systems (TAAS) : Volume 7

ACM Transactions on Autonomous and Adaptive Systems (TAAS) : Volume 6

ACM Transactions on Autonomous and Adaptive Systems (TAAS) : Volume 5

ACM Transactions on Autonomous and Adaptive Systems (TAAS) : Volume 4

ACM Transactions on Autonomous and Adaptive Systems (TAAS) : Volume 3

ACM Transactions on Autonomous and Adaptive Systems (TAAS) : Volume 2

ACM Transactions on Autonomous and Adaptive Systems (TAAS) : Volume 1

A Distributed Algorithm for Large-Scale Graph Partitioning

Content Provider	ACM Digital Library
Author	Jelasity, Mark Payberah, Amir H. Girdzijauskas, Sarunas Rahimian, Fatemeh Haridi, Seif
Copyright Year	2015
Abstract	Balanced graph partitioning is an NP-complete problem with a wide range of applications. These applications include many large-scale distributed problems, including the optimal storage of large sets of graph-structured data over several hosts. However, in very large-scale distributed scenarios, state-of-the-art algorithms are not directly applicable because they typically involve frequent global operations over the entire graph. In this article, we propose a fully distributed algorithm called JA-BE-JA that uses local search and simulated annealing techniques for two types of graph partitioning: $\textit{edge-cut}$ partitioning and $\textit{vertex-cut}$ partitioning. The algorithm is massively parallel: There is no central coordination, each vertex is processed independently, and only the direct neighbors of a vertex and a small subset of random vertices in the graph need to be known locally. Strict synchronization is not required. These features allow JA-BE-JA to be easily adapted to any distributed graph-processing system from data centers to fully distributed networks. We show that the minimal edge-cut value empirically achieved by JA-BE-JA is comparable to state-of-the-art centralized algorithms such as Metis. In particular, on large social networks, JA-BE-JA outperforms Metis. We also show that JA-BE-JA computes very low vertex-cuts, which are proved significantly more effective than edge-cuts for processing most real-world graphs.
Starting Page	1
Ending Page	24
Page Count	24
File Format	PDF
ISSN	15564665
e-ISSN	15564703
DOI	10.1145/2714568
Volume Number	10
Issue Number	2
Journal	ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-06-09
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Distributed algorithm Edge-cut partitioning Graph partitioning Load balancing Simulated annealing Vertex-cut partitioning
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering 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

JA-BE-JA: A Distributed Algorithm for Balanced Graph Partitioning

Dynamic Graph Partitioning Scheme for Supporting Load Balancing in Distributed Graph Environments

Partitioning of unstructured meshes for load balancing (1994).

Partitioning of Unstructured Meshes for Load Balancing (1995)

Partitioning of unstructured meshes for load balancing (1995).

Multiply balanced k − partitioning.

Efficient clustering and simulated annealing approach for circuit partitioning

Semi-distributed load balancing for massively parallel multicomputer systems

Dynamic Load Balancing for Large-scale Distributed System with Intelligent Fuzzy Controller

A Distributed Algorithm for Large-Scale Graph Partitioning

Similar Documents

JA-BE-JA: A Distributed Algorithm for Balanced Graph Partitioning

Dynamic Graph Partitioning Scheme for Supporting Load Balancing in Distributed Graph Environments

Partitioning of unstructured meshes for load balancing (1994).

Partitioning of Unstructured Meshes for Load Balancing (1995)

Partitioning of unstructured meshes for load balancing (1995).

Multiply balanced k − partitioning.

Efficient clustering and simulated annealing approach for circuit partitioning

Semi-distributed load balancing for massively parallel multicomputer systems

Dynamic Load Balancing for Large-scale Distributed System with Intelligent Fuzzy Controller

A Distributed Algorithm for Large-Scale Graph Partitioning