NDLI: An optimized MapReduce workflow scheduling algorithm for heterogeneous computing

Content Provider	Springer Nature Link
Author	Tang, Zhuo Liu, Min Ammar, Almoalmi Li, Kenli Li, Keqin
Copyright Year	2014
Abstract	The MapReduce framework is considered to be an effective resolution for huge and parallel data processing. This paper treats a massive data processing workflow as a DAG graph consisting of MapReduce jobs. In a heterogeneous computing environment, the computation speed can be different even on the same slot depending on various jobs. For this problem, this paper proposes an optimized MapReduce workflow scheduling algorithm. This algorithm comprises a job prioritizing phase and a task assignment phase. First, the jobs can be classified as I/O-intensive and computing-intensive, and the priorities of all jobs are computed according to their corresponding types. Then, the suitable slots are allocated for each block, and the MapReduce tasks in the workflow are scheduled with respect to data locality. The experimental results show that the optimized MapReduce workflow scheduling algorithm can improve the performance of task scheduling and the rationality of resources allocation in heterogeneous computing.
Starting Page	2059
Ending Page	2079
Page Count	21
File Format	PDF
ISSN	09208542
Journal	The Journal of Supercomputing
Volume Number	72
Issue Number	6
e-ISSN	15730484
Language	English
Publisher	Springer US
Publisher Date	2014-11-19
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Hadoop Heterogeneous cluster MapReduce Scheduling Workflow Programming Languages, Compilers, Interpreters Processor Architectures Computer Science
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Information Systems Hardware and Architecture 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

HAT: history-based auto-tuning MapReduce in heterogeneous environments

HScheduler: an optimal approach to minimize the makespan of multiple MapReduce jobs

A novel real-time scheduling algorithm and performance analysis of a MapReduce-based cloud

Optimizing the Hadoop MapReduce Framework with high-performance storage devices

Task scheduling for heterogeneous systems using an incremental approach

An improved partitioning mechanism for optimizing massive data analysis using MapReduce

Adaptive multiple-workflow scheduling with task rearrangement

A market-oriented hierarchical scheduling strategy in cloud workflow systems

Task scheduling for heterogeneous computing systems

An optimized MapReduce workflow scheduling algorithm for heterogeneous computing

Similar Documents

HAT: history-based auto-tuning MapReduce in heterogeneous environments

HScheduler: an optimal approach to minimize the makespan of multiple MapReduce jobs

A novel real-time scheduling algorithm and performance analysis of a MapReduce-based cloud

Optimizing the Hadoop MapReduce Framework with high-performance storage devices

Task scheduling for heterogeneous systems using an incremental approach

An improved partitioning mechanism for optimizing massive data analysis using MapReduce

Adaptive multiple-workflow scheduling with task rearrangement

A market-oriented hierarchical scheduling strategy in cloud workflow systems

Task scheduling for heterogeneous computing systems

An optimized MapReduce workflow scheduling algorithm for heterogeneous computing