Proceedings Article10.1109/SYSCON.2012.6189509
Cloud Computing—Task scheduling based on genetic algorithms
Eleonora Maria Mocanu,Mihai Florea,Mugurel Ionut Andreica,Nicolae Tapus +3 more
- 19 Mar 2012
- pp 1-6
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TL;DR: The goal of this project is to improve Hadoop's functionality by implementing a scheduler based on a genetic algorithm, solving the stated problem.
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Abstract: Cloud Computing is a cutting edge technology for managing and delivering services over the Internet. Map-Reduce is the programming model used in cloud computing for processing large data sets in parallel over huge clusters. In order to increase efficiency, a good task scheduling is needed. Genetic algorithms are very useful and accurate in finding solutions to large scale optimization problems, such as task scheduling. They have gained immense popularity over last few years as a robust and easily adaptable search technique. Hadoop, the open source implementation of Map-Reduce, has several task schedulers available (FIFO, Fair, Capacity Schedulers), but neither one of them is focused on minimizing the global execution time. The goal of this project is to improve Hadoop's functionality by implementing a scheduler based on a genetic algorithm, solving the stated problem.
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Citations
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TL;DR: This study was developed in order to implement some of these path planning algorithms in the near future, with the objective to find out their relative advantages and disadvantages, and in which situations their implementation is more adequate.
A novel hybrid of Shortest job first and round Robin with dynamic variable quantum time task scheduling technique
Samir Elmougy,Shahenda Sarhan,Manar Joundy +2 more
- 01 Dec 2017
TL;DR: A novel hybrid task scheduling algorithm named SRDQ is proposed combining Shortest-Job-First (SJF) and Round Robin (RR) schedulers considering a dynamic variable task quantum to balance waiting time between short and long tasks.
Scheduling using improved genetic algorithm in cloud computing for independent tasks
Pardeep Kumar,Amandeep Verma +1 more
- 03 Aug 2012
TL;DR: The three scheduling techniques Min-Min, Max-Min and Genetic Algorithm have been discussed and performance metrics of Min- Min andMax-Min have been shown and the performance of the standard Genetic Al algorithm and the proposed Improved Genetic Algorithms have been checked against the sample data.
95
Task scheduling in a cloud computing environment using HGPSO algorithm
TL;DR: An efficient task scheduling algorithm is proposed in this paper that evaluates suitable resources for the user tasks which are in the on-demand queue which is combined with the HGPSO algorithm.
69
Resource Allocation and Scheduling in Cloud Computing: Policy and Algorithm
TL;DR: Five major topics in cloud computing are presented, namely locality-aware task scheduling; reliability-aware scheduling; energy-aware RAS; Software as a Service (SaaS) layer R AS; and workflow scheduling; and performance- and cost-based RAS.
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References
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Jeffrey Dean,Sanjay Ghemawat +1 more
- 06 Dec 2004
TL;DR: This paper presents the implementation of MapReduce, a programming model and an associated implementation for processing and generating large data sets that runs on a large cluster of commodity machines and is highly scalable.
Adaptive probabilities of crossover and mutation in genetic algorithms
M. Srinivas,Lalit M. Patnaik +1 more
- 01 Apr 1994
TL;DR: An efficient approach for multimodal function optimization using genetic algorithms (GAs) and the use of adaptive probabilities of crossover and mutation to realize the twin goals of maintaining diversity in the population and sustaining the, convergence capacity of the GA are described.
Delay scheduling: a simple technique for achieving locality and fairness in cluster scheduling
Matei Zaharia,Dhruba Borthakur,Joydeep Sen Sarma,Khaled Elmeleegy,Scott Shenker,Ion Stoica +5 more
- 13 Apr 2010
TL;DR: This work proposes a simple algorithm called delay scheduling, which achieves nearly optimal data locality in a variety of workloads and can increase throughput by up to 2x while preserving fairness.
Job Scheduling for Multi-User MapReduce Clusters
Matei Zaharia,Dhruba Borthakur,Joydeep Sen Sarma,Khaled Elmeleegy,Scott Shenker,Ion Stoica +5 more
- 01 Jan 2009
TL;DR: Two simple techniques, delay scheduling and copy-compute splitting, are developed which improve throughput and response times in multi-user MapReduce workloads by factors of 2 to 10 and can also raise throughput in a single-user, FIFO workload by a factor of 2.
Dynamic proportional share scheduling in Hadoop
Thomas Sandholm,Kevin Lai +1 more
- 23 Apr 2010
TL;DR: The Dynamic Priority (DP) parallel task scheduler for Hadoop allows users to control their allocated capacity by adjusting their spending over time and enforces service levels more accurately and also scales to more users with distinct service levels than existing schedulers.