A Two-Stage Queue Model for Context-Aware Task Scheduling in Mobile Multimedia Cloud Environments

2018 ◽  
pp. 287-297
Author(s):  
Durga S ◽  
Mohan S ◽  
J. Dinesh Peter
Keyword(s):  
Task Scheduling ◽  
Mobile Multimedia ◽  
Context Aware ◽  
Two Stage ◽  
Multimedia Cloud ◽  
Cloud Environments ◽  
Queue Model

scholarly journals TMaR: a two-stage MapReduce scheduler for heterogeneous environments

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Neda Maleki ◽  
Hamid Reza Faragardi ◽  
Amir Masoud Rahmani ◽  
Mauro Conti ◽  
Jay Lofstead
Keyword(s):  
Task Scheduling ◽  
Network Traffic ◽  
Input Data ◽  
Power Reduction ◽  
Heterogeneous Environments ◽  
Two Stage ◽  
Finish Time ◽  
Remote Storage ◽  
Simulation Results

Abstract In the context of MapReduce task scheduling, many algorithms mainly focus on the scheduling of Reduce tasks with the assumption that scheduling of Map tasks is already done. However, in the cloud deployments of MapReduce, the input data is located on remote storage which indicates the importance of the scheduling of Map tasks as well. In this paper, we propose a two-stage Map and Reduce task scheduler for heterogeneous environments, called TMaR. TMaR schedules Map and Reduce tasks on the servers that minimize the task finish time in each stage, respectively. We employ a dynamic partition binder for Reduce tasks in the Reduce stage to lighten the shuffling traffic. Indeed, TMaR minimizes the makespan of a batch of tasks in heterogeneous environments while considering the network traffic. The simulation results demonstrate that TMaR outperforms Hadoop-stock and Hadoop-A in terms of makespan and network traffic and achieves by an average of 29%, 36%, and 14% performance using Wordcount, Sort, and Grep benchmarks. Besides, the power reduction of TMaR is up to 12%.

scholarly journals Evolutionary algorithm-based multi-objective task scheduling optimization model in cloud environments

2015 ◽  
Vol 18 (6) ◽  
pp. 1737-1757 ◽  
Author(s):  
Fahimeh Ramezani ◽  
Jie Lu ◽  
Javid Taheri ◽  
Farookh Khadeer Hussain
Keyword(s):  
Evolutionary Algorithm ◽  
Task Scheduling ◽  
Optimization Model ◽  
Scheduling Optimization ◽  
Multi Objective ◽  
Cloud Environments

scholarly journals Teaching-Learning based Task Scheduling Optimization in Cloud Computing Environments

2019 ◽  
Vol 8 (2) ◽  
pp. 2952-2958
Keyword(s):  
Task Scheduling ◽  
Research Work ◽  
Search Space ◽  
Scheduling Problem ◽  
Optimal Resource ◽  
Np Hard Problem ◽  
Cloud Environments ◽  
Computing Environments ◽  
Teaching Learning ◽  
First Time

Generating optimal task scheduling plans in cloud environments is a tedious task as it is a np-hard problem. The optimal resource allocation in cloud environments involves more search space and time consuming. Therefore, recent researchers are focused on implementation of artificial intelligence to solve task scheduling problem. In this paper, a new and efficient evolutionary algorithm named teaching-learning based algorithm has been implemented first time to solve the task scheduling problem in cloud environments. The current research work considers the task scheduling problem as a multi-objective optimization problem. The proposed algorithm finds the best solution by minimizing the execution time and response time while maximizing the throughput of all resources to complete the assigned tasks.

A heuristic task scheduling algorithm based on server power efficiency model in cloud environments

2018 ◽  
Vol 20 ◽  
pp. 56-65 ◽  
Author(s):  
Weiwei Lin ◽  
Weiqi Wang ◽  
Wentai Wu ◽  
Xiongwen Pang ◽  
Bo Liu ◽  
...  
Keyword(s):  
Task Scheduling ◽  
Power Efficiency ◽  
Scheduling Algorithm ◽  
Task Scheduling Algorithm ◽  
Cloud Environments

A Task Scheduling Approach for Cloud Environments Employing Evolutionary Algorithms

2021 ◽  
Vol 13 (2) ◽  
pp. 423-438
Author(s):  
B. Lakhani ◽  
A. Agrawal
Keyword(s):  
Evolutionary Algorithms ◽  
Response Time ◽  
Task Scheduling ◽  
Gradient Descent ◽  
Virtual Machines ◽  
Sufficient Information ◽  
Swarm Optimization ◽  
Cloud Environments ◽  
Time Critical ◽  
Cloud Resources

One of the key challenges in the domain of cloud computing is task scheduling and estimation of cloud workloads for time critical applications pertaining to constrained cloud resources. While effective task scheduling is necessary for balancing the load, workload forecasting is necessary to plan in advance the requirements of cloud platforms based on previous data so as to effectively utilize cloud resources. Often it is challenging to gather sufficient information about the tasks and hence allocating the tasks to virtual machines (VMs) in the most optimal way is non-trivial. In this paper, a hybrid task scheduling approach is proposed based on evolutionary algorithms. The first approach is the amalgamation of bat and particle swarm optimization (PSO) techniques. The scheduling approach also combines the processing time preemption (PTP) approach to schedule the source intensive tasks which allows to reduce the response time of the proposed system.  The second approach is a machine learning based approach employing gradient descent with momentum (GDM). The evaluation of the proposed system has been done based on the response time and mean square error of the system.

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