Center-of-Gravity Reduce Task Scheduling to Lower MapReduce Network Traffic

2012 ◽  
Author(s):  
Mohammad Hammoud ◽  
M. Suhail Rehman ◽  
Majd F. Sakr
Keyword(s):  
Task Scheduling ◽  
Network Traffic ◽  
Center Of Gravity

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%.

Center of gravity

AccessScience ◽  
2015 ◽  
Keyword(s):  
Center Of Gravity

How does manipulation of secondary task scheduling affect human performance?

2006 ◽  
Author(s):  
Patrice D. Tremoulet ◽  
Kathleen M. Stibler ◽  
Patrick Craven ◽  
Joyce Barton ◽  
Adam Gifford ◽  
...  
Keyword(s):  
Task Scheduling ◽  
Secondary Task ◽  
Human Performance

Classification of Acceleration Waveforms during Walking by Wavelet Transform

1997 ◽  
Vol 36 (04/05) ◽  
pp. 356-359 ◽  
Author(s):  
M. Sekine ◽  
M. Ogawa ◽  
T. Togawa ◽  
Y. Fukui ◽  
T. Tamura
Keyword(s):  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Center Of Gravity ◽  
Dynamic Responses ◽  
Sequential Data ◽  
Acceleration Signal ◽  
Continuous Dynamic

Abstract:In this study we have attempted to classify the acceleration signal, while walking both at horizontal level, and upstairs and downstairs, using wavelet analysis. The acceleration signal close to the body’s center of gravity was measured while the subjects walked in a corridor and up and down a stairway. The data for four steps were analyzed and the Daubecies 3 wavelet transform was applied to the sequential data. The variables to be discriminated were the waveforms related to levels -4 and -5. The sum of the square values at each step was compared at levels -4 and -5. Downstairs walking could be discriminated from other types of walking, showing the largest value for level -5. Walking at horizontal level was compared with upstairs walking for level -4. It was possible to discriminate the continuous dynamic responses to walking by the wavelet transform.

Task Scheduling Algorithm based on Resources Segregation in Cloud Environment

2018 ◽  
pp. 96-101
Author(s):  
Shailendra Raghuvanshi ◽  
Priyanka Dubey
Keyword(s):  
Load Balancing ◽  
Task Scheduling ◽  
Waiting Time ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Cloud Environment ◽  
Task Scheduling Algorithm ◽  
Machine Utilization ◽  
Independent Tasks ◽  
Optimal Machine

Load balancing of non-preemptive independent tasks on virtual machines (VMs) is an important aspect of task scheduling in clouds. Whenever certain VMs are overloaded and remaining VMs are under loaded with tasks for processing, the load has to be balanced to achieve optimal machine utilization. In this paper, we propose an algorithm named honey bee behavior inspired load balancing, which aims to achieve well balanced load across virtual machines for maximizing the throughput. The proposed algorithm also balances the priorities of tasks on the machines in such a way that the amount of waiting time of the tasks in the queue is minimal. We have compared the proposed algorithm with existing load balancing and scheduling algorithms. The experimental results show that the algorithm is effective when compared with existing algorithms. Our approach illustrates that there is a significant improvement in average execution time and reduction in waiting time of tasks on queue using workflowsim simulator in JAVA.

Detection and Classification of Anomalies in Network Traffic Using Generalized Entropies and OC-SVM with Mahalanobis Kernel

2014 ◽  
Author(s):  
Jayro Santiago-Paz ◽  
Deni Torres-Roman ◽  
Angel Figueroa-Ypiña
Keyword(s):  
Network Traffic ◽  
Generalized Entropies

A Survey on Task Scheduling For Parallel Workloads in the Cloud Computing System

2014 ◽  
Vol 03 (10) ◽  
pp. 16879-16885 ◽  
Author(s):  
ABHIJEET TIKAR ◽  
S.M.JAYB HAYE ◽  
G.R.PAT HAK
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Computing System ◽  
Cloud Computing System
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