Synthezis the strucrure of cloud computing system to control of mobile robots group

2016 ◽  
Vol 11 (1) ◽  
pp. 72-80
Author(s):  
O.V. Darintsev ◽  
A.B. Migranov
Keyword(s):  
Cloud Computing ◽  
Mobile Robots ◽  
Distinctive Feature ◽  
Information Exchange ◽  
High Performance ◽  
Computing System ◽  
Control Information ◽  
Cloud Computing System ◽  
Group Control ◽  
Reliability And Robustness

In article one of possible approaches to synthezis of group control of mobile robots which is based on use of cloud computing is considered. Distinctive feature of the offered techniques is adequate reflection of specifics of a scope and the robots of tasks solved by group in architecture of control-information systems, methods of the organization of information exchange, etc. The approach offered by authors allows to increase reliability and robustness of collectives of robots, to lower requirements to airborne computers when saving summary high performance in general.

Cognitive Visual Analytics of Multi-Dimensional Cloud System Monitoring Data

2017 ◽  
Vol 9 (1) ◽  
pp. 20-34
Author(s):  
George Baciu ◽  
Yungzhe Wang ◽  
Chenhui Li
Keyword(s):  
Cloud Computing ◽  
System Dynamics ◽  
Resource Utilization ◽  
Visual Analytics ◽  
High Performance ◽  
Large Scale ◽  
Computing System ◽  
Cloud System ◽  
Past Behavior ◽  
Cloud Computing System

Hardware virtualization has enabled large scale computational service delivery models with high cost leverage and improved resource utilization on cloud computing platforms. This has completely changed the landscape of computing in the last decade. It has also enabled large–scale data analytics through distributed high performance computing. Due to the infrastructure complexity, end–users and administrators of cloud platforms can rarely obtain a full picture of the state of cloud computing systems and data centers. Recent monitoring tools enable users to obtain large amounts of data with respect to many utilization parameters of cloud platforms. However, they fail to get the maximal overall insight into the resource utilization dynamics of cloud platforms. Furthermore, existing tools make it difficult to observe large-scale patterns, making it difficult to learn from the past behavior of cloud system dynamics. In this work, the authors describe a perceptual-based interactive visualization platform that gives users and administrators a cognitive view of cloud computing system dynamics.

Adaptive Threshold Based Scheduler for Batch of Independent Jobs for Cloud Computing System

2018 ◽  
Vol 9 (4) ◽  
pp. 20-39
Author(s):  
TAJ ALAM ◽  
PARITOSH DUBEY ◽  
ANKIT KUMAR
Keyword(s):  
Cloud Computing ◽  
Distributed Systems ◽  
High Performance ◽  
Large Scale ◽  
Real Life ◽  
Interval Estimation ◽  
Computing System ◽  
Adaptive Threshold ◽  
Cloud Computing System ◽  
Heterogeneous Distributed Systems

Distributed systems are efficient means of realizing high-performance computing (HPC). They are used in meeting the demand of executing large-scale high-performance computational jobs. Scheduling the tasks on such computational resources is one of the prime concerns in the heterogeneous distributed systems. Scheduling jobs on distributed systems are NP-complete in nature. Scheduling requires either heuristic or metaheuristic approach for sub-optimal but acceptable solutions. An adaptive threshold-based scheduler is one such heuristic approach. This work proposes adaptive threshold-based scheduler for batch of independent jobs (ATSBIJ) with the objective of optimizing the makespan of the jobs submitted for execution on cloud computing systems. ATSBIJ exploits the features of interval estimation for calculating the threshold values for generation of efficient schedule of the batch. Simulation studies on CloudSim ensures that the ATSBIJ approach works effectively for real life scenario.

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