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

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

Research Anthology on Architectures, Frameworks, and Integration Strategies for Distributed and Cloud Computing ◽

10.4018/978-1-7998-5339-8.ch110 ◽

2021 ◽

pp. 2246-2266

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 ◽

Batch Simulation ◽

Heterogeneous Distributed Systems

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Cognitive Visual Analytics of Multi-Dimensional Cloud System Monitoring Data

International Journal of Software Science and Computational Intelligence ◽

10.4018/ijssci.2017010102 ◽

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.

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

Proceedings of the Mavlyutov Institute of Mechanics ◽

10.21662/uim2016.1.011 ◽

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.

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Enhanced Security In Cloud With Multi-Level Intrusion Detection System

International Journal of Computer and Communication Technology ◽

10.47893/ijcct.2015.1265 ◽

2015 ◽

pp. 9-12

Author(s):

M. KUZHALISAI ◽

G. GAYATHRI

Keyword(s):

Cloud Computing ◽

Intrusion Detection ◽

Large Scale ◽

Detection System ◽

Computing System ◽

Cyber Attacks ◽

Security Level ◽

Security Service ◽

Cloud Computing System ◽

System Resource

Cloud computing is a new type of service which provides large scale computing resource to each customer. Cloud Computing Systems can be easily threatened by various cyber attacks, because most of Cloud computing system needs to contain some Intrusion Detection Systems (IDS) for protecting each Virtual Machine (VM) against threats. In this case, there exists a tradeoff between the security level of the IDS and the system performance. If the IDS provide stronger security service using more rules or patterns, then it needs much more computing resources in proportion to the strength of security. So the amount of resources allocating for customers decreases. Another problem in Cloud Computing is that, huge amount of logs makes system administrators hard to analyse them. In this paper, we propose a method that enables cloud computing system to achieve both effectiveness of using the system resource and strength of the security service without trade-off between them.

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Private cloud computing system based on dynamic service adaptable to large-scale data processing

Journal of Computer Applications ◽

10.3724/sp.j.1087.2012.01009 ◽

2013 ◽

Vol 32 (4) ◽

pp. 1009-1012

Author(s):

Zhu WANG ◽

Lin MEI ◽

Lei LI ◽

Tai-yin ZHAO ◽

Guang-min HU

Keyword(s):

Cloud Computing ◽

Data Processing ◽

Large Scale ◽

Computing System ◽

Private Cloud ◽

Large Scale Data ◽

Cloud Computing System ◽

Large Scale Data Processing ◽

Private Cloud Computing ◽

Scale Data

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High-Throughput Cloud Computing with the Cloudscheduler VM Provisioning Service

Computing and Software for Big Science ◽

10.1007/s41781-020-0036-1 ◽

2020 ◽

Vol 4 (1) ◽

Cited By ~ 1

Author(s):

F. Berghaus ◽

K. Casteels ◽

C. Driemel ◽

M. Ebert ◽

F. F. Galindo ◽

...

Keyword(s):

Cloud Computing ◽

High Throughput ◽

Particle Physics ◽

Large Scale ◽

Computing System ◽

Current Status ◽

Scale Production ◽

Original Design ◽

Cloud Computing System ◽

Vm Provisioning

AbstractWe describe a high-throughput computing system for running jobs on public and private computing clouds using the HTCondor job scheduler and the cloudscheduler VM provisioning service. The distributed cloud computing system is designed to simultaneously use dedicated and opportunistic cloud resources at local and remote locations. It has been used for large-scale production particle physics workloads for many years using thousands of cores on three continents. A decade after its initial design and implementation, cloudscheduler has been modernized to take advantage of new software designs, improved operating system capabilities and support packages. The updated cloudscheduler is more resilient and scalable, with expanded capabilities. We present an overview of the original design and then describe the new version of the distributed compute cloud system. We conclude with a review of the current status and future plans.

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