Hybrid Nature-inspired algorithm for efferent cloud resource utilization

2018 ◽  
Vol 7 (2.4) ◽  
pp. 26
Author(s):  
Monika . ◽  
Vivek Jaglan ◽  
Jugnesh Kumar ◽  
Akshat Agrawal
Keyword(s):  
Cloud Computing ◽  
Reaction Time ◽  
Distributed Computing ◽  
Resource Utilization ◽  
Cost Estimation ◽  
Test System ◽  
Hybrid Nature ◽  
Cloud Resource ◽  
Nature Inspired Algorithm ◽  
Best Fit

Cloud computing has come up as a standout amongst the most encouraging &reliable advancements in the IT part. However by and by there exists a noteworthy issue of load adjusting in the distributed computing condition. This paper comprises of an answer for streamlining the heap utilizing hereditary calculation. Hereditary calculation which takes after the transformative system can build up an answer near ideal arrangement. The proposed calculation is produced by consolidating two existing calculations by considering cost an incentive as the wellness work. The workload is adjusted by the considering the mix of both the heap rate and cost estimation of the assets. Allotment of assets is performed by taking the best fit esteem and lessening the reaction time and general cost. Reenactment comes about are indicated utilizing the cloud examiner test system.

scholarly journals Resource-Aware Min-Min (RAMM) Algorithm for Resource Allocation in Cloud Computing Environment

2019 ◽  
Vol 8 (3) ◽  
pp. 1863-1870 ◽  
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Resource Utilization ◽  
Completion Time ◽  
Scheduling Algorithm ◽  
Cloud Resource ◽  
Tasks Scheduling ◽  
Minimum Completion Time ◽  
Resource Aware ◽  
Time Resource

Resource allocation (RA) is a significant aspect of Cloud Computing. The Cloud resource manager is responsible to assign available resources to the tasks for execution in an effective way that improves system performance, reduce response time, lessen makespan and utilize resources efficiently. To fulfil these objectives, an effective Tasks Scheduling algorithm is required. The standard Max-Min and Min-Min Task Scheduling algorithms are not able to produce better makespan and effective resource utilization. In this paper, a Resource-Aware Min-Min (RAMM) Algorithm is proposed based on basic Min-Min algorithm. The proposed RAMM Algorithm selects shortest execution time task and assigns it to the resource which takes shortest completion time. If minimum completion time resource is busy, then the RAMM Algorithm selects next minimum completion time resource to reduce waiting time of the task and improve resource utilization. The experiment results show that the proposed RAMM Algorithm produces better makespan and load balance than Max-Min, Min-Min and improved Max-Min Algorithms.

scholarly journals A Comparative Study of Different Load Balancing Techniques

2018 ◽  
Vol 7 (4.12) ◽  
pp. 63 ◽  
Author(s):  
Jyoti Parashar ◽  
Dr. Avinash Sharma
Keyword(s):  
Cloud Computing ◽  
Distributed Computing ◽  
Load Balancing ◽  
Comparative Study ◽  
Resource Utilization ◽  
User Satisfaction ◽  
Performance Metrics ◽  
New Technology ◽  
Research Review

Cloud computing is a new technology used to manipulate, configure and can be used to access distributed computing applications in the network. It implements the load balancing approach which is used to distribute all of its workload to every node connected in the network. By using this technique resource utilization is done properly. It can also used to achieve user satisfaction and computing resources. If load balancing is used properly then it can efficiently and properly implement the fail-over, scalability, over- provisioning techniques. It can also minimize the resources used and avoid the bottleneck. In my research, review of different load balancing techniques, its usage, limitations, applications and various performance metrics are described..  

scholarly journals Load Balancing in Cloud Computing using Modified Throtled Algorithm

2019 ◽  
Vol 8 (11S) ◽  
pp. 1115-1119
Keyword(s):  
Cloud Computing ◽  
Reaction Time ◽  
Distributed Computing ◽  
Load Balancing ◽  
The Real ◽  
Focal Issue

In the coming age of computing world, cloud computing plays an important role. Cloud computing gives assets to customer on interest. The assets might be programming assets or equipment assets. Cloud computing structures are circulated, parallel and serve the requirements of various customers in various situations. This appropriated design conveys assets cloud to convey benefits productively to clients in various topographical channels. Customers in a circulated domain produce ask for haphazardly in any processor. So the real downside of this haphazardness is related with errand task. The unequal undertaking task to the processor makes imbalance i.e., a portion in the processors work a lot and some of them stay idle. To exchange the load from over- burden procedure to under stacked procedure straightforwardly is the goal of load balancing. Out of many issues involving in distributed computing, load balancing is one of the focal issue. To accomplish superior, least reaction time and high asset use proportion we have to exchange the assignments between hubs in cloud arrange. Load balancing strategy is utilized to appropriate errands from over stacked hubs to under stacked or inert hubs. In following areas we talk about cloud computing, load balancing strategies.

A Study for VM Placement Schemes in Cloud

2017 ◽  
Vol 7 (8) ◽  
pp. 208
Author(s):  
Ramandeep Kaur ◽  
Navpreet Kaur
Keyword(s):  
Cloud Computing ◽  
Reaction Time ◽  
Task Scheduling ◽  
Specific Time ◽  
Due Date ◽  
Vm Placement ◽  
Time Task ◽  
Wide Range ◽  
The Web

The cloud computing can be essentially expressed as aconveyance of computing condition where distinctive assets are conveyed as a support of the client or different occupants over the web. The task scheduling basically concentrates on improving the productive use of assets and henceforth decrease in task fruition time. Task scheduling is utilized to allot certain tasks to specific assets at a specific time occurrence. A wide range of systems has been exhibited to take care of the issues of scheduling of various tasks. Task scheduling enhances the productive use of asset and yields less reaction time with the goal that the execution of submitted tasks happens inside a conceivable least time. This paper talks about the investigation of need, length and due date based task scheduling calculations utilized as a part of cloud computing.

Design of the IDCTMV Human-Machine Ergonomics Test System

2014 ◽  
Vol 599-601 ◽  
pp. 900-903
Author(s):  
Quan Wang ◽  
Wei Ping Liu ◽  
Yi Jin ◽  
Bin He Fu
Keyword(s):  
Reaction Time ◽  
Subjective Evaluation ◽  
Test System ◽  
Test Methods ◽  
Operation Performance ◽  
Operation Procedure ◽  
Simulated Test ◽  
Control Terminal ◽  
And Control ◽  
Integrated Display

This paper presented the scenario of the IDCTMV Human-Machine Ergonomics test system with the programming idea of the modularization. Based on LabVIEW, the IDCTMV simulated test software and subjective evaluation software were designed and developed. The subjective evaluation results and operation performance data including the reaction time of crews, the rate of errors, and the rate of over reports were tested by the simulation of the integrated display and control terminal for the typical operation procedure, which solved the problems of lacking test methods for the study of the IDCTMV Human-Machine Ergonomics.

scholarly journals Fog Computing in the context of Smart Home, voice assistant and the future of IoT

2020 ◽  
Vol 21 (1) ◽  
pp. 6-12
Author(s):  
Javier Pinzón Castellanos ◽  
Miguel Antonio Cadena Carter
Keyword(s):  
Cloud Computing ◽  
Distributed Computing ◽  
Data Transmission ◽  
Smart Home ◽  
Fog Computing ◽  
Relevant Information ◽  
Raspberry Pi ◽  
Thermal Sensors ◽  
Computing Architecture ◽  
The Future

Fog Computing is the distributed computing layer that lies between the user and the cloud. A successful fog architecture reduces delay or latency and increases efficiency. This paper describes the development and implementation of a distributed computing architecture applied to an automation environment that uses Fog Computing as an intermediary with the cloud computing layer. This study used a Raspberry Pi V3 board connected to end control elements such as servomotors and relays, indicators and thermal sensors. All is controlled by an automation framework that receives orders from Siri and executes them through predetermined instructions. The cloud connection benefits from a reduced amount of data transmission, because it only receives relevant information for analysis.

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