Performance Metrics of Local Cloud Computing Architectures

2015 ◽  
Author(s):  
Travis Brummett ◽  
Pezhman Sheinidashtegol ◽  
Debadrita Sarkar ◽  
Michael Galloway
Keyword(s):  
Cloud Computing ◽  
Performance Metrics ◽  
Local Cloud

scholarly journals Hybrid Starling Social Spider Algorithm for Energy and Load Aware Task Scheduling in Cloud Computing

2019 ◽  
Vol 8 (9) ◽  
pp. 3135-3142
Keyword(s):  
Energy Efficiency ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Performance Metrics ◽  
Scheduling Algorithm ◽  
Social Spider ◽  
Task Scheduling Algorithm ◽  
Intelligent Behavior ◽  
Social Spider Algorithm ◽  
The Given

The efficiency of the cloud-based systems is greatly relying on the task scheduling algorithm which affects the performance parameters such as makespan, response time, degree of imbalance and cost. In recent years, the energy efficiency is also considered as another challenging issue which affects the efficiency of cloud computing systems. This paper proposes a Hybrid Starling Social Spider Algorithm (Starling-SSA) for Energy and Load Aware Task Scheduling in cloud computing. The Starling-SSA is designed as a hybrid algorithm inspired by the intelligent behavior of social spider and the collective response behavior of starling birds. The foraging behavior of spider is implemented to identify the best VMs for the given task with minimum makespan and degree of imbalance. In addition to this, the distance factor is incorporated inspired by starling flock distance in order identify the closeness of VM pairs and avoids the VMs that are far away, thereby VMs can be limited during the searching process. This will greatly reduce energy consumption by taking only VMs that are belongs to the distance factor. The performance metrics such as makespan, degree of imbalance and energy efficiency are evaluated against the existing algorithms such as EATS, CBAT and HC-ACO. The results presents a significant improvements when comparing to the existing algorithms

Status of Cloud Service Adoption in Climate Risk Country

2018 ◽  
Vol 2 (2) ◽  
pp. 51-54
Author(s):  
E.O. Opoku
Keyword(s):  
Cloud Computing ◽  
Data Center ◽  
Service Providers ◽  
Cloud Service ◽  
Cloud Services ◽  
Service Adoption ◽  
Cloud Computing Service ◽  
Cloud Brokerage ◽  
Brokerage Firms ◽  
Local Cloud

Ghana has attained cloud readiness indices facilitating services adoption by local enterprises through brokerage firms. Accordingto Gartner group by 2015, at least 20% of all cloud services will be consumed via internal or external cloud service brokerages,rather than directly with service providers. It means enterprises must identify local cloud brokerage firms to intermediate for cloudclients and service providers. We aimed at surveying cloud service awareness among enterprises in Ghana. We performed fieldstudy using statistical tool to analyze data collected among 45-participants spread across 20 local enterprises, using purposivesampling in the selection of strategic enterprise managers located in the second largest city, Kumasi, Ghana. We employedDelphi technique involving three Information Technology experts to validate responses in reducing margin of error in the analysis.We found that 67% respondents are unaware of local cloud service brokerage firms. Alternatively, 33% respondents mentioned atleast one local cloud brokerage firm; although experts believed some did a chess guessing to have it correct. Our Delphi expertsattributed this alarming percentile to lack of policy stakeholders involvement in ensuring cloud adoption readiness. We concludedon effective sensitization of cloud computing service adoption in optimizing data center proliferation by enterprises in Ghana.Adopting cloud computing over data center helps in reducing global warming contributed by heat emissions from computingservers.

Sharing VM Resources With Using Prediction of Future User Requests for an Efficient Load Balancing in Cloud Computing Environment

2021 ◽  
Vol 13 (2) ◽  
pp. 37-64
Author(s):  
Mousa Elrotub ◽  
Ahmed Bali ◽  
Abdelouahed Gherbi
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Performance Metrics ◽  
Virtual Machines ◽  
Machine Learning Techniques ◽  
Data Repository ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Learning Techniques

The problem of balancing user requests in cloud computing is becoming more serious due to the variation of workloads. Load balancing and allocation processes still need more optimizing methodologies and models to improve performance and increase the quality of service. This article describes a solution to balance user workload efficiently by proposing a model that allows each virtual machine (VM) to maximize the serving number of requests based on its capacity. The model measures VMs' capacity as a percentage and maps groups of user requests to appropriate active virtual machines. Finding the expected patterns from a big data repository, such as log data, and using some machine learning techniques can make the prediction more efficiently. The work is implemented and evaluated using some performance metrics, and the results are compared with other research. The evaluation shows the efficiency of the proposed approach in distributing user workload and improving results.

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 A Comparative Performance Analysis of Fog-Based Smart Surveillance System

2020 ◽  
Vol 2 (2) ◽  
pp. 78-88
Author(s):  
Sujan Shrestha ◽  
Subarna Shakya
Keyword(s):  
Cloud Computing ◽  
Surveillance System ◽  
Performance Metrics ◽  
Fog Computing ◽  
Comparative Performance ◽  
Smart Surveillance ◽  
Increasing Demand ◽  
Analytical Services ◽  
Cloud Network ◽  
Smart Surveillance System

There has been increasing demand of security and safety in public as well as private places and hence surveillance system using IoT sensors, cameras has become the most important part in our daily life. This system has to operate all times 24/7/365 and thus produces huge amounts of data. Cloud computing offers storage, processing, and analytical services for handling of such massive amounts of data. For real time applications like smart surveillance system, increased latency from centralized Cloud computing is not acceptable. Fog Computing is an extension of Cloud computing, evolved to minimize latency. A Fog-Based Smart Surveillance System has been modelled and simulated in two environments as Cloud Only Network and Fog-Based Cloud Network using iFogsim. Various performance metrics like Application Loop Delay, Energy Consumption, Execution Cost, and Network Usage has been compared between Fog-Based Cloud Network and Cloud Only Network. Results showed that Fog-Based Cloud Network performs better than Cloud Only Network.

Self-Management of Applications and Systems to Optimize Energy in Data Centers

2012 ◽  
pp. 372-394 ◽  
Author(s):  
Frederico Alvares de Oliveira ◽  
Adrien Lèbre ◽  
Thomas Ledoux ◽  
Jean-Marc Menaud
Keyword(s):  
Cloud Computing ◽  
Data Centers ◽  
Performance Metrics ◽  
Scale Up ◽  
Direct Consequence ◽  
Cloud Infrastructure ◽  
Control Loops ◽  
Improve Energy Efficiency ◽  
Energy Footprint ◽  
Cloud Infrastructures

As a direct consequence of the increasing popularity of cloud computing solutions, data centers are growing amazingly and hence have to urgently face with the energy consumption issue. Available solutions are focused basically on the system layer, by leveraging virtualization technologies to improve energy efficiency. Another body of works relies on cloud computing models and virtualization techniques to scale up/down applications based on their performance metrics. Although those proposals can reduce the energy footprint of applications and by transitivity of cloud infrastructures, they do not consider the internal characteristics of applications to finely define a trade-off between applications Quality of Service and energy footprint. In this paper, the authors propose a self-adaptation approach that considers both application internals and system to reduce the energy footprint in cloud infrastructure. Each application and the infrastructure are equipped with control loops, which allow them to autonomously optimize their executions. The authors implemented the control loops and simulated them in order to show their feasibility. In addition, the chapter shows how the solution fits in federated clouds through a motivating scenario. Finally, it provides some discussion about open issues on models and implementation of the proposal.

Green Cloud Computing

2018 ◽  
pp. 114-136
Author(s):  
Indira K. ◽  
Thangavel M.
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Web Applications ◽  
Performance Metrics ◽  
Cost Effective ◽  
Critical Issue ◽  
Efficient Solutions ◽  
Cloud Infrastructure ◽  
Total Energy Consumption ◽  
Green Cloud

Cloud computing is a highly scalable and cost-effective infrastructure for running HPC, enterprise and Web applications. However, the growing demand of Cloud infrastructure has drastically increased the energy consumption of data centers, which has become a critical issue. High energy consumption not only translates to high operational cost, which reduces the profit margin of Cloud providers, but also leads to high carbon emissions which is not environmentally friendly. Hence, energy-efficient solutions are required to minimize the impact of Cloud computing on the environment. Thus, in this chapter, we discuss various elements of Green Clouds which contribute to the total energy consumption. The chapter also explains the role of Green Cloud Performance metrics and Green Cloud Architecture.

Tracy–Singh Product and Genetic Whale Optimization Algorithm for Retrievable Data Perturbation for Privacy Preserved Data Publishing in Cloud Computing

2019 ◽  
Vol 63 (2) ◽  
pp. 239-253
Author(s):  
Thanga Revathi S ◽  
N Ramaraj ◽  
S Chithra
Keyword(s):  
Cloud Computing ◽  
Optimization Algorithm ◽  
Performance Metrics ◽  
Whale Optimization Algorithm ◽  
Data Publishing ◽  
Secret Key ◽  
Data Perturbation ◽  
Data Set ◽  
Proposed Model ◽  
Whale Optimization

Abstract This paper proposes a retrievable data perturbation model for overcoming the challenges in cloud computing. Initially, genetic whale optimization algorithm (genetic WOA) is developed by integrating genetic algorithm (GA) and WOA for generating the optimized secret key. Then, the input data and the optimized secret key are given to the Tracy–Singh product-based model for transforming the original database into perturbed database. Finally, the perturbed database can be retrieved by the client, if and only if the client knows the secret key. The performance of the proposed model is analyzed using three databases, namely, chess, T10I4D100K and retail databases from the FIMI data set based on the performance metrics, privacy and utility. Also, the proposed model is compared with the existing methods, such as Retrievable General Additive Data Perturbation, GA and WOA, for the key values 128 and 256. For the key value 128, the proposed model has the better privacy and utility of 0.18 and 0.83 while using the chess database. For the key value 256, the proposed model has the better privacy and utility of 0.18 and 0.85, using retail database. From the analysis, it can be shown that the proposed model has better privacy and utility values than the existing models.

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