scholarly journals Horizontal Scaling for Containerized Application Using Hybrid Approach

2020 ◽  
Vol 25 (6) ◽  
pp. 709-718
Author(s):  
Mahendra Pratap Yadav ◽  
Gaurav Raj ◽  
Harishchandra A. Akarte ◽  
Dharmendra Kumar Yadav
Keyword(s):  
Cloud Computing ◽  
Response Time ◽  
Hybrid Approach ◽  
End Users ◽  
The Internet ◽  
Sla Violation ◽  
Proposed Model ◽  
Maximum Response Time ◽  
Simulation Results ◽  
The Right

Cloud computing is a paradigm to provide services to end-users through the Internet. The availability of services to end-users is dependent on various factors such as the availability of computing resources as well as the number of users to access those services. To manage the real-time fluctuating workload cloud providers use elasticity mechanisms. Elasticity is one of the important characteristics of cloud computing that dynamically allocates computing resources to manage the fluctuating workload. The failure of allocation/de-allocation of computing resources at the right moment leads to SLA violation, degradation of services performance, maximum power consumption, minimum throughput, and maximum response time. To address these challenges, we have proposed a hybrid approach to perform horizontal elasticity. The proposed approach uses both reactive and proactive approaches for provisioning/de-provisioning of computing resources. The simulation results of the proposed model show that performance of system has improved in terms of CPU utilization, response time, and throughput.

Privacy Preservation Based on Separation Sensitive Attributes for Cloud Computing

2021 ◽  
pp. 840-856
Author(s):  
Feng Xu ◽  
Mingming Su ◽  
Yating Hou
Keyword(s):  
Cloud Computing ◽  
Privacy Preservation ◽  
Personal Data ◽  
The Internet ◽  
Malicious Nodes ◽  
Privacy Leakage ◽  
Computing Paradigm ◽  
Data Release ◽  
Security Levels ◽  
Simulation Results

The Cloud computing paradigm can improve the efficiency of distributed computing by sharing resources and data over the Internet. However, the security levels of nodes (or severs) are not the same, thus, sensitive tasks and personal data may be scheduled (or shared) to some unsafe nodes, which can lead to privacy leakage. Traditional privacy preservation technologies focus on the protection of data release and process of communication, but lack protection against disposing sensitive tasks to untrusted computing nodes. Therefore, this article put forwards a protocol based on task-transformation, by which tasks will be transformed into another form in the task manager before they can be scheduled to other nodes. The article describes a privacy preservation algorithm based on separation sensitive attributes from values (SSAV) to realize the task-transformation function. This algorithm separates sensitive attributes in the tasks from their values, which make the malicious nodes cannot comprehend the real meaning of the values even they get the transformed tasks. Analysis and simulation results show that the authors' algorithm is more effective.

A PSO Algorithm Based Task Scheduling in Cloud Computing

2019 ◽  
Vol 10 (4) ◽  
pp. 1-17 ◽  
Author(s):  
Mohit Agarwal ◽  
Gur Mauj Saran Srivastava
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Pso Algorithm ◽  
Emerging Technology ◽  
The Internet ◽  
Hard Problem ◽  
Swarm Optimization ◽  
Np Hard Problem ◽  
Simulation Results

Cloud computing is an emerging technology which involves the allocation and de-allocation of the computing resources using the internet. Task scheduling (TS) is one of the fundamental issues in cloud computing and effort has been made to solve this problem. An efficient task scheduling mechanism is always needed for the allocation to the available processing machines in such a manner that no machine is over or under-utilized. Scheduling tasks belongs to the category of NP-hard problem. Through this article, the authors are proposing a particle swarm optimization (PSO) based task scheduling mechanism for the efficient scheduling of tasks among the virtual machines (VMs). The proposed algorithm is compared using the CloudSim simulator with the existing greedy and genetic algorithm-based task scheduling mechanism. The simulation results clearly show that the PSO-based task scheduling mechanism clearly outperforms the others as it results in almost 30% reduction in makespan and increases the resource utilization by 20%.

A Hybrid Multiple Parallel Queuing Model to Enhance QoS in Cloud Computing

2020 ◽  
Vol 12 (1) ◽  
pp. 18-34 ◽  
Author(s):  
Shahbaz Afzal ◽  
G. Kavitha
Keyword(s):  
Cloud Computing ◽  
Response Time ◽  
Waiting Time ◽  
Queue Length ◽  
Utilization Rate ◽  
Queuing Model ◽  
Total Response Time ◽  
Proposed Model ◽  
Mathematical Equations ◽  
And Performance

Among the different QoS metrics and parameters considered in cloud computing are the waiting time of cloud tasks, execution time of tasks in VM's, and the utilization rate of servers. The proposed model was developed to overcome some of the pitfalls in the existing systems among which are sub-optimal markdown in the queue length, waiting time, response time, and server utilization rate. The proposed model contemplates on the enhancement of these metrics using a Hybrid Multiple Parallel Queuing approach with a joint implementation of M/M/1: ∞ and M/M/s: N/FCFS to achieve the desired objectives. A neoteric set of mathematical equations have been formulated to validate the efficiency and performance of the hybrid queuing model. The results have been validated with reference to the workload traces of Bit Brains infrastructure provider. The results obtained indicate the significant reduction in the queue length by 60.93 percent, waiting time in the queue by 73.85 percent, and total response time by 97.51%.

The Rise of Big Data, Cloud, and Internet of Things

2018 ◽  
pp. 201-222 ◽  
Author(s):  
Reema Abdulraziq ◽  
Muneer Bani Yassein ◽  
Shadi Aljawarneh
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
End Users ◽  
Structured Data ◽  
The Internet ◽  
Huge Amount ◽  
Volume Velocity ◽  
The Cost ◽  
The Internet Of Things

Big data refers to the huge amount of data that is being used in commercial, industrial and economic environments. There are three types of big data; structured, unstructured and semi-structured data. When it comes to discussions on big data, three major aspects that can be considered as its main dimensions are the volume, velocity, and variety of the data. This data is collected, analysed and checked for use by the end users. Cloud computing and the Internet of Things (IoT) are used to enable this huge amount of collected data to be stored and connected to the Internet. The time and the cost are reduced by means of these technologies, and in addition, they are able to accommodate this large amount of data regardless of its size. This chapter focuses on how big data, with the emergence of cloud computing and the Internet of Things (IOT), can be used via several applications and technologies.

Secure Architecture for Cloud Environment

2018 ◽  
pp. 910-925
Author(s):  
Kashif Munir ◽  
Sellapan Palaniappan
Keyword(s):  
Cloud Computing ◽  
Data Centers ◽  
End Users ◽  
Cloud Services ◽  
The Internet ◽  
Security Threats ◽  
Cloud Environment ◽  
Cloud Architecture ◽  
The World ◽  
Secure Architecture

Cloud computing is set of resources and services offered through the internet. Cloud services are delivered from data centers located throughout the world. Enterprises are rapidly adopting cloud services for their businesses, measures need to be developed so that organizations can be assured of security in their businesses and can choose a suitable vendor for their computing needs. In this chapter we identify the most vulnerable security threats/attacks in cloud computing, which will enable both end users and vendors to know about the key security threats associated with cloud computing and propose relevant solution directives to strengthen security in the cloud environment. This chapter also discusses secure cloud architecture for organizations to strengthen the security.

Privacy Preservation Based on Separation Sensitive Attributes for Cloud Computing

2019 ◽  
Vol 13 (2) ◽  
pp. 104-119 ◽  
Author(s):  
Feng Xu ◽  
Mingming Su ◽  
Yating Hou
Keyword(s):  
Cloud Computing ◽  
Privacy Preservation ◽  
Personal Data ◽  
The Internet ◽  
Malicious Nodes ◽  
Privacy Leakage ◽  
Computing Paradigm ◽  
Data Release ◽  
Security Levels ◽  
Simulation Results

The Cloud computing paradigm can improve the efficiency of distributed computing by sharing resources and data over the Internet. However, the security levels of nodes (or severs) are not the same, thus, sensitive tasks and personal data may be scheduled (or shared) to some unsafe nodes, which can lead to privacy leakage. Traditional privacy preservation technologies focus on the protection of data release and process of communication, but lack protection against disposing sensitive tasks to untrusted computing nodes. Therefore, this article put forwards a protocol based on task-transformation, by which tasks will be transformed into another form in the task manager before they can be scheduled to other nodes. The article describes a privacy preservation algorithm based on separation sensitive attributes from values (SSAV) to realize the task-transformation function. This algorithm separates sensitive attributes in the tasks from their values, which make the malicious nodes cannot comprehend the real meaning of the values even they get the transformed tasks. Analysis and simulation results show that the authors' algorithm is more effective.

scholarly journals Industry 4.0 Roadmap: Implementation for Small and Medium-Sized Enterprises

2020 ◽  
Vol 10 (23) ◽  
pp. 8566
Author(s):  
Alberto Cotrino ◽  
Miguel A. Sebastián ◽  
Cristina González-Gaya
Keyword(s):  
Cloud Computing ◽  
Literature Review ◽  
Industry 4.0 ◽  
Manufacturing Industry ◽  
Recent Literature ◽  
Low Cost ◽  
The Internet ◽  
Business Structures ◽  
The Right ◽  
The Internet Of Things

The Industry 4.0 era has resulted in several opportunities and challenges for the manufacturing industry and for small and medium-sized enterprises (SME); technologies such as the Internet of Things (IoT), Virtual Reality (VR) or Cloud Computing are changing business structures in profound ways. A literature review shows that most large-sized enterprises have rolled out investment plans, some of which are reviewed during this research and show that Industry 4.0 investments in such companies exceed the turnover of SMEs in all cases (<€50 million), which makes access to those technologies by SMEs very difficult. The research has also identified two gaps: firstly, the recent literature review fails to address the implementation of Industry 4.0 technologies in SMEs from a practical viewpoint; secondly, the few existing roadmaps for the implementation of Industry 4.0 lack a focus on SMEs. Furthermore, SMEs do not have the resources to select suitable technologies or create the right strategy, and they do not have the means to be fully supported by consultancies. To this end, a simple six-step roadmap is proposed that includes real implementations of Industry 4.0 in SMEs. Our results show that implementing Industry 4.0 solutions following the proposed roadmap helps SMEs to select appropriate technologies. In addition, the practical examples shown across this work demonstrate that SMEs can access several Industry 4.0 technologies with low-cost investments.

scholarly journals An Intelligent Cloud Service Composition Optimization Using Spider Monkey and Multistage Forward Search Algorithms

Symmetry ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 82
Author(s):  
Hassan Tarawneh ◽  
Issam Alhadid ◽  
Sufian Khwaldeh ◽  
Suha Afaneh
Keyword(s):  
Web Services ◽  
Response Time ◽  
Web Service ◽  
Service Composition ◽  
Web Service Composition ◽  
Spider Monkey ◽  
Experimental Results ◽  
Forward Search ◽  
Proposed Model ◽  
The Right

Web service composition allows developers to create and deploy applications that take advantage of the capabilities of service-oriented computing. Such applications provide the developers with reusability opportunities as well as seamless access to a wide range of services that provide simple and complex tasks to meet the clients’ requests in accordance with the service-level agreement (SLA) requirements. Web service composition issues have been addressed as a significant area of research to select the right web services that provide the expected quality of service (QoS) and attain the clients’ SLA. The proposed model enhances the processes of web service selection and composition by minimizing the number of integrated Web Services, using the Multistage Forward Search (MSF). In addition, the proposed model uses the Spider Monkey Optimization (SMO) algorithm, which improves the services provided with regards to fundamentals of service composition methods symmetry and variations. It achieves that by minimizing the response time of the service compositions by employing the Load Balancer to distribute the workload. It finds the right balance between the Virtual Machines (VM) resources, processing capacity, and the services composition capabilities. Furthermore, it enhances the resource utilization of Web Services and optimizes the resources’ reusability effectively and efficiently. The experimental results will be compared with the composition results of the Smart Multistage Forward Search (SMFS) technique to prove the superiority, robustness, and effectiveness of the proposed model. The experimental results show that the proposed SMO model decreases the service composition construction time by 40.4%, compared to the composition time required by the SMFS technique. The experimental results also show that SMO increases the number of integrated ted web services in the service composition by 11.7%, in comparison with the results of the SMFS technique. In addition, the dynamic behavior of the SMO improves the proposed model’s throughput where the average number of the requests that the service compositions processed successfully increased by 1.25% compared to the throughput of the SMFS technique. Furthermore, the proposed model decreases the service compositions’ response time by 0.25 s, 0.69 s, and 5.35 s for the Excellent, Good, and Poor classes respectively compared to the results of the SMFS Service composition response times related to the same classes.

scholarly journals CLOUD COMPUTING

2013 ◽  
Vol 4 (1) ◽  
pp. 5-7 ◽  
Author(s):  
Dr. Vinod Kumar ◽  
Er. Gagandeep Raheja ◽  
Ms. Jyoti Sodhi
Keyword(s):  
Cloud Computing ◽  
Data Access ◽  
End Users ◽  
The Internet ◽  
End User ◽  
Shared Resources ◽  
Electricity Grid ◽  
Physical Location ◽  
User Knowledge ◽  
And Storage

Cloud computing is the delivery of computing as a service rather than a product, whereby shared resources, software, and information are provided to computers and other devices as a utility (like the electricity grid) over a network (typically the internet). Cloud computing provides computation, software, data access, and storage services that do not require end-user knowledge of the physical location and configuration of the system that delivers the services. Parallel to this concept can be drawn with the electricity grid, wherein end-users consume power without needing to understand the component devices or infrastructure required to provide the service.

How to Choose the Right Cloud

2012 ◽  
pp. 1530-1552
Author(s):  
Stamatia Bibi ◽  
Dimitrios Katsaros ◽  
Panayiotis Bozanis
Keyword(s):  
Cloud Computing ◽  
Decision Problem ◽  
Data Centers ◽  
Recent Trend ◽  
Large Data ◽  
The Internet ◽  
Business Agility ◽  
Basic Parameters ◽  
User Demand ◽  
The Right

Cloud computing is a recent trend in IT that moves computing and data away from desktop and portable PCs into large data centers, and outsources the “applications” (hardware and software) as services over the Internet. Cloud computing promises to increase the velocity with which applications are deployed, increase innovation, and lower costs, all while increasing business agility. But, is the migration to the Cloud the most profitable option for every business? This chapter presents a study of the basic parameters for estimating the potential infrastructure and software costs deriving from building and deploying applications on cloud and on-premise assets. Estimated user demand and desired quality attributes related to an application are also addressed in this chapter as they are aspects of the decision problem that also influence the choice between cloud and in-house solutions.

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