Cloud Computing and Comparison based on Service and Performance between Amazon AWS, Microsoft Azure, and Google Cloud

2021 ◽  
Author(s):  
Prakarsh Kaushik ◽  
Ashwin Murali Rao ◽  
Devang Pratap Singh ◽  
Swati Vashisht ◽  
Shubhi Gupta
Keyword(s):  
Cloud Computing ◽  
Microsoft Azure ◽  
And Performance

scholarly journals Insights from Learning Analytics for Hands-On Cloud Computing Labs in AWS

2020 ◽  
Vol 10 (24) ◽  
pp. 9148
Author(s):  
Germán Moltó ◽  
Diana M. Naranjo ◽  
J. Damian Segrelles
Keyword(s):  
Cloud Computing ◽  
Learning Analytics ◽  
Cloud Provider ◽  
Online Students ◽  
Computing Services ◽  
Hands On ◽  
Data Processing Tool ◽  
Amazon Web Services ◽  
Course Length ◽  
And Performance

Cloud computing instruction requires hands-on experience with a myriad of distributed computing services from a public cloud provider. Tracking the progress of the students, especially for online courses, requires one to automatically gather evidence and produce learning analytics in order to further determine the behavior and performance of students. With this aim, this paper describes the experience from an online course in cloud computing with Amazon Web Services on the creation of an open-source data processing tool to systematically obtain learning analytics related to the hands-on activities carried out throughout the course. These data, combined with the data obtained from the learning management system, have allowed the better characterization of the behavior of students in the course. Insights from a population of more than 420 online students through three academic years have been assessed, the dataset has been released for increased reproducibility. The results corroborate that course length has an impact on online students dropout. In addition, a gender analysis pointed out that there are no statistically significant differences in the final marks between genders, but women show an increased degree of commitment with the activities planned in the course.

scholarly journals A Fast Optimization Method for Reliability and Performance of Cloud Services Composition Application

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Zhao Wu ◽  
Naixue Xiong ◽  
Yannong Huang ◽  
Qiong Gu ◽  
Chunyang Hu ◽  
...  
Keyword(s):  
Cloud Computing ◽  
State Space ◽  
Generating Function ◽  
Performance Optimization ◽  
Optimization Method ◽  
Cloud Services ◽  
Services Composition ◽  
Fast Optimization ◽  
And Performance ◽  
Universal Generating Function

At present the cloud computing is one of the newest trends of distributed computation, which is propelling another important revolution of software industry. The cloud services composition is one of the key techniques in software development. The optimization for reliability and performance of cloud services composition application, which is a typical stochastic optimization problem, is confronted with severe challenges due to its randomness and long transaction, as well as the characteristics of the cloud computing resources such as openness and dynamic. The traditional reliability and performance optimization techniques, for example, Markov model and state space analysis and so forth, have some defects such as being too time consuming and easy to cause state space explosion and unsatisfied the assumptions of component execution independence. To overcome these defects, we propose a fast optimization method for reliability and performance of cloud services composition application based on universal generating function and genetic algorithm in this paper. At first, a reliability and performance model for cloud service composition application based on the multiple state system theory is presented. Then the reliability and performance definition based on universal generating function is proposed. Based on this, a fast reliability and performance optimization algorithm is presented. In the end, the illustrative examples are given.

scholarly journals Adjacency-Hash-Table Based Public Auditing for Data Integrity in Mobile Cloud Computing

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Wenqi Chen ◽  
Hui Tian ◽  
Chin-Chen Chang ◽  
Fulin Nan ◽  
Jing Lu
Keyword(s):  
Cloud Computing ◽  
High Speed ◽  
Security Analysis ◽  
Hash Table ◽  
The State ◽  
Public Auditing ◽  
The Arts ◽  
High Speed Data ◽  
And Performance ◽  
Data Updating

Cloud storage, one of the core services of cloud computing, provides an effective way to solve the problems of storage and management caused by high-speed data growth. Thus, a growing number of organizations and individuals tend to store their data in the cloud. However, due to the separation of data ownership and management, it is difficult for users to check the integrity of data in the traditional way. Therefore, many researchers focus on developing several protocols, which can remotely check the integrity of data in the cloud. In this paper, we propose a novel public auditing protocol based on the adjacency-hash table, where dynamic auditing and data updating are more efficient than those of the state of the arts. Moreover, with such an authentication structure, computation and communication costs can be reduced effectively. The security analysis and performance evaluation based on comprehensive experiments demonstrate that our protocol can achieve all the desired properties and outperform the state-of-the-art ones in computing overheads for updating and verification.

Compensation and Convergence — Comparing and Combining Deployment Automation Approaches

2015 ◽  
Vol 24 (03) ◽  
pp. 1541001 ◽  
Author(s):  
Johannes Wettinger ◽  
Uwe Breitenbücher ◽  
Frank Leymann
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Configuration Management ◽  
Fine Grained ◽  
Design Time ◽  
Major Class ◽  
Continuous Delivery ◽  
And Performance

Leading paradigms to develop, deploy, and operate applications such as continuous delivery, configuration management, and the merge of development and operations (DevOps) are the foundation for various techniques and tools to implement automated deployment. To make such applications available for users and customers, these approaches are typically used in conjunction with Cloud computing to automatically provision and manage underlying resources such as storage and virtual servers. A major class of these automation approaches follow the idea of converging toward a desired state of a resource (e.g. a middleware component deployed on a virtual machine). This is achieved by repeatedly executing idempotent scripts to reach the desired state. Because of major drawbacks of this approach, we discuss an alternative deployment automation approach based on compensation and fine-grained snapshots using container virtualization. We perform an evaluation comparing both approaches in terms of difficulties at design time and performance at runtime. Moreover, we discuss concepts, strategies, and implementations to effectively combine different deployment automation approaches.

scholarly journals Cloud Computing based on the Load Balancing Algorithm

2018 ◽  
Vol 7 (4.7) ◽  
pp. 131
Author(s):  
NV Abhinav Chand ◽  
A Hemanth Kumar ◽  
Surya Teja Marella
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
User Satisfaction ◽  
Easy Access ◽  
Future Research ◽  
Computing Technology ◽  
Main Challenge ◽  
Load Balancing Algorithm ◽  
And Performance ◽  
Time Resource

Emerging cloud computing technology is a big step in virtual computing. Cloud computing provides services to clients through the internet. Cloud computing enables easy access to resources distributed all over the world. Increase in the number of the population has further increased the challenge. The main challenge of cloud computing technology is to achieve efficient load balancing. Load balancing is a process of assigning load to available resources in such a way that it avoids overloading of resources. If load balancing is performed efficiently, it improves QoS metric including cost, throughput, response time, resource utilization and performance. Efficient load balancing techniques also provide better user satisfaction. Various load balancing algorithms are used in different scenarios for ensuring the same. In the current research, we will study different algorithms for load balancing and benefits and limitations caused to the system due to the algorithms. In this paper, we will compare static and dynamic load balancing algorithms for various measures of efficiency. These will be useful for future research in the concerned field. 

Optimal Resource Usage in Multi-Cloud Computing Environment

2013 ◽  
Vol 3 (1) ◽  
pp. 44-57 ◽  
Author(s):  
Veena Goswami ◽  
Choudhury Nishkanta Sahoo
Keyword(s):  
Cloud Computing ◽  
Service Providers ◽  
Queueing Network ◽  
Computing Environment ◽  
New Paradigm ◽  
Cloud Computing Environment ◽  
Optimal Resource ◽  
Optimal Resource Management ◽  
And Performance ◽  
Multi Cloud

Cloud computing has emerged as a new paradigm for accessing distributed computing resources such as infrastructure, hardware platform, and software applications on-demand over the internet as services. This paper presents an optimal resource management framework for multi-cloud computing environment. The authors model the behavior and performance of applications to integrate different service-providers for end-to-end-requirements. Each service model caters to specific type of requirements and there are already number of players with own customized products/services offered. Intercloud Federation and Service delegation models are part of Multi-Cloud environment where the broader target is to achieve infinite pool of resources. They propose an analytical queueing network model to improve the efficiency of the system. Numerical results indicate that the proposed provisioning technique detects changes in arrival pattern, resource demands that occur over time and allocates multiple virtualized IT resources accordingly to achieve application Quality of Service targets.

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