Securing Business IT on the Cloud

2015 ◽  
pp. 2022-2032
Author(s):  
Bina Ramamurthy
Keyword(s):  
Large Scale ◽  
Service Providers ◽  
Cloud Service ◽  
Access Point ◽  
Cloud Provider ◽  
Service Model ◽  
Platform As A Service ◽  
Security Challenges ◽  
Modern Business ◽  
Amazon Web Services

In this chapter, the author examines the various approaches taken by the popular cloud providers Amazon Web Services (AWS), Google App Engine (GAE), and Windows Azure (Azure) to secure the cloud. AWS offers Infrastructure as a Service model, GAE is representative of the Software as a Service, and Azure represents the Platform as a Service model. Irrespective of the model, a cloud provider offers a variety of services from a simple large-scale storage service to a complete infrastructure for supporting the operations of a modern business. The author discusses some of the security aspects that a cloud customer must be aware of in selecting a cloud service provider for their needs. This discussion includes the major threats posed by multi-tenancy in the cloud. Another important aspect to consider in the security context is machine virtualization. Securing these services involves a whole range of measures from access-point protection at the client end to securing virtual co-tenants on the same physical machine hosted by a cloud. In this chapter, the author highlights the major offerings of the three cloud service providers mentioned above. She discusses the details of some important security challenges and solutions and illustrates them using screen shots of representative security configurations.

Securing Business IT on the Cloud

2014 ◽  
pp. 115-125
Author(s):  
Bina Ramamurthy
Keyword(s):  
Large Scale ◽  
Service Providers ◽  
Cloud Service ◽  
Access Point ◽  
Cloud Provider ◽  
Service Model ◽  
Platform As A Service ◽  
Security Challenges ◽  
Modern Business ◽  
Amazon Web Services

In this chapter, the author examines the various approaches taken by the popular cloud providers Amazon Web Services (AWS), Google App Engine (GAE), and Windows Azure (Azure) to secure the cloud. AWS offers Infrastructure as a Service model, GAE is representative of the Software as a Service, and Azure represents the Platform as a Service model. Irrespective of the model, a cloud provider offers a variety of services from a simple large-scale storage service to a complete infrastructure for supporting the operations of a modern business. The author discusses some of the security aspects that a cloud customer must be aware of in selecting a cloud service provider for their needs. This discussion includes the major threats posed by multi-tenancy in the cloud. Another important aspect to consider in the security context is machine virtualization. Securing these services involves a whole range of measures from access-point protection at the client end to securing virtual co-tenants on the same physical machine hosted by a cloud. In this chapter, the author highlights the major offerings of the three cloud service providers mentioned above. She discusses the details of some important security challenges and solutions and illustrates them using screen shots of representative security configurations.

scholarly journals Privacy enforcement on subscribers data in cloud computing

2021 ◽  
Vol 40 (2) ◽  
pp. 308-320
Author(s):  
S.A. Akinboro ◽  
U.J. Asanga ◽  
M.O. Abass
Keyword(s):  
Cloud Computing ◽  
Service Providers ◽  
Cloud Service ◽  
Cloud Provider ◽  
Sensitive Information ◽  
Secure Socket Layer ◽  
Brute Force ◽  
User Privacy ◽  
Platform As A Service ◽  
High Level

Data stored in the cloud are susceptible to an array of threats from hackers. This is because threats, hackers and unauthorized access are not supported by the cloud service providers as implied. This study improves user privacy in the cloud system, using privacy with non-trusted provider (PNTP) on software and platform as a service model. The subscribers encrypt the data using user’s personal Advanced Encryption Standard (AES) symmetric key algorithm and send the encrypted data to the storage pool of the Cloud Service Provider (CSP) via a secure socket layer. The AES performs a second encryption on the data sent to the cloud and generates for the subscriber a key that will be used for decryption of previously stored data. The encryption and decryption keys are managed by the key server and have been hardcoded into the PNTP system. The model was simulated using the Stanford University multimedia dataset and benchmarked with a Privacy with Trusted cloud Provider (PTP) model using encryption time, decryption time and efficiency (brute force hacking) as parameters. Results showed that it took a longer time to access the user files in PNTP than in the PTP system. The brute force hacking took a longer time (almost double) to access data stored on the PNTP system. This will give subscribers a high level of control over their data and increase the adoption of cloud computing by businesses and organizations with highly sensitive information.

scholarly journals A Survey On Cloud Computing and Its Benefits

2015 ◽  
Vol 15 (4) ◽  
pp. 6643-6648
Author(s):  
Prabhpreet Kaur ◽  
Monika Sachdeva
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Data Centers ◽  
Service Providers ◽  
Cloud Service ◽  
Platform As A Service ◽  
Large Scale Data ◽  
Research Challenges ◽  
Organizational Perspective ◽  
Privacy Issues

Cloud computing is an increasingly popular paradigm for accessing computing resources. In practice, cloud service providers tend to offer services that can be grouped into three categories: software as a service, platform as a service, and infrastructure as a service. Cloud computing represents a shift away from computing as a product that is purchased, to computing as a service that is delivered to consumers over the internet from large-scale data centers – or ‘clouds’. This paper discusses some of the research challenges for cloud computing from an enterprise or organizational perspective, and puts them in context by reviewing the existing body of literature in cloud computing. Various research challenges relating to the following topics are discussed: the organizational changes brought about by cloud computing; the economic and organizational implications of its utility billing model; the security, legal and privacy issues that cloud computing raises. It is important to highlight these research challenges because cloud computing is not simply about a technological improvement of data centers but a fundamental change in how IT is provisioned and used. This type of research has the potential to influence wider adoption of cloud computing in enterprise, and in the consumer market too.

GOVERNMENT REGULATIONS OF THE USING CLOUD SERVICES

2022 ◽  
Author(s):  
Olexander Melnikov ◽  
◽  
Konstantin Petrov ◽  
Igor Kobzev ◽  
Viktor Kosenko ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Service Providers ◽  
Cloud Service ◽  
Cloud Services ◽  
Government Agencies ◽  
Remote Work ◽  
Cloud Infrastructure ◽  
Wide Range ◽  
Technological Base

The article considers the development and implementation of cloud services in the work of government agencies. The classification of the choice of cloud service providers is offered, which can serve as a basis for decision making. The basics of cloud computing technology are analyzed. The COVID-19 pandemic has identified the benefits of cloud services in remote work Government agencies at all levels need to move to cloud infrastructure. Analyze the prospects of cloud computing in Ukraine as the basis of e-governance in development. This is necessary for the rapid provision of quality services, flexible, large-scale and economical technological base. The transfer of electronic information interaction in the cloud makes it possible to attract a wide range of users with relatively low material costs. Automation of processes and their transfer to the cloud environment make it possible to speed up the process of providing services, as well as provide citizens with minimal time to obtain certain information. The article also lists the risks that exist in the transition to cloud services and the shortcomings that may arise in the process of using them.

Cloud-TM

2015 ◽  
pp. 749-781
Author(s):  
João Barreto ◽  
Pierangelo Di Sanzo ◽  
Roberto Palmieri ◽  
Paolo Romano
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Economies Of Scale ◽  
Service Providers ◽  
Cloud Service ◽  
Capital Investments ◽  
Barriers To Entry ◽  
Capital Costs ◽  
Programming Paradigms ◽  
Pricing Schemes

By shifting data and computation away from local servers towards very large scale, world-wide spread data centers, Cloud Computing promises very compelling benefits for both cloud consumers and cloud service providers: freeing corporations from large IT capital investments via usage-based pricing schemes, drastically lowering barriers to entry and capital costs; leveraging the economies of scale for both services providers and users of the cloud; facilitating deployment of services; attaining unprecedented scalability levels. However, the promise of infinite scalability catalyzing much of the recent hype about Cloud Computing is still menaced by one major pitfall: the lack of programming paradigms and abstractions capable of bringing the power of parallel programming into the hands of ordinary programmers. This chapter describes Cloud-TM, a self-optimizing middleware platform aimed at simplifying the development and administration of applications deployed on large scale Cloud Computing infrastructures.

Client-Centric Cloud Service Composition

2017 ◽  
pp. 144-166
Author(s):  
Vivek Gaur ◽  
Praveen Dhyani ◽  
Om Prakash Rishi
Keyword(s):  
Service Composition ◽  
Large Scale ◽  
Business Processes ◽  
Service Providers ◽  
Service Selection ◽  
Cloud Service ◽  
Quality Parameters ◽  
End User ◽  
Cloud Service Composition ◽  
Tabu Search Methods

Recent computing world has seen rapid growth of the number of middle and large scale enterprises that deploy business processes sharing variety of services available over cloud environment. Due to the advantage of reduced cost and increased availability, the cloud technology has been gaining unbound popularity. However, because of existence of multiple cloud service providers on one hand and varying user requirements on the other hand, the task of appropriate service composition becomes challenging. The conception of this chapter is to consider the fact that different quality parameters related to various services might bear varied importance for different user. This chapter introduces a framework for QoS-based Cloud service selection to satisfy the end user needs. A hybrid algorithm based on genetic algorithm (GA) and Tabu Search methods has been developed, and its efficacy is analysed. Finally, this chapter includes the experimental analysis to present the performance of the algorithm.

scholarly journals A Secure IoT-Based Cloud Platform Selection Using Entropy Distance Approach and Fuzzy Set Theory

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Alakananda Chakraborty ◽  
Muskan Jindal ◽  
Mohammad R. Khosravi ◽  
Prabhishek Singh ◽  
Achyut Shankar ◽  
...  
Keyword(s):  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Large Scale ◽  
Service Providers ◽  
Cost Optimization ◽  
Cloud Service ◽  
Security And Privacy ◽  
Cloud Platform ◽  
Efficiency Cost

With the growing emergence of the Internet connectivity in this era of Gen Z, several IoT solutions have come into existence for exchanging large scale of data securely, backed up by their own unique cloud service providers (CSPs). It has, therefore, generated the need for customers to decide the IoT cloud platform to suit their vivid and volatile demands in terms of attributes like security and privacy of data, performance efficiency, cost optimization, and other individualistic properties as per unique user. In spite of the existence of many software solutions for this decision-making problem, they have been proved to be inadequate considering the distinct attributes unique to individual user. This paper proposes a framework to represent the selection of IoT cloud platform as a MCDM problem, thereby providing a solution of optimal efficacy with a particular focus in user-specific priorities to create a unique solution for volatile user demands and agile market trends and needs using optimized distance-based approach (DBA) aided by Fuzzy Set Theory.

scholarly journals CLOUDS: A NEW PLAYGROUND FOR THE XTREEMOS GRID OPERATING SYSTEM

2009 ◽  
Vol 19 (03) ◽  
pp. 435-449 ◽  
Author(s):  
CHRISTINE MORIN ◽  
YVON JÉGOU ◽  
JÉRÔME GALLARD ◽  
PIERRE RITEAU
Keyword(s):  
Operating System ◽  
Cloud Computing ◽  
Large Scale ◽  
Service Providers ◽  
Cloud Service ◽  
Virtual Organizations ◽  
New Era ◽  
Cloud Service Providers ◽  
Administrative Domains ◽  
Distributed Operating System

The emerging cloud computing model has recently gained a lot of interest both from commercial companies and from the research community. XtreemOS is a distributed operating system for large-scale wide-area dynamic infrastructures spanning multiple administrative domains. XtreemOS, which is based on the Linux operating system, has been designed as a Grid operating system providing native support for virtual organizations. In this paper, we discuss the positioning of XtreemOS technologies with regard to cloud computing. More specifically, we investigate a scenario where XtreemOS could help users take full advantage of clouds in a global environment including their own resources and cloud resources. We also discuss how the XtreemOS system could be used by cloud service providers to manage their underlying infrastructure. This study shows that the XtreemOS distributed operating system is a highly relevant technology in the new era of cloud computing where future clouds seamlessly span multiple bare hardware providers and where customers extend their IT infrastructure by provisioning resources from different cloud service providers.

scholarly journals Dynamic mutual manufacturing and transportation routing service selection for cloud manufacturing with multi-period service-demand matching

2021 ◽  
Vol 7 ◽  
pp. e461
Author(s):  
Seyed Ali Sadeghi Aghili ◽  
Omid Fatahi Valilai ◽  
Alireza Haji ◽  
Mohammad Khalilzadeh
Keyword(s):  
Service Composition ◽  
Large Scale ◽  
Business Models ◽  
Service Providers ◽  
Service Selection ◽  
Cloud Service ◽  
Cloud Manufacturing ◽  
Architecture Model ◽  
Logistics Service ◽  
Composition Problem

Recently, manufacturing firms and logistics service providers have been encouraged to deploy the most recent features of Information Technology (IT) to prevail in the competitive circumstances of manufacturing industries. Industry 4.0 and Cloud manufacturing (CMfg), accompanied by a service-oriented architecture model, have been regarded as renowned approaches to enable and facilitate the transition of conventional manufacturing business models into more efficient and productive ones. Furthermore, there is an aptness among the manufacturing and logistics businesses as service providers to synergize and cut down the investment and operational costs via sharing logistics fleet and production facilities in the form of outsourcing and consequently increase their profitability. Therefore, due to the Everything as a Service (XaaS) paradigm, efficient service composition is known to be a remarkable issue in the cloud manufacturing paradigm. This issue is challenging due to the service composition problem’s large size and complicated computational characteristics. This paper has focused on the considerable number of continually received service requests, which must be prioritized and handled in the minimum possible time while fulfilling the Quality of Service (QoS) parameters. Considering the NP-hard nature and dynamicity of the allocation problem in the Cloud composition problem, heuristic and metaheuristic solving approaches are strongly preferred to obtain optimal or nearly optimal solutions. This study has presented an innovative, time-efficient approach for mutual manufacturing and logistical service composition with the QoS considerations. The method presented in this paper is highly competent in solving large-scale service composition problems time-efficiently while satisfying the optimality gap. A sample dataset has been synthesized to evaluate the outcomes of the developed model compared to earlier research studies. The results show the proposed algorithm can be applied to fulfill the dynamic behavior of manufacturing and logistics service composition due to its efficiency in solving time. The paper has embedded the relation of task and logistic services for cloud service composition in solving algorithm and enhanced the efficiency of resulted matched services. Moreover, considering the possibility of arrival of new services and demands into cloud, the proposed algorithm adapts the service composition algorithm.

scholarly journals CoT-Enabled Robust Surveillance System using Fog Machine Learning

2021 ◽  
Author(s):  
Mohanasundaram R ◽  
Rishikesh Y Mule ◽  
Gowrison Gengavel ◽  
Muhammad Rukunuddin Ghalib ◽  
Achyut Shankar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Surveillance System ◽  
Large Scale ◽  
Service Providers ◽  
Fog Computing ◽  
Cost Effective ◽  
Cloud Service ◽  
Gas Leakage ◽  
Property Losses

Abstract Surveillance system is a method of securing resources and loss of lives against fire, gas leakage, intruder, earthquake, and weather. In today’s time, people own home, farm, factory, office etc. It has become more crucial to monitor everything for securing resources and loss of lives against fire, gas leakage, intruder, earthquake. As a part of surveillance, monitoring weather is also essential. Climate change and agriculture are interrelated processes, Today's sophisticated commercial farming like weather monitoring, suffers from a lack of precision, which results huge loss in farm. Monitoring residential and commercial arenas throughout is an efficient technique to decrease personal and property losses due to fire, gas leakage, earthquake catastrophes. Internet of Things make it possible and can be implemented separately for each thing or site. But it is very difficult to monitor each site and have centralized access of it across the world. This arises the need of heterogenous system which will monitor all IoTs and perform decision making accordingly. IoT itself a large-scale thing. For single IoT application, sensors used are more in number. These sensors generate thousands of records for an instance of time, some of those are valuable and some requires just analysis. This huge amount of data on servers requires better data processing and analytics. Maintenance is also a critical task. Cloud extends these functionalities but storing all the data on cloud entail users to pay tremendous cost to the cloud service providers. This problem is catered by “CoTsurF” framework. This paper presents novel and cost effective “CoTsurF” framework, CoT-enabled robust Surveillance system using fog machine learning, a Proof-Of-Concept implementation of heterogenous and robust surveillance system based on internet of things and cloud computing by leveraging a groundbreaking concept of Fog machine learning that is Fog Computing and machine learning in Cloud of Things.

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