Light-Weighted Decision Support Framework for Selecting Cloud Service Providers

Multi-criteria decision making(MCDM)is a technique used to achieve better outcomes for some complex business-related problems,whereby the selection of the best alternative can be made in as many cases as possible.This paper proposes a model,the multi-criteria decision support method,that allows both service providers and consumers to maximize their profits while preserving the best matching process for resource allocation and task scheduling.The increasing number of service providers with different service provision capabilities creates an issue for consumers seeking to select the best service provider.Each consumer seeks a service provider based on various preferences,such as price,service quality,and time to complete the tasks.In the literature,the problem is viewed from different perspectives,such as investigating how to enhance task scheduling and the resource allocation process,improve consumers’trust,and deal with network problems.This paper offers a novel model that considers the preferences of both service providers and consumers to find the best available service provider for each consumer.First,the model adopts the best-worst method(BWM)to gather and prioritize tasks based on consumers’and service providers’preferences.Then,the model calculates and matches similarities between the sets of tasks from the consumer’s side with the sets of tasks from the provider’s side to select the best service provider for each consumer using the two proposed algorithms.The complexity of the two algorithms is found to be O(n3).

Information Security in the Cloud: Emerging Trends and Challenges

This article explores the evolution of cloud computing, its advantages over traditional on-premises infrastructure, and its impact on information security. The study presents a comprehensive literature review covering various cloud infrastructure offerings and security models. Additionally, it deeply analyzes real-life case studies illustrating successful cloud migrations and highlights common information security threats in current cloud computing. The article concludes by offering recommendations to businesses to protect themselves from cloud data breaches and providing insights into selecting a suitable cloud services provider from an information security perspective.

Prediction-Based Resource Assignment Scheme to Maximize the Net Profit of Cloud Service Providers

In a cloud computing environment, users using the pay-as-you-go billing model can relinquish their services at any point in time and pay accordingly. From the perspective of the Cloud Service Providers (CSPs), this is not beneficial as they may lose the opportunity to earn from the relinquished resources. Therefore, this paper tackles the resource assignment problem while considering users relinquishment and its impact on the net profit of CSPs. As a solution, we first compare different ways to predict user behavior (i.e. how likely a user will leave the system before its scheduled end time) and deduce a better prediction technique based on linear regression. Then, based on the RACE (Relinquishment-Aware Cloud Economics) model proposed in [1], we develop a relinquishment-aware resource optimization model to estimate the amount of resources to assign on the basis of predicted user behavior. Simulations performed with CloudSim show that cloud service providers can gain more by estimating the amount of resources using better prediction techniques rather than blindly assigning resources to users. They also show that the proposed prediction-based resource assignment scheme typically generates more profit for a lower or similar utilization.

A Selection Algorithm of Service Providers for Optimized Data Placement in Multi-Cloud Storage Environment

The benefits of cloud storage come along with challenges and open issues about availability of services, vendor lock-in and data security, etc. One solution to mitigate the problems is the multi-cloud storage, where the selection of service providers is a key point. In this paper, an algorithm that can select optimal provider subset for data placement among a set of providers in multicloud storage architecture based on IDA is proposed, designed to achieve good tradeoff among storage cost, algorithm cost, vendor lock-in, transmission performance and data availability. Experiments demonstrate that it is efficient and accurate to find optimal solutions in reasonable amount of time, using parameters taken from real cloud providers.

A Meta-Modeling Service Paradigm for Cloud Computing and Its Implementation

It is argued in this article to shed light on service supply chain issues associated with cloud computing by examining several interrelated questions:service supply chain architecture from service perspective; basic clouds of service supply chain and development of managerial insights into these clouds. In particular,to demonstrate how those services can be utilized and the processes involved in their utilization,a hypothetical meta-modeling service of cloud computing can be given. Moreover,the paper defines the architecture of cloud managed for SV or ISP in infrastructure of cloud computing in service supply chain:IT services,business services,business processes,which creates atomic and composite software services that are used to perform business processes with business service choreographies.

Cloud Services Quality Enhancement through Control &Monitoring of Service Level Agreements at Server and Consumer Side

Several researches and case studies have been revealing a repetitive issue and that is monopolization of controlling, measuring, and metering of the consumer’s billing for the consumed Cloud Services. The Cloud Service Providers (CSPs) have been hiding, or monopolizing the controlling, metering tools and ignoring the calculation and consideration of the service defects like downtime, service outage, poor performance and client service migration delays during overloads of the servers. The main issues behind these discrepancies are the Service Level Agreement (SLA), its violation, and the monopolized Cloud Governance. Till now there is a lack of parallel monitoring and metering system of the consumed cloud services at the customer level under the provisions of SLAs. The Cloud Governance Tier has not given any flexibility or utility to monitor such consumed cloud services at the client tier in parallel to the CSP tier. This research is an effort to develop a SLA controlling/monitoring framework to solve the identified problems other than the CSP level. Further the E_Draw max was used for designing the framework. The framework consists of the components like;SLA_CSP, SLA_CSC, SLA_M&C and Cloud Services and Delivery Models. After designing the framework, a prototype was developed using CloudSim on java net beans, and MySQL database. This prototype was implemented using four scenarios to evaluate its performance and the functionalities. 1) Checking the SLA under CSP whether the resources to the consumer in the same manner with promised. 2) To check if the request from cloud consumer is less than what is expected in SLA. 3) To check by increasing the request of cloud consumer more than what is expected in SLA and 4) Using the SLA_M&C mechanism. After execution it was observed that, in the first and the second cases there is no violation, but there is violation in the third and fourth cases. This implied that there are chances of the discrepancies in resources provided to make the selves beneficiary beyond SLA under CSP and CSC was discriminated while receiving services from CSP.

An Integrated Framework for Cloud Service Selection Based on BOM and TOPSIS

Many businesses have experienced difficulties in selecting a cloud service provider(CSP)due to the rapid advancement of cloud computing services and the proliferation of CSPs.Many independent criteria should be considered when evaluating the services provided by different CSPs.It is a case of multi-criteria decision-making(MCDM).This paper presents an integrated MCDM cloud service selection framework for determining the most appropriate service provider based on the best only method(BOM)and technique for order of preference by similarity to ideal solution(TOPSIS).To obtain the weights of criteria and the relative importance of CSPs based on each criterion,BOM performs pairwise comparisons of criteria and also for alternatives on each criterion,and TOPSIS uses these weights to rank cloud alternatives.An evaluation and validation of the proposed framework have been carried out through a use-case model to prove its efficiency and accuracy.Moreover,the developed framework was compared with the analytical hierarchical process(AHP),a popular MCDM approach,based on two perspectives:efficiency and consistency.According to the research results,the proposed framework only requires 25%of the comparisons needed for the AHP approach.Furthermore,the proposed framework has a CR of 0%,whereas AHP has 38%.Thus,the proposed framework performs better than AHPwhen it comes to computation complexity and consistency,implying that it is more efficient and trustworthy.

Towards Developing Privacy-Preserved Data Security Approach(PP-DSA)in Cloud Computing Environment

In the present scenario of rapid growth in cloud computing models,several companies and users started to share their data on cloud servers.However,when the model is not completely trusted,the data owners face several security-related problems,such as user privacy breaches,data disclosure,data corruption,and so on,during the process of data outsourcing.For addressing and handling the security-related issues on Cloud,several models were proposed.With that concern,this paper develops a Privacy-Preserved Data Security Approach(PP-DSA)to provide the data security and data integrity for the out-sourcing data in Cloud Environment.Privacy preservation is ensured in this work with the Efficient Authentication Technique(EAT)using the Group Signature method that is applied with Third-Party Auditor(TPA).The role of the auditor is to secure the data and guarantee shared data integrity.Additionally,the Cloud Service Provider(CSP)and Data User(DU)can also be the attackers that are to be handled with the EAT.Here,the major objective of the work is to enhance cloud security and thereby,increase Quality of Service(QoS).The results are evaluated based on the model effectiveness,security,and reliability and show that the proposed model provides better results than existing works.

制造企业数字化服务商成熟度模型的构建研究

制造企业数字化已成为推动经济转型升级的重要工作,而高质量的数字化服务商是保障企业数字化转型能否顺利推进的决定性力量。为科学评价服务商的综合能力,本文构建了包含数字化投入、数字服务能力、项目实施能力、数字化产出的制造企业数字化服务商成熟度评价指标体系,并运用层次分析法、熵权法和博弈论组合赋权确定权重,形成了服务商成熟度模型。在此基础上,总结了基础规划级、数字规范级、综合集成级、数字驱动级和生态建设级5个成熟度等级,并基于云模型对已经在浙江省开展业务运作的W数字化服务商进行模型应用,验证了成熟度模型的合理性和科学性。最后基于构建的成熟度模型,对发展数字化服务商提出对策建议。

Windows 2000平台下CSP模块的替换方法研究

要将自行开发的CSP模块替换Windows2000系统中自带的CSP模块,必须将自己开发的CSP动态库发送给微软签名,但是在开发CSP时必需对开发的模块进行测试,本文给出了两种在测试时简单替换Windows2000系统中CSP的方法,且这些方法简单易行。

基于CSP的中小制造企业虚拟互助培训系统平台研究

针对中小制造企业在培训中面临的难题,提出了基于云服务提供商(CSP)的中小制造企业虚拟互助培训模式。首先对模式的拓扑结构及功能进行了详细分析;然后对该模式下的培训流程以及模式特点进行了阐述;最后对基于CSP的中小制造企业培训系统平台的建设方案进行了研究,给出了平台的体系结构,并对平台经营模式及发展规划进行了阐述。该模式具有方便企业管理、促进企业交流与进步、节约培训成本、提高培训效果等优点,对促进中小制造企业培训工作的开展具有现实意义。

Cloud-Based Software Development Lifecycle:A Simplified Algorithm for Cloud Service Provider Evaluation with Metric Analysis

At present,hundreds of cloud vendors in the global market provide various services based on a customer’s requirements.All cloud vendors are not the same in terms of the number of services,infrastructure availability,security strategies,cost per customer,and reputation in the market.Thus,software developers and organizations face a dilemma when choosing a suitable cloud vendor for their developmental activities.Thus,there is a need to evaluate various cloud service providers(CSPs)and platforms before choosing a suitable vendor.Already existing solutions are either based on simulation tools as per the requirements or evaluated concerning the quality of service attributes.However,they require more time to collect data,simulate and evaluate the vendor.The proposed work compares various CSPs in terms of major metrics,such as establishment,services,infrastructure,tools,pricing models,market share,etc.,based on the comparison,parameter ranking,and weightage allocated.Furthermore,the parameters are categorized depending on the priority level.The weighted average is calculated for each CSP,after which the values are sorted in descending order.The experimental results show the unbiased selection of CSPs based on the chosen parameters.The proposed parameter-ranking priority level weightage(PRPLW)algorithm simplifies the selection of the best-suited cloud vendor in accordance with the requirements of software development.

基于USB KEY的CSP实现

微软CryptoAPI/CryptoSPI是一种广泛应用的PKI(Public Key Infrastructure)应用架构,应用程序通过CryptoAPI(Crypto-graphic Application Programming Interface)公共接口使用CSP(Cryptographic Service Provider)提供的密码服务。USB KEY是一种直接在个人计算机的USB接口上使用的智能卡设备,使用方便、安全性高、运算能力强。研究和设计了一套基于USB KEY的CSP中间件模块,为PKI体系应用提供了高强度的安全保障。

Prediction of Cloud Ranking in a Hyperconverged Cloud Ecosystem Using Machine Learning

Cloud computing is becoming popular technology due to its functional properties and variety of customer-oriented services over the Internet.The design of reliable and high-quality cloud applications requires a strong Quality of Service QoS parameter metric.In a hyperconverged cloud ecosystem environment,building high-reliability cloud applications is a challenging job.The selection of cloud services is based on the QoS parameters that play essential roles in optimizing and improving cloud rankings.The emergence of cloud computing is significantly reshaping the digital ecosystem,and the numerous services offered by cloud service providers are playing a vital role in this transformation.Hyperconverged software-based unified utilities combine storage virtualization,compute virtualization,and network virtualization.The availability of the latter has also raised the demand for QoS.Due to the diversity of services,the respective quality parameters are also in abundance and need a carefully designed mechanism to compare and identify the critical,common,and impactful parameters.It is also necessary to reconsider the market needs in terms of service requirements and the QoS provided by various CSPs.This research provides a machine learning-based mechanism to monitor the QoS in a hyperconverged environment with three core service parameters:service quality,downtime of servers,and outage of cloud services.

Towards a Comprehensive Security Framework of Cloud Data Storage Based on Multi Agent System Architecture

The tremendous growth of the cloud computing environments requires new architecture for security services. Cloud computing is the utilization of many servers/data centers or cloud data storages (CDSs) housed in many different locations and interconnected by high speed networks. CDS, like any other emerging technology, is experiencing growing pains. It is immature, it is fragmented and it lacks standardization. Although security issues are delaying its fast adoption, cloud computing is an unstoppable force and we need to provide security mechanisms to ensure its secure adoption. In this paper a comprehensive security framework based on Multi-Agent System (MAS) architecture for CDS to facilitate confidentiality, correctness assurance, availability and integrity of users' data in the cloud is proposed. Our security framework consists of two main layers as agent layer and CDS layer. Our propose MAS architecture includes main five types of agents: Cloud Service Provider Agent (CSPA), Cloud Data Confidentiality Agent (CDConA), Cloud Data Correctness Agent (CDCorA), Cloud Data Availability Agent (CDAA) and Cloud Data Integrity Agent (CDIA). In order to verify our proposed security framework based on MAS architecture, pilot study is conducted using a questionnaire survey. Rasch Methodology is used to analyze the pilot data. Item reliability is found to be poor and a few respondents and items are identified as misfits with distorted measurements. As a result, some problematic questions are revised and some predictably easy questions are excluded from the questionnaire. A prototype of the system is implemented using Java. To simulate the agents, oracle database packages and triggers are used to implement agent functions and oracle jobs are utilized to create agents.

Enhanced Trust Based Access Control for Multi-Cloud Environment

Security is an essential part of the cloud environment.For ensuring the security of the data being communicated to and from the cloud server,a significant parameter called trust was introduced.Trust-based security played a vital role in ensuring that the communication between cloud users and service providers remained unadulterated and authentic.In most cloud-based data distribution environments,emphasis is placed on accepting trusted client users’requests,but the cloud servers’integrity is seldom verified.This paper designs a trust-based access control model based on user and server characteristics in a multi-cloud environment to address this issue.The proposed methodology consists of data encryption using Cyclic Shift Transposition Algorithm and trust-based access control method.In this trust-based access control mechanism framework,trust values are assigned to cloud users using direct trust degrees.The direct trust degree is estimated based on the following metrics:success and failure rate of interactions,service satisfaction index,and dishonesty level.In addition to this,trust values are assigned to cloud servers based on the metrics:server load,service rejection rate,and service access delay.The role-Based Access control policy of each user is modified based on his trust level.If the server fails to meet the minimum trust level,then another suitable server will be selected.The proposed system is found to outperform other existing systems in a multi-cloud environment.

Ensuring Security, Confidentiality and Fine-Grained Data Access Control of Cloud Data Storage Implementation Environment

With the development of cloud computing, the mutual understandability among distributed data access control has become an important issue in the security field of cloud computing. To ensure security, confidentiality and fine-grained data access control of Cloud Data Storage (CDS) environment, we proposed Multi-Agent System (MAS) architecture. This architecture consists of two agents: Cloud Service Provider Agent (CSPA) and Cloud Data Confidentiality Agent (CDConA). CSPA provides a graphical interface to the cloud user that facilitates the access to the services offered by the system. CDConA provides each cloud user by definition and enforcement expressive and flexible access structure as a logic formula over cloud data file attributes. This new access control is named as Formula-Based Cloud Data Access Control (FCDAC). Our proposed FCDAC based on MAS architecture consists of four layers: interface layer, existing access control layer, proposed FCDAC layer and CDS layer as well as four types of entities of Cloud Service Provider (CSP), cloud users, knowledge base and confidentiality policy roles. FCDAC, it’s an access policy determined by our MAS architecture, not by the CSPs. A prototype of our proposed FCDAC scheme is implemented using the Java Agent Development Framework Security (JADE-S). Our results in the practical scenario defined formally in this paper, show the Round Trip Time (RTT) for an agent to travel in our system and measured by the times required for an agent to travel around different number of cloud users before and after implementing FCDAC.

New Proposed Robust, Scalable and Secure Network Cloud Computing Storage Architecture

Cloud computing describes highly scalable computing resources provided as an external service via the internet. Economically, the main feature of cloud computing is that customers only use what they need, and only pay for what they actually use. Resources are available to be accessed from the cloud at any time, and from any location via the internet. There’s no need to worry about how things are being maintained behind the scenes—you simply purchase the IT service you require. This new, web-based generation of computing utilizes remote servers for data storage and management. One of the challenging issues tackled in the cloud computing is the security of data stored in the service providers’ site. In this paper, we propose a new architecture for secure data storage in such a way that users’ data are encrypted and split into various cipher blocks and distributed among different service providers site rather than solely depend on single provider for data storage. This architecture ensures better reliability, availability, scalability and security.

Binary Multifold Encryption Technique for Complex Cloud Systems

Data security is a major cloud computing issue due to different usertransactions in the system. The evolution of cryptography and cryptographic analysis are regarded domains of current research. deoxyribo nucleic acid (DNA) cryptography makes use of DNA as a sensing platform, which is then manipulated usinga variety of molecular methods. Many security mechanisms including knowledgebased authentication, two-factor authentication, adaptive authentication, multifactorauthentication and single password authentication have been deployed. These cryptographic techniques have been developed to ensure confidentiality, but most ofthem are based on complex mathematical calculations and equations. In the proposed approach, a novel and unique Hybrid helix scuttle-deoxy ribo nucleic acids(HHS-DNA) encryption algorithm has been proposed which is inspired by DNAcryptography and Helix scuttle. The proposed HHS-DNA is a type of multifold binary version of DNA (MF-BDNA). The major role of this paper is to present a multifold HHS-DNA algorithm to encrypt the cloud data assuring more security with lesscomplexity. The experimentation is carried out and it reduces the encryption time,cipher text size, and improves throughput. When compared with previous techniques, there is a 45% improvement in throughput, 37% fast in encryption time,54.67% cipher text size. The relevant experimental results and foregoing analysisshow that this method is of good robustness, stability, and security.

Cloud Data Encryption and Authentication Based on Enhanced Merkle Hash Tree Method

Many organizations apply cloud computing to store and effectively process data for various applications.The user uploads the data in the cloud has less security due to the unreliable verification process of data integrity.In this research,an enhanced Merkle hash tree method of effective authentication model is proposed in the multi-owner cloud to increase the security of the cloud data.Merkle Hash tree applies the leaf nodes with a hash tag and the non-leaf node contains the table of hash information of child to encrypt the large data.Merkle Hash tree provides the efficient mapping of data and easily identifies the changesmade in the data due to proper structure.The developed model supports privacy-preserving public auditing to provide a secure cloud storage system.The data owners upload the data in the cloud and edit the data using the private key.An enhanced Merkle hash tree method stores the data in the cloud server and splits it into batches.The data files requested by the data owner are audit by a third-party auditor and the multiowner authentication method is applied during the modification process to authenticate the user.The result shows that the proposed method reduces the encryption and decryption time for cloud data storage by 2–167 ms when compared to the existing Advanced Encryption Standard and Blowfish.