Cloud Broker Research Articles

Enhanced Cloud Security using Hybrid Mechanism of RSA, AES and Blowfish Data Encryption with Secure OTP

Cloud Computing has become one of the most talked about technologies in recent times and has got lots of attention from media as well as analysts because it is offering lots of opportunities. Enterprises have been determined to reduce computing costs and for that reason most of them started using it in IT technology then adapted virtualization technology. Cloud computing has revolutionized the way computing and software services are delivered to the clients on demand. It offers users the ability to connect to computing resources and access IT managed services with a previously unknown level of ease. Thus, security concerns among users of the cloud have become a major barrier to the widespread growth of cloud computing. In this research work, we have used the 3 step security mechanism for the keeping the data secure at the cloud. We have implemented the strong authentication mechanism using md5 encrypted OTP and enhanced the security of data using Cloud Broker and RSA, Blowfish and AES. When you log on to your machine and then try to access a resource, say a file server or database, something needs to assure that your username and password are valid. With sensitive data stored in the cloud of the different users, we need a strong authentication mechanism along with OTP. Data breaches because of no/weak authentication. Afterwards we have verified the integrity of data stored at cloud provider using SHA2. Multiple parameters like processing time, processing cost, AES,RSA and blowfish encryption time, OTP generation and encryption time have been calculated and analyzed. We have been able to enhance the security by optimizing the processing time as well as processing cost. After implementing the proposed methodology, it has been summarized that the cloud security can be enhanced by applying the proposed mechanism. The proposed system has reduced the complexity, processing cost which increases the overall efficiency of the system.

Diagnostic accuracy of the Maslach Burnout Inventory-Student Survey

The broad diffusion of Cloud Computing has fostered the proliferation of a large number of cloud computing providers. The need of Cloud Brokers arises for helping consumers in discovering, considering and comparing services with different capabilities and offered by different providers. Moreover, consuming services exposed by different providers may alleviate the vendor lock-in issue. While it can be straightforward to choose the best provider when deploying small and homogeneous applications, things get more challenging with large and complex applications. In this paper we propose qbrokage, a genetic approach for Cloud Brokering, aiming at finding Infrastructure-as-a-Service (IaaS) resources for satisfying Quality of Service (QoS) requirements of cloud applications. Our approach is capable of evaluating such requirements both for the single application service and for the application as whole. We performed a set of experiments with an implementation of such broker, by considering three-tier applications and scientific application workflows. Results show that our broker can find near-optimal solutions even when dealing with hundreds of providers, providing optimized deployment solutions that includes data transferring cost across multiple clouds.

A fuzzy-based decision-making broker for effective identification and selection of cloud infrastructure services

In a typical cloud environment, the task of identification and selection of required services solely depend upon the service specification furnished by the cloud user. The experienced users can specify their service requirements in precise terms by using numerical representations. On the other end, the task of service specification is always a challenging one for an inexperienced cloud user who seems to be new to the environment. In such a kind of requirement, vagueness arises in the preliminary phase of cloud service life cycle results service mismatches and affects both the cloud providers and user significantly. Hence, identifying and offering suitable services in responses to the imprecise service requirement have been emerged as an important research issue. In this paper, we have proposed a fuzzy logic-based intelligent cloud broker that clears the imprecise state of the inexperienced cloud user while furnishing the infrastructure service requirements. The proposed broker acts as an intermediate layer between the provider and consumer that finds out the appropriate services through the fuzzification and de-fuzzification process. In addition, the broker performs service aggregation through Sugeno integral and makes decision about the right services by implementing the fuzzy decision tree. The effectiveness of the broker has been validated by using MATLAB and R Studio.

Optimization‐based profitability management tool for cloud broker

AbstractThis paper represents a model that supports the choice of efficient service portfolios at a cloud service broker. Among the many types of different cloud service brokers, we focus on a firm that offers service bundles that are composed from different services of different internet software providers. The necessary integration, aggregation, and customization of services can be time consuming and costly. Whenever the cloud broker can choose from many service combinations, but has limited human resources with critical time to market, it is essential to prioritize some of the service bundles, markets, services, and internet software providers. The purpose of this paper is to facilitate this kind of decision. Moreover, both the time and resources required for creating service offerings and the customers' demand for these service bundles are subject to uncertainty. Because of this uncertainty, a cloud broker needs to be guided to potential service portfolios that give the best trade‐off between risk and profitability. Our model helps the decision maker to identify efficient service portfolios, ie, service portfolios that for a given risk have the highest profitability or for a given profitability have the lowest risk. Our paper shows the application of this model to a cloud broker that mediates mainly software as a service bundled with mobile subscriptions for telephony (calling and messaging) and internet access. The model is inspired by the ideas from financial portfolio optimization and product‐mix decisions under scarce resources. The model corresponds to a linear stochastic optimization problem with an objective function that balances risk and profitability.

Brokering in interconnected cloud computing environments: A survey

Cloud computing provides computing platforms and facilitates to optimize utilization of infrastructure resources, reduces deployment time and increases flexibility. The popularity of cloud computing led to development of interconnected cloud computing environments (ICCE) such as hybrid cloud, inter-cloud, multi-cloud, and federated cloud, enabling the possibilities to share resources among individual clouds. However, individual proprietary technologies and access interfaces employed by cloud service providers made it difficult to share resources. Interoperability and portability are two of the major challenges to be addressed to ensure seamless access and sharing of resources and services.Many cloud service providers have similar service offerings but different access patterns. It is difficult and time consuming for a cloud user to select an appropriate cloud service as per the application’s requirement. Cloud user has to gather information from various cloud service providers and analyze them. Cloud broker has been proposed to address the challenge of cloud users to get best out of cloud provider. Cloud broker is an entity which works as an independent third party between cloud users and cloud providers. Cloud broker negotiates with several cloud providers as per user’s requirements and tries to select the best services. Cloud broker coordinates the sharing of resources and provides interoperability and portability with other cloud providers.In this paper, a comprehensive survey of cloud brokering in interconnected cloud computing environments has been provided. The need and importance of cloud broker has been discussed. The existing architectures and frameworks of Cloud Brokering are reviewed. A comprehensive literature survey of various Cloud Brokering techniques is presented. A taxonomy of Cloud Brokering techniques has been presented and analyzed on the basis of their strengths and weaknesses/limitations. The taxonomy includes pricing, multi-criteria, quality of services, optimization and trust techniques. The techniques are analyzed on various performance metrics. Research challenges and open problems are identified from reviewed techniques. A model for cloud broker is proposed to address identified challenges. We hope that our work will enable researchers to launch and dive deep into Cloud Brokering challenges in interconnected cloud computing environments.

A Survey of Resource Management Challenges in Multi-cloud Environment: Taxonomy and Empirical Analysis

Cloud computing has seen a great deal of interest by researchers and industrial firms since its first coined. Different perspectives and research problems, such as energy efficiency, security and threats, to name but a few, have been dealt with and addressed from cloud computing perspective. However, cloud computing environment still encounters a major challenge of how to allocate and manage computational resources efficiently. Furthermore, due to the different architectures and cloud computing networks and models used (i.e., federated clouds, VM migrations, cloud brokerage), the complexity of resource management in the cloud has been increased dramatically. Cloud providers and service consumers have the cloud brokers working as the intermediaries between them, and the confusion among the cloud computing parties (consumers, brokers, data centres and service providers) on who is responsible for managing the request of cloud resources is a key issue. In a traditional scenario, upon renting the various cloud resources from the providers, the cloud brokers engage in subletting and managing these resources to the service consumers. However, providers’ usually deal with many brokers, and vice versa, and any dispute of any kind between the providers and the brokers will lead to service unavailability, in which the consumer is the only victim. Therefore, managing cloud resources and services still needs a lot of attention and effort. This paper expresses the survey on the systems of the cloud brokerage resource management issues in multi-cloud environments.

An MCDM method for cloud service selection using a Markov chain and the best-worst method

Due to the increasing number of cloud services, service selection has become a challenging decision for many organisations. It is even more complicated when cloud users change their preferences based on the requirements and the level of satisfaction of the experienced service. The purpose of this paper is to overcome this drawback and develop a cloud broker architecture for cloud service selection by finding a pattern of the changing priorities of User Preferences (UPs). To do that, a Markov chain is employed to find the pattern. The pattern is then connected to the Quality of Service (QoS) for the available services. A recently proposed Multi Criteria Decision Making (MCDM) method, Best Worst Method (BWM), is used to rank the services. We show that the method outperforms the Analytic Hierarchy Process (AHP). The proposed methodology provides a prioritized list of the services based on the pattern of changing UPs. The methodology is validated through a case study using real QoS performance data of Amazon Elastic Compute (Amazon EC2) cloud services.

A Classification and Comparison Framework for Cloud Service Brokerage Architectures

Cloud service brokerage and related management and marketplace concepts have been identified as key concerns for future cloud technology development and research. Cloud service management is an important building block of cloud architectures that can be extended to act as a broker service layer between consumers and providers, and even to form marketplace services. We present a three-pronged classification and comparison framework for broker platforms and applications. A range of specific broker development concerns like architecture, programming and quality are investigated. Based on this framework, selected management, brokerage and marketplace solutions will be compared, not only to demonstrate the utility of the framework, but also to identify challenges and wider research objectives based on an identification of cloud broker architecture concerns and technical requirements for service brokerage solutions. We also discuss emerging cloud architecture concerns such as commoditisation and federation of integrated, vertical cloud stacks.

A hybrid model of Internet of Things and cloud computing to manage big data in health services applications

Over the last decade, there has been an increasing interest in big data research, especially for health services applications. The adoption of the cloud computing and the Internet of Things (IoT) paradigm in the healthcare field can bring several opportunities to medical IT, and experts believe that it can significantly improve healthcare services and contribute to its continuous and systematic innovation in a big data environment such as Industry 4.0 applications. However, the required resources to manage such data in a cloud-IoT environment are still a big challenge. Accordingly, this paper proposes a new model to optimize virtual machines selection (VMs) in cloud-IoT health services applications to efficiently manage a big amount of data in integrated industry 4.0. Industry 4.0 applications require to process and analyze big data, which come from different sources such as sensor data, without human intervention. The proposed model aims to enhance the performance of the healthcare systems by reducing the stakeholders’ request execution time, optimizing the required storage of patients’ big data and providing a real-time data retrieval mechanism for those applications. The architecture of the proposed hybrid cloud-IoT consists of four main components: stakeholders’ devices, stakeholders’ requests (tasks), cloud broker and network administrator. To optimize the VMs selection, three different well-known optimizers (Genetic Algorithm (GA), Particle swarm optimizer (PSO) and Parallel Particle swarm optimization (PPSO) are used to build the proposed model. To calculate the execution time of stakeholders’ requests, the proposed fitness function is a composition of three important criteria which are CPU utilization, turn-around time and waiting time. A set of experiments were conducted to provide a comparative study between those three optimizers regarding the execution time, the data processing speed, and the system efficiency. The proposed model is tested against the state-of-the-art method to evaluate its effectiveness. The results show that the proposed model outperforms on the state-of-the-art models in total execution time the rate of 50%. Also, the system efficiency regarding real-time data retrieve is significantly improved by 5.2%.

Status of Cloud Service Adoption in Climate Risk Country

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.

DESIGN AND IMPLEMENTATION OF AN OTP BASED DATA SECURITY MODEL INCOPERATING AES AND SHA2 IN CLOUD ENVIRONMENT

Cloud computing has revolutionized the way computing and software services are delivered to the clients on demand. It offers users the ability to connect to computing resources and access IT managed services with a previously unknown level of ease. Thus, security concerns among users of the cloud have become a major barrier to the widespread growth of cloud computing. In this research work, we have used the 3 step security mechanism for the keeping the data secure at the cloud. We have implemented the strong authentication mechanism using AES encrypted OTP and enhanced the security of data using Cloud Broker and AES. When you log on to your machine and then try to access a resource, say a file server or database, something needs to assure that your username and password are valid. With sensitive data stored in the cloud of the different users, we need a strong authentication mechanism along with OTP. Data breaches because of no/weak authentication. Afterwards we have verified the integrity of data stored at cloud provider using SHA2. Multiple parameters like processing time, processing cost, AES encryption time, OTP generation and encryption time have been calculated and analyzed. We have been able to enhance the security by optimizing the processing time as well as processing cost. After implementing the proposed methodology, it has been summarized that the cloud security can be enhanced by applying the proposed mechanism. The proposed system has reduced the complexity, processing cost which increases the overall efficiency of the system.

Automatic cloud service monitoring and management with prediction-based service provisioning

As the number of users is increasing day by day over the cloud environment, service providers might be unable to offer service as per the agreement. Hence, cloud services being offered should be monitored to detect service level agreement violation. Most existing cloud service monitoring frameworks are developed to support the service provider only, which raises the question of correctness and fairness. On the other hand, if monitoring is applied at user side, then it creates overhead to the clients. To manage such issue, ontology-based automatic cloud services monitoring and management with prediction-based service provisioning approach is proposed in this paper. In this approach, monitoring is performed at cloud broker, which is an intermediate entity between the user and service provider. When service level agreement violation is detected, it sends alert to both clients and service providers, and broker automatically reschedules the tasks to reduce the further violations.

모바일 사용자 및 어플리케이션을 위한 지역 간 클라우드 인프라 연동

모바일 사용자 및 어플리케이션을 위한 지역 간 클라우드 인프라 연동

PaaSport semantic model: An ontology for a platform-as-a-service semantically interoperable marketplace

PaaS is a Cloud computing service that provides a computing platform to develop, run, and manage applications without the complexity of infrastructure maintenance. SMEs are reluctant to enter the growing PaaS market due to the possibility of being locked in to a certain platform, mostly provided by the market's giants. The PaaSport Marketplace aims to avoid the provider lock-in problem by allowing Platform provider SMEs to roll out semantically interoperable PaaS offerings and Software SMEs to deploy or migrate their applications on the best-matching offering, through a thin, non-intrusive Cloud broker. In this paper, we present the PaaSport semantic model, namely an OWL ontology, extension of the DUL ontology. The ontology is used for semantically representing (a) PaaS offering capabilities and (b) requirements of applications to be deployed. The ontology has been designed to optimally support a semantic matchmaking and ranking algorithm that recommends the best-matching PaaS offering to the application developer. The DUL ontology offers seamless extensibility, since both PaaS Characteristics and parameters are defined as classes; therefore, extending the ontology with new characteristics and parameters requires the addition of new specialized subclasses of the already existing classes, which is less complicated than adding ontology properties. The PaaSport ontology is evaluated through verification tools, competency questions, human experts, application tasks and query performance tests.

An iterative mathematical decision model for cloud migration: A cost and security risk approach

SummaryThis paper presents an iterative mathematical decision model for organizations to evaluate whether to invest in establishing information technology (IT) infrastructure on‐premises or outsourcing IT services on a multicloud environment. This is because a single cloud cannot cover all types of users’ functional/nonfunctional requirements, in addition to several drawbacks such as resource limitation, vendor lock‐in, and prone to failure. On the other hand, multicloud brings several merits such as vendor lock‐in avoidance, system fault tolerance, cost reduction, and better quality of service. The biggest challenge is in selecting an optimal web service composition in the ever increasing multicloud market in which each provider has its own pricing schemes and delivers variation in the service security level. In this regard, we embed a module in the cloud broker to log service downtime and different attacks to measure the security risk. If security tenets, namely, security service level agreement, such as availability, integrity, and confidentiality for mission‐critical applications, are targeted by cybersecurity attacks, it causes disruption in business continuity, leading to financial losses or even business failure. To address this issue, our decision model extends the cost model by using the cost present value concept and the risk model by using the advanced mean failure cost concept, which are derived from the embedded module to quantify cloud competencies. Then, the cloud economic problem is transformed into a bioptimization problem, which minimizes cost and security risks simultaneously. To deal with the combinatorial problem, we extended a genetic algorithm to find a Pareto set of optimal solutions. To reach a concrete result and to illustrate the effectiveness of the decision model, we conducted different scenarios and a small‐to‐medium business IT development for a 5‐year investment as a case study. The result of different implementation shows that multicloud is a promising and reliable solution against IT on‐premises deployment.

Multiclouds in an Enterprise – a Love-Hate Relationship

This issue of the magazine focused on the role of middleware for multiclouds. The columns in this issue cover diverse topics, ranging from whether cloud brokers are still relevant (Cloud Technologies); to describing block chain standards for compliance and trust (Cloud StandardsNow); to describing a software-defined membrane for policy-driven edge and IoT security (Blue Skies); to addressing the challenges and research opportunities for biometrics in the cloud (Cloud and the Law); and finally, describing real world experiences in a multicloud exchange (Cloud Economics),

Real-World Experience with a Multicloud Exchange

In the past decade, cloud service provider offerings and customer awareness of how to use them have evolved significantly. One of the major developments has been the evolution of customers from using a single service from a single provider to using multiple clouds in a true, integrated multicloud. Due to industry characteristics such as differential and dynamic pricing, emerging technologies such as containers and standards that facilitate workload migration, and the needs of customers to minimize spend yet maximize performance and availability, cloud brokers such as UCX are becoming an essential element of the entire ecosystem.

A Fuzzy-Based Intelligent Cloud Broker with MapReduce Framework to Evaluate the Trust Level of Cloud Services Using Customer Feedback

With the rapid development of cloud computing services, it is highly essential to ensure the quality of service offered by the service providers. Though several trust evaluation methods are available based on user feedback, it is hard to reap meaningful trust level of services when large number of cloud users are involved. To tackle the problem, we have advocated a big data processing framework for evaluating the trust level of availed services. An intelligent cloud broker with the incorporation of MapReduce framework has been put forth for the effective preprocessing of cloud users’ feedback. Besides, the broker performs the fuzzy inference system and a decision-making process for evaluating the trust level of services on the basis of processed feedback. Experimental results show that our proposed framework scores better results in terms of both trust level identification and execution efficiency.

QoS-aware genetic Cloud Brokering

The broad diffusion of Cloud Computing has fostered the proliferation of a large number of cloud computing providers. The need of Cloud Brokers arises for helping consumers in discovering, considering and comparing services with different capabilities and offered by different providers. Moreover, consuming services exposed by different providers may alleviate the vendor lock-in issue. While it can be straightforward to choose the best provider when deploying small and homogeneous applications, things get more challenging with large and complex applications. In this paper we propose qbrokage, a genetic approach for Cloud Brokering, aiming at finding Infrastructure-as-a-Service (IaaS) resources for satisfying Quality of Service (QoS) requirements of cloud applications. Our approach is capable of evaluating such requirements both for the single application service and for the application as whole. We performed a set of experiments with an implementation of such broker, by considering three-tier applications and scientific application workflows. Results show that our broker can find near-optimal solutions even when dealing with hundreds of providers, providing optimized deployment solutions that includes data transferring cost across multiple clouds.

Design and Comparative Analysis of MCDM-based Multi-dimensional Trust Evaluation Schemes for Determining Trustworthiness of Cloud Service Providers

This paper presents the design and comparative analysis of MCDM-based multi-dimensional trust evaluation schemes for determining trustworthiness of Cloud Service Providers (CSPs) in a cloud environment. The trustworthiness has been defined as the degree of compliance provided by a CSP to the promised QoS parameters as per the Service Level Agreement (SLA). Due to large number of CSPs offering similar kinds of services in a cloud environment, it has become a challenging task for Cloud Clients (CCs) to identify and differentiate between trustworthy and untrustworthy CSPs. We present the design and comparative analysis of three MCDM-based techniques (AHP, TOPSIS and PROMETHEE) that enable CCs to determine the trustworthiness of a CSP from different perspectives. All the three schemes enable us to assess the trustworthiness of a CSP from CC's perspective, Cloud Auditor's perspective, Cloud Broker's perspective and Peers' perspective. Experimental results show that all the techniques are effective and stable in differentiating between trustworthy and untrustworthy CSPs. The PROMETHEE technique has shown advantage over the AHP and the TOPSIS methods in the sense that it has reported less uncertainty during evaluations. The applicability and validity of the schemes have been demonstrated with the help of a case study involving the use of real cloud data.