Cloud Service Broker Research Articles

The Role of Cloud Service Brokers: Enhancing Security and Compliance in Multi-cloud Environments


 The goal of this study is to put encryption key management strategies into practice while taking legal restrictions and the necessity for data accessibility into account. In order to reduce the danger of unauthorized access to cloud resources, this article explores the creation of Identity and Access Management (IAM) solutions that successfully apply role-based access control and abide by the least privilege principle. The advancement of digital business is both facilitated and disrupted by the usage of cloud technologies. Spending on cloud computing platforms and infrastructure is expected to rise significantly, with a compound annual growth rate (CAGR) of 30% predicted for the period between 2013 and 2018. In comparison, the corporate IT industry as a whole is anticipated to develop at a rate that is quite slow—5%. Additionally, an increasing number of businesses—precisely 85%—are seeing the benefits of using several cloud platforms to boost employee productivity, encourage collaboration, and spur corporate innovation. Regulations and standards are quite complicated, with possibilities for repetition and occasionally discrepancies. Utilizing existing standard models, patterns, architectures, and best practices is one efficient way to manage the difficulties brought on by compliance complexity, uncertainties, and overlaps. Examining regulatory practices and looking for overlaps have been efforts.

Dynamic congestion management system for cloud service broker

<span lang="EN-US">The cloud computing model offers a shared pool of resources and services with diverse models presented to the clients through the internet by an on-demand scalable and dynamic pay-per-use model. The developers have identified the need for an automated system (cloud service broker (CSB)) that can contribute to exploiting the cloud capability, enhancing its functionality, and improving its performance. This research presents a dynamic congestion management (DCM) system which can manage the massive amount of cloud requests while considering the required quality for the clients’ requirements as regulated by the service-level policy. In addition, this research introduces a forwarding policy that can be utilized to choose high-priority calls coming from the cloud service requesters and passes them by the broker to the suitable cloud resources. The policy has made use of one of the mechanisms that are used by Cisco to assist the administration of the congestion that might take place at the broker side. Furthermore, the DCM system is used to help in provisioning and monitoring the works of the cloud providers through the job operation. The proposed DCM system was implemented and evaluated by using the CloudSim tool.</span>

Fog Computing, Cloud Computing and IoT Environment: Advanced Broker Management System

Cloud computing is a massive amount of dynamic ad distributed resources that are delivered on request to clients over the Internet. Typical centralized cloud computing models may have difficulty dealing with challenges caused by IoT applications, such as network failure, latency, and capacity constraints. One of the introduced methods to solve these challenges is fog computing which makes the cloud closer to IoT devices. A system for dynamic congestion management brokerage is presented in this paper. With this proposed system, the IoT quality of service (QoS) requirements as defined by the service-level agreement (SLA) can be met as the massive amount of cloud requests come from the fog broker layer. In addition, a forwarding policy is introduced which helps the cloud service broker to select and forward the high-priority requests to the appropriate cloud resources from fog brokers and cloud users. This proposed idea is influenced by the weighted fair queuing (WFQ) Cisco queuing mechanism to simplify the management and control of the congestion that may possibly take place at the cloud service broker side. The system proposed in this paper is evaluated using iFogSim and CloudSim tools, and the results demonstrate that it improves IoT (QoS) compliance, while also avoiding cloud SLA violations.

Decision Making Models on the Market of Cloud Services

Introduction. Optimization can be applied in developing profitability management tools for a cloud service broker working according to a certain business model. On behalf of the managing telecommunications holding company (telecommunications operator), this broker integrates, aggregates and configures software and data storage services of third-party Internet software vendors. Such a broker receives only fixed commissions from this company, based on the subscription fee, but does not pay royalties to an Internet software vendor and does not receive payments from the sale of service packages. The purpose. The cloud broker faces the problem of limited human resources required to carry out the relevant legal, technical and economic activities. In addition, the broker faces the problem of uncertainty in sales, service prices, the share of resource use, or the risk of losing operational and financial goals. Results. To run a broker?s business efficiently, one needs to find services and their bundles that increase profitability and reduce financial risk by solving certain optimization problems. Information on such services is needed to support negotiations on fixed and variable commissions, as well as to prioritize services and their packages to be provided. Thus, for the cloud services broker, both profitability management tools and services portfolio development tools are useful. In general, a cloud service broker is an organization that negotiates the relationships between cloud service clients and Internet software vendors. Cloud broker can be created on the basis of different business models regarding the type of service (platform, infrastructure, software), type of clients (enterprise, household), functions performed (identity management, accounting, billing, location, etc.), the degree of rebranding, measures of aggregation of services and other criteria. Conclusions. Different cloud brokers have different attitudes to choice of important solutions for their businesses. Solutions can relate to pricing, capacity planning and utilization in combination with service quality, security, scalability and other issues. Keywords: optimization, portfolio, uncertainty, Boolean variables, revenue generation.

Profit Maximization for Cloud Broker in Cloud Computing

As cloud computing evolves, more and more applications are moving to the cloud. Cloud brokers are are like Middlemen between cloud service providers and cloud users. Thus, cloud brokers can significantly reduce the cost of consumers. In addition to reducing the cost per user, the cloud broker can also accommodate the price difference between on-demand virtual machines and dedicated virtual machines. The problem with the current system is that if many customers request a large amount of cloud services at once, the cloud service broker cannot purchase enough cloud services from CSP to meet the needs of all customers. Then there is a peak demand problem where the customer cannot complete the job. As a result, dynamic conditions not only lead to financial problems, but can also negatively impact the customer experience. To solve this problem, the system focuses on guaranteed quality of service for all requests, reduces waste of resources, increases security and maximizes revenue. All jobs are scheduled by the job scheduler and assigned to different VMs in a centralized way. Many factors such as market demand, application volume, SLA, service rental cost, etc. are taken into account to formulate an optimal configuration problem of profit maximization.

Optimal Cloud Service Provider Selection Based on QoS Metrics

There are thousands of providers obtainable in the market, and more and more are being added to the service list every day. All of these providers claim that the services given to them are distinctive and hassle-free. To check their claim, the cloud service broker (CSB) verifies the quality of service (QoS) of the cloud service providers (CSPs) and the level of user needs. Depending on the requirements of the cloud consumer (CC), CSB allocates a CSP to it. This paper proposed optimal cloud service provider selection (OCSPS) based on QoS metrics. The CC would handovers the demand to the CSB for optimal CSP selection using QoS. Once the CSP selection process is complete, the outcomes would become back to the CC. Entire demands created to CCs are saved in the request buffer (RB) in the CSB. When a specific request is fulfilled, the subsequent demand would take from this RB. The experimental result shows that the proposed OCSPS algorithm takes less time for CSP ranking and optimal CSP selection.

Efficient and Cost-effective Service Broker Policy Based on User Priority in VIKOR for Cloud Computing

Cloud computing has become a practical solution for processing big data. Cloud service providers have heterogeneous resources and offer a wide range of services with various processing capabilities. Typically, cloud users set preferences when working on a cloud platform. Some users tend to prefer the cheapest services for the given tasks, whereas other users prefer solutions that ensure the shortest response time or seek solutions that produce services ensuring an acceptable response time at a reasonable cost. The main responsibility of the cloud service broker is identifying the best data centre to be used for processing user requests. Therefore, to maintain a high level of quality of service, it is necessity to develop a service broker policy that is capable of selecting the best data centre, taking into consideration user preferences (e.g. cost, response time). This paper proposes an efficient and cost-effective plan for a service broker policy in a cloud environment based on the concept of VIKOR. The proposed solution relies on a multi-criteria decision-making technique aimed at generating an optimized solution that incorporates user preferences. The simulation results show that the proposed policy outperforms most recent policies designed for the cloud environment in many aspects, including processing time, response time, and processing cost. KEYWORDS Cloud computing, data centre selection, service broker, VIKOR, user priorities

A Cloud Service Broker for Cost Effective Infrastructure Selection using Multiple Deployment Options

A Cloud Service Broker for Cost Effective Infrastructure Selection using Multiple Deployment Options

Approach for Selecting and Integrating Cloud Services to Construct Hybrid Cloud

With the popularization of cloud computing, various cloud services have emerged, and hybrid clouds that can take advantage of combining public and private clouds are attracting attention. However, because of their variety, determining a combination of cloud services suited to the user’s current environment and requirements is expensive when deploying a hybrid cloud. Even if the required services are available, there is a lack of tools to connect them, manage them in batches, and utilize the integrated environment. To solve these problems, this paper proposes a cloud selection and integration process (C-SIP), which selects and integrates a combination of cloud services through a hybrid cloud service broker (hybrid CSB), which is an automation solution supporting hybrid cloud deployment. Moreover, the proposed method is realized using a script including the application programming interface of each cloud service. The proposed C-SIP will be used as a core approach toward the hybrid CSB, which is expected to facilitate the introduction of hybrid clouds and the acquisition of cloud strategies.

Service Pricing and Selection for IoT Applications Offloading in the Multi-Mobile Edge Computing Systems

With the rapid developing of Internet of Things (IoT) technologies, various kinds of IoT devices are connected over the Internet. Consequently, how to meet the requirements of executing IoT applications is becoming a critical issue. Offloading the IoT applications to the public cloud is an efficient approach to enhance the computing capabilities of these IoT devices. However, as there is a long distance from the IoT devices to the remote public cloud, transmission delay will be caused. Mobile edge computing (MEC) provides an effective solution to this issue since IoT devices are near to the servers of the MEC systems. Pricing and load balancing are two important factors for cloud service provision. Pricing is of paramount importance for cloud service provision, and load balancing is fully considered when cloud users select an edge cloud service provider (ESP) as it has a direct relation with the quality of cloud service. In multi-cloud systems, a cloud service broker (CSB) reserves cloud resources from multiple CSPs to provision cloud services to users. While existing work has put a lot of attention on IoT applications offloading to the MEC, many of them only considered one edge cloud scenario, ignoring the multi-MEC scenario. In this article, we investigate service pricing and selection for IoT applications offloading in a multi-MEC system with multiple ESPs. Specifically, we take load balancing into account. The studied problem is formulated as a Stackelberg game, where CSB first sets service price and load balancing strategies for the cloud services trying to get its maximized revenue. Then, IoT users make their decisions on which ESP they select service. By applying the backward induction approach, the optimal solutions are derived. The proposed scheme is verified through simulation results.

Risk analysis of cloud service providers by analyzing the frequency of occurrence of problems using E-Eclat algorithm

Cloud is a large scale distributed system where the resource provisioning is a challenging and important problem. It may be statically or dynamically provisioned and must meet the QoS parameters such as throughput, availability, security, transit delay and thereby ensuring SLA. Nowadays the number of service providers tends to increase tremendously in the market, hence selecting an optimal service provider is a tedious process which leads to trust a third party known as cloud service brokers. They analyzes the problems with the provider and explores the providers to the consumers to make the service available. But the risks with the providers are not known by the consumers and as well as the trusted cloud service brokers. Hence the proposed work indulges in finding the risk with the providers in the broker’s registry by analyzing the past histories of them. The analysis will be carried out for a period of time for all the providers and the most important problems that occur during the period will be considered for prioritizing the providers. Hence this paper proposes a novel Enhanced Eclat algorithm for finding the frequent problems at the providers level and also the frequency of occurrence of the same with each provider. The proposed E-Eclat algorithm is compared with two other algorithms and found that it consumes reduced time than other algorithms. It is also observed that the complexity is much low than other two algorithms as transpose scanning is used.

Cloud Brokering with Bundles: Multi-objective Optimization of Services Selection

Abstract Cloud computing has become one of the major computing paradigms. Not only the number of offered cloud services has grown exponentially but also many different providers compete and propose very similar services. This situation should eventually be beneficial for the customers, but considering that these services slightly differ functionally and non-functionally -wise (e.g., performance, reliability, security), consumers may be confused and unable to make an optimal choice. The emergence of cloud service brokers addresses these issues. A broker gathers information about services from providers and about the needs and requirements of the customers, with the final goal of finding the best match. In this paper, we formalize and study a novel problem that arises in the area of cloud brokering. In its simplest form, brokering is a trivial assignment problem, but in more complex and realistic cases this does not longer hold. The novelty of the presented problem lies in considering services which can be sold in bundles. Bundling is a common business practice, in which a set of services is sold together for the lower price than the sum of services’ prices that are included in it. This work introduces a multi-criteria optimization problem which could help customers to determine the best IT solutions according to several criteria. The Cloud Brokering with Bundles (CBB) models the different IT packages (or bundles) found on the market while minimizing (maximizing) different criteria. A proof of complexity is given for the single-objective case and experiments have been conducted with a special case of two criteria: the first one being the cost and the second is artificially generated. We also designed and developed a benchmark generator, which is based on real data gathered from 19 cloud providers. The problem is solved using an exact optimizer relying on a dichotomic search method. The results show that the dichotomic search can be successfully applied for small instances corresponding to typical cloud-brokering use cases and returns results in terms of seconds. For larger problem instances, solving times are not prohibitive, and solutions could be obtained for large, corporate clients in terms of minutes.

Adaptive user-oriented fuzzy-based service broker for cloud services

This paper presents an adaptive fuzzy-based cloud service brokering algorithm (AFBSB). The proposed algorithm employs an adaptive fuzzy-based engine to select the most appropriate data center for user cloud service requests considering user preferences in terms of cost and performance. The algorithm is implemented using an open-source cloud computing simulation tool. The algorithm results are tested against the results of other existing techniques within two types of cloud environments. First, a performance-constrained environment in which performance improvement is the main objective for cloud users. Second, a preference-aware environment, in which cloud users have different cost and performance preferences. Simulation results show that the proposed algorithm can achieve significant performance improvement in performance-constrained environments. As for the preference-aware environment, they show that AFBSB can operate in user-oriented manner that guarantees performance/cost improvement, as compared to other algorithms.

Optimized hybrid service brokering for multi-cloud architectures

A cloud computing platform provides access to shared resources along with diverse services including computation and storage to its users. The ubiquitous access to resources requires the service providers to ensure an efficient, reliable, and a fault-tolerant infrastructure. In this context, the cloud brokering mechanisms enable the cloud computing platforms to manage cloud resources through mediation between cloud service providers and cloud users. Corresponding to user requests, the cloud service brokering and load balancing aim at efficient real-time provision of services with minimal monetary cost through selection of appropriate data centers and virtual machines. This paper proposes a normalization-based hybrid service brokering approach integrated with throttled round-robin load balancing to improve resource management through cost- and performance-aware provision of cloud services. The proposed approach incorporates a hybrid (static and dynamic) evaluation criteria using normalization for determining the impact of cost and performance-oriented parameters in a multi-cloud environment. The subsequent selection of the most appropriate service provider along with throttled round-robin load balancing optimizes cloud resource management. The experiments performed with diverse number of user bases and data centers show that the proposed cloud service brokering approach outperforms other well-known approaches by improving response time, data center processing time, and monetary cost up to 17.39%, 31.35%, and 7.06%, respectively.

The Cloud Service Broker in Multicloud Demand Response

Because of cloud-computing development, choosing cloud services can be complicated and time-consuming for customers. To facilitate cloud service delivery, the authors propose a cloud service broker that resides in the multicloud model, provides automated selection of suitable cloud services, and assures the best performance, reliability, and cost efficiency.

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.

Cloud service selection using cloud service brokers: approaches and challenges

Cloud computing users are faced with a wide variety of services to choose from. Consequently, a number of cloud service brokers (CSBs) have emerged to help users in their service selection process. This paper reviews the recent approaches that have been introduced and used for cloud service brokerage and discusses their challenges accordingly. We propose a set of attributes for a CSB to be considered effective. Different CSBs’ approaches are classified as either single service or multiple service models. The CSBs are then assessed, analyzed, and compared with respect to the proposed set of attributes. Based on our studies, CSBs with multiple service models that support more of the proposed effective CSB attributes have wider application in cloud computing environments.

C-RCE: an Approach for Constructing and Managing a Cloud Service Broker

Recent years have seen a paradigm shift from PC-centric computing to cloud computing. The advent of cloud computing has led to the emergence of various cloud services and providers. Cloud service brokers (CSBs) were introduced to serve as intermediaries between cloud service providers and cloud users who wish to select an appropriate cloud service. A CSB requires intermediation technologies with service recommendation, contract management, and cloud service usage assistance (such as evaluation) capabilities. These intermediation technologies enable CSBs to increase the quality of cloud service usage. However, currently commercially available CSBs fail to satisfy user requirements. In addition, many open research problems remain in the technologies and approaches underpinning CSB intermediation technologies. This paper proposes Cloud Service—Recommendation, Contract, and Evaluation (C-RCE), which supports CSB processes, including the management and operation of each proposed process. We implement a prototype of the proposed C-RCE process in a CSB to evaluate its performance and confirm that it is superior to existing CSBs. The proposed C-RCE process may be used as a guideline and reference model for constructing, operating, and managing actual CSBs.

Collaboration environment for JointCloud computing

JointCloud computing is a new generation of cloud computing mode based on the cooperation among multicloud service entities. Through the deep integration of multicloud resources, it facilitates developers to customize cloud services by the method of “software definition, and to create cloud value. Achieving successful cloud service transactions is the most important challenge that JointCloud faces. This paper originally puts forward a supporting environment of cross-stakeholders peer-to-peer transactions, including resources and services, named JointCloud Cooperation Environment (JCCE). The core of JCCE architecture involves distributed cloud transaction, distributed cloud community, distributed cloud monitoring, and the blockchain-based distributed ledger system. In such primary services, the related information exchange techniques, especially value exchange techniques, are conducive to breaking the information asymmetry among the participants in JointCloud, creating win-win opportunities for each party, and forming strong support for the JointCloud computing business innovation. From the perspective of cloud service consumers, cloud service providers, and cloud service brokers, JCCEs consumption service mode, supply service mode, and consultation service mode are put forward to guide the transaction participants to apply the value-exchange service of JCCE efficiently and conveniently. Finally, with regard to the support service of value exchange, the operation mechanism of JCCE is expounded in different scenarios including cloud resource transaction, data transaction, and custom software service transaction.

An Economical and SLO-Guaranteed Cloud Storage Service Across Multiple Cloud Service Providers

It is important for cloud service brokers to provide a multi-cloud storage service to minimize their payment cost to cloud service providers (CSPs) while providing service level objective (SLO) guarantee to their customers. Many multi-cloud storage services have been proposed or payment cost minimization or SLO guarantee. However, no previous works fully leverage the current cloud pricing policies (such as resource reservation pricing) to reduce the payment cost. Also, few works achieve both cost minimization and SLO guarantee. In this paper, we propose a multi-cloud Economical and SLO-guaranteed Storage Service (ES <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ), which determines data allocation and resource reservation schedules with payment cost minimization and SLO guarantee. ES3 incorporates (1) a coordinated data allocation and resource reservation method, which allocates each data item to a datacenter and determines the resource reservation amount on datacenters by leveraging all the pricing policies; (2) a genetic algorithm based data allocation adjustment method, which reduce data Get/Put rate variance in each datacenter to maximize the reservation benefit. We also propose several algorithms to enhance the cost efficient and SLO guarantee performance of ES <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> including i) dynamic request redirection, ii) grouped Gets for cost reduction, iii) lazy update for cost-efficient Puts, and iv) concurrent requests for rigid Get SLO guarantee. Our trace-driven experiments on a supercomputing cluster and on real clouds (i.e., Amazon S3, Windows Azure Storage and Google Cloud Storage) show the superior performance of ES <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> in payment cost minimization and SLO guarantee in comparison with previous methods.