Cloud Broker Research Articles

Modeling and solving cloud service purchasing in multi-cloud environments

Nowadays, the range of cloud services offered by cloud providers varies sharply and poses a challenge for cloud consumers aiming for the most cost-effective and compliant solutions, especially when operating highly scalable microservice architectures. A remedy can be provided by a cloud brokerage intelligent mechanism selecting cloud services across multiple clouds on behalf of consumers by considering individual goals and requirements. In this paper, we present the Cloud Service Purchasing Problem (CSPP) which aims to minimize costs while incorporating specific consumer and application task requirements. To solve this problem, we propose a mixed-integer programming model and two large neighborhood search approaches. Using well-defined problem instances incorporating data from real cloud providers, we conduct several computational experiments to evaluate the performance of the proposed algorithms. They exhibit a competitive performance providing solutions for all scenarios within short computational times. Finally, the significance of this problem and decision support approaches is analyzed by comparing usage patterns with respect to different nowadays cloud providers and offered virtual machine types for various scenarios.

A cloud broker architecture for cloud service selection based on multi-criteria decision making and rough set theory

Cloud computing is a rising field providing computation resources. It represents a new paradigm of utility computing and enormously growing phenomenon in the present IT industry and economy hype. The companies which provide services to customers are called as cloud service providers. The cloud users (CUs) increase and require secure, reliable and trustworthy cloud service providers (CSPs) from the market. So, it's a challenge for a new user to choose the best provider. In this paper, we propose cloud broker architecture to help a new customer to find out the best CP. This architecture is based on a combination of rough set theory and multi-criteria analysis of some parameters related to the quality of service of the available providers. A mathematical model is used to do this analysis; it integrates multi-agent systems to present an intelligent, distributed and collaborative method in order to add assistance to each actor in cloud computing environment.

A cloud broker architecture for cloud service selection based on multi-criteria decision making and rough set theory

A cloud broker architecture for cloud service selection based on multi-criteria decision making and rough set theory

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.

A Service Context-Aware QoS Prediction and Recommendation of Cloud Infrastructure Services

Service recommendation is an active research area in cloud computing since mapping of right services as per the desired requirements of user is a challenging task. With the rapid development of cloud services, the recommendation techniques are facing the problem in predicting the better QoS values because of neglecting the contextual information of cloud services. The paper proposes a service context-aware-based cloud broker that extracts the service details by considering the contextual information of cloud services and computes service similarities on the basis of QoS values. Further, it tackles the cold start problem by adopting the matrix factorization principle and predicts better QoS values for newly arrived services. To validate our approach, we have conducted experimental works on benchmark datasets and the result shows that the proposed approach outperforms better results than the model-based approaches. In particular, our proposed system produces improved response time for the dataset of sparse nature.

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.

Extensible Conditional Privacy Protection Authentication Scheme for Secure Vehicular Networks in a Multi-Cloud Environment

With an increasing number of cloud service providers (CSPs), research works on multi-cloud environments to provide solutions to avoid vendor lock-in and deal with the single-point failure problem have expanded considerably. However, a few schemes focus on the conditional privacy protection authentication of vehicular networks under a multi-cloud environment. In this regard, we propose a robust and extensible authentication scheme for vehicular networks to fulfil the ever-growing diversified service demands from users. According to our solution, the vehicles need to register with the trusted authority (TA) only once to achieve a fast and efficient authentication with CSPs. Additionally, as long as the new CSP is successfully registered in TA, it can participate in vehicular service. A cloud broker, which is managed by the TA, is responsible for connecting all the cloud services; consequently, the complexity involved in the selection of CSPs is hidden from the users’ view. A detailed security analysis establishes that our scheme can fulfil conditional privacy protection and achieve the security objectives of vehicular networks. Our scheme is based on elliptic curve cryptography and does not employ the complex bilinear pairing operation. An evaluation of performance of the proposed scheme indicates that it is suitable for applications involving vehicular networks.

Efficient Load Balancing and Service Broker Policy Using Local and Global Representatives in Cloud Computing

Cloud computing is a technology in the field of computing which offer services to the customer from anywhere at any time [1]. In the cloud, resources are shared all around the work for quick servicing to the customer. The aggregation of two terms is referred as cloud computing. The term “cloud” is a pool of different resources offers services to the end customers and “computing” is done based on the Service Level Agreement (SLA) to make the resources efficiently to the customers. Load balancing is an important challenge in the environment of the cloud to increase the utilization of resources [3]. Here we proposed an algorithm which is based on load balancing and service broker policy. We user two representative thin the proposed approach local representative and global representative Local user representative is used to predict the parameters of user task and based on priority it allocate the task to the Virtual Machine (VM). Then for scheduling the task and provide the services to the users based on the available cloud brokers global user representative used Dynamic Optimal Load-Aware Service Broker (DOLASB).we used two scenario with different no. of user requests , in these scenario result of our proposed method is better as compared with the other existing methods in terms s of Execution Time, Makespan, Waiting Time, Energy Efficiency and Throughput.

Enhancing the Performance of Healthcare Service in IoT and Cloud Using Optimized Techniques

Big data research for health service applications, from the approval of the cloud computing and the Internet of Things (IoT) model of healthcare brought drastic changes in the medical field and improved healthcare services. But the required source to bring the data in a cloud-IoT environment poses a big challenge. In this research work, optimization techniques of virtual machines (VMs) in cloud environment are introduced. The performance of healthcare methods by decreasing the stakeholder’s requirements, execution time, CPU utilization, and storing the patient’s digitals is considered in this research work. The proposed structure defines different steps such as customer devices, customer request (tasks), cloud broker, and network administrator. Three optimization methods such as the Cuckoo Search Algorithm, Particle Swarm Optimization, and Artificial Bee Colony Optimization (ABCO) are employed in the research to optimize the execution time of the stakeholder’s request. The fitness function consists of three fields such as CPU utilization, turn-around time, and waiting time. The experimental result shows the details about three optimization techniques in order to enhance execution time, data processing time, and system efficiency. The simulation result shows that the proposed method decreases the performance rate of total execution time and the system efficiency regarding the real-time improvement of the system. After comparing the proposed optimization methods, ABCO achieves a better efficiency rate of 92.5%, for use in industries.

Cloud broker proposal based on multicriteria decision‐making and virtual infrastructure migration

SummaryThe adoption of infrastructure as a Service (IaaS) is a reality for academic, industrial, and governmental institutions. Cloud tenants request dynamically provisioned virtual infrastructures (VIs) tailored to their application requirements, detailing not only the virtual compute/storage resources but also the network components, topology, and services. The creation of a large number of cloud providers came along with the widespread use of VIs. The selection of an appropriate provider is a challenging task due to the diversity of the IaaS market and formally is a multicriteria analysis (NP‐hard). Notwithstanding the provider selection complexity, the mobility of VI‐hosted applications is limited due to the optimization anchors introduced by providers. Although the existing IaaS cloud brokers can indicate a hosting provider, they lack on conceptual and technical skills to migrate a VI and all its internal components between providers. This work enhances the state‐of‐the‐art on IaaS cloud brokerage by proposing virtual infrastructure multicriteria allocation and migration–based broker (VIMAM), which performs a multicriteria analysis of providers and VI migration. VIMAM is driven by an analytic hierarchy process (AHP) to select an IaaS provider, offering a set of predefined weighting schemas to represent distinct tenant perspectives. Moreover, to migrate a VI, VIMAM takes into account the virtual machines, containers, switches, and other topology elements. In addition to discussing the AHP ranking weights and frequency of providers selection, the experimental analysis details the implementation of an OpenStack and Docker–based prototype for VI migration.

Бізнес-моделі хмарного надання ІТ-послуг

Бізнес-моделі хмарного надання ІТ-послуг

Analysis of Resource Management and Security Management in Cloud Computing Environment

This paper elaborates the concept of Cloud Computing towards the Resource Management and Security issues. In current scenario most of the people tend towards the cloud computing because of its enormous usage and many features. This paper gives descriptive information. Cloud Computing gives and access to the applications. It enables users to setup, design and personalize business usance. The network or Internet refers the Cloud Computing. The Cloud means the information which is present at remote place where users data is saved. This paper gives an information about the management of the cloud computing. The cloud brokerage suppliers manage the solution and their fulfilment. The main resources such as load balance, storage, production, backups, capacity and utilization etc. The main concern in cloud computing is security. Data should store the encrypted form so others can’t read or modify data. The cloud computing service provider should give the protection of the user’s sensitive data.

Trust based Cloud Brokerage Approach using Cryptographic and Weighed Technique

Trust based Cloud Brokerage Approach using Cryptographic and Weighed Technique

Dynamic provisioning of resources based on load balancing and service broker policy in cloud computing

Dynamic resource allocation is the key objective of the paper motivated due to a large number of user’s service request and increasing network infrastructure complexity. Load balancing and Service Broker Policy are taken as two main key areas for the dynamic provision of resources to the cloud user in order to meet the QoS requirement. While provisioning the resources, the conventional approaches degrade due to QoS performance limits such as time delay, energy, etc. To overcome those problems, we proposed a new approach to provide dynamic provisioning of resources based on load balancing and service brokering. Initially, the Multi-agent Deep Reinforcement Learning-Dynamic Resource Allocation (MADRL-DRA) is used in the Local User Agent (LUA) to predict the environmental activities of user task and allocate the task to the Virtual Machine (VM) based on priority. Next, a Load balancing (LB) is performed in the VM, which increases the throughput and reduces the response time in the resource allocation task. Secondly, the Dynamic Optimal Load-Aware Service Broker (DOLASB) is used in the Global User Agent (GUA) for scheduling the task and provide the services to the users based on the available cloud brokers (CBs). In the global agent, cloud brokers are the mediators between users and providers. The optimization problem in Global Agent (GA) is formulated by the programming of mixed integers, and Bender decomposition algorithm. The result of our proposed method is better as compared with the conventional techniques in terms of Execution Time, Waiting Time, Energy Efficiency, Throughput, Resource Usage, and Makespan.

Recent advances in cloud data centers toward fog data centers

Recent advances in cloud data centers toward fog data centers

Cloud Brokerage

Background —The proliferation of cloud services has opened a space for cloud brokerage services. Brokers intermediate between cloud customers and providers to assist the customer in selecting the most suitable service, helping to manage the dimensionality, heterogeneity, and uncertainty associated with cloud services. Objective —Unlike other surveys, this survey focuses on the customer perspective. The survey systematically analyses the literature to identify and classify approaches to realise cloud brokerage, presenting an understanding of the state-of-the-art and a novel taxonomy to characterise cloud brokers. Method —A systematic literature survey was conducted to compile studies related to cloud brokerage and explore how cloud brokers are engineered. These studies are then analysed from multiple perspectives, such as motivation, functionality, engineering approach, and evaluation methodology. Results —The survey resulted in a knowledge base of current proposals for realising cloud brokers. The survey identified differences between the studies’ implementations, with engineering efforts directed at combinations of market-based solutions, middlewares, toolkits, algorithms, semantic frameworks, and conceptual frameworks. Conclusion —Our comprehensive meta-analysis shows that cloud brokerage is still a formative field. Although significant progress has been achieved in this field, considerable challenges remain to be addressed, which are also identified in this survey.

Profit Maximization for Cloud Brokers in Cloud Computing

Along with the development of cloud computing, more and more applications are migrated into the cloud. An important feature of cloud computing is pay-as-you-go. However, most users always should pay more than their actual usage due to the one-hour billing cycle. In addition, most cloud service providers provide a certain discount for long-term users, but short-term users with small computing demands cannot enjoy this discount. To reduce the cost of cloud users, we introduce a new role, which is cloud broker . A cloud broker is an intermediary agent between cloud providers and cloud users. It rents a number of reserved VMs from cloud providers with a good price and offers them to users on an on-demand basis at a cheaper price than that provided by cloud providers. Besides, the cloud broker adopts a shorter billing cycle compared with cloud providers. By doing this, the cloud broker can reduce a great amount of cost for user. In addition to reduce the user cost, the cloud broker also could earn the difference in prices between on-demand and reserved VMs. In this paper, we focus on how to configure a cloud broker and how to price its VMs such that its profit can be maximized on the premise of saving costs for users. Profit of a cloud broker is affected by many factors such as the user demands, the purchase price and the sales price of VMs, the scale of the cloud broker, etc.. Moreover, these factors are affected mutually, which makes the analysis on profit more complicated. In this paper, we first give a synthetically analysis on all the affecting factors, and define an optimal multiserver configuration and VM pricing problem which is modeled as a profit maximization problem. Second, combining the partial derivative and bisection search method, we propose a heuristic method to solve the optimization problem. The near-optimal solutions can be used to guide the configuration and VM pricing of the cloud broker. Moreover, a series of comparisons are given which show that a cloud broker can save a considerable cost for users.

A Strategy Game System for QoS-Efficient Dynamic Virtual Machine Consolidation in Data Centers

In this paper, we develop a model for the competitive relationship between cloud service providers and cloud brokers during the virtual machine (VM) placement. The model adopts the idea of a strategy game, in which the cloud service provider and all VMs are participants. First, we define the strategy for the participants in the strategy game model according to the difference between the number of physical resources provided by the provider, and that requested for the VMs by the brokers. Next, payment values between the providers and brokers are obtained from each strategy in the game matrix, which corresponds to the strategy set of VM placement. In addition, the Nash equilibrium solution of the current strategy game demonstrates the trade-off between reducing energy consumption and guaranteeing QoS. On the basis of this, the optimal VM placement scheme is obtained, and the ongoing VM consolidation is examined. Finally, a strategy-game-based VM consolidation (SG-VMC) method is proposed. The extensive experimental results show that the proposed SG-VMC method can significantly reduce energy consumption while guaranteeing QoS.

A game-based virtual machine pricing mechanism in federated clouds

In a federated cloud environment, diverse pricing schemes among different IaaS service providers (ISPs) form a complex economic landscape that nurtures the market of cloud brokers. Although pricing mechanisms have been proposed in the past few years, few of them address the issue of competitive and cooperative behaviours among different ISPs. In this paper, we employ the learning curve to model the operation cost of ISPs, and introduce a novel algorithm that determines the cooperative pricing mechanism among different ISPs. The cooperation decision algorithm uses the operation cost computed based on the learning curve model and price policies obtained from the competition part as parameters to calculate the final revenue when outsourcing or locally satisfying users' resource requests. Extensive experiments are conducted in a real-world federated cloud platform, and the experimental results are compared with three existing pricing mechanisms. Our experimental results show that the proposed pricing mechanism is effective to improve resource utilisation as well as reduce the profit loss caused by request rejection.

A Novel Model for Optimizing Selection of Cloud Instance Types

With the development of cloud computing, the cloud market is becoming more and more complicated. In a cloud data center, there are many cloud instance types with different computing capacity and price, which brings users some confusions when they select cloud instance types. In order to solve this selection problem, a cloud brokering architecture is proposed. In this architecture, the selection problem is modeled as a multi-objective optimization problem, and through analysis, we get the relationship between complete Pareto set and solution space. Based on this, a two-stage Cloud Instance Type Selection Model (CITSM) is proposed to help users select the cloud instance types. The first stage is Complete Pareto Set Generation Algorithm (CPSGA) which can generate a complete Pareto set of the cloud instance type selection schemes. Then, the Optimal cloud instance type selection Scheme Screening Algorithm (OSSA) is used to select one scheme from the complete Pareto set. We perform some experiments to prove the proposed CITSM is efficient and effective. The proposed method can also solve the single objective optimization problem by modifying OSSA, which illustrates the scalability.