Cloud computing is one of major dynamically evolving area which provides business agencies to extend their process across the globe. Cloud broker mediates between cloud service provider and cloud consumers through API. Initially, cloud user submits the specification to the cloud broker and desires for the best cloud provider. Request from cloud users are processed by the cloud broker and best suited provider is allocated to them.
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This paper proposed an idea of introducing a MultiObjective Optimization technique in selecting a best provider for the cloud consumers. Once the service level agreement is assured, connection to appropriate cloud provider is established through cloud API. The negotiation can be modeled as middleware, and its services can be provided as application programming interfaces. Infrastructure-as-a-Service (IaaS) specification of each provider is considered and compared with requirement specified by cloud user.
Keywords: Cloud computing, Cloud Broker, MultiObjective Optimization.
A cloud refers the interconnection of huge number of computer systems in a network. The cloud provider extends service through virtualization technologies to cloud user. Client credentials are stored on the company server at a remote location. Every action initiated by the client is executed in a distributed environment and as a result, the complexity of maintaining the software or infrastructure is minimized. The services provided by cloud providers are classified into three types: Infrastructure-as-a-Service (IaaS), Software-as-a-Service (SaaS), and Platform-as-a-Service (PaaS). Cloud computing makes client to store information on remote site and hence there is no need of storage infrastructure. Web browser act as an interface between client and remote machine to access data by logging into his/her account. The intent of every customer is to use cloud resources at a low cost with high efficiency in terms of time and space. If more number of cloud service providers is providing almost same type of services, customers or users will have difficulty in choosing the right service provider. To handle this situation of negotiating with multiple service providers, Cloud Broker Services (CBS) play a major role as a middleware. Cloud broker acts as a negotiator between cloud user and cloud service provider. Initially, cloud provider registers with cloud broker about its specification on offerings and user submits request to broker. Based on type of service, and requirements, best provider is suggested to the cloud user. Upon confirmation from the user, broker establishes the connection to the provider.
RELATED WORKS OF CLOUD BROKERAGE SERVICES (CBS)
Foued Jrad et al  introduced Intercloud Gateway and Open Cloud Computing Interface specification (OCCI) cloud API to overcome lack of interoperability and heterogeneity. Cloud users cannot identify appropriate cloud providers through the assistance of existing Cloud Service Broker (CSB). By implementing OCCI in Intercloud Gateway, it acts as server for service providers and OCCI act as a client in abstract cloud API. Cloud Broker satisfies users of both functional and non-functional requirements through Service Level Agreement (SLA). Intercloud Gateway acts as a front end for cloud providers and interacts with cloud broker. Identity Manager handles user authentication through unique ID.SLA Manager is responsible for negotiates SLA creation and storing. Match Manager takes care of selecting suitable resources for cloud users. Monitoring and Discovery Manager monitor SLA metrics in various resource allocations. Deployment manager is in charge of deploying services to cloud user. Abstract cloud API provides interoperability. The user submits a request to SLA Manager and it parses the request into SLA parameters which is given to Match Maker. By applying algorithm Match Maker find best suited solution and response is passed to the user. Upon user acceptance a connection is provided by service providers. Through this architecture, interoperability is achieved, but this cannot assure best matching cloud service provider to the client. Tao Yu and Kwei-Jay Lin  introduces Quality of Service (QoS) broker module in between cloud service providers and cloud users. The role of QoS information is collecting information about active servers, suggesting appropriate server for clients, and negotiate with servers to get QoS agreements. The QoS information manager collects information required for QoS negotiation and analysis. It checks with the Universal Description Discovery and Integration (UDDI) registry to get the server information and contacts servers for QoS information such as server send their service request and QoS load and service levels. After receiving clients functional and QoS requirements, the QoS negotiation manager searches through the broker’s database to look for qualified services. If more than one candidate is found, a decision algorithm is used to select the most suitable one. The QoS information from both server and QoS analyzer will be used to make the decision. By using this architecture load balancing factor of server is maintained for a large number of users, but not efficient in delivering best suited provider to the client.
HQ and RQ allocation algorithm is proposed to maximize server resource while minimizing QoS instability for each client. The HQ allocation algorithm is to evenly divide available resource among required client based on active clients. RQ assigns a different service level to client based on requirements.
Josef Spillner et al  provided solution is to subdivide resource reservation into either serial or parallel segments. Nested virtualization provides services to cloud user. The outcome is a highly virtualizing cloud resource broker. The system supports hierarchically nested virtualization with dynamically reallocate capable resources. A base virtual machine is dedicated to enabling the nested cloud with other virtual machines is referred to as sub-virtual machine running at a higher virtualization level. The nested cloud virtual machine is to be deployed by the broker and offers control facilities through the broker configurator which turn it into a lightweight infrastructure manager. The proposed solution yields the higher reselling power of unused resources, but hardware cost of running virtual machine will be high to obtain the desired performance.
Chao Chen et al  projected objectives of negotiation are minimize price and guaranteed QoS within expected timeline, maximize profit from the margin between the customers financial plan and the providers negotiated price, maximize profit by accepting as many requests as possible to enlarge market share. The proposed automated negotiation framework uses Software–as-a-Service (SaaS) broker which is utilized as the storage unit for customers. This helps the user to save time while selecting multiple providers. The negotiation framework helps user to assist in establishing a mutual agreement between provider and client through SaaS broker. The main objective of the broker is to maintain SLA parameters of cloud provider and suggesting best provider to customer. Negotiation policy translator maps customers QoS parameters to provider specification parameters. Negotiation engine includes workflows which use negotiation policy during the negotiation process. The decision making system uses decision making criteria to update the negotiation status. The minimum cost is incurred for resource utilization. Renegotiation for dynamic customer needs is not solved.
Wei Wang et al  proposed a new cloud brokerage service that reserves a large pool of instances from cloud providers and serves users with price discounts. A practical problem facing cloud users is how to minimize their costs by choosing among different pricing options based on their own demands. The broker optimally exploits both pricing benefits of long-term instance, reservations and multiplexing gains. Dynamic approach for the broker to make instant reservations with the objective of minimizing its service cost is achieved. This strategy controls, dynamic programming and algorithms to quickly handle large demands. A smart cloud brokerage service that serves cloud user demands with a large pool of computing instances that are dynamically launched on-demand from IaaS clouds. Partial usage of the billing cycle incurs a full cycle charge, this makes user to pay more than they actually use. This broker uses single instance to serve many users by time-multiplexing usage, reducing cost of cloud user.
Lori MacVittie  introduces broker as a solution to integrate hybrid policy without affecting control in services. The integration between cloud and datacenter is done with cloud broker integration at the process layer. Brokers deploy vast amount of applications for customer through infrastructure defined by corporate enforced policies. Identity broker module communicates with datacenter through authorization and authentication mechanism. The real-time implementation of cloud broker is achieved by two types of architectures: Full-proxy broker and Half-proxy broker. In Full-proxy broker requests are processed through the tunneling and implemented in many ways such as VPN. In Half-proxy broker only validation of the request is done by broker, successive communication established directly. This model defines how the request can be handled in late binding. A cloud delivery broker can make decision, such as where to revert user upon request. Hybrid cloud must be able to describe capabilities such as bandwidth, location, cost, type of environment.
The proposed system works based on MultiObjective Optimization technique. Cloud broker consists of two phases namely, resource manager and pareto analysis.
The resource manager is involved in storing specification of the each cloud service provider which is stored in the local database of the cloud broker. Upon request from the cloud user, based on user specification, appropriate cloud provider is assigned. The specification can be of IaaS or Software-as-a-Service (SaaS) or Platform-as-a-Service (PaaS) type needed by user.
Pareto analysis is procedure of making decision based on importance of input parameters specified by user. This process assigns scores to each parameter which makes large impact on the output. The first step in analysis is to identify the factors which have large influence on output and then sort out objectives based on user preferences. Pareto analysis uses MultiObjective Optimization (MOO) technique in deciding best cloud provider for user requirements.
Fig 1 Framework for Cloud Brokerage Services
From the above figure it is evident that optimized solution can be obtained from proposed algorithm in the cloud broker.
MultiObjective Optimization Evolutionary Algorithm (MOEA)
The Non-dominated Sorting Approach-2 (NSGA-2) algorithm is computationally fast among all non-dominated sorting approach in MOEA. This algorithm is used to select optimized output for the user specified requirement. The algorithm works as follows:
Fig. 2. Modified NSGA-2 Algorithm for Cloud Brokerage Services (CBS).
The optimized objective is made to tournament selection  and recombination procedure for best cloud provider.
CONCLUSIONS AND FUTURE WORKS
The development of a cloud brokerage services framework is getting momentum since its usage is pervasive in all verticals. The works till now considered the scenario of more than two cloud service provider providing the same level of requirements to the user. This scenario will able to identify optimized cloud providers for the users to choose an appropriate provider. The Cloud Broker Services will act on behalf of the user to choose a particular service provider for providing service to the user. If Cloud Broker Service becomes a standard middleware framework, many chores of cloud service providers can be taken by CBS.
- Foued Jrad, Jie Tao, Achim Streit, SLA Based Service Brokering in Intercloud Environments. Proceedings of the 2nd International Conference on Cloud Computing and Services Science, pp. 76-81, 2012.
- Tao Yu and Kwei-Jay Lin, The Design of QoS Broker Algorithms for QoS-Capable Web Services, Proceedings of IEEE International Conference on e-Technology, e-Commerce and e-Service, pp. 17-24, 2004.
- Josef Spillner, Andrey Brito, Francisco Brasileiro, Alexander Schill, A Highly-Virtualising Cloud Resource Broker, IEEE Fifth International Conference on Utility and Cloud Computing, pp.233-234, 2012.
- Linlin Wu, Saurabh Kumar Garg, Rajkumar Buyya, Chao Chen, Steve Versteeg, Automated SLA Negotiation Framework for Cloud Computing, 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, pp.235-244, 2013.
- Wei Wang, Di Niu, Baochun Li, Ben Liang, Dynamic Cloud Resource Reservation via Cloud Brokerage, Proceedings of the 33rd International Conference on Distributed Computing Systems (ICDCS), Philadelphia, Pennsylvania, July 2013.
- Lori MacVittie, Integrating the Cloud: Bridges, Brokers, and Gateways, 2012.
- Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and T. Meyarivan, A Fast and Elitist Multiobjective Genetic Algorithm:NSGA-II. IEEE Transactions on Evolutionary Computation, Vol. 6, No. 2, April 2002.
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