EFFICIENT FEATURES EXTRACTION TECHNIQUE FOR CLOUD BASED APPLICATION WITH INTEROPERABILITY USING EVOLUTIONARY COMPUTING

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1 EFFICIENT FEATURES EXTRACTION TECHNIQUE FOR CLOUD BASED APPLICATION WITH INTEROPERABILITY USING EVOLUTIONARY COMPUTING * C. Saravanakumar and C. Arun 1 Department of Computer Science and Engineering, Sathyabama University,Tamilnadu, Chennai, India 2 Department of Electronics and Communication Engineering, R. M. K. College of Engineering and Technology, Chennai, Tamil Nadu, India *Author for Correspondence ABSTRACT The cloud computing is a technology for retrieving the services from various heterogeneous resources which are available at the centralized location. The problem of cloud computing is not a standard way of accessing a cloud service from the cloud environment i.e., different Cloud Service Providers (CSP s) have different services. The existing work focused on the service separation among the cloud services in order to provide the quality service to the Cloud Service Users. Later the interoperability is implemented over the software applications which are used to provide the quality of services to the Cloud Service Users. Various deployment models are used for those software applications they are private cloud, public cloud, hybrid cloud and community cloud. The interoperability can be implemented over community cloud because some of the CSP share the services. The features are extracted from software application of various CSP by using the feature extractor then it is stored into the storage. The extracted features are collectively sent to the feature deployment model. The Cloud Service User can select the suitable services and ss the request to the cloud. This paper focuses on the features of the Software as a Service (SaaS) with common feature deployment model. The cloud service features are evaluated using various method of evolutionary computing. In future this work can be exted to the Platform and Infrastructure Level. Key Words: Interoperability, Cloud Computing, Word Processing and Evolutionary Computing INTRODUCTION Cloud computing is the delivery of different computing service rather than resources, software, and information which are used by the user. There are different types of services which satisfies the users request namely Software as a Service (SaaS) (Jaroodi & Mohamed, 2011) Platform as a Service (PaaS), Infrastructure as a Service (IaaS) (Ferrer et al., (2011) and etc. Multi-tenancy is one of the features of the cloud computing which can be used to enable sharing of resources with the important characteristics namely centralization, peak load capacity and utilization with efficiency. The sharing of services in the cloud computing comprises of different layers such as Application Layer, Platform layer and Infrastructure layer. Different types of deployment model are used to provide the cloud services to the user namely Public Cloud, Community Cloud, Hybrid Cloud and Private Cloud. In a public cloud all services are accessed over the internet i.e., all the services are available globally. Community cloud shares infrastructure between several organizations from a specific community with common concerns whether it is managed internally or by a third-party and hosted internally or externally. The Hybrid cloud is a combination of public, community and private cloud in order to provide services to the user. Private cloud is used to provide the services within the organization which are managed internally. The cloud services are classified into high level services and low level services for addressing the interoperability between different CSP s. There are three different high level services are proposed. First, the data processing service in which the data is submitted by the user are processed, and the result is sent back to the user without storing the value into the cloud storage. Second, if the user wants to process the data and then they wants to store the result of the processed data into the cloud storage service. Finally, the cloud storage services only stores the data coming from the user without processing the information (Saravanakumar & Arun, 2011). Darwin frameworks which is used to reduce both the cost and risk of migration workload and enhances the speed of the workload which is migrated from one source to destination and to reduce the risk occur during migrations (Ward et al., 2010). A deployment model for cloud computing based application lacks in the security. If the user wants to access the data available in the remote system may become unavailable due to system failure because it deps on a single service provider. In order to identify this security problem by using different deployment models such as Separation model, Availability model, Migration model, Tunnel model and Cryptography model (Zhao et al., 2010). The cloud security management issues and interoperability challenges of the collaborative cloud focused on the cloud security management infrastructure, which is used to manage and integrate the cloud security management system (Kretzschmar & Hanigk, 2010). The service oriented applications 74

2 are deployed over the cloud which is used to migrate the security threats with the help of dynamic web service policy framework for the cloud environment (Bertram et al., 2010). Smart metering is a technique used for optimizing the cloud based data center infrastructure usage, by using the Application Programming Interface. The on-line delivery and consumption model were used for IT and non-it which are commonly referred as everything as a Service (XaaS). Smart metering provides different tariffs to the customer to get the best price (Singh & Yara, 2009). An accounting is a process which is used to record the accounting requirements and a list of pricing schemes. The pricing model is based on SLA (Service Level Agreement) between the user and the provider. Accounting is made at each and every layers of the cloud computing. Internet Protocol Detail Recorder (IPDR) is a reference model is especially designed for accounting in order to get a flexible pricing. SLA is managed between the customer and service provider using Service Level Agreement Management (SLAM) (Greenwood et al., 2006). A web hosting provider which is called as Cloud Hosting Provider (CHP) has an unlimited set of resources though outsourcing technique which is able to run any web application in which many users can use the resources. The CHP operations such as monitoring service performance, response time, and scheduler operations are used to maximize the earning (Fito et al., 2010). A new approach which describes an interaction and competition among the user s network provider and service provider in a service oriented internet. It is a simple and economic model which provides service not only as simple content but also software, computing and storage resources etc., (Zhang et al., 2010). The cloud service providers are maintaining the separate software applications in order to provide the reliable service to the cloud service users. The separate usage of application gives complexity over the providers which are not providing reliable services to the cloud users. The common services can be handled with the help of service level agreement established between the cloud service providers (Saravanakumar & Arun, 2011). The above problems can be overcome by using the common deployment model with standardized protocol which provides to the cloud users to feel more comfortable in the cloud usage. The web based word processor is taken as an example for implementation process. The features are extracted from various web based word processor and given the summary report to the Cloud Service User (CSU) in order to get reliable cloud services for their expectations. The paper is organized as follows; Section 1 represents the overview of cloud computing and related concepts. Section 2 and 3 describes the related work and new approach respectively. Section 4 represents the framework of the proposed system. Section 5 represents the introduction to evolutionary computing. Section 6 proposed the method of evaluating the cloud service. Section 7 represents the various algorithms of the cloud computing. Finally, Section 8 represents conclusion and future work. RELATED WORK The cloud service can be separated in order to provide the reliable services to the Cloud Service Users. There are three types of services can be introduced namely Data Processing Services, Data Processing & Storage Services and Cloud Storage Services. In data processing service, the data are submitted by the user to the Cloud environment for processing, and the result is sent back to the user without storing the result into the cloud storage. In this service the security is high because the data is not stored in the cloud storage and which cannot be accessed by unauthorized users in storage level. In the data processing and storage service, the user wants to process the data and then the processed result is stored in to the cloud storage. There are two levels of security are applicable namely the process level and the storage level. In the cloud storage service the user s data are moved or accessed to/from the cloud storage directly without processing the data. These service separations are mainly introduced for providing an efficient way to access the cloud services, according to the cloud user s interests. The problem is that there is no common standard available for CSP, so the ambiguous occur in CSU for selecting a suitable service. The CSP s provides the services to CSU s which are indepent of each other even though they are providing the same service. The result which leads so much of confusion among the CSU s for selecting suitable and reliable services. The problem is that each CSP s are providing same services with different features. Some user needs simple features and others need advanced features of the cloud services. PROPOSED APPROACH The objective of this paper is to create an awareness among the cloud users to get the prior knowledge about the service, what they actually access from the cloud computing. All cloud users are sping so much of money for selecting a suitable services i.e., every users can pay same amount irrespective of the usage. In order to provide the service with high quality by introducing a common deployment model for accessing a cloud applications. The application features are extracted from the cloud application and gives a prior knowledge to the user for selecting and accessing a suitable service. The web based word processor is taken as an example in this work. 75

3 FRAMEWORK OF THE PROPOSED SYSTEM Service Request Monitor receives a request from the user and interacts with the common deployment model for the required services. The authentication of the user is validated before the request enters into the cloud environment. Once the validation process has been completed the features are extracted from the cloud applications which are related to different cloud service providers using the common deployment model. The features are segregated as two different type s namely basic and advanced features. The user can able to select their expectation very easily by using these types of services. Feature extractor can extract all the features and then store it in the database for comparison. The Features Pool Manager manages the Common Deployment Model (CDM) and the Feature Extractor handles the multiple requests from the users. If multiple user requests the same service then the requests are managed by the Feature Pool Manager with queuing model i.e. a Queue is based on First in First out (FIFO) queuing model. Each and every CSP s follows SLA for maintaining a feature list. The CSP s are connected with a community cloud because the most similar services are easily handled by the CDM. The SLA scheduler information with Quality of Service manager is scheduled using the Scheduling policy. If the features are selected by the CSU then the suitable CSP provides the user specific services. The accounting and metering section maintains a cost for the usage. Fig.1 shows an overall process of the CDM and cloud related principles. Figure 1: Framework of the proposed system INTRODUCTION TO EVOLUTIONARY ALGORITHM Evolutionary algorithm operates on a population of potential solutions for applying the principle of survival of the fittest to produce better and better approximations to get the solution. At each generation, a new set of approximations are created by the process of selecting individuals according to their level of fitness in the problem domain and using operators from natural genetics [6]. There are some processes which are used to perform the evolutionary computation namely selection, recombination, mutation, migration, locality and neighborhood. The Selection process is used to choose the individuals from the population for survival. Recombination is the process to produce new individuals (offspring s) by combining the information contained in the parents of the population. Mutation is a genetic operator that alters one or more gene values in a chromosome from its initial state. The result may be a new gene value is being added to the gene population (Engelbrecht, 2007). These approaches are used in 76

4 the cloud services for extracting the features of the Cloud applications which are used to provide Quality of Service (QoS) to the cloud service Users with their expectation. A comparison of different web based word processor with the available features of various Cloud Service Providers is shown in Table 1.The cloud based word processing application features can be selected based on the following cost calculation. COST= (PROB (current features support / total number of features))* 100 Where, PROB is a probability of the feature occurrences For example: The google docs support 4 features (current features support) from 7 features (total number of features) with the probability COST = (PROB (4 / 7)) * 100 = 57 Table 1: Comparison of various CSP s based on the word Processing Application [12] Cloud Service Provider name Online/ Offline Features Support Technology Based Sharing and collaboration Back bone Portable device support Image Support Feature Support (Count) Google DOC yes Ajax Yes 4 57 docs View KBdocs Export No 1 14 Glide & Glide Ajax Yes 4 57 write Chat Mobile Peeple Offline Web Permission Yes 3 43 Editing Writer Ajax writer Think Free Write Adobe buzzword Zoho Writer (X) IE No 1 14 Java based Need Adobe edit& format Java based Flash based Ajax, Java Portable clipart & Flickr COST 5 71 Yes 3 43 Yes HTML, Ajax, Flash Yes 4 57 Docly Copy right 1 14 Write RSS Board format Yes 2 29 inet revert Word back Yes 2 29 CLOUD SERVICE EVALUATION USING EVOLUTIONARY ALGORITHM Fitness Evaluation The fitness of the cloud services can be evaluated using the following functions with related parameters (x i ) = W1 ( memory_size(x i )) + W2( resource_speed(x i )) + W3( request_size(x i )) + W4( utilization_time(x i )) 1.1 memory_size(x i ) = free_memory_size(x i ) - occupied_memory_size(x i ) resource_speed(x i ) ={List of resources like cloud size, storage capacity etc.,} RTT(x i )= diff(request time, response time) utilization_time (x i )= RTT(x i ) / Service Time 1.5 Where 77

5 (x) is a fitness value RTT is a Round Trip Time W1, W2, W3, W4 are weight constant The equations 1.1 to 1.5 are used to find out the fitness value of the cloud services which related to various cloud performance parameters. Service Selection The service selection are calculated based on the equation 1.6 service_selection(x i )= Mutation The new features are updated with already existing cloud application features by using the mutation process which is referred using the equation 1.7 P old = list of the Features which are available in the Current CSP P new = list of the new Features which will add to the existing CSP Mutation {P} = 1.7 Cost calculation The cost of the cloud based word processing service can be calculated by using the equations 1.8 to 1.12 Cost = Cost 1 +Cost 2 +Cost 3 +Cost Cost 1 = Requesting_Cost (i.e., bandwidth) 1.9 Cost 2 = Response_Cost (i.e., bandwidth) 1.10 Cost 3 = Cloud _Utilization_Cost 1.11 Cost 4 = Client_Utilization_Cost 1.12 ALGORITHMS FOR CLOUD SERVICE INTEROPERABILITY The different types of algorithms which are used to evaluate the cloud services are explained in the following sections. Cloud Request Selection The cloud request selection which is used to select the suitable services from the Cloud application with the help of Cloud Request Selection algorithm Algorithm for Cloud Request Selection Initialize the parameter P; Collect all application features form Common Deployment Model; Initialize the population C; Initialize the Cloud Service User Request R with the parameter P; Set the Basic Features population B; Set the Advanced Features population A; for each client, C i Є C do for each request, R i Є R do for each features P i Є P do if (features Є P and Request_ Type Є B) then add P i into B s the B i to Cloud Service Users Request for the CSP S service with B i Є B features; else if (features Є P and Request_Type Є A) then add P i into A s the B i to Cloud Service Users Request for the CSP S service with A i Є A features; 78

6 Cloud Interoperability The Interoperability algorithm is used to select the suitable services with evolutionary computing process. Algorithm for Interoperability Let t=0, be the generation counter; Create and initialize the n x cloud population C (0) to consist of ns cloud Service Providers; While stopping condition(s) not true do Fitness_Evaluation (); Cloud_service _Selection (); Mutation (); Advanced to the new generation i.e. t=t+1; Cloud Service Selection The Cloud Service Selection algorithm is used to select the suitable services with by calculating the fitness in evolutionary computing process. Algorithm for Cloud Service Selection Let i=1, where i denotes the cloud service Providers; Let S be the software services; Calculate the fitness function service_selection(x i ) using the equation 1.6 For each service S i є S do If ( service_selection(x i )== true) then Return x i as the selected service; Mutation The Mutation algorithm is used to add new features over the already existing features which are available in the cloud applications. Algorithm for Mutation select the mutation M with cloud application features P for j=i nx do // nx are the cloud Application Features with Cloud Service provider C if P update is true then Add new features P new to P old in P with CSP C; CONCLUSION AND FUTURE WORK The user requests a service from cloud environment for performing web based word processing application. The community cloud provides different text editors with different features. The features of the cloud service providers differ from one CSP to the other, by extracting those features and comparing with the users request. The proposed approach gives the list of features to the CSU for selecting a suitable service which satisfies the user need with more efficient manner. By providing this comparison user will get prior knowledge about the CSP application features and get the service with more reliable over all aspects such as usage, cost etc.,. This paper focused on the feature extraction method of the Software application with common deployment model. Evaluation of the cloud services are done by using various evolutionary computing. In future this work can be exted to the platform and infrastructure level of the cloud computing. REFERENCES Andries P. Engelbrecht (2007). Computational Intelligence: An Introduction. 2 nd Edition University of Pretoria, South Africa Ana Juan Ferrer et al., (2011). OPTIMIS: A holistic approach to cloud service provisioning. Future Generation Computer Systems, 28(1)

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