QoS aware Multimedia Cloud Computing A Survey

Transcription

1 QoS aware Multimedia Cloud Computing A Survey K.Syed Ibrahim Research Scholar, Baharathidasan University, Tiruchirappalli, India ibrahimkaliba1978@gmail.com Dr. A.R.MohamedShanavas, Associate Professor, Jamal Mohamed College, Tiruchirappalli, India. arms3375@gmail.com Abstract: Cloud computing systems facilitate pay-per usage model for computing services provided to the users throughout the globe over the internet. Nowadays the demand for cloud services thru the Mobile devices has increased as organizations and individuals have now started using various handheld mobile devices such as laptop PCs, smart phones, tablets and other such devices. Therefore Cloud Service Providers need to offer services based on the expected quality requirements over the mobile devices. One of the challenges created by cloud application is quality of service (QoS) management, which is the problem of allocating resources to the applications to guarantee the service based on performance, availability and reliability. In this paper, we have presented a survey on QoS in Mobile Cloud Computing with respect to techniques used, advantages and disadvantages. Keywords- Mobile cloud computing, quality of Service (QoS), Service level Agreement (SLA). I. INTRODUCTION In recent years, accessing the multimedia content using mobile devices becomes very popular. The demand for realtime multimedia services, such as video streaming, video conferencing, gaming and IPTV is increasing rapidly. As the result of enhancements in ICT technologies mobile devices, such as smart phones, PDAs, and laptops, become more and more powerful, and are capable to access and present rich multimedia contents. Multimedia streaming data is one of the major factors that force the development of broadband wireless networks. Broadband wireless networks, such as WiMAX (Worldwide Interoperability for Microwave Access) and4g (4 th Generation Mobile Telecommunications), are broadly used for wireless and mobile Internet access. The pervasive and ubiquitous computing becomes possible as the rapid development in the heterogeneous mobile devices and widely deployed wireless networks. This means that the accessing the multimedia data from anywhere at any time becomes so easy nowadays. Multimedia streaming applications for video conferencing, mobile TV, mobile gaming, etc. have become the most popular applications on the mobile devices. Multimedia data are also widely used in surveillance systems, homeland security, transportation, e-learning, health care, etc. The Internet Service Providers (ISPs) are demanded to provide multimedia services via various wireless networking technologies, such as WLAN (Wireless Local Area Network), 4G, and WiMAX. However, there are many challenges with the resources in the mobile devices including battery life, storage, bandwidth and connectivity like mobility and security [1]. The limited resources significantly affect the improvement of service qualities. With the rapid development of mobile applications and the support of Cloud Computing for a variety of services for mobile users, Mobile Cloud Computing (MCC) is introduced as an integration of Cloud Computing with the mobile computing environment. MCC offers new types of services and facilitates the mobile users to take full advantages of Cloud Computing. This paper presents a brief survey on MCC. Section 2 provides a brief overview of MCC including definition, architecture, and its advantages. Section 3 discusses the various existing researches for providing QoSin MCC. Finally, we summarize and conclude the survey in Section 4. RES Publication 2012 Page 68 II. OVERVIEW OF MOBILE CLOUDCOMPUTING The term mobile cloud computing refers to the technology which uses the mobile devices in the cloud computing. It attracts the interest of entrepreneurs as a profitable business opportunity since it reduces the cost for developing and executing mobile applications. It attracts the mobile users to realize rich experience of a variety of mobile services for minimum cost in one hand, and the researchers as a promising solution for green IT in other hand [2]. In this section, we provide an overview of MCC including definition, architecture, and advantages of MCC. A. Definition of mobile cloud computing. The MCC forum defines MCC as follows [3]: Mobile cloud computing at its simplest refers to an infrastructure

2 where both the data storage and data processing happen outside of the mobile device. Mobile cloud applications move the computing power and data storage away from mobile phones and into the cloud, bringing applications and MC to not just smart phone users but a much broader range of mobile subscribers. CPU processing capacity, memory space etc., since the complicated computing process can be carried out in the cloud. MCC is described as a new paradigm for mobile applications by Aepona [4], in which the processing and storage of data are migrated from mobile device to a powerful and centralized computing platforms located in clouds. These centralized applications are then accessed over the mobile network. Alternatively, MCC can be defined as a combination of mobile web and CC [5,6], which is the most popular tool for mobile users to access applications and services on the Internet. Briefly, MCC provides mobile users with the data processing and storage services in clouds. It is not required for the mobile devices to have a powerful configuration like Figure 1. The Architecture of Mobile Cloud Computing B. Mobile Cloud Computing Architecture The architecture of MCC is shown in figure 1. In this architecture, mobile devices like smart phone, PDA or laptop are connected to the mobile networks through Base Stations (BSs). The BSs can be in different forms such as Base Transceiver Station (BTS), Access Point (AP), or Satellite which establish and control the connections (air links) and functional interfaces between the mobile devices and the mobile network. All the requests from the mobile users and the basic information such as ID and location are communicated to the central processors. The central processors are connected to servers which are responsible for providing mobile network services. RES Publication 2012 Page 69

3 Software as a Service (e.g., Microsoft s Live Mesh) Platform as a Service (e.g., Google App Engine, Microsoft Azure) Infrastructure as a Service (e.g., EC2, S3) hardware, servers, and networking facilities. The client typically pays on a per-use basis. PaaS - Platform as a Service offers an advanced integrated atmosphere for constructing, testing, and deploying custom applications. The examples of PaaS are Google App Engine, Microsoft Azure, and Amazon Map Reduce/Simple Storage Service. SaaS. Software as a Service supports a software delivery with specific requirements. In this layer, the users can access an application and information remotely via the Internet and pay only for that they use. Salesforce and. Microsoft s Live Mesh are good examples for this service Data centers III. QUALITY OF SERVICE Figure 2. Service Oriented Cloud Computing Architecture The mobile network operators can provide services to mobile users as authentication, authorization, and accounting (AAA) based on the Home Agent (HA) and subscribers data stored in databases. After that, the subscribers requests are conveyed to the cloud through the Internet. In the cloud, Cloud Controllers are responsible for providing the cloud services to the mobile users with the corresponding cloud services by processing the requests. These services are developed with the concepts of virtualization, utility computing and service-oriented architecture (SOA) for example web application, and database servers. The architecture of cloud computing can be viewed differently indifferent contexts. For example, in [7] four layer architectures described to compare cloud computing with grid computing. On the other hand, a service-oriented architecture (SOA), called Aneka, is presented in [8] to empower developers to construct Microsoft.NET applications using Application Programming Interfaces (APIs) and multiple programming models. The authors of [9] present architecture for building market-oriented clouds and the researcher in [10] propose architecture for webdelivered business services. In this paper, we focus on a layered model of Cloud Computing (CC) architecture as shown in Figure 2 which is generally used to illustrate the effectiveness of the CC in meeting the users requirements [11]. In General, CC is a large-scale distributed network system constructed using a number of servers in datacenters. The cloud services are generally classified as: Infrastructure as a Service (IaaS) Platform asa Service (PaaS) and Software as a Service (SaaS) Data centers layer. This layer provides the hardware facility and infrastructure for clouds in which a number of servers are linked with highspeed networks to provide services for customers. IaaS -Infrastructure as a Service is built on top of the data center layer. IaaS provides the storage, Quality of Service (QoS) represents the amount of performance, reliability and availability provided by an application and by the platform or infrastructure which provides that application. QoS is the basic requirement of the cloud users, which should be provided by the Cloud Service Providers (CSP) to deliver the advertised characteristics and CSP who need to arrange the right tradeoff between QoS levels and operational cost [12]. Providing QoS in MCC involves Service Level Agreements (SLA) which states QoS objectives and economic punishments in the case of SLA violations. A number of researchers are investigating automated QoS management systems which can enhance the scheduling of hardware and software resources in the cloud [13].M. Salam et al. [14] projected a QoS-oriented associated cloud computing framework where several autonomous cloud providers can cooperate seamlessly to provide scalable QoSassured services. The significant components for enabling cloud federation used were Cloud Coordinators (CC) and Federation Coordinators (FC). The unique feature of the proposed federation framework is its QoS-orientation that can generate the on-demand resource provisioning between multiple providers. Thus this framework helps to maximize QoS targets and resources usage, reduce SLA violations and improve SLA formalization. However, the main drawback of this approach is that it is not supporting for constructing Complex services by using a combination of services from different cloud providers and it is not secured enough against the distributed denial of service (DDoS) attacks. M. M. Hassan et al. [15] analyzed and examined the Amazon cloud EC2 for Big data workload by running a group of typical Big data jobs. They have created as imulation and compared the proposed scheme with other approaches. Although the proposed scheme was costeffective, they have not considered the parameters like delay, delay variable and throughput for performance evaluation. A formal model was proposed by W. C. Chu et al. [16] to assist the Enterprise Cloud Computing (ECC) services design and construction through SaaS, PaaS, IaaS. This model also supports the concurrent monitoring and dynamic analysis on the QoS issues for the promises from QoS RES Publication 2012 Page 70

4 service providers and the SLAs for several ECC consumers. An analysis model and testing model were also generated to support runtime monitoring as well as automatic testing to promise the satisfaction to the SLA constraints. There are some limitations in this research such as they have not adapted the features and solutions of IOT issues into the framework and the field experiment. The architecture proposed by S. Lee et al. [17] employed the agent technology in order to monitor the requested QoS requirements and SLA, to support verification and validation. Moreover, the agent-based technology dynamically analyzed resources allocation and deployment. The limitation of this scheme is the lack of a self-learning algorithm to regulate the timing of automatic resource allocation. P. Zhang et al. [18] proposed a QoS framework for and an adaptive QoS management system to accomplish QoS assurance in MCC environment. Moreover, they presented a QoS management model based on a fuzzy cognitive map (FCM). A multiple QoS constrained scheduling strategy of multiworkflows (MQMW) is proposed by M. Xu et al.[19] to tackle the issue of multiple workflows with diverse QoS requirements. The proposed approach his able to schedule multiple workflows which could be started at any time though QoS constraints like availability and reliability. Y. Xiao et al. [20] and proposed an effective reputationbased QoS provisioning scheme, which can reduce the cost of computing resources, while satisfying the preferred QoS metrics. They have taken the statistical probability of the response time as a real-time metric instead of the typical mean response time. Moreover QoS provisioning algorithm was not used to integrate the security and privacy metrics. IV. CONCLUSION Mobile Cloud Computing is a significant mobile technology trend in the future since it combines technical advancements and user benefits of both MC and CC, hence delivering optimal services for mobile users Thereby, we can realize that MCC is possibly a dominant technology trend with plentiful applications and services in the near future. In this paper, we have surveyed various research proposals on QoS techniques in MCC to determine the level in which the QoS challenge can be resolved. Many researchers have provided scheduling techniques, dynamic resource provisioning, traffic control admission control, etc focusing to manage the issue of QoS in cloud computing. We hope that this paper will be useful for giving clues the way ahead and enable a gigantic integration of mobile computing and cloud computing. 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5 Computational Sc. and Computational Intelligence, pp , [16] W. C. Chu, C. Yang, C. Lu, C. Chang, N. Hsueh, T. Hsu, S.Hung, An approach of quality of service assurance for enterprise cloud computing (QoSAECC), in Proc. Int. Conf. on Trustworthy Systems and their Applications, pp. 7-13, [17] S. Lee, D. Tang, T. Chen, W. C. Chu, A QoS assurance middleware model for enterprise cloud computing, in Proc. IEEE 36th Int. Conf. on Computer Software and Application Workshops, pp , [18] P. Zhang, and Z. Yan, A QoS-aware system for mobile cloud computing, in Proc. of IEEE, pp , [19] M. Xu, L. Cui, H. Wang, Y. B. Bi, A Multiple QoS constrained scheduling strategy of multiple workflows from cloud computing, IEEE International Symposium on Parallel and Distributed Proceeding with Applications, pp , [20] Y. Xiao, C. Lin, Y. Yiang, X. Chu, X. Shen, Reputation-based QoS provisioning in cloud computing via Dirichlet multinomial model, IEEE ICC Proceedings, pp AUTHOR S BIOGRAPHY K.Syed Ibrahim is a research scholar in Bharathidasan University, Tiruchirappalli, TamilNadu, India. He has completed Master degree in Information Technology and M.Phil in computer Science. His research interest includes Cloud Computing, Mobile Cloud Computing, and Quality of Service etc. Dr. A.R.MohamedShanavas, presently working as Associate Professor in the Department of Computer Science, Jamal Mohamed College,Tiruchirappalli,India.He has published several research papers in reputed journals and presented many papers in National and International conferences. His research interest includes Cloud Computing, Image Processing and Data Mining. RES Publication 2012 Page 72