A Survey On Quality Of Service

Published Date: 02 Nov 2017

Mobile Cloud Computing

Abstract - Mobile Cloud Computing (MCC) is an integration of cloud computing in the mobile environment which is used to improve the overall performance of the mobile devices. Mobile Cloud Computing faces a number of challenges both in mobile side and computing side, one of which is providing Quality-of Service (QoS). In this paper we present the survey of the current research related to QoS provision in mobile cloud computing.

The provision of QoS in mobile cloud computing is a challenging task. This is because of the dynamic characteristics of mobile networks and limited resources of mobile devices such as bandwidth, delay, packet loss ratio, battery life, storage capacity, computational power, security, etc. In this paper we discuss various QoS issues presented in MCC and discuss various approaches and architectures proposed to overcome the QoS issues. Finally the future research directions towards QoS provision in MCC also has been outlined.

Index Terms— Mobile cloud computing, quality-of-service, QoS.

Introduction

Nowadays, mobile devices such as Cell phones, Smartphone, Tablet pcs, etc are rapidly becoming an important part of human life as the most effective and convenient communication tools which are not restricted by time and place. Mobile users get various services from mobile applications (e.g., iPhone apps, Google apps, Android apps, etc), which run on the devices. After the invention of mobile cloud computing (MCC), these applications can be accessed by the mobile devices from remote servers (cloud servers) via wireless networks. The rapid progress of mobile computing (MC) [1] becomes a powerful trend in the development of IT technology as well as commerce and industry fields However, the mobile devices are facing many challenges in their resources (e.g., battery life, storage, and bandwidth) and communications (e.g., mobility and security) [2]. The limited resource significantly reduces quality of the services. Mobile Cloud Computing faces number of challenges both at mobile side and computing side, one of which is Quality-of Service (QoS). i.e., how a service provider can ensure QoS for its cloud services.

Mobile Cloud computing (MCC) has been recognized as the next generation’s computing infrastructure where both the data storage and the 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. Services can be accessed through mobile devices, such as smart phone, iphones, etc. It brings new types of services and facilities for mobile users to take full advantages of cloud computing.

This paper presents a comprehensive survey on quality-of-service provision in mobile cloud computing. Section II provides a brief overview of MCC including definition, architecture, advantages and its applications. Section III discusses the various QoS issues that presents in MCC. Section IV explains various existing frameworks which provide quality-of-service to mobile cloud computing environment. In Section V the future research directions towards QoS provision in MCC are outlined. Finally, we summarize and conclude the survey in section VI.

Overview of Mobile Cloud Computing

In this section we discuss the overview of mobile cloud computing that includes the definitions, architecture, advantages and applications.

A. What is Mobile Cloud Computing?

The Mobile Cloud Computing Forum defines MCC as follows [3]: "Mobile Cloud Computing at its simplest refers to an infrastructure where both the data storage and the 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 mobile computing to not just smartphone users but a much broader range of mobile subscribers".

Aepona [4] describes MCC as a new paradigm for mobile applications whereby the data processing and storage are moved from the mobile device to powerful and centralized computing platforms locate in clouds. These centralized applications are then accessed over the wireless connection based on a thin native client or web browser on the mobile devices.

Alternatively, MCC can be defined as a combination of mobile web and cloud computing [5], [6], which is the most popular tool for mobile users to access applications and services on the Internet.

Together with an explosive growth of the mobile applications and emerging of cloud computing concept, mobile cloud computing (MCC) has been introduced to be a potential technology for mobile services. A term "Mobile Cloud Computing" has been devised for a combination of cloud computing and mobile applications. In mobile cloud computing, data storage and data processing occurs outside the mobile device and results are displayed through screen or speakers. GPRS, Gmail, and Google Maps are already being used are pioneer examples of mobile cloud computing. In a few years time we can expect a major shift from traditional mobile application technology to mobile cloud computing.

Fig.1. Architecture of Mobile Cloud ComputingMobile cloud computing can give mobile device users a number of advantages. Company users are able to share resources and applications without a high level of capital expenditure on hardware and software resources. Due to the nature of cloud applications, users do not need to have highly technical hardware to use applications as complex computing operations are run within the cloud. This lessens the cost of mobile computing to the client. End users will see an excess of unique features enhancing their phones because of mobile cloud computing. A few examples of such applications can be seen to emerge such as applications that give users the ability to watch home security systems and others which allow users to create location based social networks.

B. Architectures of Mobile Cloud Computing:

The general architecture of MCC can be shown in Fig. 1. In Fig. 1, mobile devices are connected to the mobile networks via base stations (e.g., base transceiver station (BTS), access point, or satellite) that establish and control the connections (air links) and functional interfaces between the networks and mobile devices. Mobile users’ requests and information (e.g., ID and location) are transmitted to the central processors that are connected to servers providing mobile network services. Here, mobile network operators can provide services to mobile users as AAA (for authentication, authorization, and accounting) based on the home agent (HA) and subscribers’ data stored in databases. After that, the subscribers’ requests are delivered to a cloud through the Internet. In the cloud, cloud controllers process the requests to provide mobile users with the corresponding cloud services. These services are developed with the concepts of utility computing, virtualization, and service-oriented architecture (e.g., web, application, and database servers).

C. Advantages of Mobile Cloud Computing:

Cloud computing is known to be a promising solution for mobile computing due to many reasons (e.g., mobility, communication, and portability [7]). In the following, we describe how the cloud can be used to overcome obstacles in mobile computing, thereby pointing out advantages of MCC.

Extending battery lifetime:

Battery is one of the main concerns for mobile devices. Several solutions have been proposed to enhance the CPU performance [8], [9] and to manage the disk and screen in an intelligent manner [10], [11] to reduce power consumption. However, these solutions require changes in the structure of mobile devices, or they require a new hardware that results in an increase of cost and may not be feasible for all mobile devices. Computation offloading technique is proposed with the objective to migrate the large computations and complex processing from resource-limited devices (i.e., mobile devices) to resourceful machines (i.e., servers in clouds). This avoids taking a long application execution time on mobile devices which results in large amount of power consumption.

Improving data storage capacity and processing power:

Storage capacity is also a constraint for mobile devices. MCC is developed to enable mobile users to store/access the large data on the cloud through wireless networks. First example is the Amazon Simple Storage Service (Amazon S3) [12] which supports file storage service. Another example is Image Exchange which utilizes the large storage space in clouds for mobile users [13]. This mobile photo sharing service enables mobile users to upload images to the clouds immediately after capturing. Users may access all images from any devices. With cloud, the users can save considerable amount of energy and storage space on their mobile devices since all images are sent and processed on the clouds. Flickr [14] and ShoZu [15] are also the successful mobile photo sharing applications based on MCC. Facebook [16] is the most successful social network application today, and it is also a typical example of using cloud in sharing images.

Improving reliability:

Storing data or running applications on clouds is an effective way to improve the reliability since the data and application are stored and backed up on a number of computers. This reduces the chance of data and application lost on the mobile devices. In addition, MCC can be designed as a comprehensive data security model for both service providers and users. For example, the cloud can be used to protect copyrighted digital contents (e.g., video, clip, and music) from being abused and unauthorized distribution [17]. Also, the cloud can remotely provide to mobile users with security services such as virus scanning, malicious code detection, and authentication [18]. Also, such cloud-based security services can make efficient use of the collected record from different users to improve the effectiveness of the services.

Dynamic provisioning:

Dynamic on-demand provisioning of resources on a fine-grained, self-service basis is a flexible way for service providers and mobile users to run their applications without advanced reservation of resources.

Scalability:

The deployment of mobile applications can be performed and scaled to meet the unpredictable user demands due to flexible resource provisioning. Service providers can easily add and expand an application and service without or with little constraint on the resource usage.

Multi-tenancy:

Service providers (e.g., network operator and data center owner) can share the resources and costs to support a variety of applications and large number of users.

Ease of Integration:

Multiple services from different service providers can be integrated easily through the cloud and the Internet to meet the users’ demands.

D. Applications of Mobile Cloud Computing:

Mobile applications are a rapidly developing segment of the global mobile market. They consist of software that runs on a mobile device and perform certain tasks for the user of the mobile phone. As reported by World Mobile Applications Market, about 7 billion (free and paid) application downloads were made globally in 2009 alone from both native and third-party application stores, generating revenues of $3.9 billion in the same year. Various mobile applications have taken the advantages of MCC. In this section, some typical MCC applications are discussed.

Mobile Commerce

Mobile commerce (m-commerce) is a business model for commerce using mobile devices. The m-commerce applications generally fulfill some tasks that require mobility (e.g., mobile transactions and payments, mobile messaging, and mobile ticketing).

Mobile Learning

Mobile learning today is becoming more popular as there are many people using mobile devices to enhance their learning. Mobile learning (m-learning) is not only electronic learning (e-learning) but e-learning plus mobility. It is clear that learning via mobile brings many benefits for mobile users. It brings the convenience for them since they can learn anywhere they want in any convenient time from a portable device.

Mobile Healthcare

The development of telecommunication technology in the medical field helped diagnosis and treatment become easier for many people. This can helps patients regularly monitor their health and have timely treatment. Also, it leads to increase accessibility to healthcare providers, more efficient tasks and processes, and the improvement about quality of the healthcare services.

Mobile Gaming

Mobile game (m-game) is a potential market generating revenues for service providers. M-game can completely offload game engine requiring large computing resource (e.g., graphic rendering) to the server in the cloud, and gamers only interact with the screen interface on their devices.

QoS Issues of Mobile Cloud Computing

In MCC, mobile users need to access the servers located in a cloud when requesting services and resources in the cloud. However, the mobile users may face some problems such as congestion due to the limitation of wireless bandwidths, network delay, network disconnection, and the signal attenuation caused by mobile users’ mobility. They cause delays when users want to communicate with the cloud, so QoS is reduced significantly. This section lists several research issues in MCC, which are related to quality of service to the mobile cloud computing.

Low Bandwidth:

Bandwidth is one of the big issues in MCC since the radio resource for wireless networks is much scarce as compared with the traditional wired networks.

Availability:

Service availability becomes more important issue in MCC than that in the cloud computing with wired networks. Mobile users may not be able to connect to the cloud to obtain service due to traffic congestion, network failures, and the out-of-signal.

Unreliable physical channels:

Wireless channels are highly unreliable and have limited bandwidth. Wireless channels have high packet loss rate and bit error rate because of fading and multipath effects. The wireless medium is shared by multiple stations and the bandwidth allocation to one station will be affected by the neighboring stations. Because of the contention characteristics of the channels and MAC layer access methods, it is hard to provide the guaranteed end-to-end delays for the mobile device.

Node mobility:

Mobile devices are roaming and switching the wireless networks they connect to. To provide a continuous service, the mobile device should be able to connect to the wireless network that is available. For example, a mobile phone may switch from one cell covered by one base station to another cell covered by another based station, or switch from the cellular phone network to a Wireless LAN. The application should be able to provide seamless handoff among different wireless networks and provide an uninterrupted playback of the video with an acceptable QoS.

Routing

Because of the movement of the mobile devices, the topology of the mobile ad hoc networks varies dynamically. The existing routes may either not be available or not be able to support the QoS, which requires the changes of the routings. The selection of routes should be able to accommodate the changes of the topology and provide the QoS.

Resource constraints:

There are a number of limited resources on mobile devices, such as limited battery life, screen size, and input methods. The QoS is affected by the limited resources at the mobile devices, so the design of a mobile multimedia system should consider all those factors. The current battery technology is not evolving as fast as the memories and computer hardware. Both the processing and transmission of multimedia data consume power. With the limited power, it requires a power efficient design for both multimedia processing and transmission in mobile environment. The screen size of the mobile device is small, and mobile device is not equipped with full-size keyboards. All these limitations in the input and output pose many challenges in the design of the user interface.

Heterogeneity:

MCC will be used in the highly heterogeneous networks in terms of wireless network interfaces. Different mobile nodes access to the cloud through different radio access technologies such as WCDMA, GPRS, WiMAX, CDMA2000, and WLAN. As a result, an issue of how to handle the wireless connectivity while satisfying MCC’s requirements arises (e.g., always-on connectivity, on-demand scalability of wireless connectivity, and the energy efficiency of mobile devices).The heterogeneity of the mobile devices, access networks, and infrastructure networks makes the end-to-end QoS provision more difficult.

QoS Provision Architectures for Mobile Cloud Computing

In this section we discuss various existing approaches and frameworks proposed to overcome the quality-of-service issues of mobile cloud computing. Also discuss various existing works carried out towards QoS provision in mobile cloud computing.

Low Bandwidth:

Bandwidth is one of the big issues in MCC since the radio resource for wireless networks is much scarce when compared with the traditional wired networks. X. Jin and Y. K. Kwok [19] proposed a solution to share the limited bandwidth among mobile users who are located in the same area (e.g., a workplace, a station, and a stadium) and involved in the same content (e.g., a video file). The authors model the interaction among the users as a coalitional game. However, the proposed solution is only applied in the case when the users in a certain area are interested in the same contents. Also, it does not consider a distribution policy (e.g., who receives how much and which part of contents) which leads to a lack of fairness about each user’s contribution to a coalition.

In [20] E. Jung et al used the data distribution policy which determines when and how much portions of available bandwidth are shared among users from which networks (e.g., WiFi and WiMAX). It collects user profiles (e.g., calling profile, signal strength profile, and power profile) periodically and creates decision tables by using Markov Decision Process (MDP) algorithm. Based on the tables, the users decide whether or not to help other users download some contents that they cannot receive by themselves due to the bandwidth limitation, and how much it should help (e.g., 10% of contents). The authors build a framework, named RACE (Resource-Aware Collaborative Execution), on the cloud to take advantages of the computing resources for maintaining the user profiles. This approach is suitable for users who share the limited bandwidth, to balance the trade-off between benefits of the assistance and energy costs.

Availability:

Service availability becomes the most important issue in MCC than that in the cloud computing with wired networks. Mobile users may not be able to connect to the cloud to obtain service due to traffic congestion, network failures, and the out-of-signal.

G. Huerta-Canepa et al [21] and L. Zhang et al [22] proposed solutions to help mobile users in the case of the disconnection from clouds. In [21], the authors describe a discovery mechanism to find the nodes in the vicinity of a user whose link to cloud is unavailable. After detecting nearby nodes that are in a stable mode, the target provider for the application is changed. In this way, instead of having a link directly to the cloud, mobile user can connect to the cloud through neighboring nodes in an ad hoc manner. However, it does not consider the mobility, capability of devices, and privacy of neighboring nodes. [22] tried to overcome the drawbacks of [21]. In particular, [22] proposed a WiFi based multi-hop networking system called MoNet and a distributed content sharing protocol for the situation without any infrastructure.

Heterogeneity:

MCC will be used in the highly heterogeneous networks in terms of wireless network interfaces. A. Klein et al [23] proposed architecture to provide an intelligent network access strategy for mobile users to meet the application requirements. This architecture is built based on a concept of Intelligent Radio Network Access (IRNA) [24]. IRNA is an effective model to deal with the dynamics and heterogeneity of available access networks. To apply IRNA in MCC environment, the authors propose context management architecture (CMA) with the purpose to acquire, manage, and distribute context information.

Network Delay:

In MCC, mobile users need to access the servers located in a cloud when requesting services and resources in the cloud. Due to network delay the users unable to communicate with the cloud efficiently; it significantly reduced the QoS of MCC. Two new research directions are CloneCloud and Cloudlets that are expected to reduce the network delay.

CloneCloud brings the power of cloud computing to your smart phones [25]. CloneCloud uses onearby computers or data centers to increase the speed of running smart phone applications. The idea is to clone the entire set of data and applications from the smartphone onto the cloud and to selectively execute some operations on the clones, reintegrating the results back into the smartphone. One can have multiple clones for the same smartphone, and clones pretend to be more powerful smartphones, etc.

A cloudlet is a trusted, resource-rich computer or cluster of computers which is well-connected to the Internet and available for use by nearby mobile devices. Thus, when mobile devices do not want to offload to the cloud (maybe due to delay, cost, etc), they can find a nearby cloudlet. [26] builds an architecture through exploiting virtual machine technology to rapidly instantiate customized service software on a nearby cloudlet and then uses that service over a wireless LAN. This technology can help mobile users overcome the limits of cloud computing as WAN latency and low bandwidth.

Mobility Management:

Because of its roaming characteristic, a mobile device needs to switch over to different networks to maintain the pervasive and ubiquitous service. Recently, mobile devices have multiple wireless interfaces which enable them to access heterogeneous networks. Handover management determines which access network to switch to when there are multiple wireless networks in the vicinity. Handover can be categorized into horizontal handover [27] and vertical handover [28].

Some mobility management systems with the focus on vertical handover have been designed to support QoS of multimedia applications. Fernandes and Karmouch [28] proposed the Context-Aware Mobility Management System (CAMMS) for vertical mobility management. CAMMS is a cross-layer architecture where the handover decision is based on the information from at least two network layers, from the data link layer to the application layer. It considers the context information, power consumption, user preferences, and network conditions. The cross-layer handover scheme proposed in [29] tries to balance the load among different networks. It achieves the maximization of the overall system QoS and user perceived QoE by efficiently utilizing the available communication resources. Wu, Yang and Hwang [30] proposed a handover decision scheme using IEEE 802.21 [31] MIH services in WLAN and WiMAX networks to maintain nearly identical QoS in the handover.

QoS Enabled Architectures for Mobile Cloud Computing:

In this section we discuss about the various existing architectures proposed towards QoS provision in mobile cloud computing.

Peng Zhang and Zheng Yan [32] proposed a QoS framework for mobile cloud computing and an adaptive QoS management process to manage QoS assurance in mobile cloud computing environment also presented a QoS management model based on Fuzzy Cognitive Map (FCM).

Fig 2. QoS framework for mobile cloud computing

Fig.2. shows a QoS management framework for mobile cloud computing. In a mobile device, a QoS agent monitors QoS status at run time, e.g., percentage of memory and CPU consumption, connection speed, remaining battery percentage and packet loss rate, etc. The QoS status will be reported to a QoS management center in cloud side. The QoS management center aggregates and analyzes the huge set of QoS data, and dynamically adjusts resources to meet QoS requirements of each mobile cloud service.

Peng Zhang and Zheng [32] Yan also proposed a self adaptive QoS management process for mobile cloud services QoS Predication is an mechanism to predict performance of a set of cloud service modes before selecting a service mode. Mode selection is a mechanism to select the best service mode based on previous prediction results. QoS Assessment is a mechanism to monitor and assess the QoS status according to users’ QoS requirements. For the QoS requirements of a service, the QoS values can be predicted by assuming a service mode is selected. Based on prediction results, a service mode can be selected and set as system configuration. The QoS assessment mechanism evaluates the QoS status by monitoring the performance of the cloud service. According to the assessment results, the system adjusts the parameters of QoS control model to reflect real status. The adjustment happens when the evaluation result is below a threshold that is defined by users. The process runs over to achieve the self adaptive QoS management in the dynamic mobile cloud environment. In [32] apply Fuzzy Cognitive Map [FCM] to model the factors considered in adaptive QoS management.

Lodi et al [33] proposed middle-ware architecture for enabling Service Level Agreement (SLA)-driven clustering of QoS-aware application servers. That middleware architecture supports application server technologies with dynamic resource management: Application servers can dynamically change the amount of clustered resources assigned to hosted applications on-demand so as to meet application-level Quality of Service (QoS) requirements. These requirements can include timeliness, availability, and high throughput and are specified in SLAs.

In [33] the authors designed a middleware architecture incorporating three principle QoS-aware middleware services: a Configuration Service, a Monitoring Service, and a Load Balancing Service. The services were developed to minimize their interdependency with specific J2EE implementations and maximize the portability of our software architecture. This architecture is designed to be deployed in a cluster of application servers. The cluster consists of application server instances (termed nodes). Each node hosts a replica of the above services and the architecture implements a primary-backup replication scheme [11] for fault-tolerance purposes. The QoS-aware middleware services cooperate with each other to ensure hosting SLA enforcement and monitoring. Fig. 3 shows how the clients interact with each other.

Fig. 3. QoS-aware middleware services interaction

In Fig. 3, client requests are intercepted by the Load Balancing Service. For each request, the QoS delivered by the cluster is compared to the desired level of QoS specified in the hosting SLA in order to monitor adherence to this SLA. To this end, the Configuration Service makes the hosting SLA content available to the Monitoring Service. The Monitoring Service cooperates with the Load Balancing Service to obtain the QoS delivered by the cluster. Based on the retrieved QoS data, the Monitoring Service computes and updates the monitoring parameters, which serve to check whether the cluster operational conditions are close to violating the hosting SLA. Hence, the Monitoring Service first monitors the SLA Client Responsibilities of the hosting SLA. If clients send a higher number of requests than that allowed, clients are violating the SLA.

Wang et al [34] proposed an adaptive QoS management framework for VoD (Video On Demand) cloud service centers. The VoD cloud service center can attract service providers by offering them the opportunity for reducing or eliminating costs associated with provision of streaming media services. When users on the Internet visit the VoD cloud Service Center, they will be charged for the media content they accessed in a "pay-as-you-go" manner. However, in another point of view, it is essential that the providers should promise guarantees on service delivery so that the users would like to pay for the service due to its high quality. Since users are sending requests continuously from time to time, VoD service providers need methodologies and mechanisms for managing run-time QoS. Fig. 4 shows the architecture of the VoD Cloud Service Center.

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Fig. 4 Architecture of the VoD Cloud Service Center

As shown in Fig. 4, the solid round-comer rectangle depicts a VoD cloud service center comprised of clustered streaming media servers, which delivers video-on-demand services continuously to the users on the Internet. The architecture uses different kind of nodes such as, storage nodes, deliver nodes and control nodes. All nodes of the cluster can communicate with each other simultaneously through the high-speed interconnection network.

Ye et al [35] proposed a framework for handling QoS and power management (QPM) for mobile devices in the service cloud environment. In this framework, the service QoS profiles capturing the services’ QoS and power behaviors and user profiles capturing users’ service usage patterns are defined. Based on the information, service QoS behaviors and power consumption patterns can be predicted to facilitate decisions regarding whether to run a service locally or remotely and how to configure the mobile device such that the power usage can be minimized without violating QoS requirements. The QPM framework is illustrated in Fig. 5.

Fig. 5. QPM framework

QPM framework that tries to minimize the power consumption on the mobile device while satisfying the QoS requirements through the coordination of the mobile device and the service cloud.

Li [36] proposed a VM-based architecture for adaptive management of virtualized resources in cloud computing using feedback control and model an adaptive controller that dynamically adjusts multiple virtualized resources utilization to achieve application Service Level Objective (SLO) in cloud computing. Feedback control offers a conceptual tool to address the dynamic of resource management, especially

Fig.6. VM-based Architecture Adaptive Management of Virtualized Resources in Cloud Computing

unpredictable changes and disturbances in workloads and configuration and is emerging as a viable approach for the design of self-managed and autonomic computing systems. The complete architecture [8] is depicted in Fig. 6.

Xiao [37] proposed a reputation-based QoS provisioning in cloud computing via Dirichlet multinomial model, which can minimize the cost of computing resources, while satisfying the desired QoS metrics. A typical reputation-based QoS provisioning architecture is shown in Fig.7.

Fig.7. Reputation-based QoS Provisioning Architecture

When a service provider receives a service request to make QoS provisioning, he first enable the service broker to discover available Cloud computing service resources, then the service selector is enabled to select appropriate service from all the available service sets according to some metrics such as service cost, response time and reputation to make resource provisioning. If users’ requirements can be satisfied by the available services, the service provider will inform the task dispatcher to be ready to dispatch the user’s task, and at the same time service monitor will monitor the status of service execution. When the service providers accomplish the designated tasks, they send the results back to the users. The billing service is in charge of the fee according to used resources per unit time. The reputation management service is in charge of updating the reputation value of services according to the users’ feedback and other reports constantly and providing decision service for service selectors.

Yan [38] proposed an adaptive trust control model to specify, evaluate, establish, and ensure the trust relationships among system entities. This model concerns the quality attributes of the entity and a number of trust control modes supported by the system. In particular, its parameters can be adaptively adjusted based on runtime trust assessment in order to reflect real system context and situation. In [38] the author applied several trust control modes, each containing a number of control mechanisms or operations. The trust control mode can be treated as a special configuration of trust management provided by the system. In this procedure, trustworthiness prediction is a mechanism to anticipate the performance or feasibility of applying control modes. It predicts the trustworthiness values supposing application of some control modes before deciding to initiate them. Trust control mode selection is a mechanism to select the most suitable trust control modes based on the prediction results. Trust assessment is conducted based on the trustor’s criteria by evaluating the trustee entity’s quality attributes. Particularly, the quality attributes of the entity can be controlled or improved by applying a number of trust control modes, especially at system runtime.

Future Research Directions

We have discussed several research works carried out towards the development of quality-of-service provision in MCC in the previous section. However, there are still some issues which need to be addressed and improved. In this section we discuss possible research directions towards the development of quality-of-service provision in MCC and also the future research direction of general MCC issues.

In [32] the authors proposed an adaptive QoS management system for mobile cloud computing. This solution facilitates QoS prediction, establishment, assessment and assurance. They introduced the influence of QoS properties and service modes into the model, which supports adaptive QoS management according to QoS assessment based on runtime QoS observation. They applied the FCM theory into QoS management. They used FCM concept to predict the performance of cloud service modes in order to select the best one. Some cases the predicted services will not be efficient. So we have to develop a new approach which exactly chooses the QoS service models. In future the neural networks concept can be replaced by FCM, in which the QoS service model can be evaluated against the client requirement and the exact model can be chosen.

We have seen many issues related to QoS provision in MCC and various possible architectures to solve these issues. Still there are many general issues which are possible in MCC and various research directions in development of MCC. Some issues like Mobile devices has limited storage and processing capacity so work in direction of to how efficient use of these limited resources can be performed for cloud computing can be done. Various operating systems are available for mobile devices like Android, Symbian, and Chrome etc. So work related to does a general access platform for mobile cloud computing is possible on top of these various operating system platform can be done. In future the research related to security can be done as there are various security threats both inside and outside the cloud. Mobility is the general issue Because of user mobility, the wide diversity of applications, and the varying wireless channel status, the mobile cloud computing context is highly dynamic. Future research should therefore focus on the design of a comprehensive framework that integrates the existing solutions and activates the most appropriate one depending on the current device, network, and cloud server status.

Conclusion

Mobile cloud computing is the emerging mobile technology for future because it integrates the advantages of both mobile computing and cloud computing. Mobile Cloud Computing refers to an infrastructure where both data processing and data storage can happen outside the mobile device. Mobile devices do not need to have powerful configurations like high speed processor or large storage space because all processing is performed on the cloud that is outside the mobile device. User can access the services via mobile devices. It is expected that more than 240 million businesses will be going to use cloud computing on their mobile devices by 2015. This paper has provided an overview of mobile cloud computing including its definitions, architecture, advantages and applications. The various QoS issues presented in MCC has also been summarized. Next the various architectures and approaches proposed to overcome the QoS issues have been discussed. Finally the future research directions towards QoS provision in MCC has been outlined.