User experience of mobile cloud applications - current state and future directions

The increasing penetration rate of feature rich mobile devices such as smartphones and tablets in the global population has resulted in a large number of applications and services being created or modified to support mobile devices. Mobile cloud computing is a proposed paradigm to address the resource scarcity of mobile devices in the face of demand for more computing intensive tasks. Several approaches have been proposed to confront the challenges of mobile cloud computing, but none has used the user experience as the primary focus point. In this paper we evaluate these approaches in respect of the user experience, propose what future research directions in this area require to provide for this crucial aspect, and introduce our own solution

Integrating mobile and cloud resources management using the cloud personal assistant

The mobile cloud computing model promises to address the resource limitations of mobile devices, but effectively implementing this model is difficult. Previous work on mobile cloud computing has required the user to have a continuous, high-quality connection to the cloud infrastructure. This is undesirable and possibly infeasible, as the energy required on the mobile device to maintain a connection, and transfer sizeable amounts of data is large; the bandwidth tends to be quite variable, and low on cellular networks. The cloud deployment itself needs to efficiently allocate scalable resources to the user as well. In this paper, we formulate the best practices for efficiently managing the resources required for the mobile cloud model, namely energy, bandwidth and cloud computing resources. These practices can be realised with our mobile cloud middleware project, featuring the Cloud Personal Assistant (CPA). We compare this with the other approaches in the area, to highlight the importance of minimising the usage of these resources, and therefore ensure successful adoption of the model by end users. Based on results from experiments performed with mobile devices, we develop a no-overhead decision model for task and data offloading to the CPA of a user, which provides efficient management of mobile cloud resources

The cloud personal assistant for providing services to mobile clients

This paper introduces the original concept of a cloud personal assistant, a cloud service that manages the access of mobile clients to cloud services. The cloud personal assistant works in the cloud on behalf of its owner: it discovers services, invokes them, stores the results and history, and delivers the results to the mobile user immediately or when the user requests them. Preliminary experimental results that demonstrate the concept are included

Mobile cloud contextual awareness with the cloud personal assistant

This paper presents our efforts to bridge the gap between mobile context awareness, and mobile cloud services, using the Cloud Personal Assistant (CPA). The CPA is a part of the Context Aware Mobile Cloud Services (CAMCS) middleware, which we continue to develop. Specifically, we discuss the development and evaluation of the Context Processor component of this middleware. This component collects context data from the mobile devices of users, which is then provided to the CPA of each user, for use with mobile cloud services. We discuss the architecture and implementation of the Context Processor, followed by the evaluation. We introduce context profiles for the CPA, which influence its operation by using different context types. As part of the evaluation, we present two experimental context-aware mobile cloud services to illustrate how the CPA works with user context, and related context profiles, to complete tasks for the user

Context aware mobile cloud services: a user experience oriented middleware for mobile cloud computing

Existing research on implementing the mobile cloud computing paradigm is typically based on offloading demanding computation from mobile devices to cloud-based servers. A continuous, high quality connection to the cloud infrastructure is normally required, with frequent high-volume data transfer, which can have a detrimental impact on the user experience of the application or service. In this paper, the Context Aware Mobile Cloud Services (CAMCS) middleware is presented as a solution that can deliver an integrated user experience of the mobile cloud to users. Such an experience respects the resource limitations of the mobile device. This is achieved by the Cloud Personal Assistant (CPA), the user’s trusted representative within CAMCS, which completes user-assigned tasks using existing cloud-based services, with an asynchronous, disconnected approach. A thin client mobile application, the CAMCS Client, allows the mobile user to send descriptions of tasks to his/her CPA, and view task results saved at the CPA, when convenient. The design and implementation of the middleware is presented, along with results of experimental evaluation on Amazon EC2. The resource usage of the CAMCS client is also studied. Analysis shows that CAMCS delivers an integrated user experience of mobile cloud applications and services

A modified Muskingum routing approach for floodplain flows: theory and practice

Hydrological or hydraulic flood routing methods can be used to predict the floodplain influences on a flood wave as it passes along a river reach. While hydraulic routing uses both the equation of continuity and the equation of momentum to describe the dynamics of river flows, the simpler data requirements of hydrological routing makes it useful for preliminary estimates of the time and shape of a flood wave at successive points along a river. This paper presents a modified linear Muskingum hydrological routing method where the floodplain effects on flood peak attenuation and flood wave travel time are included in routing parameters. Developing the routing parameters initially involved routing hydrographs of different flood peak and duration through a 1-dimensional model of a generalised river reach in which a range of geometrical and resistance properties were varied. Comparison of upstream and simulated downstream hydrographs for each condition investigated, allowed the attenuation and travel time (storage constant, K, in standard Muskingum routing) of the flood wave to be estimated. Standard Muskingum 1 routing was then used to develop downstream hydrographs for each K value together with assumed storage weighting factors (x) ranging from 0 to 0.5. Flood peak attenuations were again determined through comparison of the upstream and routed downstream hydrographs and with these, linear relationships between x and these attenuations were developed. Actual weighting factors, corresponding to storage constants, were subsequently determined using these relationships for all attenuations determined from the 1-dimensional model simulations. Using multi-variate regression analysis, the computed values of K and x were correlated to catchment and hydrograph properties and expressions for determining both K and x in terms of these properties were developed. The modified Muskingum routing method based on these regressed expressions for K and x was applied to a case study of the River Suir in Ireland where good agreement between measured and routed hydrographs was observed.Deposited by bulk importTS 11.02.1

Investigation into a best practice model for providing an integrated user experience with mobile cloud applications

Mobile Cloud Computing promises to overcome the physical limitations of mobile devices by executing demanding mobile applications on cloud infrastructure. In practice, implementing this paradigm is difficult; network disconnection often occurs, bandwidth may be limited, and a large power draw is required from the battery, resulting in a poor user experience. This thesis presents a mobile cloud middleware solution, Context Aware Mobile Cloud Services (CAMCS), which provides cloudbased services to mobile devices, in a disconnected fashion. An integrated user experience is delivered by designing for anticipated network disconnection, and low data transfer requirements. CAMCS achieves this by means of the Cloud Personal Assistant (CPA); each user of CAMCS is assigned their own CPA, which can complete user-assigned tasks, received as descriptions from the mobile device, by using existing cloud services. Service execution is personalised to the user's situation with contextual data, and task execution results are stored with the CPA until the user can connect with his/her mobile device to obtain the results. Requirements for an integrated user experience are outlined, along with the design and implementation of CAMCS. The operation of CAMCS and CPAs with cloud-based services is presented, specifically in terms of service description, discovery, and task execution. The use of contextual awareness to personalise service discovery and service consumption to the user's situation is also presented. Resource management by CAMCS is also studied, and compared with existing solutions. Additional application models that can be provided by CAMCS are also presented. Evaluation is performed with CAMCS deployed on the Amazon EC2 cloud. The resource usage of the CAMCS Client, running on Android-based mobile devices, is also evaluated. A user study with volunteers using CAMCS on their own mobile devices is also presented. Results show that CAMCS meets the requirements outlined for an integrated user experience