Multimedia Storage System Providing QoS in Cloud Based Environment

Cloud computing and mobile computing are moderately new trend in Information Technology which are growing rapidly. Mobile devices are replacing personal computers by joining large mobile networks and are effortlessly switching between different network providers. Currently, to maintain network connectivity all the time different service handover mechanisms is used so that cloud services can be accessed by user without any interruption. In this scenario, if user mobility is considered, then he is connected to its local cloud to access the different cloud services. As user is moving from one geographical location to another because of this mobility factor network congestion increases which causes degradation in QoS. For this reason a framework is introduced which will deliver services to the users to improve QoS in order to provide better QoE to the clients. In this paper, we are further developing this framework in which an algorithm is designed in service delivery layer which will help for better solution to the efficient management of network resources while providing a high QoE for the clients. And as the demand for specific services increases in a location, using this framework it will be more efficient to move those services closer to that location. This framework will help to reduce high traffic loads due to multimedia streams and will offer service providers an automated resource allocation and management mechanism for their services

Biometric Identification and Authentication Providence using Fingerprint for Cloud Data Access

The raise in the recent security incidents of cloud computing and its challenges is to secure the data. To solve this problem, the integration of mobile with cloud computing, Mobile biometric authentication in cloud computing is presented in this paper. To enhance the security, the biometric authentication is being used, since the Mobile cloud computing is popular among the mobile user. This paper examines how the mobile cloud computing (MCC) is used in security issue with finger biometric authentication model. Through this fingerprint biometric, the secret code is generated by entropy value. This enables the person to request for accessing the data in the desk computer. When the person requests the access to the authorized user through Bluetooth in mobile, the Authorized user sends the permit access through fingerprint secret code. Finally this fingerprint is verified with the database in the Desk computer. If it is matched, then the computer can be accessed by the requested person

Research on Mobile Cloud Computing in Teaching and Learning: A Conceptual Model

Advances in cloud computing technology coupled with increasing volumes of data has driven the growth and differentiation of cloud-based solutions in teaching and learning. The cloud computing industry has matured over the past decade and the number of publications steadily rose, to build on the maturity of the field researchers investigating cloud computing research in the mobile teaching and learning domain need to be cognisant of the state of the art. The objective of this paper is to analyse the available literature in the field of cloud computing for mobile teaching and learning to identify the main categories of research, the prevalent methodologies and research gaps, and then integrate the findings in a conceptual framework representing the current state of the field in terms of research opportunities. A systematic mapping study on relevant publications in journals and conferences was conducted. Mapping studies are a suitable method for structuring a research field concerning research questions about contents, methods and trends in the available publications. A systematic literature review and mapping was used to select 107 articles from a total of 21 822 publications in five prominent databases, namely ACM, ERIC, IEEE, Google Scholar and Springer. The analysis was done in October 2017 on papers published between 2013 and 2017. The contribution is to classify existing work and suggest future opportunities based on a systematic mapping of mobile cloud computing (MCC) for teaching and learning research. The analysis provides an overview of the field in terms of what is researched, how that is researched and where the future research contributions may lie. The findings are integrated to present a non-prescriptive, conceptual framework on mobile cloud computing research for teaching and learning. Researchers can use the proposed framework as a point of reference in starting or aligning their own projects and establishing where future research opportunities exist.School of Computin

Energy Efficiency Multi task Offloading and Resource Allocation in Mobile Edge Computing

On edge computing, mobile devices can offload some computing intensive tasks to the cloud so that the time delay and battery losses can be reduced. Different from cloud computing, an edge computing model is under the constraint of radio transmitting bandwidth, power and etc. With regard to most models in presence, each user is assigned to a single mission, transmitting power or local CPU frequency on mobile terminals is deemed to be a constant. Furthermore, energy consumption has a positive correlation with the above two parameters. In a context of multitask, such values could be increased or reduced according to workload to save energy. Additionally, the existing offloading methods are inappropriate if all the compute densities of multiple tasks are high. In this paper, a single-user multi-task with high computing density model is proposed and partial task is offloaded when use the different offload algorithm. Simulated annealing algorithm is the best method to select offloading tasks, which can enhance the offloading ratio and save energy consumption

Live Prefetching for Mobile Computation Offloading

The conventional designs of mobile computation offloading fetch user-specific data to the cloud prior to computing, called offline prefetching. However, this approach can potentially result in excessive fetching of large volumes of data and cause heavy loads on radio-access networks. To solve this problem, the novel technique of live prefetching is proposed in this paper that seamlessly integrates the task-level computation prediction and prefetching within the cloud-computing process of a large program with numerous tasks. The technique avoids excessive fetching but retains the feature of leveraging prediction to reduce the program runtime and mobile transmission energy. By modeling the tasks in an offloaded program as a stochastic sequence, stochastic optimization is applied to design fetching policies to minimize mobile energy consumption under a deadline constraint. The policies enable real-time control of the prefetched-data sizes of candidates for future tasks. For slow fading, the optimal policy is derived and shown to have a threshold-based structure, selecting candidate tasks for prefetching and controlling their prefetched data based on their likelihoods. The result is extended to design close-to-optimal prefetching policies to fast fading channels. Compared with fetching without prediction, live prefetching is shown theoretically to always achieve reduction on mobile energy consumption.Comment: To appear in IEEE Trans. on Wireless Communicatio