Efficient multimedia data storage in cloud environment

With the rapid adoption of social media, people are more habituated to utilize the images and video for expressing themselves. Future communication will replace the conventional means of social interaction with the video or images. This, in turn, requires huge data storage and processing power. This paper reports a compression/decompression module for image and video sequences for the cloud computing environment. The reported mechanism acts as a submodule of IaaS layer of the cloud. The compression of the images is achieved using redundancy removal using block matching algorithm. The proposed module had been evaluated with three different video compression algorithms and variable macroblock size. The experimentations has been carried out on a cloud host environment by using VMWarework station platform. Apart from being simple in execution, the proposed module does not incur an additional monetary burden, hardware or manpower to achieve the desired compression of the image data. Experimental analysis has shown a considerable reduction in data storage requirement as well as the processing time.Web of Science39444243

On Optimal and Fair Service Allocation in Mobile Cloud Computing

This paper studies the optimal and fair service allocation for a variety of mobile applications (single or group and collaborative mobile applications) in mobile cloud computing. We exploit the observation that using tiered clouds, i.e. clouds at multiple levels (local and public) can increase the performance and scalability of mobile applications. We proposed a novel framework to model mobile applications as a location-time workflows (LTW) of tasks; here users mobility patterns are translated to mobile service usage patterns. We show that an optimal mapping of LTWs to tiered cloud resources considering multiple QoS goals such application delay, device power consumption and user cost/price is an NP-hard problem for both single and group-based applications. We propose an efficient heuristic algorithm called MuSIC that is able to perform well (73% of optimal, 30% better than simple strategies), and scale well to a large number of users while ensuring high mobile application QoS. We evaluate MuSIC and the 2-tier mobile cloud approach via implementation (on real world clouds) and extensive simulations using rich mobile applications like intensive signal processing, video streaming and multimedia file sharing applications. Our experimental and simulation results indicate that MuSIC supports scalable operation (100+ concurrent users executing complex workflows) while improving QoS. We observe about 25% lower delays and power (under fixed price constraints) and about 35% decrease in price (considering fixed delay) in comparison to only using the public cloud. Our studies also show that MuSIC performs quite well under different mobility patterns, e.g. random waypoint and Manhattan models