Abstract-Despite the advances in hardware for hand-held mobile devices, resource-intensive applications (e.g., video and image storage and processing or map-reduce type) still remain off bounds since they require large computation and storage capabilities. Recent research has attempted to address these issues by employing remote servers, such as clouds and peer mobile devices. For mobile devices deployed in dynamic networks (i.e., with frequent topology changes because of node failure/unavailability and mobility as in a mobile cloud), however, challenges of reliability and energy efficiency remain largely unaddressed. To the best of our knowledge, we are the first to address these challenges in an integrated manner for both data storage and processing in mobile cloud, an approach we call k-out-of-n computing. In our solution, mobile devices successfully retrieve or process data, in the most energy-efficient way, as long as k out of n remote servers are accessible. Through a real system implementation we prove the feasibility of our approach. Extensive simulations demonstrate the fault tolerance and energy efficiency performance of our framework in larger scale networks.
Recent advances in mobile technologies have enabled a plethora of new applications. The hardware capabilities of mobile devices, however, are still insufficient for real-time stream data processing (e.g., real-time video stream). In order to process real-time streaming data, most existing applications offload the data and computation to a remote cloud service, such as Apache Storm or Apache Spark Streaming. Offloading streaming data, however, has high costs for users, e.g., significant service fees and battery consumption. To address these challenges, we design, implement and evaluate Mobile Storm, the first stream processing platform for mobile clouds, leveraging clusters of local mobile devices to process real-time stream data. In Mobile Storm, we model the workflow of a real-time stream processing job and decompose it into several tasks so that the job can be executed concurrently and in a distributed manner on multiple mobile devices. Mobile Storm was implemented on Android phones and evaluated extensively through a real-time HD video processing application. The result shows that Mobile Storm effectively processes HD Video Stream in a mobile cloud, which would be impossible on a single mobile device. ii DEDICATION To my parents, sister and girl friend. iii Jay Chen for all he has done to help me with my thesis. Finally, I am very much grateful to my family for their constant support.
With the advance of mobile devices, cloud computing has enabled people to access data and computing resources without spatiotemporal constraints. A common assumption is that mobile devices are well connected to remote data centers and the data centers securely store and process data. However, for systems like mobile cloud deployed in infrastructureless dynamic networks (i.e., with frequent topology changes because of node failure/unavailability and mobility), reliability and energy efficiency remain largely unaddressed challenges. To address these issues, we develop the first "k-out-of-n computing" framework that ensures nodes retrieve or process data stored in mobile cloud with minimum energy consumption as long as k out of n storage/processing nodes are accessible. We demonstrate the feasibility and performance of our framework through both hardware implementation and extensive simulations.
Mobile Cloud Storage (MCS) systems -cloud storage on mobile devices without access to remote data center-type cloud resources, are not only interesting from a theoretical point of view, as they pose the most challenging design settings, but also important in enabling real-world applications such as disaster relief, military operation, and mining in remote areas. Central to MCS design is how to minimize the energy consumption of the battery-powered devices while still maintaining the data reliability and availability. Unfortunately, existing solutions do not model the energy-efficiency and data reliability of MCS in an integrated manner. Their formulations predominantly make use of heuristics, which may over-emphasize energy efficiency and not provide sufficient data reliability for some applications. In this paper, we design an energy-efficient distributed data storage framework in MCS under explicit data reliability requirement. The novel formulations produce a reliability-compliant and energy-efficient MCS system. The performance characteristics of our solutions are extensively evaluated through both real-world and synthetic mobility traces.
Abstract-The new generations of mobile devices have high processing power and storage, but they lag behind in terms of software systems for big data storage and processing. Hadoop is a scalable platform that provides distributed storage and computational capabilities on clusters of commodity hardware. Building Hadoop on a mobile network enables the devices to run data intensive computing applications without direct knowledge of underlying distributed systems complexities. However, these applications have severe energy and reliability constraints (e.g., caused by unexpected device failures or topology changes in a dynamic network). As mobile devices are more susceptible to unauthorized access, when compared to traditional servers, security is also a concern for sensitive data. Hence, it is paramount to consider reliability, energy efficiency and security for such applications. The MDFS (Mobile Distributed File System) [1] addresses these issues for big data processing in mobile clouds. We have developed the Hadoop MapReduce framework over MDFS and have studied its performance by varying input workloads in a real heterogeneous mobile cluster. Our evaluation shows that the implementation addresses all constraints in processing large amounts of data in mobile clouds. Thus, our system is a viable solution to meet the growing demands of data processing in a mobile environment.
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