MADServer: An Architecture for Opportunistic Mobile Advanced Delivery

Rapid increases in cellular data traffic demand creative alternative delivery vectors for data. Despite the conceptual attractiveness of mobile data offloading, no concrete web server architectures integrate intelligent offloading in a production-ready and easily deployable manner without relying on vast infrastructural changes to carriers’ networks. Delay-tolerant networking technology offers the means to do just this. We introduce MADServer, a novel DTN-based architecture for mobile data offloading that splits web con- tent among multiple independent delivery vectors based on user and data context. It enables intelligent data offload- ing, caching, and querying solutions which can be incorporated in a manner that still satisfies user expectations for timely delivery. At the same time, it allows for users who have poor or expensive connections to the cellular network to leverage multi-hop opportunistic routing to send and receive data. We also present a preliminary implementation of MADServer and provide real-world performance evaluations

An SOA-Based Framework of Computational Offloading for Mobile Cloud Computing

Mobile Computing is a technology that allows transmission of audio, video, and other types of data via a computer or any other wireless-enabled device without having to be connected to a fixed physical link. Despite increasing usage of mobile computing, exploiting its full potential is difficult due to its inherent problems such as resource scarcity, connection instability, and limited computational power. In particular, the advent of connecting mobile devices to the internet offers the possibility of offloading computation and data intensive tasks from mobile devices to remote cloud servers for efficient execution. This proposed thesis develops an algorithm that uses an objective function to adaptively decide strategies for computational offloading according to changing context information. By following the style of Service-Oriented Architecture (SOA), the proposed framework brings cloud computing to mobile devices for mobile applications to benefit from remote execution of tasks in the cloud. This research discusses the algorithm and framework, along with the results of the experiments with a newly developed system for self-driving vehicles and points out the anticipated advantages of Adaptive Computational Offloading

A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing

Edge computing is promoted to meet increasing performance needs of data-driven services using computational and storage resources close to the end devices, at the edge of the current network. To achieve higher performance in this new paradigm one has to consider how to combine the efficiency of resource usage at all three layers of architecture: end devices, edge devices, and the cloud. While cloud capacity is elastically extendable, end devices and edge devices are to various degrees resource-constrained. Hence, an efficient resource management is essential to make edge computing a reality. In this work, we first present terminology and architectures to characterize current works within the field of edge computing. Then, we review a wide range of recent articles and categorize relevant aspects in terms of 4 perspectives: resource type, resource management objective, resource location, and resource use. This taxonomy and the ensuing analysis is used to identify some gaps in the existing research. Among several research gaps, we found that research is less prevalent on data, storage, and energy as a resource, and less extensive towards the estimation, discovery and sharing objectives. As for resource types, the most well-studied resources are computation and communication resources. Our analysis shows that resource management at the edge requires a deeper understanding of how methods applied at different levels and geared towards different resource types interact. Specifically, the impact of mobility and collaboration schemes requiring incentives are expected to be different in edge architectures compared to the classic cloud solutions. Finally, we find that fewer works are dedicated to the study of non-functional properties or to quantifying the footprint of resource management techniques, including edge-specific means of migrating data and services.Comment: Accepted in the Special Issue Mobile Edge Computing of the Wireless Communications and Mobile Computing journa

Separation Framework: An Enabler for Cooperative and D2D Communication for Future 5G Networks

Soaring capacity and coverage demands dictate that future cellular networks need to soon migrate towards ultra-dense networks. However, network densification comes with a host of challenges that include compromised energy efficiency, complex interference management, cumbersome mobility management, burdensome signaling overheads and higher backhaul costs. Interestingly, most of the problems, that beleaguer network densification, stem from legacy networks' one common feature i.e., tight coupling between the control and data planes regardless of their degree of heterogeneity and cell density. Consequently, in wake of 5G, control and data planes separation architecture (SARC) has recently been conceived as a promising paradigm that has potential to address most of aforementioned challenges. In this article, we review various proposals that have been presented in literature so far to enable SARC. More specifically, we analyze how and to what degree various SARC proposals address the four main challenges in network densification namely: energy efficiency, system level capacity maximization, interference management and mobility management. We then focus on two salient features of future cellular networks that have not yet been adapted in legacy networks at wide scale and thus remain a hallmark of 5G, i.e., coordinated multipoint (CoMP), and device-to-device (D2D) communications. After providing necessary background on CoMP and D2D, we analyze how SARC can particularly act as a major enabler for CoMP and D2D in context of 5G. This article thus serves as both a tutorial as well as an up to date survey on SARC, CoMP and D2D. Most importantly, the article provides an extensive outlook of challenges and opportunities that lie at the crossroads of these three mutually entangled emerging technologies.Comment: 28 pages, 11 figures, IEEE Communications Surveys & Tutorials 201