Social-aware hybrid mobile offloading

Mobile offloading is a promising technique to aid the constrained resources of a mobile device. By offloading a computational task, a device can save energy and increase the performance of the mobile applications. Unfortunately, in existing offloading systems, the opportunistic moments to offload a task are often sporadic and short-lived. We overcome this problem by proposing a social-aware hybrid offloading system (HyMobi), which increases the spectrum of offloading opportunities. As a mobile device is always co- located to at least one source of network infrastructure throughout of the day, by merging cloudlet, device-to-device and remote cloud offloading, we increase the availability of offloading support. Integrating these systems is not trivial. In order to keep such coupling, a strong social catalyst is required to foster user's participation and collaboration. Thus, we equip our system with an incentive mechanism based on credit and reputation, which exploits users' social aspects to create offload communities. We evaluate our system under controlled and in-the-wild scenarios. With credit, it is possible for a device to create opportunistic moments based on user's present need. As a result, we extended the widely used opportunistic model with a long-term perspective that significantly improves the offloading process and encourages unsupervised offloading adoption in the wild

Cloudlet computing : recent advances, taxonomy, and challenges

A cloudlet is an emerging computing paradigm that is designed to meet the requirements and expectations of the Internet of things (IoT) and tackle the conventional limitations of a cloud (e.g., high latency). The idea is to bring computing resources (i.e., storage and processing) to the edge of a network. This article presents a taxonomy of cloudlet applications, outlines cloudlet utilities, and describes recent advances, challenges, and future research directions. Based on the literature, a unique taxonomy of cloudlet applications is designed. Moreover, a cloudlet computation offloading application for augmenting resource-constrained IoT devices, handling compute-intensive tasks, and minimizing the energy consumption of related devices is explored. This study also highlights the viability of cloudlets to support smart systems and applications, such as augmented reality, virtual reality, and applications that require high-quality service. Finally, the role of cloudlets in emergency situations, hostile conditions, and in the technological integration of future applications and services is elaborated in detail. © 2013 IEEE

Adaptive Q-learning-supported Resource Allocation Model in Vehicular Fogs

Urban computing has become a significant driver in supporting the delivery and sharing of services, being a strong ally to intelligent transportation. Smart vehicles present computing and communication capabilities that allow them to enable many autonomous vehicular safety and infotainment applications. Vehicular Cloud Computing (VCC) has already proven to be a technology shifting paradigm harnessing the computation resources from on board units from vehicles to form clustered computing units to solve real world computing problems. However, with the rise of vehicular application use and intermittent network conditions, VCC exhibits many drawbacks. Vehicular Fog computing appears as a new paradigm in enabling and facilitating efficient service and resource sharing in urban environments. Several vehicular resource management works have attempted to deal with the highly dynamic vehicular environment following diverse approaches, e.g. MDP, SMDP, and policy-based greedy techniques. However, the high vehicular mobility causes several challenges compromising consistency, efficiency, and quality of service. RL-enabled adaptive vehicular Fogs can deal with the mobility for properly distributing load and resources over Fogs. Thus, we propose a mobility-based cloudlet dwell time estimation method for accurately estimating vehicular resources in a Fog. Leveraging the CDT estimation model, we devise an adaptive and highly dynamic resource allocation model using mathematical formula for Fog selection, and reinforcement learning for iterative review and feedback mechanism for generating optimal resource allocation policy

SAMI: Service-Based Arbitrated Multi-Tier Infrastructure for Mobile Cloud Computing

Mobile Cloud Computing (MCC) is the state-ofthe- art mobile computing technology aims to alleviate resource poverty of mobile devices. Recently, several approaches and techniques have been proposed to augment mobile devices by leveraging cloud computing. However, long-WAN latency and trust are still two major issues in MCC that hinder its vision. In this paper, we analyze MCC and discuss its issues. We leverage Service Oriented Architecture (SOA) to propose an arbitrated multi-tier infrastructure model named SAMI for MCC. Our architecture consists of three major layers, namely SOA, arbitrator, and infrastructure. The main strength of this architecture is in its multi-tier infrastructure layer which leverages infrastructures from three main sources of Clouds, Mobile Network Operators (MNOs), and MNOs' authorized dealers. On top of the infrastructure layer, an arbitrator layer is designed to classify Services and allocate them the suitable resources based on several metrics such as resource requirement, latency and security. Utilizing SAMI facilitate development and deployment of service-based platform-neutral mobile applications.Comment: 6 full pages, accepted for publication in IEEE MobiCC'12 conference, MobiCC 2012:IEEE Workshop on Mobile Cloud Computing, Beijing, Chin

A LITERATURE STUDY ON VARIOUS FACTORS AFFECTING COMPUTATIONAL OFFLOADING PERFORMANCE

Mobile systems, such as smart phones, tablets, have become the primary resources of computation for many users. Many surveys have shown that longer battery lifetime as the most important feature of such systems.  Consumers spend more time on media through mobile applications. Many mobile applications are too computation intensive to perform on a mobile system such as games, image processing and many more. The hardware limitations of mobile devices for higher performance and/or energy savings can be addressed by offloading computationally intensive task to external resource.  There are many issues associated with computation offloading such as network bandwidth, intermittent connectivity, the transmission delays, the distance of remote computing resources from primary computing resource. This paper presents a literature study on research work done till date with respect to computation offloading strategies developed to overcome these challenges

Time-Optimized Task Offloading Decision Making in Mobile Edge Computing

Mobile Edge Computing application domains such as vehicular networks, unmanned aerial vehicles, data analytics tasks at the edge and augmented reality have recently emerged. Under such domains, while mobile nodes are moving and have certain tasks to be offloaded to Edge Servers, choosing an appropriate time and an ideally suited server to guarantee the quality of service can be challenging. We tackle the offloading decision making problem by adopting the principles of Optimal Stopping Theory to minimize the execution delay in a sequential decision manner. A performance evaluation is provided by using real data sets compared with the optimal solution. The results show that our approach significantly minimizes the execution delay for task execution and the results are very close to the optimal solution