Cooperative Caching Framework for Mobile Cloud Computing

Due to the advancement in mobile devices and wireless networks mobile cloud computing, which combines mobile computing and cloud computing has gained momentum since 2009. The characteristics of mobile devices and wireless network makes the implementation of mobile cloud computing more complicated than for fixed clouds. This section lists some of the major issues in Mobile Cloud Computing. One of the key issues in mobile cloud computing is the end to end delay in servicing a request. Data caching is one of the techniques widely used in wired and wireless networks to improve data access efficiency. In this paper we explore the possibility of a cooperative caching approach to enhance data access efficiency in mobile cloud computing. The proposed approach is based on cloudlets, one of the architecture designed for mobile cloud computing

Implementation and Evaluation of Mobile-Edge Computing Cooperative Caching

Recent expanding rise of mobile device users for cloud services leads to resource challenges in Mobile Network Operator's (MNO) network. This poses significant additional costs to MNOs and also results in poor user experience. Studies illustrate that large amount of traffic consumption in MNO's network is originated from the similar requests of users for the same popular contents over Internet. Therefore such networks suffer from delivering the same content multiple times through their connected gateways to the Internet backhaul. On the other hand, in content delivery networks (CDN), the delay caused by network latency is one of the biggest issues which impedes the efficient delivery and desirable user experience. Cooperative caching is one of the ways to handle the extra posed traffic by requesting popular contents repeatedly in MNO's network. Furthermore Mobile-Edge Computing (MEC) offers a resource rich environment and data locality to cloud applications. This helps to reduce the network latency time in CDN services. Thus in this Thesis an aggregation between Cooperative Caching and MEC concept has been considered. This Thesis demonstrates a design, implementation and evaluation for a Mobile-Edge computing Cooperative Caching system to deliver content to mobile users. A design is presented in a failure resilient and scalable practice using a light-weight synchronizing method. The system is implemented and deployed on Nokia Networks Radio Application Cloud Servers(Nokia Networks RACS) as intelligent MEC base-stations and finally the outcome of the system and the effect on bandwidth saving, CDN delay and user experience are evaluated

MEC vs MCC: performance analysis of real-time applications

Hoje em dia, numerosas são as aplicações que apresentam um uso intensivo de recursos empurrando os requisitos computacionais e a demanda de energia dos dispositivos para além das suas capacidades. Atentando na arquitetura Mobile Cloud, que disponibiliza plataformas funcionais e aplicações emergentes (como Realidade Aumentada (AR), Realidade Virtual (VR), jogos online em tempo real, etc.), são evidentes estes desafios directamente relacionados com a latência, consumo de energia, e requisitos de privacidade. O Mobile Edge Computing (MEC) é uma tecnologia recente que aborda os obstáculos de desempenho enfrentados pela Mobile Cloud Computing (MCC), procurando solucioná-los O MEC aproxima as funcionalidades de computação e de armazenamento da periferia da rede. Neste trabalho descreve-se a arquitetura MEC assim como os principais tipos soluções para a sua implementação. Apresenta-se a arquitetura de referência da tecnologia cloudlet e uma comparação com o modelo de arquitetura ainda em desenvolvimento e padronização pelo ETSI. Um dos propósitos do MEC é permitir remover dos dispositivos tarefas intensivas das aplicações para melhorar a computação, a capacidade de resposta e a duração da bateria dos dispositivos móveis. O objetivo deste trabalho é estudar, comparar e avaliar o desempenho das arquiteturas MEC e MCC para o provisionamento de tarefas intensivas de aplicações com uso intenso de computação. Os cenários de teste foram configurados utilizando esse tipo de aplicações em ambas as implementações de MEC e MCC. Os resultados do teste deste estudo permitem constatar que o MEC apresenta melhor desempenho do que o MCC relativamente à latência e à qualidade de experiência do utilizador. Além disso, os resultados dos testes permitem quantificar o benefício efetivo tecnologia MEC.Numerous applications, such as Augmented Reality (AR), Virtual Reality (VR), real-time online gaming are resource-intensive applications and consequently, are pushing the computational requirements and energy demands of the mobile devices beyond their capabilities. Despite the fact that mobile cloud architecture has practical and functional platforms, these new emerging applications present several challenges regarding latency, energy consumption, context awareness, and privacy enhancement. Mobile Edge Computing (MEC) is a new resourceful and intermediary technology, that addresses the performance hurdles faced by Mobile Cloud Computing (MCC), and brings computing and storage closer to the network edge. This work introduces the MEC architecture and some of edge computing implementations. It presents the reference architecture of the cloudlet technology and provides a comparison with the architecture model that is under standardization by ETSI. MEC can offload intensive tasks from applications to enhance computation, responsiveness and battery life of the mobile devices. The objective of this work is to study and evaluate the performance of MEC and MCC architectures for provisioning offload intensive tasks from compute-intensive applications. Test scenarios were set up with use cases with this kind of applications for both MEC and MCC implementations. The test results of this study enable to support evidence that the MEC presents better performance than cloud computing regarding latency and user quality of experience. Moreover, the results of the tests enable to quantify the effective benefit of the MEC approach

Street Smart in 5G : Vehicular Applications, Communication, and Computing

Recent advances in information technology have revolutionized the automotive industry, paving the way for next-generation smart vehicular mobility. Specifically, vehicles, roadside units, and other road users can collaborate to deliver novel services and applications that leverage, for example, big vehicular data and machine learning. Relatedly, fifth-generation cellular networks (5G) are being developed and deployed for low-latency, high-reliability, and high bandwidth communications. While 5G adjacent technologies such as edge computing allow for data offloading and computation at the edge of the network thus ensuring even lower latency and context-awareness. Overall, these developments provide a rich ecosystem for the evolution of vehicular applications, communications, and computing. Therefore in this work, we aim at providing a comprehensive overview of the state of research on vehicular computing in the emerging age of 5G and big data. In particular, this paper highlights several vehicular applications, investigates their requirements, details the enabling communication technologies and computing paradigms, and studies data analytics pipelines and the integration of these enabling technologies in response to application requirements.Peer reviewe

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