A Case Study of Edge Computing Implementations: Multi-access Edge Computing, Fog Computing and Cloudlet

With the explosive growth of intelligent and mobile devices, the current centralized cloud computing paradigm is encountering difficult challenges. Since the primary requirements have shifted towards implementing real-time response and supporting context awareness and mobility, there is an urgent need to bring resources and functions of centralized clouds to the edge of networks, which has led to the emergence of the edge computing paradigm. Edge computing increases the responsibilities of network edges by hosting computation and services, therefore enhancing performances and improving quality of experience (QoE). Fog computing, multi-access edge computing (MEC), and cloudlet are three typical and promising implementations of edge computing. Fog computing aims to build a system that enables cloud-to-thing service connectivity and works in concert with clouds, MEC is seen as a key technology of the fifth generation (5G) system, and Cloudlet is a micro-data center deployed in close proximity. In terms of deployment scenarios, Fog computing focuses on the Internet of Things (IoT), MEC mainly provides mobile RAN application solutions for 5G systems, and cloudlet offloads computing power at the network edge. In this paper, we present a comprehensive case study on these three edge computing implementations, including their architectures, differences, and their respective application scenario in IoT, 5G wireless systems, and smart edge. We discuss the requirements, benefits, and mechanisms of typical co-deployment cases for each paradigm and identify challenges and future directions in edge computing

Fog Computing: A Taxonomy, Survey and Future Directions

In recent years, the number of Internet of Things (IoT) devices/sensors has increased to a great extent. To support the computational demand of real-time latency-sensitive applications of largely geo-distributed IoT devices/sensors, a new computing paradigm named "Fog computing" has been introduced. Generally, Fog computing resides closer to the IoT devices/sensors and extends the Cloud-based computing, storage and networking facilities. In this chapter, we comprehensively analyse the challenges in Fogs acting as an intermediate layer between IoT devices/ sensors and Cloud datacentres and review the current developments in this field. We present a taxonomy of Fog computing according to the identified challenges and its key features.We also map the existing works to the taxonomy in order to identify current research gaps in the area of Fog computing. Moreover, based on the observations, we propose future directions for research

Cloudlet Deployment to Balance Energy Consumption in Wireless Networks: A Survey, Issues and Challenges

Cloud computing and wireless networks, both are two different important components in information technology (IT) world. These both wide network components are unlike from each other and their characteristics are providing huge services. By using these services, users getting several computing services at low cost while providing the security and privacy. According to the one survey till end of the 2016, gross payments from the e-commerce business management was spent $4 billion for maintenance of datacenters. Energy consumption is the key component in the information technology world, because due to the wastage usage of computing hardware components like servers, datacenters and network bandwidth etc., business profits will be go down. So, by integration of these two areas, online business management will get more revenues while providing the QoS. The proposed mechanism, deploying the cloudlets for executing the actions, which are belongs to the wireless networks such as Local Area Network (LAN), Metropolitan Area Network (MAN) and Wide Area Network (WAN) etc. The proposed idea addressed better solution to avoid Service Level Agreement�s (SLA) violations and poor QoS

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

A survey on mobility-induced service migration in the fog, edge, and related computing paradigms

The final publication is available at ACM via http://dx.doi.org/10.1145/3326540With the advent of fog and edge computing paradigms, computation capabilities have been moved toward the edge of the network to support the requirements of highly demanding services. To ensure that the quality of such services is still met in the event of users’ mobility, migrating services across different computing nodes becomes essential. Several studies have emerged recently to address service migration in different edge-centric research areas, including fog computing, multi-access edge computing (MEC), cloudlets, and vehicular clouds. Since existing surveys in this area focus on either VM migration in general or migration in a single research field (e.g., MEC), the objective of this survey is to bring together studies from different, yet related, edge-centric research fields while capturing the different facets they addressed. More specifically, we examine the diversity characterizing the landscape of migration scenarios at the edge, present an objective-driven taxonomy of the literature, and highlight contributions that rather focused on architectural design and implementation. Finally, we identify a list of gaps and research opportunities based on the observation of the current state of the literature. One such opportunity lies in joining efforts from both networking and computing research communities to facilitate future research in this area.Peer ReviewedPreprin

Context-Aware Computation Offloading for Mobile Cloud Computing: Requirements Analysis, Survey and Design Guideline

AbstractAlong with the rise of mobile handheld devices the resource demands of respective applications grow as well. However, mobile devices are still and will always be limited related to performance (e.g., computation, storage and battery life), context adaptation (e.g., intermittent connectivity, scalability and heterogeneity) and security aspects. A prominent solution to overcome these limita- tions is the so-called computation offloading, which is the focus of mobile cloud computing (MCC). However, current approaches fail to address the complexity that results from quickly and constantly changing context conditions in mobile user scenarios and hence developing effective and efficient MCC applications is still challenging. Therefore, this paper first presents a list of re- quirements for MCC applications together with a survey and classification of current solutions. Furthermore, it provides a design guideline for the selection of suitable concepts for different classes of common cloud-augmented mobile applications. Finally, it presents open issues that developers and researchers should be aware of when designing their MCC-approach

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

A game-theoretic approach to computation offloading in mobile cloud computing

We consider a three-tier architecture for mobile and pervasive computing scenarios, consisting of a local tier ofmobile nodes, a middle tier (cloudlets) of nearby computing nodes, typically located at the mobile nodes access points but characterized by a limited amount of resources, and a remote tier of distant cloud servers, which have practically infinite resources. This architecture has been proposed to get the benefits of computation offloading from mobile nodes to external servers while limiting the use of distant servers whose higher latency could negatively impact the user experience. For this architecture, we consider a usage scenario where no central authority exists and multiple non-cooperative mobile users share the limited computing resources of a close-by cloudlet and can selfishly decide to send their computations to any of the three tiers. We define a model to capture the users interaction and to investigate the effects of computation offloading on the users’ perceived performance. We formulate the problem as a generalized Nash equilibrium problem and show existence of an equilibrium.We present a distributed algorithm for the computation of an equilibrium which is tailored to the problem structure and is based on an in-depth analysis of the underlying equilibrium problem. Through numerical examples, we illustrate its behavior and the characteristics of the achieved equilibria