Multi-access edge computing (MEC) is an emerging ecosystem, which aims at converging telecommunication and IT services, providing a cloud computing platform at the edge of the radio access network MEC offers storage and computational resources at the edge, reducing latency for mobile end users and utilizing more efficiently the mobile backhaul and core networks This paper introduces a survey on MEC and focuses on the fundamental key enabling technologies It elaborates MEC orchestration considering both individual services and a network of MEC platforms supporting mobility, bringing light into the different orchestration deployment options In addition, this paper analyzes the MEC reference architecture and main deployment scenarios, which offer multi-tenancy support for application developers, content providers, and third parties Finally, this paper overviews the current standardization activities and elaborates further on open research challenges

/pdf/on-multi-access-edge-computing-a-survey-of-the-emerging-5g-q09cwte3qi.pdf

On Multi-Access Edge Computing: A Survey of the Emerging 5G Network Edge Cloud Architecture and Orchestration

All one needs to know about fog computing and related edge computing paradigms: A complete survey

Ubiquitous sensors and smart devices from factories and communities are generating massive amounts of data, and ever-increasing computing power is driving the core of computation and services from the cloud to the edge of the network. As an important enabler broadly changing people's lives, from face recognition to ambitious smart factories and cities, developments of artificial intelligence (especially deep learning, DL) based applications and services are thriving. However, due to efficiency and latency issues, the current cloud computing service architecture hinders the vision of "providing artificial intelligence for every person and every organization at everywhere". Thus, unleashing DL services using resources at the network edge near the data sources has emerged as a desirable solution. Therefore, edge intelligence, aiming to facilitate the deployment of DL services by edge computing, has received significant attention. In addition, DL, as the representative technique of artificial intelligence, can be integrated into edge computing frameworks to build intelligent edge for dynamic, adaptive edge maintenance and management. With regard to mutually beneficial edge intelligence and intelligent edge, this paper introduces and discusses: 1) the application scenarios of both; 2) the practical implementation methods and enabling technologies, namely DL training and inference in the customized edge computing framework; 3) challenges and future trends of more pervasive and fine-grained intelligence. We believe that by consolidating information scattered across the communication, networking, and DL areas, this survey can help readers to understand the connections between enabling technologies while promoting further discussions on the fusion of edge intelligence and intelligent edge, i.e., Edge DL.

Convergence of Edge Computing and Deep Learning: A Comprehensive Survey

With the Internet of Things (IoT) becoming part of our daily life and our environment, we expect rapid growth in the number of connected devices. IoT is expected to connect billions of devices and humans to bring promising advantages for us. With this growth, fog computing, along with its related edge computing paradigms, such as multi-access edge computing (MEC) and cloudlet, are seen as promising solutions for handling the large volume of security-critical and time-sensitive data that is being produced by the IoT. In this paper, we first provide a tutorial on fog computing and its related computing paradigms, including their similarities and differences. Next, we provide a taxonomy of research topics in fog computing, and through a comprehensive survey, we summarize and categorize the efforts on fog computing and its related computing paradigms. Finally, we provide challenges and future directions for research in fog computing.

All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey

The Internet of Things (IoT) aims to connect billions of smart objects to the Internet, which can bring a promising future to smart cities. These objects are expected to generate large amounts of data and send the data to the cloud for further processing, especially for knowledge discovery, in order that appropriate actions can be taken. However, in reality sensing all possible data items captured by a smart object and then sending the complete captured data to the cloud is less useful. Further, such an approach would also lead to resource wastage (e.g., network, storage, etc.). The Fog (Edge) computing paradigm has been proposed to counterpart the weakness by pushing processes of knowledge discovery using data analytics to the edges. However, edge devices have limited computational capabilities. Due to inherited strengths and weaknesses, neither Cloud computing nor Fog computing paradigm addresses these challenges alone. Therefore, both paradigms need to work together in order to build a sustainable IoT infrastructure for smart cities. In this article, we review existing approaches that have been proposed to tackle the challenges in the Fog computing domain. Specifically, we describe several inspiring use case scenarios of Fog computing, identify ten key characteristics and common features of Fog computing, and compare more than 30 existing research efforts in this domain. Based on our review, we further identify several major functionalities that ideal Fog computing platforms should support and a number of open challenges toward implementing them, to shed light on future research directions on realizing Fog computing for building sustainable smart cities.

/pdf/fog-computing-for-sustainable-smart-cities-a-survey-14m3ggumxt.pdf

Fog Computing for Sustainable Smart Cities: A Survey

High latency, network congestion and network bottleneck are some of problems in cloud computing. Moving from centralized to decentralized paradigm, Edge computing could offload the processing to the edge which indirectly reduces application response time and improves overall user experience. This paper evaluate Docker, a container based technology as a platform for Edge Computing. 4 fundamental criteria were evaluated 1) deployment and termination, 2) resource & service management, 3) fault tolerance and 4) caching. Based on our evaluation and experiment Docker provides fast deployment, small footprint and good performance which make it potentially a viable Edge Computing platform.

Evaluation of Docker as Edge computing platform

Cloud Computing and IoT have been around for quite some time. The use of Cloud Computing to compute or to store IoT data is useful as Cloud can offer such an effective and cheap solution to access resources from the edge. However, extensively use such resources may face degradation since the data and applications are processed in the cloud where every bit of data is transferred over the network from the edge. In this paper, we describe our work on extending our Cloud Computing infrastructure to the edge environment by exploiting the use of Docker containers as a mean of virtualization that run at the edge. Possible scenarios of cyber infrastructure to support IoT services that may take advantage of edge computing concept are described. Next, we describe the edge requirement and discuss the challenges that are arisen for those scenarios. Experiment sets to see the potentiality of Docker container as a mean ofvirtualization for the edge environment are also described and discussed. Finally, we summarize our work and discuss the future work.

Extending Cloud Resources to the Edge: Possible Scenarios, Challenges, and Experiments

Containerization provides a definite platform for faster deployment of application and hence widely adopted in most of the existing and emerging technologies. Applications being deployed on such platform need to be monitored and most importantly it becomes essential to manage the logs generated by each application which helps in identifying and keep track of any issues generated during run time. Several log management tools were available in market and each tool has distinct functions to process on the log data. Some of these tools can be integrated for better log management solutions. However, there requires a framework which could monitor the integrated tools and manage the overall smooth function of the log management system. In this paper, we discuss the challenges required for any log management system in a containerized environment, available log management tools and our proposed framework.

CLOF: A proposed containerized log management orchestration framework

Cloud, Micro-service, World Wide Web, big data spill of a huge amount of data. Collecting data from multiple sources and putting into a unified silo is now a common practice. From there, we perform analytics, statistics, trends, or behavioral analysis to expose hidden information within. There is a vital need to retrieve these voluminous data in a quick way. Search Engine can solve this. This paper discusses several use case of Search Engine in recent trends. We explain the history of Search Engine, how data movement and possible requirement consideration in hosting a search infrastructure. A reference architecture is provided to guide the user in understanding how to acquire data and retrieve data using Search Engine. The presented work is intended to provide a bird's eye view on how search can be integrated benefited into the existing application system.

Reference architecture for search infrastructure

Collecting data from multiple sources and putting into a unified silo is now a common practice. It is important to retrieve these voluminous data in a quick and efficient manner. Using search engine indexing capabilities gives the user a low-latency query towards the data. But often to set up and maintain such an infrastructure is complex and hard to maintain. In this paper, we discuss the challenges and requirement to host a search engine as a service. We take the requirement based on labs and department requirements. We propose a framework to host search engine on top of private cloud deployment. It uses a mixture of containerization and virtualization for faster deployment and easy maintenance.

Ehsan Mostajeran Goortani

Papers

Evaluation of Docker as Edge computing platform

Extending Cloud Resources to the Edge: Possible Scenarios, Challenges, and Experiments

CLOF: A proposed containerized log management orchestration framework

Reference architecture for search infrastructure

A Proposed Framework for Search as a Service on Private Cloud