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

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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

https://pureadmin.qub.ac.uk/ws/files/134629726/paper_v2.pdf

Next generation cloud computing

The landscape of cloud computing has significantly changed over the last decade. Not only have more providers and service offerings crowded the space, but also cloud infrastructure that was traditionally limited to single provider data centers is now evolving. In this paper, we firstly discuss the changing cloud infrastructure and consider the use of infrastructure from multiple providers and the benefit of decentralising computing away from data centers. These trends have resulted in the need for a variety of new computing architectures that will be offered by future cloud infrastructure. These architectures are anticipated to impact areas, such as connecting people and devices, data-intensive computing, the service space and self-learning systems. Finally, we lay out a roadmap of challenges that will need to be addressed for realising the potential of next generation cloud systems.

Next Generation Cloud Computing: New Trends and Research Directions

Safety is the most important requirement for autonomous vehicles; hence, the ultimate challenge of designing an edge computing ecosystem for autonomous vehicles is to deliver enough computing power, redundancy, and security so as to guarantee the safety of autonomous vehicles. Specifically, autonomous driving systems are extremely complex; they tightly integrate many technologies, including sensing, localization, perception, decision making, as well as the smooth interactions with cloud platforms for high-definition (HD) map generation and data storage. These complexities impose numerous challenges for the design of autonomous driving edge computing systems. First, edge computing systems for autonomous driving need to process an enormous amount of data in real time, and often the incoming data from different sensors are highly heterogeneous. Since autonomous driving edge computing systems are mobile, they often have very strict energy consumption restrictions. Thus, it is imperative to deliver sufficient computing power with reasonable energy consumption, to guarantee the safety of autonomous vehicles, even at high speed. Second, in addition to the edge system design, vehicle-to-everything (V2X) provides redundancy for autonomous driving workloads and alleviates stringent performance and energy constraints on the edge side. With V2X, more research is required to define how vehicles cooperate with each other and the infrastructure. Last, safety cannot be guaranteed when security is compromised. Thus, protecting autonomous driving edge computing systems against attacks at different layers of the sensing and computing stack is of paramount concern. In this paper, we review state-of-the-art approaches in these areas as well as explore potential solutions to address these challenges.

Edge Computing for Autonomous Driving: Opportunities and Challenges

This paper argues for the utility of back-end driven onloading to the edge as a way to address bandwidth use and latency challenges for future device-cloud interactions. Supporting such edge functions (EFs) requires solutions that can provide (i) fast and scalable EF provisioning and (ii) strong guarantees for the integrity of the EF execution and confidentiality of the state stored at the edge. In response to these goals, we (i) present a detailed design space exploration of the current technologies that can be leveraged in the design of edge function platforms (EFPs), (ii) develop a solution to address security concerns of EFs that leverages emerging hardware support for OS agnostic trusted execution environments such as Intel SGX enclaves, and (iii) propose and evaluate AirBox, a platform for fast, scalable and secure onloading of edge functions.

Fast, Scalable and Secure Onloading of Edge Functions Using AirBox

Application-level DDoS attacks mounted using compromised IoT devices are emerging as a critical problem. The application-level and seemingly legitimate nature of traffic in such attacks renders most existing solutions ineffective, and the sheer amount and distribution of the generated traffic make mitigation extremely costly. This paper proposes a new approach which leverages edge computing to deploy edge functions that gather information about incoming traffic and communicate that information via a fast-path with a nearby detection service. This accelerates the detection and the arrest of such attacks, limiting their damaging impact. Preliminary investigation shows promise for up to 10x faster detection that reduces up to 82% of the Internet traffic due to IoT-DDoS.

/pdf/towards-iot-ddos-prevention-using-edge-computing-tz76j1cofp.pdf

Towards IoT-DDoS Prevention Using Edge Computing

End user experiences on mobile devices with their rich sets of sensors are constrained by limited device battery lives and restricted form factors, as well as by the 'scope' of the data available locally. The 'Personal Cloud' distributed software abstractions address these issues by enhancing the capabilities of a mobile device via seamless use of both nearby and remote cloud resources. In contrast to vendor-specific, middleware-based cloud solutions, Personal Cloud instances are created at hypervisor- level, to create for each end user the federation of networked resources best suited for the current environment and use. Specifically, the Cirrostratus extensions of the Xen hypervisor can federate a user's networked resources to establish a personal execution environment, governed by policies that go beyond evaluating network connectivity to also consider device ownership and access rights, the latter managed in a secure fashion via standard Social Network Services. Experimental evaluations with both Linux- and Android-based devices, and using Facebook as the SNS, show the approach capable of substantially augmenting a device's innate capabilities, improving application performance and the effective functionality seen by end users.

https://www.cc.gatech.edu/~kbhardwa/papers/pclouds.pdf

Personal Clouds: Sharing and Integrating Networked Resources to Enhance End User Experiences

With an ever increasing number of networked devices used in mobile settings, or residing in homes, offices, and elsewhere, there is a plethora of potential computational infrastructure available for providing end users with new functionality and improved experiences for their interactions with the cyber physical world. The goal of our research is to permit end user applications to take advantage of dynamically available, local and remote computational infrastructure, without requiring applications to be explicitly rewritten and/or reconfigured for each scenario and with minimal end user intervention. Edge Cloud Composites (ECC) make possible the dynamic creation of virtual computational platforms that (i) can be composed from specific capabilities - competences - of participating devices, (ii) are guided by end user-centric abstractions capturing current user context and user intent, and (iii) use dynamic methods for device discovery and ECC maintenance. In contrast to datacenter clouds, ECC participants can include both virtualized and non-virtualized devices, and in addition, services running remotely, made possible by ECC's CIC abstractions, where C(ompetence) captures the functional capabilities of accessible devices and/or remote services, (I)ntent articulates end user desires, and (C)ontext describing the current operating environment. Concrete examples prototyped in this work include Android applications for distributed video playback, collaborative UI, and a distributed augmented reality application. For all such applications, an ECC composed from available devices, and guided by ECC's CIC notions, obtains up to 86% performance improvements and reductions in energy consumption of up to 37% compared to running on a single device. A resultant advantage in using ECCs to run applications is the ability to avoid the unpredictable latency variations seen in device-remote cloud interactions.

ECC: Edge Cloud Composites

Applications incorporating DNN-based visual analytics are growing in demand. This class of data-intensive and latency-sensitive workloads has an opportunity to benefit from the emerging edge computing tier. However, to decouple the growing resource demand of DNN models, from the characteristics and resource limitations of the infrastructure elements available at the edge, new methods are needed to quickly slice the DNNs into appropriately-sized components, and to deploy those DNN slices to be executed on the edge infrastructure stacks. This paper presents Couper, a practical solution that provides for quick creation of slices of production DNNs for visual analytics, and enables their deployment in contemporary container-based edge software stacks. Couper is evaluated with 7 production DNNs, under varying edge configurations.

Ketan Bhardwaj

Papers

Fast, Scalable and Secure Onloading of Edge Functions Using AirBox

Towards IoT-DDoS Prevention Using Edge Computing

Personal Clouds: Sharing and Integrating Networked Resources to Enhance End User Experiences

ECC: Edge Cloud Composites

Couper: DNN model slicing for visual analytics containers at the edge