Addressing the Node Discovery Problem in Fog Computing

In recent years, the Internet of Things (IoT) has gained a lot of attention due to connecting various sensor devices with the cloud, in order to enable smart applications such as: smart traffic management, smart houses, and smart grids, among others. Due to the growing popularity of the IoT, the number of Internet-connected devices has increased significantly. As a result, these devices generate a huge amount of network traffic which may lead to bottlenecks, and eventually increase the communication latency with the cloud. To cope with such issues, a new computing paradigm has emerged, namely: fog computing. Fog computing enables computing that spans from the cloud to the edge of the network in order to distribute the computations of the IoT data, and to reduce the communication latency. However, fog computing is still in its infancy, and there are still related open problems. In this paper, we focus on the node discovery problem, i.e., how to add new compute nodes to a fog computing system. Moreover, we discuss how addressing this problem can have a positive impact on various aspects of fog computing, such as fault tolerance, resource heterogeneity, proximity awareness, and scalability. Finally, based on the experimental results that we produce by simulating various distributed compute nodes, we show how addressing the node discovery problem can improve the fault tolerance of a fog computing system

Addressing the Challenges in Federating Edge Resources

This book chapter considers how Edge deployments can be brought to bear in a global context by federating them across multiple geographic regions to create a global Edge-based fabric that decentralizes data center computation. This is currently impractical, not only because of technical challenges, but is also shrouded by social, legal and geopolitical issues. In this chapter, we discuss two key challenges - networking and management in federating Edge deployments. Additionally, we consider resource and modeling challenges that will need to be addressed for a federated Edge.Comment: Book Chapter accepted to the Fog and Edge Computing: Principles and Paradigms; Editors Buyya, Sriram

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

ENORM: A Framework For Edge NOde Resource Management

Current computing techniques using the cloud as a centralised server will become untenable as billions of devices get connected to the Internet. This raises the need for fog computing, which leverages computing at the edge of the network on nodes, such as routers, base stations and switches, along with the cloud. However, to realise fog computing the challenge of managing edge nodes will need to be addressed. This paper is motivated to address the resource management challenge. We develop the first framework to manage edge nodes, namely the Edge NOde Resource Management (ENORM) framework. Mechanisms for provisioning and auto-scaling edge node resources are proposed. The feasibility of the framework is demonstrated on a PokeMon Go-like online game use-case. The benefits of using ENORM are observed by reduced application latency between 20% - 80% and reduced data transfer and communication frequency between the edge node and the cloud by up to 95\%. These results highlight the potential of fog computing for improving the quality of service and experience.Comment: 14 pages; accepted to IEEE Transactions on Services Computing on 12 September 201

Fog-enabled Edge Learning for Cognitive Content-Centric Networking in 5G

By caching content at network edges close to the users, the content-centric networking (CCN) has been considered to enforce efficient content retrieval and distribution in the fifth generation (5G) networks. Due to the volume, velocity, and variety of data generated by various 5G users, an urgent and strategic issue is how to elevate the cognitive ability of the CCN to realize context-awareness, timely response, and traffic offloading for 5G applications. In this article, we envision that the fundamental work of designing a cognitive CCN (C-CCN) for the upcoming 5G is exploiting the fog computing to associatively learn and control the states of edge devices (such as phones, vehicles, and base stations) and in-network resources (computing, networking, and caching). Moreover, we propose a fog-enabled edge learning (FEL) framework for C-CCN in 5G, which can aggregate the idle computing resources of the neighbouring edge devices into virtual fogs to afford the heavy delay-sensitive learning tasks. By leveraging artificial intelligence (AI) to jointly processing sensed environmental data, dealing with the massive content statistics, and enforcing the mobility control at network edges, the FEL makes it possible for mobile users to cognitively share their data over the C-CCN in 5G. To validate the feasibility of proposed framework, we design two FEL-advanced cognitive services for C-CCN in 5G: 1) personalized network acceleration, 2) enhanced mobility management. Simultaneously, we present the simulations to show the FEL's efficiency on serving for the mobile users' delay-sensitive content retrieval and distribution in 5G.Comment: Submitted to IEEE Communications Magzine, under review, Feb. 09, 201

Orchestrating Service Migration for Low Power MEC-Enabled IoT Devices

Multi-Access Edge Computing (MEC) is a key enabling technology for Fifth Generation (5G) mobile networks. MEC facilitates distributed cloud computing capabilities and information technology service environment for applications and services at the edges of mobile networks. This architectural modification serves to reduce congestion, latency, and improve the performance of such edge colocated applications and devices. In this paper, we demonstrate how reactive service migration can be orchestrated for low-power MEC-enabled Internet of Things (IoT) devices. Here, we use open-source Kubernetes as container orchestration system. Our demo is based on traditional client-server system from user equipment (UE) over Long Term Evolution (LTE) to the MEC server. As the use case scenario, we post-process live video received over web real-time communication (WebRTC). Next, we integrate orchestration by Kubernetes with S1 handovers, demonstrating MEC-based software defined network (SDN). Now, edge applications may reactively follow the UE within the radio access network (RAN), expediting low-latency. The collected data is used to analyze the benefits of the low-power MEC-enabled IoT device scheme, in which end-to-end (E2E) latency and power requirements of the UE are improved. We further discuss the challenges of implementing such schemes and future research directions therein