Edge computing

About: Edge computing is a research topic. Over the lifetime, 11657 publications have been published within this topic receiving 148533 citations.

An Evaluation of Open Source Serverless Computing Frameworks Support at the Edge

Abstract: The proliferation of Internet of Things (IoT) and the success of resource-rich cloud services have pushed the data processing horizon towards the edge of the network. This has the potential to address bandwidth costs, and latency, availability and data privacy concerns. Serverless computing, a cloud computing model for stateless and event-driven applications, promises to further improve Quality of Service (QoS) by eliminating the burden of always-on infrastructure through ephemeral containers. Open source serverless frameworks have been introduced to avoid the vendor lock-in and computation restrictions of public cloud platforms and to bring the power of serverless computing to on-premises deployments. In an IoT environment, these frameworks can leverage the computational capabilities of devices in the local network to further improve QoS of applications delivered to the user. However, these frameworks have not been evaluated in a resource-constrained, edge computing environment. In this work we evaluate four open source serverless frameworks, namely, Kubeless, Apache OpenWhisk, OpenFaaS, Knative. Each framework is installed on a bare-metal, single master, Kubernetes cluster. We use the JMeter framework to evaluate the response time, throughput and success rate of functions deployed using these frameworks under different workloads. The evaluation results are presented and open research opportunities are discussed.

BloCkEd: Blockchain-Based Secure Data Processing Framework in Edge Envisioned V2X Environment

Abstract: There has been an increasing trend of moving computing activities closer to the edge of the network, particularly in smart city applications (e.g., vehicle-to-everything – V2X). Such a paradigm allows the end user’s requests to be handled/processed by nodes at the edge of the network; thus, reducing latency, and preserving privacy of user data/activities. However, there are a number of challenges in such an edge computing ecosystem. Examples include (1) potential inappropriate utilization of resources at the edge nodes, (2) operational challenges in cache management and data integrity due to data migration between edge nodes, particularly when dealing with vehicular mobility in a V2X application, and (3) high energy consumption due to continuous link breakage and subsequent reestablishment of link(s). Therefore in this paper, we design a blockchain-based secure data processing framework for an edge envisioned V2X environment (hereafter referred to as BloCkEd ). Specifically, a multi-layered edge-enabled V2X system model for BloCkEd is presented, which includes the formulation of a multi-objective optimization problem. In addition, BloCkEd comprises an optimal container-based data processing scheme, and a blockchain-based data integrity management scheme, designed to minimize link breakage and reducing latency. Using Chandigarh City, India, as the scenario, we implement and evaluate the proposed approach in terms of its latency, energy consumption, and service level agreement compliance.

A Game-Theoretic Approach to Computation Offloading in Satellite Edge Computing

Abstract: Mobile edge computing (MEC) is proposed as a new paradigm to meet the ever-increasing computation requirements, which is caused by the rapid growth of the Internet of Things (IoT) devices. As a supplement to the terrestrial network, satellites can provide communication to terrestrial devices in some harsh environments and natural disasters. Satellite edge computing is becoming an emerging topic and technology. In this paper, a game-theoretic approach to the optimization of computation offloading strategy in satellite edge computing is proposed. The system model for computation offloading in satellite edge computing is established, considering the intermittent terrestrial-satellite communication caused by satellites orbiting. We conduct a computation offloading game framework and compute the response time and energy consumption of a task based on the queuing theory as metrics of optimizing performance. The existence and uniqueness of the Nash equilibrium is theoretically proved, and an iterative algorithm is proposed to find the Nash equilibrium. Simulation results validate the proposed algorithm and show that the game-based offloading strategy can greatly reduce the average cost of a device.

Energy-Efficient and Delay-Guaranteed Workload Allocation in IoT-Edge-Cloud Computing Systems

Abstract: Edge computing has recently emerged as an extension to cloud computing for quality of service (QoS) provisioning particularly delay guarantee for delay-sensitive applications. By offloading the computationally intensive workloads to edge servers, the quality of computation experience, e.g., network transmission delay and transmission energy consumption, could be improved greatly. However, the computation resource of an edge server is so scarce that it cannot respond quickly to the bursting computation requirements. Accordingly, queuing delay is un-negligible in a computationally intensive environment, e.g., a computing environment consists of the Internet of Things (IoT) applications. In addition, the computation energy consumption in edge servers may be higher than that in clouds when the workload is heavy. To provide QoS for end users while achieving green computing for computing systems, the cooperation between edge servers and the cloud is significantly important. In this paper, the energy-efficient and delay-guaranteed workload allocation problem in an IoT-edge-cloud computing system are investigated. We formulate a delay-based workload allocation problem which suggests the optimal workload allocations among local edge server, neighbor edge servers, and cloud toward the minimal energy consumption as well as the delay guarantee. The problem is then tackled using a delay-base workload allocation (DBWA) algorithm based on Lyapunov drift-plus-penalty theory. The theoretical analysis and simulation results have been conducted to demonstrate the efficiency of the proposal for energy efficiency and delay guarantee in an IoT-edge-cloud system.

HCP: Heterogeneous Computing Platform for Federated Learning Based Collaborative Content Caching Towards 6G Networks

Abstract: A heterogeneous computing architecture is essential to facilitate intelligent network traffic control for a joint computation, communication, and collaborative caching optimization in 6G networks to provide stringent Quality of Experience (QoE) guarantees. In this paper, we consider a 6G integrated aerial-terrestrial network model where Unmanned Aerial Vehicles (UAVs) and terrestrial Remote Radio Heads (RRHs) jointly serve as heterogeneous Base Stations (hgNBs) of a Cloud Radio Access Network (HCRAN) serving different mobile user (UE) types. We propose a distributed heterogeneous computing platform (HCP) across the UAVs and terrestrial Base Stations (BSs) by utilizing their caching and cooperative communication capabilities. In order to preserve the privacy of the content of the UEs, we propose a 2-stage federated learning algorithm among the UEs, UAVs/BSs, and HCP to collaboratively predict the content caching placement by jointly considering traffic distribution, UE mobility and localized content popularity. An asynchronous weight updating method is adopted to avoid redundant learning transfer in the federated learning. Once the global model is learnt by the HCP, it transfers the learned model to the UEs to facilitate the much desired edge intelligence in the considered 6G tiny cell. The effectiveness of the proposal is evaluated by extensive numerical analysis.