Edge Infrastructure Research Articles

VonEdgeSim: A Framework for Simulating IoT Application in Volunteer Edge Computing

Recently, various emerging technologies have been introduced to host IoT applications. Edge computing, utilizing volunteer devices, could be a feasible solution due to the significant and underutilized resources at the edge. However, cloud providers are still reluctant to offer it as an edge infrastructure service because of the unpredictable nature of volunteer resources. Volunteer edge computing introduces challenges such as reliability, trust, and availability. Testing this infrastructure is prohibitively expensive and not feasible in real-world scenarios. This emerging technology will not be fully realized until dedicated research and development efforts have substantiated its potential for running reliable services. Therefore, this paper proposes VonEdgeSim, a simulation of volunteer edge computing. To the best of our knowledge, it is the first and only simulation capable of mimicking volunteer behavior at the edge. Researchers and developers can utilize this simulation to test and develop resource management models. We conduct experiments with various IoT applications, including Augmented Reality, Infotainment, and Health Monitoring. Our results show that incorporating volunteer devices at the edge can significantly enhance system performance by reducing total task delay, and improving task execution time. This emphasizes the potential of volunteers to provide reliable services in an edge computing environment. The simulation code is publicly available for further development and testing.

The Impact of Edge Computing on Real-Time Data Processing

Purpose: The study sought to explore the impact of edge computing on real-time data processing. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to the impact of edge computing on real-time data processing. Preliminary empirical review reveled that edge computing significantly reduced latency and enhanced efficiency in real-time data processing across various industries by bringing computational resources closer to data sources. It highlighted the technology's ability to handle large volumes of IoT-generated data, improve security by localizing data processing, and drive innovation and economic growth through new applications and services. Edge computing's decentralized approach proved essential for reliable and robust data handling, particularly in critical sectors like healthcare and finance, ultimately solidifying its importance in the digital transformation landscape. Unique Contribution to Theory, Practice and Policy: The Diffusion of Innovations Theory, Resource-Based View (RBV) and Sociotechnical Systems Theory may be used to anchor future studies on edge computing on real-time data processing. The study recommended expanding theoretical frameworks to include the unique aspects of edge computing, investing in robust edge infrastructure, and developing standardized protocols and best practices. It emphasized the need for government incentives and supportive regulatory frameworks to promote adoption, and suggested that academic institutions incorporate edge computing into curricula. Additionally, the study called for ongoing research to address emerging challenges and opportunities, ensuring continuous advancement and effective implementation of edge computing technologies.

Cometes: Cross-Device Mapping for Energy and Time-Aware Deployment on Edge Infrastructures

As the Internet of Things (IoT) application development falls more on libraries and tools, software developers design applications targeting power-constrained Edge-computing devices. However, not only the Quality-of-Service (QoS) but also energy savings remain major challenges. This article presents Cometes, a complete tool-flow that extends modern SDK tools, assisting developers in selecting resources towards the desired trade-off between execution time and energy consumption, without exceeding the power limitations of the Edge clusters. Cometes runs at application design time, on top of Kubernetes, and exploits cross-device estimation methods as well as traditional multi-objective optimizers to enable efficient resource allocation. The presented solution achieves up to 66% energy savings, up to 50% speed-up and an average improvement of 7%, compared to the placement policy of the lightweight Kubernetes (k3s).

A DRL-Based Service Offloading Approach Using DAG for Edge Computational Orchestration

Edge infrastructure and Industry 4.0 required services are offered by edge-servers (ESs) with different computation capabilities to run social application's workload based on a leased-price method. The usage of Social Internet of Things (SIoT) applications increases day-to-day, which makes social platforms very popular and simultaneously requires an effective computation system to achieve high service reliability. In this regard, offloading high required computational social service requests (SRs) in a time slot based on directed acyclic graph (DAG) is an NP-complete problem. Most state-of-art methods concentrate on the energy preservation of networks but neglect the resource sharing cost and dynamic subservice execution time (SET) during the computation and resource sharing. This article proposes a two-step deep reinforcement learning (DRL)-based service offloading (DSO) approach to diminish edge server costs through a DRL influenced resource and SET analysis (RSA) model. In the first level, the service and edge server cost is considered during service offloading. In the second level, the R-retaliation method evaluates resource factors to optimize resource sharing and SET fluctuations. The simulation results show that the proposed DSO approach achieves low execution costs by streamlining dynamic service completion and transmission time, server cost, and deadline violation rate attributes. Compared to the state-of-art approaches, our proposed method has achieved high resource usage with low energy consumption.

Optimal service caching, pricing and task partitioning in mobile edge computing federation

Mobile Edge Computing (MEC) federations aim to establish a joint edge service model between Edge Infrastructure Providers (EIPs) and clouds, facilitating the sharing and utilization of MEC services and resources. However, in such a hierarchical multi-EIP MEC federation environment, optimizing service caching, task partitioning, and pricing strategies to maximize the profits gained by EIPs and minimize the cost of mobile devices (MDs) is challenging. To address this challenge, This paper first formulates a two-stage multi-leader, multi-follower Stackelberg game between EIPs and MDs. Then, a new method combining a Stackelberg-based Multi-Agent Deep Deterministic Policy Gradient (STMADDPG) algorithm and a Transformer-based Popularity Prediction (TPP) model is devised to learn the optimal strategies. Extensive experiments with real-world datasets are conducted to demonstrate the excellent performance of the proposed STMADDPG-TPP method.

Analysis of micro- vs. macro-flows management in QKD-secured edge computing

Quantum Key Distribution (QKD) holds the promise of a secure exchange of cryptographic material between applications that have access to the same network of QKD nodes, interconnected through fiber optic or satellite links. Worldwide several such networks are being deployed at a metropolitan level, where edge computing is already offered by the telco operators to customers as a viable alternative to both cloud and on-premise hosting of computational resources. In this paper, we investigate the implications of enabling QKD for edge-native applications from a practical perspective of resource allocation in the QKD network and the edge infrastructure. Specifically, we consider the dichotomy between aggregating all the applications on the same source–destination path vs. adopting a more flexible micro-flow approach, inspired from Software Defined Networking (SDN) concepts. Our simulation results show that there is a fundamental trade-off between the efficient use of resources and the signaling overhead, which we managed to diminish with the use of suitable hybrid solutions.

Sustainable edge computing: Challenges and future directions

AbstractThe advent of edge computing holds immense promise for advancing the digitization of society, ushering in critical applications that elevate the overall quality of life. Yet, the practical implementation of the edge paradigm proves more challenging than anticipated, encountering disruptions primarily due to the constraints of applying conventional cloud‐based strategies at the network's periphery. Increasingly influenced by sustainability commitments, industry regulations currently view edge computing as a potential threat, primarily due to the energy inefficiency of solutions situated in close proximity to data generation sources and the rising density of computing. This paper presents a proactive strategy to transform the perceived threat into an opportunity, steering the sustainable evolution of future edge infrastructures to make them both environmentally and economically competitive for accelerated adoption. The vision outlined addresses key challenges associated with edge deployment and operation, emphasizing energy efficiency, fault‐tolerant automation, and collaborative orchestration. The proposed approach integrates two‐phase immersion cooling, formal modeling, machine learning, and federated management to effectively harness heterogeneity, propelling the sustainability of edge computing. To substantiate the efficacy of this approach, the paper details initial efforts towards establishing the sustainability of an edge infrastructure designed for an Advanced Driver Assistance Systems application.

Leveraging Data Plane Programmability to enhance service orchestration at the edge: A focus on industrial security

The Edge Computing paradigm is increasingly gaining traction in modern telecommunication scenarios, as it enables the offloading of computational tasks from end devices to a variety of nodes located in close proximity to them. This approach is essential for meeting the ever-stricter Quality of Service requirements imposed by modern applications. Concurrently, the advent of Data Plane Programmability allows for unmatched flexibility on the networking plane, supporting processing of multiple protocols in a logically centralized fashion with simple in-line computation, and offering the possibility to offload additional services to networking equipment. Reaping those benefits necessitates heedful management of resources and infrastructure. This, in turn, calls for the introduction of a service orchestration entity, capable of taking advantage of device heterogeneity to enable efficient and swift service provisioning. This work delves into the potential of introducing an orchestration system able to cope with the challenges of offloading security tasks at the Edge. This effort involves developing and implementing novel architectural components that capitalize on the heterogeneous nature of the Edge infrastructure as well as of the Programmable Data Plane as a potential tool for service offloading. To establish the feasibility and performance of this approach, an industrial scenario is considered, where the integrity of data from legacy devices must be ensured. Following an evaluation of the hashing performance of the Programmable Data Plane in comparison to general-purpose devices, a simulation study is conducted on the overall orchestration system, demonstrating the viability of the proposed approach.

Intelligent Resource Orchestration for 5G Edge Infrastructures

The adoption of edge infrastructure in 5G environments stands out as a transformative technology aimed at meeting the increasing demands of latency-sensitive and data-intensive applications. This research paper presents a comprehensive study on the intelligent orchestration of 5G edge computing infrastructures. The proposed Smart 5G Edge-Cloud Management Architecture, built upon an OpenNebula foundation, incorporates a ONEedge5G experimental component, which offers intelligent workload forecasting and infrastructure orchestration and automation capabilities, for optimal allocation of virtual resources across diverse edge locations. The research evaluated different forecasting models, based both on traditional statistical techniques and machine learning techniques, comparing their accuracy in CPU usage prediction for a dataset of virtual machines (VMs). Additionally, an integer linear programming formulation was proposed to solve the optimization problem of mapping VMs to physical servers in distributed edge infrastructure. Different optimization criteria such as minimizing server usage, load balancing, and reducing latency violations were considered, along with mapping constraints. Comprehensive tests and experiments were conducted to evaluate the efficacy of the proposed architecture.

Application Scenarios of Digital Twins for Smart Crop Farming through Cloud–Fog–Edge Infrastructure

In the last decade, digital twin (DT) technology has received considerable attention across various domains, such as manufacturing, smart healthcare, and smart cities. The digital twin represents a digital representation of a physical entity, object, system, or process. Although it is relatively new in the agricultural domain, it has gained increasing attention recently. Recent reviews of DTs show that this technology has the potential to revolutionise agriculture management and activities. It can also provide numerous benefits to all agricultural stakeholders, including farmers, agronomists, researchers, and others, in terms of making decisions on various agricultural processes. In smart crop farming, DTs help simulate various farming tasks like irrigation, fertilisation, nutrient management, and pest control, as well as access real-time data and guide farmers through ‘what-if’ scenarios. By utilising the latest technologies, such as cloud–fog–edge computing, multi-agent systems, and the semantic web, farmers can access real-time data and analytics. This enables them to make accurate decisions about optimising their processes and improving efficiency. This paper presents a proposed architectural framework for DTs, exploring various potential application scenarios that integrate this architecture. It also analyses the benefits and challenges of implementing this technology in agricultural environments. Additionally, we investigate how cloud–fog–edge computing contributes to developing decentralised, real-time systems essential for effective management and monitoring in agriculture.

Implementing Virtualization on Single-Board Computers: A Case Study on Edge Computing

The widespread adoption of cloud computing has resulted in centralized datacenter structures; however, there is a requirement for smaller-scale distributed infrastructures to meet the demands for speed, responsiveness, and security for critical applications. Single-Board Computers (SBCs) present numerous advantages such as low power consumption, low cost, minimal heat emission, and high processing power, making them suitable for applications such as the Internet of Things (IoT), experimentation, and other advanced projects. This paper investigates the possibility of adopting virtualization technology on Single-Board Computers (SBCs) for the implementation of reliable and cost-efficient edge-computing environments.The results of this study are based on experimental implementations and testing conducted in the course of a case study performed on the edge infrastructure of a financial organization, where workload migration was achieved from a traditional to an SBC-based edge infrastructure. The performance of the two infrastructures was studied and compared during this process, providing important insights into the power efficiency gains, resource utilization, and overall suitability for the organization’s operational needs.

Controller-driven vector autoregression model for predicting content popularity in programmable named data networking devices.

Named Data Networking (NDN) has emerged as a promising network architecture for content delivery in edge infrastructures, primarily due to its name-based routing and integrated in-network caching. Despite these advantages, sub-optimal performance often results from the decentralized decision-making processes of caching devices. This article introduces a paradigm shift by implementing a Software Defined Networking (SDN) controller to optimize the placement of highly popular content in NDN nodes. The optimization process considers critical networking factors, including network congestion, security, topology modification, and flowrules alterations, which are essential for shaping content caching strategies. The article presents a novel content caching framework, Popularity-aware Caching in Popular Programmable NDN nodes (PaCPn). Employing a multi-variant vector autoregression (VAR) model driven by an SDN controller, PaCPn periodically updates content popularity based on time-series data, including 'request rates' and 'past popularity'. It also introduces a controller-driven heuristic algorithm that evaluates the proximity of caching points to consumers, considering factors such as 'distance cost,' 'delivery time,' and the specific 'status of the requested content'. PaCPn utilizes customized DATA named packets to ensure the source stores content with a valid residual freshness period while preventing intermediate nodes from caching it. The experimental results demonstrate significant improvements achieved by the proposed technique PaCPn compared to existing schemes. Specifically, the technique enhances cache hit rates by 20% across various metrics, including cache size, Zipf parameter, and exchanged traffic within edge infrastructure. Moreover, it reduces content retrieval delays by 28%, considering metrics such as cache capacity, the number of consumers, and network throughput. This research advances NDN content caching and offers potential optimizations for edge infrastructures.

Intent-driven orchestration of serverless applications in the computing continuum

Following the emergence of Internet of Things (IoT) and edge computing technologies and the development of distributed applications with strict Quality of Service (QoS) requirements, a transition is underway from centralized computing models to distributed computing continuum systems. The latter provide abstractions of the available resources, while they lead to the development of orchestration mechanisms with increased distributed intelligence and autonomy characteristics. These characteristics make the computing continuum suitable for managing serverless computing applications, considering their loose coupling with the resources and the need for reactiveness and efficiency to serve dynamic workloads in the edge and cloud part of the infrastructure. This paper presents an intent-based approach for the orchestration of a function chain over an edge and cloud infrastructure. We propose a Reinforcement Learning (RL) based autoscaling method that incorporates a reward function that aims to provide optimal utilization of resources and timely autoscaling of the functions, taking advantage of methods for workload prediction. Based on a high-level intent, the proposed optimization problem dictates the scheduling of the functions, minimizing the communication overhead and transformation cost while also considering the energy efficiency of the infrastructure. The proposed solution is evaluated with other state-of-the-art techniques for autoscaling and scheduling of serverless functions in a small edge infrastructure. Our solution provides 1.52% QoS violations with a slight increase in the deployed resources, while also significantly reducing the total cost and power consumption for the deployment of the function chain.

Blockchain-Based Decentralized Storage Design for Data Confidence Over Cloud-Native Edge Infrastructure

Blockchain-Based Decentralized Storage Design for Data Confidence Over Cloud-Native Edge Infrastructure

Optimizing the Micro-Architectural Performance of the Current and Emerging Edge Infrastructure

Optimizing the Micro-Architectural Performance of the Current and Emerging Edge Infrastructure

Designing of Edge Infrastructure for Establishing Signal Transmission in Palm Oil Field

Edge computing is real-time processing that gathers, analyses, and transports data at the edge of the network using sensor technologies. It has become one of the IoT era's computing solutions because it improves signal transmission while preventing network problems like delay, congestion, or latency. It is advantageous for farm management to be able to monitor farm activities like cropping and fertilization in real-time, which helps increase palm oil production. However, the farm's remote location limits the amount of signal transmission available from its current Internet service providers, which prevents real-time processing. Hence, suitable edge infrastructure must be designed for establishing signal accessibility for data communication. It also required determining the optimal number of edge nodes to be allocated in the palm field due to different terrain and various tree sizes. The number of edge nodes will have an impact on cost utilization because it relates to setup costs and parameters. In response to data communication issues in the palm oil field, our work focuses on developing the edge computing infrastructure to maintain stable signal transmission for real-time processing. A network topology is designed to identify suitable parameters and settings that aligned with topography of the sample palm oil field. Furthermore, by having the right topology and signal frequency settings, the number of edge nodes required in the field can be determined. In our simulated design, the appropriate setting is to add a router with less than 4K million instructions per second (MIPS) compared to connecting through three mobile hot spots. Note that different palm oil fields will have different settings for their edge infrastructure. Optimistically, our study can provide guidance for promoting signal transmission in areas with limited network accessibility.

HADES: An NFV solution for energy-efficient placement and resource allocation in heterogeneous infrastructures

Network Function Virtualization (NFV) aims to replace traditional network functions running in proprietary hardware with software instances (i.e., Virtual Network Functions, VNFs) embedded in general-purpose virtualization solutions. Aware that the transition to a fully virtualized network infrastructure will pay a high energy cost, especially in IoT systems composed of a myriad of devices, energy efficiency is one of the key innovative targets of future networks. Edge computing should be considered in IoT environments to save time and energy by processing data near the producer devices. However, applying NFV in the context of IoT/Edge/Cloud environments complicates the placement of VNFs, due to the inherent heterogeneous nature of such environments and the variety of resource demands. This paper proposes an energy-aware placement of service function chains of VNFs and a resource-allocation solution for heterogeneous edge infrastructures that considers the computation and communication delays according to the VNFs’ location in the infrastructure. The solution has been integrated with the ETSI-sponsored project Open Source Management and Orchestration (OSM) as an extension called HADES, which allows the configuration of VNFs and their subsequent resource allocation and deployment at the edge, minimizing energy consumption and ensuring a quality of service. We have applied the deployment of augmented reality services in real and simulated scenarios. The results show up to a 59% reduction in power consumption and QoS compliance in all scenarios considered compared to default OSM placement and four other allocation policies. We prove that our solution has negligible power overhead, and validate the scalability and applicability of HADES.

CollOR: Distributed collaborative offloading and routing for tasks with QoS demands in multi-robot system

Multi-access edge computing (MEC) offers prospective opportunities for robots that have various computational tasks to execute in Industry 4.0, smart cities and many other fields. Computational offloading, as the key of this emerging paradigm, can leverage resourceful edge infrastructure nearby to provide computational support for these resource-limited robots. However, existing computational offloading schemes not only rarely consider that robots are geo-distributed, but also ignore the diversified quality of service (QoS) of robotic applications. Thus in this paper, the collaborative computational offloading problem for multi-robot system in MEC is studied, and a distributed scheme is proposed to jointly optimize offloading and routing for computational tasks of robots. First, we introduce the collaborative communication model of robots, construct the multi-criteria utility function related to both the QoS attributes of applications and the energy efficiency of robot, and formulate the joint offloading and routing problem. Afterwards, the QoS preference analysis mechanism is established to characterize the heterogeneous requirements of robotic applications. Next, by designing a novel collaborative robotics framework, we realize the distributed processing and adaptive strategy-making of multi-robots, and integrate the game-theoretic approach to propose a utility-aware collaborative computational offloading algorithm. Finally, simulation results validate that the proposed algorithm has significant performance improvement over the representative algorithms in different evaluation metrics, and can achieve stable and scalable performance in the scenario of the increasing demand for QoS and the growing scale of robots.

Online Distributed Learning-Based Load-Aware Heterogeneous Vehicular Edge Computing

Vehicular edge computing is an emerging enabler in strengthening driving efficiency and traffic safety. However, both performance bottlenecks and low resource efficiency of heterogeneous edge servers arise concurrently because of the inhomogeneous load distribution among the servers. Further, the unsaturated infrastructure coverage situation can deteriorate the concurrent issues. Although transmitting raw task data with large sizes among heterogeneous edge servers can relieve the concurrent issues, it distinctly degrades the core network’s efficiency, especially during rush hours. Meanwhile, it cannot settle the unsaturated coverage situation. To relieve the concurrent issues without degrading the core network’s efficiency, we introduce an aerial relay station that can flexibly relay vehicular tasks to nearby heterogeneous edge servers. The long-term task scheduling problem without any prior environment knowledge for the considered vehicular edge system is crucial but still up in the air. We formulate the system latency minimization problem as a partially observable stochastic game. Then a model-free multi-agent reinforcement learning algorithm is developed to search the real-time load-aware scheduling policy. Besides, we design a practical factor named offloading latency gain to assist the training process of the learning algorithm. Simulation experiments show that our proposed algorithm can better exploit idle computation resources of heterogeneous edge infrastructures and significantly reduce the average system latency up to 15-20% over existing algorithms.

On Jointly Optimizing Partial Offloading and SFC Mapping: A Cooperative Dual-Agent Deep Reinforcement Learning Approach

Multi-access edge computing (MEC) and network function virtualization (NFV) are promising technologies to support emerging IoT applications, especially those computation-intensive. In NFV-enabled MEC environment, service function chain (SFC), i.e., a set of ordered virtual network functions (VNFs), can be mapped on MEC servers. Mobile devices (MDs) can offload computation-intensive applications, which can be represented by SFCs, fully or partially to MEC servers for remote execution. This paper studies the partial offloading and SFC mapping joint optimization (POSMJO) problem in an NFV-enabled MEC system, where the data from an incoming task is partitioned into two parts, with one part executed locally and the other offloaded to the edge infrastructure for execution. These two parts are independent of each other, but both need to be processed by the same SFC. The objective is to minimize the average cost in the long term which is a combination of execution delay, MD's energy consumption, and usage charge for edge computing. This problem consists of two closely related decision-making steps, namely task partition and VNF placement, which is highly complex and quite challenging. To address this, we propose a cooperative dual-agent deep reinforcement learning (CDADRL) algorithm, where two agents interact with each other. Simulation results show that the proposed algorithm outperforms three combinations of deep reinforcement learning algorithms with respect to cumulative reward and it overweighs a number of baseline algorithms in terms of execution delay, energy consumption, and usage charge.