Edge Computing Paradigm Research Articles

Adaptive and Priority-Based Resource Allocation for Efficient Resources Utilization in Mobile-Edge Computing

Edge computing (EC) offers cloud-like services at the edge of mobile networks to satisfy the delay-sensitive and rapid computation applications in meeting the demands of rapidly increasing mobile devices and other Internet of Things. EC is known to be constrained with limited resources that its efficacy greatly depends on an effective and efficient resource allocation to provide optimal resource utilization. Focusing on the fact, this article presents an adaptive resource allocation mechanism, abbreviated as A-PBRA, for effective resources utilization in the EC paradigm. To realize optimal utilization, the available resources are allocated dynamically (adaptability) by considering the nature of the incoming requests. The proposed scheme shall adapt to the resource demands and priorities of the incoming requests. After identifying the received request which can be either the priority-based or normal request, each of them is processed with three possibilities. The available resources are thus allocated as per the priorities of the incoming requests to satisfy the constraints accordingly. The proposed mechanism is adaptable to a maximum number of incoming requests along with optimizing the utilization of limited resources at the edge node. Extensive simulations were performed through ifogsim to evaluate the performance of the proposed method. Critical comparisons were made against closely related algorithms and techniques, i.e., the novel bioinspired hybrid algorithm and the CORA-GT. The simulation results from the proposed scheme optimistically showing that it performed better in terms of resources utilization, average response time, task execution time, and energy consumption.

Techno-Economic Analysis of MEC Clustering Models for Seamless CCAM Service Provision

The latency requirements of delay-sensitive applications such as cooperative, connected, and automated mobility (CCAM) services challenge the capabilities of traditional vehicular radio access technologies (i.e., IEEE 802.11P and cellular networks). To this end, the 5G cellular network is adopting the multi-access edge computing (MEC) paradigm. However, the use of this technology comes with several challenges. In this article, MEC placement challenges and their impact on network deployment costs are studied. We propose three MEC clustering models and compare them from a cost perspective. Results show that all the MEC clustering models outperform the non-clustering approach. In addition, the conditions in which specific clustering models yield the most optimal results are analyzed. Results aim to provide insights into cost-effective MEC deployment models.

Decentralized Scheduling in Edge Computing Paradigms: End-to-End Decision Analytics and the Price of Anarchy

Decentralized Scheduling in Edge Computing Paradigms: End-to-End Decision Analytics and the Price of Anarchy

A Distributed Fuzzy Optimal Decision Making Strategy for Task Offloading in Edge Computing Environment

With the technological evolution of mobile devices, 5G and 6G communication and users’ demand for new generation applications viz. face recognition, image processing, augmented reality, etc., has accelerated the new computing paradigm of Mobile Edge Computing (MEC). It operates in close proximity to users by facilitating the execution of computational-intensive tasks from devices through offloading. However, the offloading decision at the device level faces many challenges due to uncertainty in various profiling parameters in modern communication technologies. Further, with the increase in the number of profiling parameters, the fuzzy-based approaches suffer inference searching overheads. In this context, a fuzzy-based approach with an optimal inference strategy is proposed to make suitable offloading decision. The proposed approach utilizes Classification and Regression Tree (CART) mechanism at the inference engine with reduced time complexity of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (| <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</i> | <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">log</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> | <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> |)), as compared to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (| <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> | <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">|V |</sup> ) of state-of-the-art, conventional fuzzy-based offloading approaches, and has been proved to be more efficient. The performance of the proposed approach is evaluated and compared with contemporary offloading algorithms in a python-based fog and edge simulator, YAFS. The simulation results show the reduction in average task processing time, average task completion time, energy consumption, improved server utilization, and tolerance to latency and delay sensitivity for the offloaded tasks in terms of reduced task failure rates.

Offloading dependent tasks in MEC-enabled IoT systems: A preference-based hybrid optimization method

The rapid development of IoT-based services has resulted in an exponential increase in the number of connected smart mobile devices (SMDs). Processing the massive data generated by the large number of SMDs is becoming a big problem for mobile devices, servers, and wireless communication channels. A Multi-access Edge Computing (MEC) paradigm partially mitigates this problem by deploying edge server nodes at the edge of wireless networks nearby SMDs, but the challenge still remains due to the limited computation capacity of MEC servers and the bandwidth of wireless channels. In addition, the dependency of tasks generated by applications on SMDs increases the complexity of the problem. In this paper, we propose a constrained multiobjective computation offloading optimization solution to resolve the problem of task dependency under limited resources. This solution improves the Quality of Service (QoS) through minimizing the latency, energy consumption, and rate of task failure caused by limited resources. We propose a two-staged hybrid computation offloading optimization method to solve the problem. In the first stage, the computation offloading decisions are made based on the preferences of tasks. Then, in the second stage, nearly optimal solutions are found using the modified Non-Dominated Sorting Genetic Algorithm (NSGA-III). The overall efficiency of the proposed method is increased owing to the preference-based algorithm reinforcing the NSGA-III algorithm by generating a better initial population. The results of extensive experiments show that the efficiency of the proposed method is significantly better than the existing methods.

LightMAN: A Lightweight Microchained Fabric for Assurance- and Resilience-Oriented Urban Air Mobility Networks

Rapid advancements in the fifth generation (5G) communication technology and mobile edge computing (MEC) paradigm have led to the proliferation of unmanned aerial vehicles (UAV) in urban air mobility (UAM) networks, which provide intelligent services for diversified smart city scenarios. Meanwhile, the widely deployed Internet of drones (IoD) in smart cities has also brought up new concerns regarding performance, security, and privacy. The centralized framework adopted by conventional UAM networks is not adequate to handle high mobility and dynamicity. Moreover, it is necessary to ensure device authentication, data integrity, and privacy preservation in UAM networks. Thanks to its characteristics of decentralization, traceability, and unalterability, blockchain is recognized as a promising technology to enhance security and privacy for UAM networks. In this paper, we introduce LightMAN, a lightweight microchained fabric for data assurance and resilience-oriented UAM networks. LightMAN is tailored for small-scale permissioned UAV networks, in which a microchain acts as a lightweight distributed ledger for security guarantees. Thus, participants are enabled to authenticate drones and verify the genuineness of data that are sent to/from drones without relying on a third-party agency. In addition, a hybrid on-chain and off-chain storage strategy is adopted that not only improves performance (e.g., latency and throughput) but also ensures privacy preservation for sensitive information in UAM networks. A proof-of-concept prototype is implemented and tested on a micro-air–vehicle link (MAVLink) simulator. The experimental evaluation validates the feasibility and effectiveness of the proposed LightMAN solution.

Prediction of health monitoring with deep learning using edge computing

Today's modern computing environment provides a smart healthcare monitoring system for early prediction of fall detection. The Internet of Things-based health model plays a significant role in the health care service area and helps to improve the processing of data and its prediction. Transferring reports or data from one place to another takes too much time and energy, and it will cause high latency and energy issues. To handle these kinds of hazards, edge computing provides solutions. In this paper w presents smart healthcare system issues, services, and applications. Furthermore, propose a CNN-based prediction model with the use of edge computing and IoT paradigms. Edge computing is a distributed environment framework that enables rapid resource availability and response time through local edge servers computed at the end of IoT devices. The CNN model is used to analyse the health data collected by IoT devices. Furthermore, the role of edge devices is to provide doctors and patients with timely health-prediction reports via edge servers. The proposed mechanism can be analysed using accuracy and error rate performance parameters. In the proposed mechanism, the accuracy is 99.23% in comparison with other techniques.

Price and Risk Awareness for Data Offloading Decision-Making in Edge Computing Systems

The proliferation of multiaccess edge computing (MEC) paradigm has created a challenging multiuser–multiserver–multiaccess edge computing competitive environment, which brings the problem of data offloading decision-making to the forefront of research. In this article, we address this issue while jointly studying the impact of the user behavioral characteristics and the MEC servers pricing policies on determining the optimal user data offloading strategies. Prospect theory is exploited to reflect the user satisfaction and subjectivity from the data offloading, while the MEC servers’ probability of failure owing to the potential overexploitation by the users, is modeled via the theory of tragedy of the commons. A multileader multifollower Stackelberg game is formulated among the MEC servers (leaders) and the users (followers), to determine the servers’ optimal pricing policies and the users’ optimal data offloading strategies. The users’ data offloading decision-making is formulated as a noncooperative game among them and a Nash equilibrium is determined, while the MEC servers’ optimal computing service prices are obtained either through a semiautonomous game-theoretic approach, or through a fully autonomous reinforcement learning-based approach. The performance evaluation and demonstration of the superiority of the proposed framework against other benchmarking alternatives is achieved via modeling and simulation.

A multi-layer guided reinforcement learning-based tasks offloading in edge computing

The breakthrough in Machine Learning (ML) techniques and the popularity of the Internet of Things (IoT) has increased interest in applying Artificial Intelligence (AI) techniques to the new paradigm of Edge Computing. One of the challenges in edge computing architectures is the optimal distribution of the generated tasks between the devices in each layer (i.e., cloud-fog-edge). In this paper, we propose to use Reinforcement Learning (RL) to solve the Task Assignment Problem (TAP) at the edge layer and then we propose a novel multi-layer extension of RL (ML-RL) techniques that allows edge agents to query an upper-level agent with more knowledge to improve the performance in complex and uncertain situations. We first formulate the task assignment process considering the trade-off between energy consumption and execution time. We then present a greedy solution as a baseline and implement our multi-layer RL proposal in the PureEdgeSim simulator. Finally several simulations of each algorithm are evaluated with different numbers of devices to verify scalability. The simulation results show that reinforcement learning solutions outperformed the heuristic-based solutions and our multi-layer approach can significantly improve performance in high device density scenarios.

A survey of mobility-aware Multi-access Edge Computing: Challenges, use cases and future directions

Many mobile and pervasive applications avail cloud services to reduce overheads in on-device computation. The performance of these services depends on the available bandwidth of the underlying network, the physical proximity of the cloud server and the end devices, the volume of data, the computational capacity of the server, and, importantly, the mobility of the devices hosting the applications. Edge computing promises to provide better performance by bringing services (e.g., a video streaming service) from the cloud to servers near the user. It also enables partial or full offloading of the computation (tasks) and storage functionalities from the User Equipment (UE) to the edge of the network. This saves power and benefits from relatively more powerful devices at the edge. Multi-access Edge Computing (MEC), which supports wireless and wired access technologies, has gained significant research interest. When UEs move, services must continue to operate, tasks may need to be offloaded again, and states related to tasks and services may need to be migrated. In this paper, we focus on four functional components (task/service offloading, resource allocation, content/task caching, and service/task migration) of MEC. We survey the challenges to these and their solutions in the context of UE mobility. Mobility creates challenges during offloading resource-intensive tasks as the user may move while the task is being offloaded. Some of the other challenges are how to jointly allocate computing and communication resources, minimize service down time during migration, and share the backhaul network if the same MEC host must continue to be used. Some key research areas include intelligent task offloading and service migration algorithms, exploiting group mobility to improve task migration time, studying the interplay of MEC parameters such as capabilities of the target MEC host, etc. In addition, predicting the mobile trajectory through intelligent methods and implementations with datasets from real-world scenarios are required.We compare this paper on 11 parameters (service migration, task offloading, resource allocation, content caching, mobility, use cases, architecture, computing paradigm, mobility model, system model, virtualization/Software Defined Networks) with 31 other survey papers from 2018 to April 2022 in MEC and related domains. We discuss the Edge Computing paradigm, the system architecture and model descriptions, and use cases. We briefly explain the relevant challenges and future directions in emerging domains, such as the Internet of drones and Digital twins. We also discuss future research directions in task/service migration, offloading, resource management, distributed computing, reliability, and Quality of Service, all related to mobility in MEC.

STMGCN: Mobile Edge Computing-Empowered Vessel Trajectory Prediction Using Spatio-Temporal Multigraph Convolutional Network

The revolutionary advances in machine learning and data mining techniques have contributed greatly to the rapid developments of maritime Internet of Things (IoT). In maritime IoT, the spatio-temporal vessel trajectories, collected from the hybrid satellite-terrestrial automatic identification system (AIS) base stations, are of considerable importance for promoting traffic situation awareness and vessel traffic services, etc. To guarantee traffic safety and efficiency, it is essential to robustly and accurately predict the AIS-based vessel trajectories (i.e., the future positions of vessels) in maritime IoT. In this work, we propose a spatio-temporal multigraph convolutional network (STMGCN)-based trajectory prediction framework using the mobile edge computing (MEC) paradigm. Our STMGCN is mainly composed of three different graphs, which are, respectively, reconstructed according to the social force, the time to closest point of approach, and the size of surrounding vessels. These three graphs are then jointly embedded into the prediction framework by introducing the spatio-temporal multigraph convolutional layer. To further enhance the prediction performance, the self-attention temporal convolutional layer is proposed to further optimize STMGCN with fewer parameters. Owing to the high interpretability and powerful learning ability, STMGCN is able to achieve superior prediction performance in terms of both accuracy and robustness. The reliable prediction results are potentially beneficial for traffic safety management and intelligent vehicle navigation in MEC-enabled maritime IoT.

Energy-Minimized Partial Computation Offloading for Delay-Sensitive Applications in Heterogeneous Edge Networks

Mobile Devices (MDs) support various delay-sensitive and computation-intensive applications. Yet they only have limited battery energy and computing resources, thereby failing to totally run all applications. A mobile edge computing (MEC) paradigm has been proposed to provide additional computation, storage, and networking resources for MDs. Servers in MEC are often deployed in both macro base stations (MBSs) and small base stations (SBSs). Thus, it is highly challenging to associate resource-limited MDs to them with high performance, and realize partial computation offloading among them for minimizing total energy consumption of an MEC system. To tackle these challenges, this work proposes a novel computation offloading approach for delay-sensitive applications with multiple separable tasks in hybrid networks including MDs, SBSs, and an MBS. To achieve it, this work formulates total energy consumption minimization as a constrained mixed integer non-linear program. To solve it, this work designs an improved meta-heuristic optimization algorithm called <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u> article swarm optimization based on <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</u> enetic <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</u> earning (PGL), which integrates strong local search capacity of a particle swarm optimizer, and genetic operations of a genetic algorithm. PGL jointly optimizes task offloading among MDs, SBSs, and MBS, users’ connection to SBSs, MDs’ CPU speeds and transmission power, SBSs and MBS, and bandwidth allocation of available channels. Simulations with real-world data collected from Google cluster trace demonstrate that PGL significantly outperforms other existing methods in total energy consumption of an entire system.

Edge-Computing Meshed Wireless Acoustic Sensor Network for Indoor Sound Monitoring.

This work presents the design of a wireless acoustic sensor network (WASN) that monitors indoor spaces. The proposed network would enable the acquisition of valuable information on the behavior of the inhabitants of the space. This WASN has been conceived to work in any type of indoor environment, including houses, hospitals, universities or even libraries, where the tracking of people can give relevant insight, with a focus on ambient assisted living environments. The proposed WASN has several priorities and differences compared to the literature: (i) presenting a low-cost flexible sensor able to monitor wide indoor areas; (ii) balance between acoustic quality and microphone cost; and (iii) good communication between nodes to increase the connectivity coverage. A potential application of the proposed network could be the generation of a sound map of a certain location (house, university, offices, etc.) or, in the future, the acoustic detection of events, giving information about the behavior of the inhabitants of the place under study. Each node of the network comprises an omnidirectional microphone and a computation unit, which processes acoustic information locally following the edge-computing paradigm to avoid sending raw data to a cloud server, mainly for privacy and connectivity purposes. Moreover, this work explores the placement of acoustic sensors in a real scenario, following acoustic coverage criteria. The proposed network aims to encourage the use of real-time non-invasive devices to obtain behavioral and environmental information, in order to take decisions in real-time with the minimum intrusiveness in the location under study.

A Bilevel Programming Framework for Joint Edge Resource Management and Pricing

The emerging edge computing (EC) paradigm promises to provide low latency and ubiquitous computation to numerous mobile and Internet of Things (IoT) devices at the network edge. How to efficiently allocate geographically distributed heterogeneous edge resources to a variety of services is a challenging task. While this problem has been studied extensively in recent years, most of the previous work has largely ignored the preferences of the services when making edge resource allocation decisions. To this end, this article introduces a novel bilevel optimization model, which explicitly takes the service preferences into consideration, to study the interaction between an EC platform and multiple services. The platform manages a set of edge nodes (ENs) and acts as the leader while the services are the followers. Given the service placement and resource pricing decisions of the leader, each service decides how to optimally divide its workload to different ENs. The proposed framework not only maximizes the profit of the platform but also minimizes the cost of every service. When there is a single EN, we derive a simple analytic solution for the underlying problem. For the general case with multiple ENs and multiple services, we present a Karush–Kuhn–Tucker-based solution and a duality-based solution, combining with a series of linearizations, to solve the bilevel problem. Extensive numerical results are shown to illustrate the efficacy of the proposed model.

A secure edge power system based on a Docker container

Thanks to the advantages of low latency, high efficiency, and oneself-security, the edge computing (EC) paradigm is expected to widely be applied in a large number of scenarios. However, because EC equipment is closer to a wide variety of terminals in a realistic environment, it is also more vulnerable to terminal attacks. The security of edge computing equipment itself cannot be ignored. Docker is a lightweight container technology that closely fits the existing needs of edge computing for portability, security isolation, and convenience. In this paper, Docker technology is taken as an edge computing security support engine and a security monitoring system based on the Docker container is built. In addition, combined with container monitoring and objective weighting method, a node security judgment method is proposed. Finally, according to the results of the node security judgment, a method for evaluating the security of the unmonitored node is put forward. Consequently, an edge computing security monitoring system based on the Docker container is built, and its performance in the real environment of a smart grid system is implemented to verify the proposed method. The results prove the efficiency and security protection of the novel edge power system.

A Network-Embedding-Based Approach for Scalable Network Navigability in Content-Centric Social IoT

Social Internet of Things (SIoT) boosts the Internet of Things (IoT) by integrating the concept of social networking to advance the discovery of content and service. Another common and promising solution to IoT’s improvement is adopting the emerging multiaccess edge computing (MEC) paradigm by bringing computational and storage capacity at the edge. Concerning the MEC-enabled SIoT, this article aims to propose a network-embedding-based solution for scalable network navigation via leveraging the social similarity of SIoT. Different from the traditional SIoT, we characterize the social relationship from the contents point of view since MEC enables the edge with cache storage. Then, a novel heuristic embedding algorithm termed HeurEmb is proposed to embed the SIoT into the hyperbolic space to facilitate coordinate-based navigability. HeurEmb is a decentralized approach that leverages the social similarity to compute coordinates, bypassing the heavy computation of numerically maximizing the likelihood. Moreover, HeurEmb infers the virtual coordinates on multiple-level hyperbolic disks, which enabled the efficiently vertical and horizontal search for the destination. We analyze the complexity of HeurEmb and then evaluate its performance via the simulation study. The simulation results show that HeurEmb is efficient, achieving a decrease of up to two orders of magnitude of running time compared with the latest work, and effective, improving the success ratio and navigation path length. Finally, we apply HeurEmb in a content retrieval scenario of SIoT using a real-world trace. We make only a minor modification to HeurEmb-based forwarding, and the resulting strategy can achieve similar performance as the best benchmark.

Learning Based Energy Efficient Task Offloading for Vehicular Collaborative Edge Computing

Extensive delay-sensitive and computation-intensive tasks are involved in emerging vehicular applications. These tasks can hardly be all processed by the resource constrained vehicle alone, nor fully offloaded to edge facilities (like road side units) due to their incomplete coverage. To this end, we refer to the new paradigm of vehicular collaborative edge computing (VCEC) and make the best use of vehicles’ idle and redundant resources for energy consumption reduction within the VCEC system. To realize this target, we are faced with several nontrivial challenges, including short-term decision making coupled with long-term queue delay constraints, information uncertainty, and task offloading conflicts. Accordingly, we apply Lyapunov optimization to decouple the original problem into three sub-problems and then tackle them one by one: the first sub-problem is resolved by Lagrange multiplier method; the second is handled by UCB learning-matching approach; the third is addressed by a carefully designed greedy method. Scenarios without volatility and real-world road topology with realistic vehicular traffics are utilized to evaluate the proposed solution. Results from extensive numerical simulations demonstrate that our solution can achieve superior performances compared with the benchmark methods, in terms of energy consumption, learning regret, task backlog, and end-to-end delay.

Flow Assignment and Processing on a Distributed Edge Computing Platform

The evolution of telecommunication networks toward the fifth generation of mobile services (5G), along with the increasing presence of cloud-native applications, and the development of Cloud and Mobile Edge Computing (MEC) paradigms, have opened up new opportunities for the monitoring and management of logistics and transportation. We address the case of distributed streaming platforms with multiple message brokers to develop an optimisation model for the real-time assignment and load balancing of event streaming generated data traffic among Edge Computing facilities. The performance indicator function to be optimised is derived by adopting queuing models with different granularity (packet- and flow-level) that are suitably combined. A specific use case concerning a logistics application is considered and numerical results are provided to show the effectiveness of the optimisation procedure, also in comparison to a “static” assignment proportional to the processing speed of the brokers.

Edge Robotics: Edge-Computing-Accelerated Multirobot Simultaneous Localization and Mapping

With the wide penetration of smart robots in multifarious fields, the simultaneous localization and mapping (SLAM) technique in robotics has attracted growing attention in the community. Yet collaborating SLAM over multiple robots still remains challenging due to performance contradiction between the intensive graphics computation of SLAM and the limited computing capability of robots. While traditional solutions resort to the powerful cloud servers acting as an external computation provider, we show by real-world measurements that the significant communication overhead in data offloading prevents its practicability to real deployment. To tackle these challenges, this article promotes the emerging edge-computing paradigm into multirobot SLAM and proposes RecSLAM, a multirobot laser SLAM system that focuses on accelerating the map construction process under the robot–edge–cloud architecture. In contrast to the conventional multirobot SLAM that generates graphic maps on robots and completely merges them on the cloud, RecSLAM develops a hierarchical map fusion technique that directs robots’ raw data to edge servers for real-time fusion and then sends to the cloud for global merging. To optimize the overall pipeline, an efficient multirobot SLAM collaborative processing framework is introduced to adaptively optimize robot-to-edge offloading tailored to heterogeneous edge resource conditions, meanwhile ensuring the workload balancing among the edge servers. Extensive evaluations show RecSLAM can achieve up to 39.31% processing latency reduction over the state of the art. Besides, a proof-of-concept prototype is developed and deployed in real scenes to demonstrate its effectiveness.

Knowledge reuse in edge computing environments

To cope with the challenge of managing numerous computing devices, humongous data volumes and models in Internet-of-Things environments, Edge Computing (EC) has emerged to serve latency-sensitive and compute-intensive applications. Although EC paradigm significantly eliminates latency for predictive analytics tasks by deploying computation on edge nodes’ vicinity, the large scale of EC infrastructure still has huge inescapable burdens on the required resources. This paper introduces a novel paradigm where edge nodes effectively reuse local completed computations (e.g., trained models) at the network edge, coined as knowledge reuse. Such paradigm releases the burden from individual nodes, where they can save resources by relying on reusing models for various predictive analytics tasks (e.g., regression and classification). We study the feasibility of our paradigm by involving pair-wise (dis)similarity metrics among datasets over nodes based on statistical learning techniques (kernel-based Maximum Mean Discrepancy and eigenspace Cosine Dissimilarity). Our paradigm is enhanced with computationally lightweight monitoring mechanisms, which rely on Holt-Winters to forecast future violations and updates of the reused models. Such mechanisms predict when ‘borrowed’ models are insufficient for being reused, triggering a new process of finding more appropriate models to be reused at the network edge. We provide comprehensive performance evaluation and comparative assessment of our algorithms over different experimental scenarios using real and synthetic datasets. Our findings showcase the ability and robustness of our paradigm to maintain up-to-date reused models at the edge trading off quality of analytics and resource utilization.