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The edge-cloud computing continuum effectively uses fog and cloud servers to meet the quality of service (QoS) requirements of tasks when edge devices cannot meet those requirements. This paper focuses on the workflow offloading problem in edge-cloud computing and formulates this problem as a nonlinear mathematical programming model. The objective function is to minimize the monetary cost of executing a workflow while satisfying constraints related to data dependency among tasks and QoS requirements, including security and deadlines. Additionally, it presents a genetic algorithm for the workflow offloading problem to find near-optimal solutions with the cost minimization objective. The performance of the proposed mathematical model and genetic algorithm is evaluated on several real-world workflows. Experimental results demonstrate that the proposed genetic algorithm can find admissible solutions comparable to the mathematical model and outperforms particle swarm optimization, bee life algorithm, and a hybrid heuristic-genetic algorithm in terms of workflow execution costs.

Edge clouds have emerged as an essential architecture, revolutionizing data processing and analysis by bringing computational capabilities closer to data sources and end-users at the edge of the network. Anomaly detection is crucial in these settings to maintain the reliability and security of edge-based systems and applications despite limited computational resources. It plays a vital role in identifying unexpected patterns, which could indicate security threats or performance issues within the decentralized and real-time nature of edge cloud environments. For example, in critical edge applications like autonomous vehicles, augmented reality, and smart healthcare, anomaly detection ensures the consistent and secure operation of these systems, promptly detecting anomalies that might compromise safety, performance, or user experience. However, the adoption of anomaly detection within edge cloud environments poses numerous challenges. This thesis aims to contribute by addressing the problem of anomaly detection in edge cloud environments. Through a comprehensive exploration of anomaly detection methods, leveraging machine learning techniques and innovative approaches, this research aims to enhance the efficiency and accuracy of detecting anomalies in edge cloud environments. The proposed methods intend to overcome the challenges posed by resource limitations, the lack of labeled data specific to edge clouds, and the need for accurate detection of anomalies. By focusing on machine learning approaches like transfer learning, knowledge distillation, reinforcement learning, deep sequential models, and deep ensemble learning, this thesis endeavors to establish efficient and accurate anomaly detection systems specific for edge cloud environments. The results demonstrate the improvements achieved by employing machine learning methods for anomaly detection in edge clouds. Extensive testing and evaluation in real-world edge environments show how machine learning-driven anomaly

The research aims to reduce the network resource pressure on cloud centers (CC) and edge nodes, to improve the service quality and to optimize the network performance. In addition, it studies and designs a kind of edge–cloud collaboration framework based on the Internet of Things (IoT). First, raspberry pi (RP) card working machines are utilized as the working nodes, and a kind of edge–cloud collaboration framework is designed for edge computing. The framework consists mainly of three layers, including edge RP (ERP), monitoring & scheduling RP (MSRP), and CC. Among the three layers, collaborative communication can be realized between RPs and between RPs and CCs. Second, a kind of edge–cloud​ matching algorithm is proposed in the time delay constraint scenario. The research results obtained by actual task assignments demonstrate that the task time delay in face recognition on edge–cloud collaboration mode is the least among the three working modes, including edge only, CC only, and edge–CC collaboration modes, reaching only 12 s. Compared with that of CC running alone, the identification results of the framework rates on edge–cloud collaboration and CC modes are both more fluent than those on edge mode only, and real-time object detection can be realized. The total energy consumption of the unloading execution by system users continuously decreases with the increase in the number of users. It is assumed that the number of pieces of equipment in systems is 150, and the energy-saving rate of systems is affected by the frequency of task generation. The frequency of task generation increases with the corresponding reduction in the energy-saving rate of systems. Based on object detection as an example, the system energy consumption is decreased from 18 W to 16 W after the assignment of algorithms. The included framework improves the resource utility rate and reduces system energy consumption. In addition, it provides theoretical and practical references for the implem

The explosive growth of end devices that generate massive amounts of data requires close-proximity computing resources for processing at the network’s edge. Having geographic distributions and limited resources of edge nodes or servers opens several doors for attackers to exploit them primarily to the detriment of deployed services; one of the recent attacks is Very Short Intermittent Distributed Denial of Services (VSI-DDoS). Deep learning-based models have been developed to detect and mitigate such attacks but cause the degrading quality of models due to covariate shifts when deployed in real-world environments. Therefore, we propose a new approach, called MetaVSID, to detect VSI-DDoS attacks in edge clouds using meta-reinforcement learning followed by ensemble learning to increase the robustness of the model in detecting VSI-DDoS attacks early. The proposed model can capture dynamic patterns of VSI-DDoS attacks, from which it identifies manipulated services and increase service availability when covariate shifts at deployment time. We carry out extensive experiments to validate the MetaVSID using both testbed and benchmark datasets. Via the meta-reinforced downsampling process, the proposed method improves sample efficiency, leading to cost-effective policies. Moreover, the optimized policies are generalized to adapt to dynamic changes in the training distribution. Our experimental results demonstrate that MetaVSID stably achieves better performance in multiple evaluation settings with the difference from baseline models from 1.5% to 7.5% in terms of AUC for both VSI-DDoS and DDoS detection, especially under covariate shift settings.

OF@TEIN Playground is a multi-site cloud with distributed edge nodes, which, while offering reduced application latency by processing data at the edge of the network, also introduces new security challenges. To ensure the continuous and secure operation of these nodes, the SmartX Multi-View Visibility and SmartX MultiSec frameworks were introduced. However, these frameworks have limitations regarding AI-powered security mechanisms. The recent surge in data and AI technologies has amplified the need for AI-based and data-driven smart services that can process large amounts of real-time data and provide actionable insights to users. In particular, AI-inspired DevSecOps services can be highly effective in enhancing cybersecurity of the OF@TEIN Playground. However, developing and deploying these services at scale can be a challenge, due to the high costs associated with procuring and maintaining the necessary hardware and software resources. Therefore, this work aims to upgrade the OF@TEIN Playground to flexibly support cloud based AI-inspired DevSecOps services and to adequately meet the current and future demands of playground users. The results show that our effort has resulted in a more reliable, cost-effective, and flexible edge computing research and experimentation testbed.

Edge-driven software applications often deployed as online services in the cloud-to-edge continuum lack significant protection for services and infrastructures against emerging cyberattacks. Very-Short Intermittent Distributed Denial of Service (VSI-DDoS) attack is one of the biggest factor for diminishing the Quality of Services (QoS) and Quality of Experiences (QoE) for users on edge. Unlike conventional DDoS attacks, these attacks live for a very short time (on the order of a few milliseconds) in the traffic to deceive users with a legitimate service experience. To provide protection, we propose a novel and efficient approach for detecting VSI-DDoS attacks using reinforced transformer learning that mitigates the tail latency and service availability problems in edge clouds. In the presence of attacks, the users’ demand for availing ultra-low latency and high throughput services deployed on the edge, can never be met. Moreover, these attacks send very-short intermittent requests towards the target services that enforce longer delays in users’ responses. The assimilation of transformer with deep reinforcement learning accelerates detection performance under adverse conditions by adapting the dynamic and the most discernible patterns of attacks (e.g., multiplicative temporal dependency, attack dynamism). The extensive experiments with testbed and benchmark datasets demonstrate that the proposed approach is suitable, effective, and efficient for detecting VSI-DDoS attacks in edge clouds. The results outperform state-of-the-art methods with 0.9%-3.2% higher accuracy in both datasets.

Edge cloud systems reduce the latency between users and applications by offloading computations to a set of small-scale computing resources deployed at the edge of the network. However, since edge resources are constrained, they can become saturated and bottlenecked due to increased load, resulting in an exponential increase in response times or failures. In this paper, we argue that an application can be split between the edge and the cloud, allowing for better performance compared to full migration to the cloud, releasing precious resources at the edge. We model an application's internal call-Graph as a Directed-Acyclic-Graph. We use this model to develop MicroSplit, a tool for efficient splitting of microservices between constrained edge resources and large-scale distant backend clouds. MicroSplit analyzes the dependencies between the microservices of an application, and using the Louvain method for community detection---a popular algorithm from Network Science---decides how to split the microservices between the constrained edge and distant data centers. We test MicroSplit with four microservice based applications in various realistic cloud-edge settings. Our results show that Microsplit migrates up to 60% of the microservices of an application with a slight increase in the mean-response time compared to running on the edge, and a latency reduction of up to 800% compared to migrating the entire application to the cloud. Compared to other methods from the State-of-the-Art, MicroSplit reduces the total number of services on the edge by up to five times, with minimal reduction in response times.

Web application services and networks become a major target of low-rate Distributed Denial of Service (DDoS) attacks such as Very Short Intermittent DDoS (VSI-DDoS). These threats exploit the TCP congestion control mechanism to cause transient resource outage and impute delays for legitimate users’ requests, while they bypass the secure systems. Besides that, cross-layer VSI-DDoS attacks, where the performed attacks are towards the different layers of the edge cloud infrastructures, are able to cause violation of customers’ Service-Level Agreements (SLAs) with less visible behavioral patterns. In this work, we propose a novel Deep Ensemble Learning Approach named DELA for detection of cross-layer VSI-DDoS on the edge cloud. This approach is developed based on Long Short-Term Memory (LSTM), ensemble learning, and a new voting mechanism based on Feed-Forward Neural Network (FFNN). In addition, it employs a novel training and detection algorithm to combat such attacks in web services and networks. The model shows improved results due to the utilization of historical information in decision- making and also the usage of neural network as aggregator instead of a static threshold-based aggregation. Moreover, we propose a novel overlapped data chunking algorithm that is able to ameliorate the detection performance. Furthermore, the evaluation of DELA shows its superior performance over our testbed and benchmark datasets. Accordingly, DELA achieves on average 4.88% higher F 1 score compared to state-of-the-art methods.

Due to cloud computing’s limitations, edge computing has emerged to address computation-intensive and time-sensitive applications. In edge computing, users can offload their tasks to edge servers. However, the edge servers’ resources are limited, making task scheduling everything but easy. In this paper, we formulate the scheduling of tasks between the user equipment, the edge, and the cloud as a Mixed-Integer Linear Programming (MILP) problem that aims to minimize the total system delay. To solve this MILP problem, we propose an Enhanced Healed Genetic Algorithm solution (EHGA). The results with EHGA are compared with those of CPLEX and a few heuristics previously proposed by us. The results indicate that EHGA is more accurate and reliable than the heuristics and Quicker than CPLEX at solving the MILP problem.

The paper presents an approach to solve the software and hardware related failures in edge-cloud environments, more precisely, in cloud manufacturing environments. The proposed approach, called TOLERANCER, is composed of distributed components that continuously interact in a peer to peer fashion. Such interaction aims to detect stress situations or node failures, and accordingly, TOLERANCER makes decisions to avoid or solve any potential system failures. The efficacy of the proposed approach is validated through a set of experiments, and the performance evaluation shows that it responds effectively to different faults scenarios.

In this paper, we present a hierarchical model for time predictable task scheduling in edge-cloud computing architecture for industrial cyber-physical systems. Regarding the scheduling problem, we also investigate the common problem-solving approaches and discuss our preliminary plan to realize the proposed architecture. Furthermore, an Integer linear programming (ILP) model is proposed for task scheduling problem in the cloud layer. The model considers timing and security requirements of applications and the objective is to minimize the financial cost of their execution.

ANIARA (https://www.celticnext.eu/project-ai-net) attempts to enhance edge architectures for smart manufacturing and cities. AI automation, orchestrated lightweight containers, and efficient power usage are key components of this three-year project. Edge infrastructure, virtualization, and containerization in future telecom systems enable new and more demanding use cases for telecom operators and industrial verticals. Increased service flexibility adds complexity that must be addressed with novel management and orchestration systems. To address this, ANIARA will provide en-ablers and solutions for services in the domains of smart cities and manufacturing deployed and operated at the network edge(s). © 2021 Owner/Author.

The advent of crucial areas such as smart healthcare and autonomous transportation, bring in new requirements on the computing infrastructure, including higher demand for real-time processing capability with minimized latency and maximized availability. The traditional cloud infrastructure has several deficiencies when meeting such requirements due to its centralization. Edge clouds seems to be the solution for the aforementioned requirements, in which the resources are much closer to the edge devices and provides local computing power and high Quality of Service (QoS). However, there are still security issues that endanger the functionality of edge clouds. One of the recent types of such issues is Very Short Intermittent Distributed Denial of Service (VSI-DDoS) which is a new category of low-rate DDoS attacks that targets both small and large-scale web services. This attack generates very short bursts of HTTP request intermittently towards target services to encounter unexpected degradation of QoS at edge clouds. In this paper, we formulate the problem with a sequence modeling approach to address short intermittent intervals of DDoS attacks during the rendering of services on edge clouds using Long Short-Term Memory (LSTM) with local attention. The proposed approach ameliorates the detection performance by learning from the most important discernible patterns of the sequence data rather than considering complete historical information and hence achieves a more sophisticated model approximation. Experimental results confirm the feasibility of the proposed approach for VSI-DDoS detection on edge clouds and it achieves 2% more accuracy when compared with baseline methods.

We consider the computation offloading problem in an edge computing system in which an operator jointly manages wireless and computing resources across devices that make their offloading decisions autonomously with the objective to minimize their own completion times. We develop a game theoretical model of the interaction between the devices and an operator that can implement one of two resource allocation policies, a cost minimizing or a time fair resource allocation policy. We express the optimal cost minimizing resource allocation policy in closed form and prove the existence of Stackelberg equilibria for both resource allocation policies. We propose two efficient decentralized algorithms that devices can use for computing equilibria of offloading decisions under the cost minimizing and the time fair resource allocation policies. We establish bounds on the price of anarchy of the games played by the devices and by doing so we show that the proposed algorithms have bounded approximation ratios. Our simulation results show that the cost minimizing resource allocation policy can achieve significantly lower completion times than the time fair allocation policy. At the same time, the convergence time of the proposed algorithms is approximately linear in the number of devices, and thus they could be effectively implemented for edge computing resource management. ., QC 20220613

The rapid development of the Internet-of-Things (IoT) makes it convenient to sense and collect real-world information with different kinds of widely distributed sensors. With plenty of web services providing diverse functions on the cloud, the collected information can be sufficiently used to complete complex tasks after being uploaded. However, the latency brought by long-distance communication and network congestion limits the development of IoT platforms. A feasible approach to solve this problem is to establish an edge-cloud collaboration (ECC) system based on the multi-access edge computing (MEC) paradigm where the collected information can be refined with the services deployed on nearby edge servers. However, as the edge servers are resource-limited, we should be more careful in allocating the edge resource to services, as well as designing the traffic scheduling strategy. In this paper, we investigated the edge-cloud cooperation mechanism of service provisioning in ECC systems, and to that end, proposed an energy-consumption model for it; we also proposed a performance model and balancing model to quantify the running state of ECC systems. Based on these, we further formulated the energy-effective ECC system optimization problem as a joint optimization problem whose decision variables are the resource allocation strategy and traffic scheduling strategy. With the convexity of this problem proved, we proposed an algorithm to solve it and conducted a series of experiments to evaluate its performance. The results showed that our approach can improve at least 4.3 % of the performance compared with representative baselines.

The Guifi.net community network has evolved during the past 15 years into a telecommunications infrastructure that offers Internet access to more than 80.000 people. The monitoring system currently in place for this network is lagging behind the growth of the infrastructure, requiring manual intervention and counting several single points of failure. In this paper we present REDEMON, a resilient decentralized monitoring system, hosted on distributed and interconnected edge devices, for a reliable, eventually-consistent monitoring of the Guifi.net network, leveraging CRDT-based data structures implemented on AntidoteDB. We developed the REDEMON system as a prototype featuring resilience, decentralization and automation, in order to replace the legacy monitoring system. To assess the system, this prototype was deployed on resource-constraint edge nodes in the Guifi.net production network and evaluated under realistic conditions. The decentralized assignment mechanism successfully achieves setting the minimum number of monitoring servers per network device that satisfies the established system requirements. Besides, by concentrating the workload on the minimum required number of servers running at their maximum capacity, the remaining devices can idle away, reducing the consumption footprint of the system. With regard to computing resources, we measure a moderate CPU and RAM usage by the monitoring system on low-capacity devices, while we observe that a considerable network traffic is required for achieving a resilient and consistent data storage layer. This resilient and decentralized architecture could lay the basis for other edge applications in the cloud computing domain that need to coordinate over distributed and consistent shared data., This work was supported by the European H2020 framework programme project LightKone (H2020-732505), by the Spanish State Research Agency (AEI) under contracts PCI2019- 111850-2 and PCI2019-111851-2, and the Catalan government AGAUR SGR 990., Peer Reviewed, Postprint (author's final draft)

Community-built telecommunication networks such as Guifi.net demonstrate how end users can actively collaborate in the self-provision of network services, for instance by operating a self-organized distributed monitoring system. Network monitoring is performed by many small servers at the users' premises but data are only accessible via a centralized interface. Besides, due to network partitions and churn of the monitoring servers, failures in the monitoring system are frequent, leaving parts of the network unmonitored. Distributed databases are a promising solution for data replication under network partition condition, but they suffer from a trade-off between data consistency and availability. Furthermore, these databases are used in data centers with abundant computing resources, not in light edge networks. In this work we present DIMON, a reliable edge-based, eventually-consistent monitoring system that leverages CRDT-based data structures implemented in AntidoteDB. Conflict-free replicated data types (CRDTs) are able to converge to a consistent state in environments with network partitions as those found in edge networks. Our results give insights on the load of AntidoteDB on edge devices under different scenarios of read and write operations. The experiments carried out in a production network with a real system implemented contribute to the research community's knowledge about the available technologies for a consistent replicated data storage layer to support edge computing clouds., This work was supported by the European H2020 framework programme project LightKone (H2020-732505), by the Spanish government under contract TIN2016-77836-C2-2-R and the Catalan government AGAUR SGR 990., Peer Reviewed, Postprint (author's final draft)