Segmented Network Architecture for Promoting High Availability in Fog Computing through Middleware

SummaryThe authors introduce an enhanced HTML5 (H5) Cross‐Site Scripting (H5‐XSS) attack vector defensive model that safeguards and maintains the confidentiality of users accessing the web applications hosted on the fog nodes. Initially, the model performs in self‐training manner and estimates some features of H5 script code at the simulated desktop host systems of cloud infrastructure. Second, a process of H5 feature estimation gets re‐executed for the online generated HTTP response on the edge network of fog device. Any oddity in these two features sets results in the consequent nested context‐familiar sanitization of the distrustful script code on an installed fog nodes. The author design their framework structure on an open application infrastructural structure of Cisco IOx framework of various networked edge devices as fog nodes. The authors configure the tested bed of H5 web platforms on the nodes of the fog computing network for evaluating the malicious script alleviation capability of their model. Evaluation outcomes expose that the author's technique is skilled sufficient for sensing and excluding the suspicious H5 code with sufferable percentage of False Negatives (FNs), False Positives (FPs), and reduced overall performance overhead during the peak congestion of generation of sanitized HTTP response on the fog nodes.

Meso-level Comparison of Mental Health Service Availability and Use in Chile and Spain

Wireless nodes are one of the main components in different applications that are offered in a smart city. These wireless nodes are responsible to execute multiple tasks with different priority levels. As the wireless nodes have limited processing capacity, they offload their tasks to cloud servers if the number of tasks exceeds their task processing capacity. Executing these tasks from remotely placed cloud servers causes a significant delay which is not required in sensitive task applications. This execution delay is reduced by placing fog computing nodes near these application nodes. A fog node has limited processing capacity and is sometimes unable to execute all the requested tasks. In this work, an optimal task offloading scheme that comprises two algorithms is proposed for the fog nodes to optimally execute the time-sensitive offloaded tasks. The first algorithm describes the task processing criteria for local computation of tasks at the fog nodes and remote computation at the cloud server. The second algorithm allows fog nodes to optimally scrutinize the most sensitive tasks within their task capacity. The results show that the proposed task execution scheme significantly reduces the execution time and most of the time-sensitive tasks are executed.

Calculations of service availability of a high-availability (HA) cluster are usually based on the assumption of load- independent machine availabilities. In this paper, we study the issues and show how the service availabilities can be calculated under the assumption that machine availabilities are load dependent. We present a Markov chain analysis to derive the steady-state service availabilities of a load-dependent machine availability HA cluster. We show that with a load-dependent machine availability, the attained service availability is now policy dependent. After formulating the problem as a Markov decision process, we proceed to determine the optimal policy to achieve the maximum service availabilities by using the method of policy iteration. Two greedy assignment algorithms are studied: least load and first derivative length (FDL) based, where least load corresponds to some load balancing algorithms. We carry out the analysis and simulations on two cases of load profiles: In the first profile, a single machine has the capacity to host all services in the HA cluster; in the second profile, a single machine does not have enough capacity to host all services. We show that the service availabilities achieved under the first load profile are the same, whereas the service availabilities achieved under the second load profile are different. Since the service availabilities achieved are different in the second load profile, we proceed to investigate how the distribution of service availabilities across the services can be controlled by adjusting the rewards vector.

The rapid growth of Internet of Things (IoT) applications poses a great challenge to the computation capability of smart mobile equipments (SMEs). Fog Computing, as a promising technology, provides fast computing services for resource-limited SMEs in various applications. In this paper, we consider a blockchain-based fog computing network consisting of SMEs, fog nodes (FNs) and the cloud server. To optimize the delay and energy consumption of processing computation-intensive tasks, two offloading models are introduced, namely, task offloading to the device-to-device (D2D) cooperation group and to a nearby FN. Additionally, the blockchain technology is enabled to prevent malicious nodes from modifying with transaction information by maintaining a continuous tamper-proof ledger database. To reduce the delay and energy consumption of the traditional consensus mechanism, we propose the voting-based delegated proof of stake consensus mechanism, in which the FNs with the top half of votes will form a verification set, and the FNs will take turns being the manager to generate new blocks. Furthermore, to minimize the network cost, we jointly optimize task offloading decision, transmission rata allocation and computing resource allocation under various constraints. Finally, the effectiveness of the proposed scheme is demonstrated.

Ensuring optimal service continuity remains a critical challenge in cloud computing, especially when dealing with high traffic loads and system failure potential that can cause losses. To address this, this research presents the implementation of a high availability (HA) cloud system using the Least Connection load balancing algorithm implemented with Nginx, integrated with early anomaly detection and alert mechanisms. The HA architecture is implemented across two geographically distributed cloud service providers, Alibaba Cloud and Google Cloud, to analyze latency and performance differences under high load conditions. The system's resilience and scalability were evaluated through load testing using K6, simulating workloads ranging from 100 to 1000 Virtual Users (VUs) for single server configurations and 200 to 2000 VUs for HA configurations. The experiment results showed a significant improvement in service availability, reaching 100% uptime with the HA configuration compared to a peak of 98.79% in the single server environment. The Least Connection strategy effectively balanced traffic by monitoring active connections, resulting in a 29.73% increase in processed requests and a 42% reduction in system load at 1000 VUs. Additionally, the alert system successfully sent real-time Telegram notifications for delays or failures, enabling proactive mitigation. These results confirm that combining dynamic load balancing with proactive alerts can significantly improve service reliability, resource efficiency, and resilience to failures in distributed cloud infrastructure providing a viable model for robust and scalable cloud service architectures.

In the TBFC (Tree-Based Fog Computing) and TWTBFC (Two-Way TBFC) models the electric energy consumed by fog nodes and servers can be reduced in the fog computing (FC) model. Here, fog nodes are hierarchically structured in a height-balanced tree, where a root node is a cloud of servers, leaf nodes are edge nodes which communicate with devices, and each node receives data from child nodes and sends the processed data to a parent node. In the TWTBFC model, nodes send processed data to not only a parent node but also each child node. In order to reduce the network traffic in the TWTBFC model, only aggregate nodes at some level collect the output data of every other aggregate node, i.e. aggregate data. Since only target actuators are to be activated, the aggregate data has to be only delivered to target actuators. Nodes whose descendant actuators are target ones are relay nodes. On receipt of aggregate data, only relay nodes forward the aggregate data to the child nodes. We evaluate the new TWTBFC model in terms of energy consumption of nodes and number of messages transmitted to deliver aggregate data to edge nodes.

Fog computing (also known as edge computing) is a decentralized computing architecture that seeks to minimize service latency and average response time in IoT applications by providing compute and network services physically close to end-users. Fog environment consists of a network of fog nodes and IoT applications are composed of containerized microservices communicating with each other. Due to limited resources of fog nodes, it is often not possible to deploy all the containers of an application on a single fog node. Therefore, communicating containers need to be distributed on multiple fog nodes. Distribution and management of containerized IoT applications is always a critical issue to the system performance in a fog environment. Kubernetes, an open-source system, has grown into a container orchestration standard by simplifying the deployment and management of containerized applications. Despite the progress made by the academia and industry with respect to container management and the wide-scale acceptance of Kubernetes in cloud environments, container management in fog environment is still in the early stage in terms of research and practical deployment. This article aims to fill this gap by analyzing the expediency of Kubernetes container orchestration tool in the fog computing model. The paper also highlights limitations with the current Kubernetes approach and provide ideas for further research to adapt to the needs of the fog environment. Lastly, we provide experiments that demonstrate the feasibility and industrial practicality of deploying and managing containerized IoT applications in the fog computing environment.

Recently, there has been rapid growth in the Internet of things, the Internet of vehicles, fog computing, and social Internet of vehicles SIoV , which can generate large amounts of real-time data. Now, researchers have begun applying fog computing to the SIoV to reduce the computing pressure on cloud servers. However, there are still security challenges in SIoV . In this paper, we propose a lightweight and authenticated key agreement protocol based on fog nodes in SIoV . The protocol completes the mutual authentication between entities and generates the session key for subsequent communication. Through a formal analysis of the Burrows–Abadi–Needham (BAN) logic, real-oracle random (ROR) model, and ProVerif, the security, validity, and correctness of the proposed protocol are demonstrated. In addition, informal security analysis shows that our proposed protocol can resist known security attacks. We also evaluate the performance of the proposed protocol and show that it achieves better performance in terms of computing power and communication cost.

The rapidly growing amount of healthcare data, propelled by wearable sensors, personal health records (EHRs), medical monitoring systems, and Internet of Things (IoT) devices, poses serious problems for conventional cloud-based solutions. High latency, higher network bandwidth use, and concerns about data security and privacy are some of these difficulties. This research suggests a fog computing-based approach to managing, processing, and assessing health data in real-time in hospital settings in order to address these issues. To process, filter, and do preliminary analysis near the data sources, the recommended framework makes the most of fog nodes that are set up strategically. This decentralized system efficiently lowers latency and improves the responsiveness of healthcare applications via the combination of edge devices, fog nodes, and cloud services. By enhancing patient monitoring and emergency response capabilities, the fog computing model lessens reliance on cloudbased services for real-time healthcare decision-making. When compared to conventional cloud-only systems, models of hospitals revealed significant gains, such as fewer delays in data transfer, lower network bandwidth requirements, and improved real-time processing. These results demonstrate how well the concept works for important medical applications. The fog system offers scalable data management, improves data privacy, and accelerates the care of patients. Use cases like as realtime emergency warnings, predictive analytics-based early disease identification, and remote patient monitoring are ideal for it. Strategies for tackling issues with data security, resource allocation, and fog node optimization have been addressed. The fog level machine learning integration of the system extensively optimizes its abilities to support proactive healthcare, which increases patient outcomes by equipping healthcare providers to respond with greater speed and effectiveness.

Fog computing provides distributed processing and storage solutions for real-time applications that demand low latency and fast response times. This makes fog solutions suitable for internet-of-things (IoT) devices that request various network functions. To offer multiple functions for IoT applications, fog nodes can leverage network function virtualization (NFV) for a scalable and elastic function modification, i.e., without the need for dedicated hardware. Despite the saliencies achieved from the synergistic combination between fog and NFV technologies, a key challenge here is the limited resources at the fog nodes. This makes the latter susceptible to rapid node saturation and network congestion at high traffic volumes. Along this, efficient resource utilization and load distribution mechanisms are necessary to enhance admission rates and quality-of-service (QoS). Hence, this paper proposes novel load migration schemes for NFV-based fog networks that aim to reduce the overhead of the migration process. Namely, a coarse-grained load diffusion scheme is adopted to reduce migration frequencies, incurred delay, and cost. Further, destination nodes are selected based on least-load (LL) or least-delay (LD) mechanisms to accommodate delay-sensitive, delay-tolerant, and computation-intensive IoT applications.

Smart industries enabling automation and data exchange in manufacturing technologies demanding real-time processing, nearby storage, and reliability, all of which can be satisfied by the fog computing architecture. With the emergence of smart devices coupled with a diverse range of application requirements, it is essential to have an intelligent fog network where intelligence is spread across all network segments, taking network nodes self-aware and self-decision making. In fog networks, an optimal distribution decision faces challenges due to uncertainties associated with user workload and available resources at the fog nodes and also the wide range of node’s computing power. Given this challenge, a computational offloading and CPU resource scheduling method for minimizing energy consumption is proposed. To investigate the characteristics for offloading and optimizing their allocation, we consider two types of tasks, namely, offloadable and nonoffloadable tasks. The independent fog nodes adopt the same strategy without prior knowledge of the dynamic statistics and global observations, aiming to maximize a common goal with cooperative behaviors. Then, the deep recurrent <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -network (DRQN) is applied to deal with the partial-observability from limited information. The proposed DRQN-based method requires comparatively less computational complexity than the conventional <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning algorithm. The simulation results show that the proposed method can effectively deal with both transmission and CPU energy consumptions while guaranteeing convergence in a limited time.

In this paper, we study the problem of co-migrating a set of service replicas residing on one or more redundant virtual servers in clouds in order to satisfy a sequence of mobile batch-request demands in a cost effective way. With such a migration, we can not only reduce the service access latency for end users but also minimize the network costs for service providers. The co-migration can be achieved at the cost of bulk-data transfer and increases the overall monetary costs for the service providers. To gain the benefits of service migration while minimizing the overall costs, we propose a co-migration algorithm <i>Migk</i> for multiple servers, each hosting a service replicas. <i>Migk</i> is a randomized algorithm with a competitive cost of <inline-formula><tex-math> $O(\frac{\gamma\, \log \,n}{\min \lbrace \frac{1}{\kappa },\frac{\mu }{\lambda \,+\,\mu }\rbrace })$</tex-math> </inline-formula> to migrate <inline-formula><tex-math>$\kappa$</tex-math></inline-formula> services in a static <inline-formula><tex-math>$n$</tex-math></inline-formula> -node network where <inline-formula> <tex-math>$\gamma$</tex-math> </inline-formula> is the maximal ratio of the migration costs between any pair of neighbor nodes in the network, and where <inline-formula><tex-math>$\lambda$</tex-math></inline-formula> and <inline-formula><tex-math>$\mu$</tex-math></inline-formula> represent the maximum wired transmission cost and the wireless link cost respectively. For comparison, we also study this problem in its static off-line form by proposing a parallel dynamic programming (hereafter DP) based algorithm that integrates the branch&bound strategy with sampling techniques in order to approximate the optimal DP results. We validate the advantage of the proposed algorithms via extensive simulation studies using various requests patterns and cloud network topologies. Our simulation results show that the proposed algorithms can effectively adapt to mobile access patterns to satisfy the service request sequences in a cost-effective way.

Cloud computing is a distributed architecture that has shared resources, software, and information is provided to computers and other devices on a scalable platform and demand. Availability of the cloud services is one of the key security issues in it. Distributed Denial of Service (DDoS) is an attack that threats the availability of the cloud services. In this paper, effect of the DDoS attack on the cloud is investigated. Therefore, a model for attack based on the DDoS is designed, and then we simulate a cloud system on the experimental environment. Experiments show that the cloud system is vulnerable to this attack. In order to confronting this attack, several solutions are proposed in this paper. We suggest load balancing and honeypots beside the intrusion detection systems (IDS) is used to defend from the attack.

In this paper, a Random Forest classifier was used to predict leak locations for two differently sized water distribution networks based on pressure sensor measurements. The prediction model is trained on simulated leak scenarios with randomly chosen parameters - leak location, leak size, and base node demand uncertainty. Leak localization methods found in literature that rely on numerical simulations can only predict network nodes as leak nodes; however, since a leak can occur at any point along a pipe segment, additional spatial discretization of suspect pipe is proposed in this paper. It was observed that pipe segmentation of the whole network is a non-feasible approach since it rapidly increases the number of potential leak locations, consequently increasing the complexity of the prediction model. Therefore, a novel approach is proposed, in which a prediction model is trained on scenarios with leaks occurring in original network nodes only, but with its accuracy assessed against pressure sensor measurements from scenarios in which leaks occur in points between network nodes. It was observed that this approach can successfully narrow down the suspect leak area and, followed by additional segmentation of that network area and subsequent prediction, a precise leak localization can be achieved. The proposed approach enables incorporation of various uncertainties by simulating leak scenarios under different conditions. Investigation of leak size uncertainty and base demand variation showed that several different scenarios can produce similar sensor measurements which makes it difficult to unambiguously determine leak location using the prediction model. Therefore, future approaches of coupling prediction modeling with optimization methods are proposed.