Utilization Of Network Resources Research Articles

HybOff: a Hybrid Offloading approach to improve load balancing in fog environments

Load balancing is crucial in distributed systems like fog computing, where efficiency is paramount. Offloading with different approaches is the key to balancing the load in distributed environments. Static offloading (SoA) falls short in heterogeneous networks, necessitating dynamic offloading to reduce latency in time-sensitive tasks. However, prevalent dynamic offloading (PoA) solutions often come with hidden costs that impact sensitive applications, including decision time, networks congested and distance offloading. This paper introduces the Hybrid Offloading (HybOff) algorithm, which substantially enhances load balancing and resource utilization in fog networks, addressing issues in both static and dynamic approaches while leveraging clustering theory. Its goal is to create an uncomplicated low-cost offloading approach that enhances IoT application performance by eliminating the consequences of hidden costs regardless of network size. Experimental results using the iFogSim simulation tool show that HybOff significantly reduces offloading messages, distance, and decision-offloading consequences. It improves load balancing by 97%, surpassing SoA (64%) and PoA (88%). Additionally, it increases system utilization by an average of 50% and enhances system performance 1.6 times and 1.4 times more than SoA and PoA, respectively. In summary, this paper tries to introduce a new offloading approach in load balancing research in fog environments.

Towards efficient RAN slicing: A deep hierarchical reinforcement learning approach

Radio access network (RAN) slicing can significantly improve network flexibility and resource utilization efficiency. Generally, deep reinforcement learning (DRL) is a prevailing approach to implementing practicable resource management in RAN slicing. However, this demand-aware resource allocation suffers from long training time and slow execution efficiency. This can lead to the inability to quickly achieve the desired spectral efficiency and service level agreement satisfaction rate. To tackle this issue, we propose a novel RAN slicing resource allocation framework based on a deep hierarchical RL framework for efficient resource scheduling. Structurally, our framework consists of a policy selection network and a policy evaluation network. In particular, a newly built action acceleration unit can achieve quick reward accumulation, thus speeding up the optimal policy search. Extensive simulations show that our proposal has higher system utility and faster convergence compared to the state-of-the-art DRL algorithms.

SPONGE+: A NoC-based power gating scheme for overall router column opening and closing control

Power gating, a general-purpose power saving technique, can be used to turn off inactive routers in Networks-on-Chip (NoCs) to reduce static power consumption. SPONGE is an advanced power gating scheme. When the router is dormant, SPONGE maintains the transmission of straight packets, which occupy a large percentage of the network, through a direct bypass design. At the same time, the energized router column’s can ensure the normal transmission of turning packets. However, it suffers from increased latency due to excessive detours, aggravated load imbalance, and violation of the breakeven time (BET) constraint. To address the above problems, we propose an improved energy-efficient router architecture SPONGE+. First, we design a new router bypass architecture that enables powered-off routers to have the ability to transmit straight ahead and turn in the X-to-Y direction to reduce the bypass cost of packet transmission. Secondly, we propose a congestion-aware turning routing algorithm to balance the load among powered-on routers and improve network resource utilization. Finally, we pin improves the power gating mechanism for router columns to satisfy the BET constraint and further reduce the static power expenses. Experimental results indicate that, under real application scenarios, our scheme achieves a 27.3% reduction in static power consumption and a 10.5% decrease in packet latency compared to SPONGE, with commendable scalability.

Reconfigurable-Intelligent-Surface-Enhanced Dynamic Resource Allocation for the Social Internet of Electric Vehicle Charging Networks with Causal-Structure-Based Reinforcement Learning

Charging stations and electric vehicle (EV) charging networks signify a significant advancement in technology as a frontier application of the Social Internet of Things (SIoT), presenting both challenges and opportunities for current 6G wireless networks. One primary challenge in this integration is limited wireless network resources, particularly when serving a large number of users within distributed EV charging networks in the SIoT. Factors such as congestion during EV travel, varying EV user preferences, and uncertainties in decision-making regarding charging station resources significantly impact system operation and network resource allocation. To address these challenges, this paper develops a novel framework harnessing the potential of emerging technologies, specifically reconfigurable intelligent surfaces (RISs) and causal-structure-enhanced asynchronous advantage actor–critic (A3C) reinforcement learning techniques. This framework aims to optimize resource allocation, thereby enhancing communication support within EV charging networks. Through the integration of RIS technology, which enables control over electromagnetic waves, and the application of causal reinforcement learning algorithms, the framework dynamically adjusts resource allocation strategies to accommodate evolving conditions in EV charging networks. An essential aspect of this framework is its ability to simultaneously meet real-world social requirements, such as ensuring efficient utilization of network resources. Numerical simulation results validate the effectiveness and adaptability of this approach in improving wireless network efficiency and enhancing user experience within the SIoT context. Through these simulations, it becomes evident that the developed framework offers promising solutions to the challenges posed by integrating the SIoT with EV charging networks.

A hybrid framework for routing and channel assignment in WMN’s

Wireless mesh network (WMNs) can replace the physical existence of wired network; nevertheless, to solve the problem of traffic and congestion, they must carefully arrange radio resource assignment. In this paper, we focus on the major issues while transferring the data over the network and optimization of resources. WMN mainly suffers from congestion problem when numbers of channel transfer data at a same time which indirectly leads to overlapping channels and sometime data cannot be mitigated to destination. To overcome this problem and provide optimal solution we proposed hybrid channel assignment technique which offers four phase solution technique, in this technique firstly by mitigating the traffic than assigning the optimal path based on greedy and BFS. The purpose of this congestion control hybrid ant based greedy-BFS-load balancing (CCHAGBL) technique is to diminish the number of used slots, which is directly related to the overall resource assignment. We also provide optimal solution using Ant colony optimization technique with hybrid algorithm to achieve the network efficiency and resource utilization with randomly generated traffic. With the help of hybrid ant based greedy-BFS-load balancing (GBL) channel assignment algorithm throughput and packet delivery ratio is upgraded and delay is reduced to minimum.

Co-design of distributed dynamic event-triggered scheme and extended dissipative consensus control for singular Markov jumping multi-agent systems under periodic Denial-of-Service jamming attacks

This paper introduces a distributed dynamic event-triggered (DDET) controller meticulously crafted to achieve resilient consensus control in singular Markov jumping multi-agent systems (SMJMAs) even in the face of periodic Denial-of-Service (DoS) jamming attacks. With a dual focus on optimizing network resource utilization and fortifying against cyber threats, we propose a distributed dynamic event-triggered scheme (DDETS) grounded in the principles of sampled data consensus. In the presence of cyber attacks, we demonstrate the transformation of the original consensus control challenge within the singular multi-agent system into a robust stability control problem, centering around a singular switching error system governed by the DoS attack signal. Acknowledging the disturbances that typify real-world scenarios, this paper establishes criteria for attaining exponential stability and stochastic admissibility within the singular Markov jumping switching error system under the extended dissipative performance index. Furthermore, we present a hybrid design for the consensus controller and DDETS, delineated through a set of solvable linear matrix inequalities (LMIs). Finally, a comprehensive array of numerical examples meticulously illustrates the compelling effectiveness and unequivocal superiority of our proposed methodology.

Handover management procedures for future generations mobile heterogeneous networks

Handover (HO) management in Heterogeneous Networks (HetNets) poses challenges arising from network densification and dynamic environmental behaviors. Existing HO decision algorithms struggle to efficiently utilize network resources and ensure a high-quality user experience amidst the complexity of HetNets and the burgeoning growth of mobile users. This paper introduces a robust and data-driven HO decision model designed to enhance HO performance in HetNets. Initially, a conventional HO decision algorithm is developed based on users' Reference Signal Received Power (RSRP) values in MATLAB. Various simulation cases explore different HO parameters to observe their impact on handover performance. To address these challenges, a data-driven HO decision model leveraging Long Short-Term Memory (LSTM), a deep learning technique, is proposed for the regression task. The LSTM model is trained and tested using obtained RSRP values, and the future RSRP values predicted by the model are employed to trigger HO decisions in the proposed algorithm. Results from the traditional HO decision algorithm are compared with those of the proposed machine learning-based approach across various simulation runs, considering average Signal-to-Interference-plus-Noise Ratio (SINR), RSRP, user throughput values, the number of HOs and the Radio Link Failure (RLF) ratio. Different user speeds are also considered to establish a relationship between HO frequency and mobile user speed. The proposed model achieved reducing the rate of radio link failure to levels that are deemed acceptable in order to ensure a continuous connection.

High-fidelity entanglement routing in quantum networks

Quantum networks, endowed with their distinct quantum properties, are poised to emerge as indispensable platforms for the implementation of quantum information technology applications in the near future. Within the realm of quantum networks, the transmission of remote quantum information presents a pivotal and intricate challenge, commonly referred to as the entanglement routing problem. While previous efforts have delved into the intricacies of entanglement routing, there has been a notable oversight concerning the crucial parameter of entanglement fidelity. Hence, this paper introduces a novel Purification-enabled Entanglement Routing Algorithm (PERA). Initially, PERA utilizes the network throughput as the routing metric and ensures fidelity guarantees on all nodes in the link through a hop-by-hop purification process. Subsequently, acknowledging the probabilistic nature of entanglement purification, a strategy involving multiple attempts at purification is employed. Finally, a comprehensive utility function is deployed to address the path selection problem for multiple source–destination pairs. Numerical simulations confirm that PERA establishes entanglement connections with outstanding fidelity guarantees. Concurrently, PERA outperforms the baseline algorithm in terms of network throughput and resource utilization.

Secure Virtual Machine Migration and Host Overload Detection Using Modified Pelican Optimization with Variable Load Mean Function

Low-resource utilization and high-energy consumption have become progressively protuberant issues in cloud data centers. Virtual Machine (VM) migration is the key objective to resolve this issue. Moreover, extreme VM migration might empower Service-Level Agreement (SLA) violations. Few works are considered for optimizing throughput and energy consumption. An efficient VM migration must consider different parameters like network communication overhead, migration overhead, resource utilization, energy consumption and quality of service which is a multi-objective issue. Hence, in this paper, a Modified Pelican Optimization-based Variable Load Mean Function (MPO-VLMF)-based host overload detection is presented and security enhancement is developed. The main motive of this study is to achieve host overload detection and security enhancement. To obtain host overload detection, the variable load mean function is developed. In this mean function, the weight parameter is selected by considering Modified Pelican Optimization (MPO). The Levy flight (LF) is considered for enhancing the updating process of Pelican Optimization (PO). To enhance the security of the system, the Digital Signature-based Encryption (DSE) is developed. Based on this proposed approach, security and host overload detection are obtained. The proposed technique is implemented and evaluated by performance measures. It is compared with the conventional approaches to justify the performance of the system.

Intelligent load balancing in data center software‐defined networks

AbstractIn response to the increasing demand for efficient resource utilization in data center networks (DCNs), the development of intelligent load‐balancing algorithms has become crucial. This article introduces the dual double deep Q network (DDQN) algorithm, designed for software‐defined networking (SDN) environments within data centers. By leveraging deep reinforcement learning, DDQN addresses the challenges posed by dynamic traffic patterns, diverse flow requirements, and the coexistence of elephant and mice flows. Our algorithm adopts a comprehensive SDN approach, evaluating the network's status by analyzing switch load and bandwidth utilization. Using convolutional neural networks for elephant and mice flows in DCN, our algorithm enables adaptive learning and training tailored to the specific demands of elephant flows. Employing a double deep Q network architecture (DDQN), DDQN optimizes paths for both elephant and mice flows independently. Real‐time adaptation mechanisms make routing decisions based on the robust learning capabilities of DDQN, enhancing network utilization and reducing packet loss by generating optimal forwarding paths according to the current network state and traffic patterns. Simulations conducted in a Mininet environment with RYU as the controller, utilizing a fat‐tree data center topology, validate the efficacy of DDQN. The results demonstrate its effectiveness in achieving higher throughput, lower latency, and superior load balancing compared to traditional algorithms like equal‐cost multipath and Hedera.

Multi-Agent Deep Reinforcement Learning-Based Fine-Grained Traffic Scheduling in Data Center Networks

In data center networks, when facing challenges such as traffic volatility, low resource utilization, and the difficulty of a single traffic scheduling strategy to meet demands, it is necessary to introduce intelligent traffic scheduling mechanisms to improve network resource utilization, optimize network performance, and adapt to the traffic scheduling requirements in a dynamic environment. This paper proposes a fine-grained traffic scheduling scheme based on multi-agent deep reinforcement learning (MAFS). This approach utilizes In-Band Network Telemetry to collect real-time network states on the programmable data plane, establishes the mapping relationship between real-time network state information and the forwarding efficiency on the control plane, and designs a multi-agent deep reinforcement learning algorithm to calculate the optimal routing strategy under the current network state. The experimental results demonstrate that compared to other traffic scheduling methods, MAFS can effectively enhance network throughput. It achieves a 1.2× better average throughput and achieves a 1.4–1.7× lower packet loss rate.

An expert system for hybrid edge to cloud computational offloading in heterogeneous MEC–MCC environments

Mobile Cloud Computing (MCC) integrates cloud computing into the mobile environment, effectively tackling performance, environmental, and security constraints. However, the proliferation of clouds and applications introduces complexities in offloading decisions. Mobile Edge Computing (MEC) has emerged as a solution to bolster MCC performance, capitalizing on the proximity to edge devices. However, optimizing computation offloading remains paramount for heterogeneous smart (mobile phones, wearables, and IoT) devices, necessitating efficient utilization of network resources and computational offloading. To address these challenges, this paper introduces NSANNOM (Network and Device Resources Utilization through Smart ANN-based Offloading Mechanism), an expert system designed for optimal computational offloading decision-making and efficient network resource allocation mechanisms. NSANNOM employs Artificial Neural Networks (ANN) for precise decision-making by validating real-world datasets, underscoring ANN’s superiority over existing algorithms, and showcasing enhanced energy savings, cost efficiency, and latency response. Experimental evaluations demonstrate that the proposed ANN model for the offloading decision-making algorithm achieves a training accuracy of 97% and a validation accuracy of 99%. The system consumes minimal energy (10 MJ) for task scheduling and exhibits remarkable accuracy in resource utilization across multiple tasks (10–50) ranging in size (from 1 to 16 GB). Additionally, it minimizes time delays (in milliseconds) during the offloading process.

A highly efficient resource slicing and scheduling optimization algorithm for power heterogeneous communication networks based on hypergraph and congruence entropy

New services, such as distributed photovoltaic regulation and control, pose new service requirements for communication networks in the new power system. These requirements include low latency, high reliability, and large bandwidth. Consequently, power heterogeneous communication networks face the challenge of maintaining quality of service (QoS) while enhancing network resource utilization. Therefore, this paper puts forward a highly efficient optimization algorithm for resource slicing and scheduling in power heterogeneous communication networks. Our first step involves establishing an architectural description model of heterogeneous wireless networks for electric power based on hypergraph. This model characterizes complex dynamic relationships among service requirements, virtual networks, and physical networks. The system congruence entropy characterizes the degree of matching between the service demand and resource supply. Then an optimization problem is formed to maximize the system congruence entropy through dynamic resource allocation. To solve this problem, a joint resource allocation and routing method based on Lagrangian dual decomposition is proposed. These methods provide the optimal solutions of the nodes and link mappings of service function chains. The simulation results demonstrate that the proposed algorithm in this paper can greatly enhance resource utilization and also meet the QoS requirements of various services.

An integrated SDN framework for early detection of DDoS attacks in cloud computing

Cloud computing is a rapidly advancing technology with numerous benefits, such as increased availability, scalability, and flexibility. Relocating computing infrastructure to a network simplifies hardware and software resource monitoring in the cloud. Software-Defined Networking (SDN)-based cloud networking improves cloud infrastructure efficiency by dynamically allocating and utilizing network resources. While SDN cloud networks offer numerous advantages, they are vulnerable to Distributed Denial-of-Service (DDoS) attacks. DDoS attacks try to stop genuine users from using services and drain network resources to reduce performance or shut down services. However, early-stage detection of DDoS attack patterns in cloud environments remains challenging. Current methods detect DDoS at the SDN controller level, which is often time-consuming. We recommend focusing on SDN switches for early detection. Due to the large volume of data from diverse sources, we recommend traffic clustering and traffic anomalies prediction which is of DDoS attacks at each switch. Furthermore, to consolidate the data from multiple clusters, event correlation is performed to understand network behavior and detect coordinated attack activities. Many existing techniques stay behind for early detection and integration of multiple techniques to detect DDoS attack patterns. In this paper, we introduce a more efficient and effectively integrated SDN framework that addresses a gap in previous DDoS solutions. Our framework enables early and accurate detection of DDoS traffic patterns within SDN-based cloud environments. In this framework, we use Recursive Feature Elimination (RFE), Density Based Spatial Clustering (DBSCAN), time series techniques like Auto Regressive Integrated Moving Average (ARIMA), Lyapunov exponent, exponential smoothing filter, dynamic threshold, and lastly, Rule-based classifier. We have evaluated the proposed RDAER model on the CICDDoS 2019 dataset, that achieved an accuracy level of 99.92% and a fast detection time of 20 s, outperforming existing methods.

Efficient User Association Strategy for Maximizing User Satisfaction and Resource Utilization in Heterogeneous Cloud Radio Access Networks (H-CRANs)

The primary aim of this study is to present a User Association Strategy that can effectively optimize the Heterogeneous Cloud Radio Access Network (H CRAN). The primary objective of this strategy is to enhance customer satisfaction by optimizing the utilization of available network resources, such as transmission power and bandwidth. To achieve this objective, the proposed approach employs a logarithmic barrier function to address inequality constraints and transform the optimization problem into a formulation that facilitates effective convergence towards the optimal solution.

Energy efficiency in 5G systems: A systematic literature review

To ensure Energy Efficiency (EE) and better Quality of service (QoS), it is necessary to analyze the energy saving possibilities for low resource utilization in the current networks caused by rigorous QoS requirements and implementing EE approach in the planned model for performance improvement. Distributed Denial of Service (DDoS) attacks aim to exhaust the network’s processing and communication capacity by saturating it with packets and generating malicious traffic. There are numerous advantages that make Digital Twin (DT) and Intrusion Detection technique (ID) an effective remedy for a range of (fifth generation) 5G problems. A DDoS attack must be immediately detected and stopped before a legitimate user can access the target of the attacker for the 5G network to provide an efficient energy service. Although they clearly show promise in assisting with the creation and implementation of the challenging 5G environment, Digital Twins is still a relatively new technology for 5G networks but will increase EE. In this research, a thorough examination of the materials was carried out to identify the most cutting-edge DT and ID methods. The purpose of this study was to comprehend the problems with Energy Efficiency, the need for DT, and the methods for dealing with large-scale attack by DDoS on Energy Efficient networks. Only 94 of the 1555 articles produced by the procedure were determined to be relevant using inclusion and exclusion criteria. The outcome demonstrates that in 5G networks, DT, and its fundamental approaches, like QoS and DDoS attack mitigation, can be used to regulate the network’s Energy Efficiency. Numerous practical applications focusing on 5G Systems use their own principles. The effectiveness of these strategies was evaluated using several assessment criteria, including DT, Intrusion Detection, QoS, Energy Efficiency, and 5G Systems. Each study issue is thoroughly explained, along with typical methods, advantages, disadvantages, and performance metrics. Energy economy, network reliability, privacy, and cost reduction are all considerably increased by the implementation of intrusion detection technology in 5G systems. The decision is supported by the technology’s demonstrated efficacy, scalability, real-time detection capacities, low error level, and personalized learning attributes, all of which contribute to the long-term viability of 5G networks as an entire system.

A Resource Allocation Scheme for Edge Computing Network in Smart City Based on Attention Mechanism

In recent years, the number of devices and terminals connected to the smart city has increased significantly. Edge networks face a greater variety of connected objects and massive services. Considering that a large number of services have different QoS requirements, it has always been a huge challenge for smart city to optimally allocate limited computing resources to all services to obtain satisfactory performance. In particular, delay is intolerable for services in certain applications, such as medical, industrial applications, etc, that such applications require the high priority. Therefore, through flexibly dynamic scheduling, it is crucial to schedule services to the optimal node to ensure user experience. In this paper, we propose a resource allocation scheme for hierarchical edge computing network in smart city based on attention mechanism, for extracting a small number of features that can represent services from a large amount of information collected from edge nodes. The attention mechanism is used to quickly determine the priority of the services. Based on this, task deployment and resource allocation for different task priorities are developed to ensure the quality of service in smart cities by introducing Q-learning. Simulation results show that the proposed scheme can effectively improve the edge network resource utilization, reduce the average delay of task processing, and effectively guarantee the quality of service.

Resource allocation strategy based on service function Chaining in Multi-Access edge computing network

The Multi-access Edge Computing (MEC) network enhances the processing capability of Internet of Things (IoT) terminals by deploying 3C resources (computing resources, cache resources, and communication resources) at the IoT edge side, effectively improving the quality of service (QoS) of IoT applications. The majority of current MEC network research focuses on task offloading and energy management, with few studies looking at resource allocation from a service provisioning perspective. In this paper, we focus on the resource allocation problem when processing multiple tasks requested by IoT terminal devices in the MEC network. Firstly, we transform various tasks requested by IoT end devices into an SFC-based resource allocation problem. Then, we model the problem as a graph theory-based virtualized network function (VNF) node deployment cost optimization problem. Furthermore, we propose two resource allocation and deployment strategies by jointly considering the computing cost and security cost in VNF node deployment. Finally, extensive simulation experiments show that the proposed method can significantly improve the success rate of SFC task processing by more than 30% and network resource utilization by more than 20% when compared to other deployment methods for processing multiple SFC tasks with dynamic requests.

EDIR: Efficient Distributed Image Retrieval of Novel Objects in Mobile Networks

Crowdsourcing data collection from a network of mobile devices is useful in various applications. Mobile devices store a large amount of visual data that can aid in different application scenarios. Trained Convolutional Neural Networks (CNNs) can be deployed on mobile devices to be used in searching for objects of interest. Querying for novel objects, for which models have not been trained yet, presents some unique challenges. When novel objects are queried, new models must be trained and distributed to all edge devices. In this paper, we propose an efficient method and a system, called EDIR, which enables answering these queries while taking into account the bandwidth limitations encountered in wireless networks, as well as the limited energy and computational power on mobile devices. Through extensive experimentation, we show that using distance-based classifiers, specifically those relying on the Cosine distance, leads to more efficient utilization of network resources by reducing the number of false positives. We perform analysis that enables the requester to tune the parameters of interest before issuing the query, and validate our theoretical results. EDIR reduces the amount of transferred data by more than 45% compared to other approaches while simultaneously achieving a good F1 score.

A Robust Controller Deployment Algorithm under Multiple Constraints for SDON

The deployment of controller for SDON (Software Defined Optical Networks) is an NP-hard multi-objective optimization problem, and it is difficult to find an optimal solution that satisfies all objectives. Therefore, to improve the performance of control plane, the model we proposed is constructed based on three constraints on the survivability, delay, and control redundancy, which has three advantages. first, the survivability is improved by not sharing the risk between the main control channel and the protection control channel, which also improves the utilization of network resources. Second, the accuracy of delay calculation is improved by using a delay optimization model, and a threshold value is set to constrain the delay. Finally, the greedy strategy idea is used to achieve the full network coverage of the control range and improve the convergence speed. Simulation results show that the algorithm improves the fault recovery probability by an average of 15% compared with the algorithm of KSP computed routing. In addition, the algorithm in this paper achieves the same survivability as the C-MPC algorithm while using an average 11.1% reduction in the number of controllers.