Advancements in wireless communication technology have led to the widespread use of smart devices—including computers, mobile phones, tablets, wearable devices, and vehicles—which has significantly increased the demand for high-quality content. This growing demand puts pressure on the backhaul links in cellular networks, resulting in congestion and content delivery delays. To address this, cache-enabled networks and edge caching, such as caching in user devices, have emerged as promising solutions to reduce backhaul traffic. By caching content locally and using device-to-device (D2D) communication for retrieval, content delivery can be made more efficient. However, limited cache capacity requires intelligent content selection strategies. The popularity of the content is dynamic and varies with user preferences, where less than 20% of the users generate 80% of multimedia traffic. Many existing methods fail to consider this user heterogeneity, often assuming uniform preferences throughout the network. This paper proposes a novelMachine Learning-Driven Content Popularity PredictionandCache Optimization(ML-CPCO) framework that dynamically predicts user- and cluster-level content demand, incorporates user willingness to participate in caching, and optimizes cache placement inD2D-enabled clustered networks. The system predicts future content requests using machine learning algorithms and estimates content popularity at the cluster level. Based on these predictions, cache placement decisions are made to maximize efficiency. The simulation results show that the proposed approach performs well under various network conditions, achieving a cache utilization rate of nearly 97%—the highest among the methods compared. In addition, it offers an improved hit rate with an acceptable execution time, resulting in reduced backhaul traffic and enhanced user experience.

Summary Nowadays data center networks (DCNs) should handle the ever‐growing load generated by diverse applications. It particularly occurs under concurrent flow requests and so‐called mice flows (MFs): elephant flows (EFs) ratio. Concentrating on a binary vision over flow size classification (EF or MF) results in subsequent unpredicted load imbalance due to neglecting EF's distinctions including a wide range of sizes. As a result, some EFs might utilize a path owing qualities beyond the given EF's demands, while another EF with higher requirements is attending to use an over‐utilized path. This article proposes FMap, a fuzzy map for scheduling EFs through our proposed variant of traveling salesperson problem (TSP) toward DCNs. FMap represents a novel EF scheduling scheme that integrates flow prioritization and routing decisions under the event pf parallel incoming flows besides the cooperation of the controller and OpenFlow switches in software‐defined networking (SDN) paradigm. FMap adopts fuzzy inference process to overcome the vagueness over EF's resource allocation. Mainly, FMap proposes a new variant of TSP (optimized by genetic algorithm) which enables EF's group forwarding with a minimum cost. FMap reduces the total hop count of EFs through considering a single optimal path for delivering groups of EFs that contain a same tag (priority). The outstanding results represent a major improvement as compared with equal cost multiple path, Hedera, Sonoum, and Size‐KP‐PSO. Particularly, the results illustrate an outperforming by 3.76×, 0.21×, 0.15×, 0.03×, and 0.03× in terms of total hop count, EFs FCT, packet loss, goodput, and received packets as compared with Size‐KP‐PSO, respectively.

Due to the rapid growth of the Internet of Vehicles (IoV) and the rise of multimedia services, IoV networks’ servers and switches are facing resource crises. Multimedia vehicles connected to the Internet of Things are increasing; there are millions of vehicles and heavy multimedia traffic in the IoV network. The network’s scarcity of resources results in overload, which, in turn, leads to a degradation of both Quality of Service (QoS) and Quality of Experience (QoE). Conversely, when resources are abundant, it leads to unnecessary energy wastage. Managing IoV network resources optimally while considering constraints such as Energy, Load, QoS, and QoE is a complex challenge. To address this, the study proposes a solution by decomposing the problem and designing a modular architecture named$\textit {ELQ}^{\vphantom {D^{j}}2}$. This architecture enables simultaneous control of the mentioned constraints, effectively reducing overall complexity. To achieve this objective, Network Softwarization and Virtualization concepts are employed. This modern architecture allows dynamically adjusting of the scale of the resources on demand, effectively reducing energy usage. Additionally, this architecture provides some other potentials, such as “the distribution of multimedia traffic among servers”, “determining the route with high QoS for traffic”, and “selecting a media with high QoE”. A real test field is provided by Floodlight Controller, Open vSwitch, and Kamailio Server tools to evaluate the performance of${ELQ}^{2}$. The findings suggest that the utilization of${ELQ}^{2}$holds promise in reducing the count of active servers and switches via effective resource management. Additionally, it demonstrates enhancements in various QoS and QoE parameters, encompassing throughput, multimedia delay, R Factor, and MOS, accomplished through load balancing strategies. As an illustration, the deployment of flows has achieved a commendable success rate of 95% owing to the utilization of SDN-based and comprehensive management practices encompassing all network resources.

Internet of Multimedia Things (IoMT) is becoming attractive day by day and provides more services to the Internet users. The ever-increasing multimedia applications and services led to an outburst in IoMT. The multimedia things connected to IoMT are also increasing; millions of devices and high volume of traffic. This large traffic is directed toward the servers of the service provider in the IoMT cloud through the network switches. As a result, the IoMT infrastructure is facing the crisis of the resource management of switches and servers from two aspects: 1) load imbalance and 2) energy loss. This article proves that the problem of optimal resource management of IoMT networks with the energy and load constraints simultaneously is an NP-hard problem that has a high time complexity. The problem has decomposed. Then, we propose a modular system of energy and load control in IoMT using the concepts of network softwarization and virtual resources. The proposed controller first dynamically adjusts the resources through accurate determination of the IoMT network size. It then distributes the load between the IoMT servers as well as routes the traffic between switches to the desired server. The Open vSwitch, Floodlight Controller, and Kaa Servers are, respectively, used for implementing the switches, controller, and servers of IoMT in the test platform. The results show that the proposed system both minimizes the number of IoMT active servers and switches and distributes the load between them. As a result, the parameters for evaluating the quality of service and quality of experience, such as throughput, multimedia delay, R factor, and mean opinion score improved.

Summary Nowadays, the Voice Over IP (VoIP) technology is an important component of the communications industry as well as a low‐cost alternative to Public Switched Telephone Networks. Communication in VoIP networks consists of two main phases, for example, signaling and media exchange. VoIP servers are responsible for signaling exchange using the Session Initiation Protocol (SIP) as the signaling protocol. The saturation of SIP server resources is one of the issues with the VoIP network, which causes problems such as overload or loss of energy. Resource saturation occurs mainly due to a lack of integrated server resource management. In the traditional VoIP networks, management and routing are distributed among all equipment, including servers. These servers are overloaded during peak times and face energy loss during idle times. Given the importance of this issue, this paper introduces a framework based on Software‐Defined Networking technology for SIP server resource management. The advantage of this framework is to have a global view of all the server resources. In this framework, the resource allocation optimization problem and resource autoscaling are presented to deal with the problems posed. The goal is to maximize total throughput and minimize energy consumption. In this regard, we seek to strike a balance between efficiency and energy. The proposed framework is implemented in the actual testbed. The results show that the proposed framework has succeeded in achieving these goals.

With the rapid growth of different massive applications and parallel flow requests in Data Center Networks (DCNs), today’s providers are confronting challenges in flow forwarding decisions. Since Software Defined Networking (SDN) provides fine granular control, it can be intelligently programmed to distinguish between flow requirements. The present article proposes a knapsack model in which the link bandwidth and incoming flows are modeled as a knapsack capacity and items, respectively. Furthermore, each flow consists of two size and value aspects, acquired through flow size extraction and the type of service value assigned by the SDN controller decision. Indeed, the current work splits the incoming flow size range into Type of Service (ToS) decimal value numbers. The lower the flow size category, the higher the value dedicated to the flow. Particle Swarm Optimization (PSO) optimizes the knapsack problem and first forwards the selected-flows by KP-PSO, and the non-selected-flows second. To address the shortcomings of these methods in the event of dense parallel flow detection, the present study puts the link under the threshold of a 70% load by simultaneous requests. Experimental results indicate that the proposed method outperforms Sonum, Hedera, and ECMP in terms of flow completion time, packet loss rate, and goodput regarding flow size requirements.

Internet of Things (IoT) offers a variety of solutions to control industrial environments. The new generation of IoT consists of millions of machines generating huge traffic volumes; this challenges the network in achieving the Quality-of-Service (QoS) and avoiding overload. Diverse classes of applications in IoT are subject to specific QoS treatments. In addition, traffic should be distributed among IoT servers based on their available capacity. In this article, we propose a novel framework based on software-defined networking (SDN) to fulfill the QoS requirements of various IoT services and to balance traffic between IoT servers simultaneously. At first, the problem is formulated as an integer linear programming (ILP) model that is NP-hard. Then, a predictive and proactive heuristic mechanism based on time-series analysis and fuzzy logic is proposed. Afterward, the proposed framework is implemented in a real testbed, which consists of the Open vSwitch, Floodlight controller, and Kaa servers. To evaluate the performance, various experiments are conducted under different scenarios. The results indicate the improved IoT QoS parameters, including throughput and delay, and illustrate the nonoccurrence of overload on IoT servers in heavy traffic. Furthermore, the results show improved performance compared to similar methods.

The advanced metering infrastructure (AMI) is one of the main services of smart grid (SG), which collects data from smart meters (SMs) and sends them to utility company meter data management systems (MDMSs) via a communication network. In the next generation AMI, both the number of SMs and the meter sampling frequency will dramatically increase, thus creating a huge traffic load which should be efficiently routed and balanced across the communication network and MDMSs. This paper initially formulates the global load-balanced routing problem in the AMI communication network as an integer linear programming model, which is NP-hard. Then, to overcome this drawback, it is decomposed into two subproblems and a novel software defined network-based AMI communication network is proposed called OpenAMI. This paper also extends the OpenAMI for the cloud computing environment in which some virtual MDMSs are available. OpenAMI is implemented on a real test bed, which includes Open vSwitch, Floodlight controller, and OpenStack, and its performance is evaluated by extensive experiments and scenarios. Based on the results, OpenAMI achieves low end-to-end delay and a high delivery ratio by balancing the load on the entire AMI network.

VoIP is becoming a low-priced and efficient replacement for PSTN in communication industries. With a widely growing adoption rate, session initiation protocol (SIP) is an application layer signaling protocol, standardized by the IETF, for creating, modifying, and terminating VoIP sessions. Generally speaking, SIP routes a call request to its destination by using SIP proxies. With the increasing use of SIP, traditional configurations pose certain drawbacks, such as ineffective routing, un-optimized management of proxy resources (including CPU and memory), and overload conditions. This paper presents OpenSIP to upgrade the SIP network framework with emerging technologies, such as software-defined networking (SDN) and network function virtualization (NFV). SDN provides for management that decouples the data and control planes along with a software-based centralized control that results in effective routing and resource management. Moreover, NFV assists SDN by virtualizing various network devices and functions. However, current SDN elements limit the inspected fields to layer 2-4 headers, whereas SIP routing information resides in the layer-7 header. A benefit of OpenSIP is that it enforces policies on SIP networking that are agnostic to higher layers with the aid of a deep packet inspection engine. Among the benefits of OpenSIP is programmability, cost reduction, unified management, routing, as well as efficient load balancing. This paper implements OpenSIP on a real testbed which includes Open vSwitch and the Floodlight controller. The results show that the proposed architecture has a low overhead and satisfactory performance and, in addition, can take advantage of a flexible scale-out design during application deployment.

Network functions virtualization provides opportunities to design, deploy, and manage networking services. It utilizes cloud computing virtualization services that run on high-volume servers, switches, and storage hardware to virtualize network functions. Virtualization techniques can be used in IP multimedia subsystem (IMS) cloud computing to develop different networking functions (e.g., load balancing and call admission control). IMS network signaling happens through session initiation protocol (SIP). An open issue is the control of overload that occurs when an SIP server lacks sufficient CPU and memory resources to process all messages. This paper proposes a virtual load balanced call admission controller (VLB-CAC) for the cloud-hosted SIP servers. VLB-CAC determines the optimal “call admission rates” and “signaling paths” for admitted calls along with the optimal allocation of CPU and memory resources of the SIP servers. This optimal solution is derived through a new linear programming model. This model requires some critical information of SIP servers as input. Further, VLB-CAC is equipped with an autoscaler to overcome resource limitations. The proposed scheme is implemented in smart applications on virtual infrastructure (SAVI) which serves as a virtual testbed. An assessment of the numerical and experimental results demonstrates the efficiency of the proposed work.

The Session Initiation Protocol (SIP) is an application-layer control protocol for creating, modifying and terminating multimedia sessions. An open issue is the control of overload that occurs when a SIP server lacks sufficient CPU and memory resources to process all messages. We prove that the problem of overload control in SIP network with a set of n servers and limited resources is in the form of NP-hard. This paper proposes a Load-Balanced Call Admission Controller (LB-CAC), based on a heuristic mathematical model to determine an optimal resource allocation in such a way that maximizes call admission rates regarding the limited resources of the SIP servers. LB-CAC determines the optimal "call admission rates" and "signaling paths" for admitted calls along optimal allocation of CPU and memory resources of the SIP servers through a new linear programming model. This happens by acquiring some critical information of SIP servers. An assessment of the numerical and experimental results demonstrates the efficiency of the proposed method.