Diverse Quality Of Service Research Articles

5G Network Slicing as a Service Enabler for the Automotive Sector

ABSTRACTNetwork slicing, a key technology introduced in 5G standards, enables mobile networks to simultaneously support a wide range of heterogeneous use cases with diverse quality of service (QoS) requirements. This work discusses the potential benefits of network slicing for the automotive sector, encompassing manufacturing processes and vehicular communications. The review of the state of the art reveals a clear gap regarding the application of network slicing from the perspective of industrial verticals such as automotive use cases and their specific requirements. Departing from this observation, we first identify limitations of previous cellular technologies and open challenges for supporting the data services required. Then we describe network slicing as an enabler to face these challenges. We present an analysis of the cost equilibrium for network slicing to be effective for car manufacturers, and tests in real 5G networks that demonstrate the performance improvement in OTA updates coexisting with other services.

Quality of Service-Aware Multi-Objective Enhanced Differential Evolution Optimization for Time Slotted Channel Hopping Scheduling in Heterogeneous Internet of Things Sensor Networks.

The emergence of the Internet of Things (IoT) has attracted significant attention in industrial environments. These applications necessitate meeting stringent latency and reliability standards. To address this, the IEEE 802.15.4e standard introduces a novel Medium Access Control (MAC) protocol called Time Slotted Channel Hopping (TSCH). Designing a centralized scheduling system that simultaneously achieves the required Quality of Service (QoS) is challenging due to the multi-objective optimization nature of the problem. This paper introduces a novel optimization algorithm, QoS-aware Multi-objective enhanced Differential Evolution optimization (QMDE), designed to handle the QoS metrics, such as delay and packet loss, across multiple services in heterogeneous networks while also achieving the anticipated service throughput. Through co-simulation between TSCH-SIM and Matlab, R2023a we conducted multiple simulations across diverse sensor network topologies and industrial QoS scenarios. The evaluation results illustrate that an optimal schedule generated by QMDE can effectively fulfill the QoS requirements of closed-loop supervisory control and condition monitoring industrial services in sensor networks from 16 to 100 nodes. Through extensive simulations and comparative evaluations against the Traffic-Aware Scheduling Algorithm (TASA), this study reveals the superior performance of QMDE, achieving significant enhancements in both Packet Delivery Ratio (PDR) and delay metrics.

Workflow scheduling based on asynchronous advantage actor–critic algorithm in multi-cloud environment

Recently, the multi-cloud environment (MCE) has increasingly become the preferred choice of users. As with the cloud environment, efficient workflow scheduling in a MCE remains crucial for identifying the cost efficiency and overall performance of the MCE. In MCE, the resources exhibit heterogeneity, complexity, and dynamism. Simultaneously, the intricate inter-task dependencies among workflow tasks, diverse Quality of Service (QoS) metrics for users, and multiple cloud service providers’ (CSPs) billing mechanisms significantly amplify the workflow scheduling challenge. Motivated by the application of reinforcement learning (RL) in workflow scheduling in a cloud environment, this paper proposes a scheduling algorithm that takes advantage of the asynchronous advantage actor–critic algorithm (A3C) to balance cost, makespan and resource utilization in workflow scheduling in a MCE. By analyzing the elements in the MCE, we design and define multiple agents in the MCE, and each cloud service provider will have an agent to record the state and update the local parameters. For the workflow task submitted by the user, the action is selected according to the initialization policy and submitted to the scheduling action to allocate the task to a designated virtual machine in the MCE so that each agent can more clearly perceive the environment change and adapt to the MCE. In contrast to the traditional A3C algorithm, we design a new critic network according to the data characteristics of real-world scientific workflows so that each agent is more suitable for real-world scientific workflow data. Through multiple sets of simulation experiments, the workflow scheduling algorithm based on the A3C algorithm in the MCE (MCWS-A3C) was compared with three benchmark methods. The experimental results show that the proposed method has better advantages than other methods in terms of cost, makespan, and resource utilization. Specifically, on the Montage_100 dataset, the average cost was reduced by 55.12% compared to other methods. The pioneering introduction of the A3C algorithm that adapts to the dynamic environment into the MCE brings more possibilities to address the issue of workflow scheduling in the MCE.

Empowering Light Fidelity-Enabled Internet of Things with Hybrid Fuzzy-Particle Swarm Optimization Power Allocation for Enhanced Energy Efficiency and Quality of Service

This paper proposes a hybrid fuzzy-Particle Swarm Optimization (PSO) approach for power allocation in bidirectional Light Fidelity (LiFi)-enabled Internet of Things (IoT) communication systems. The approach integrates fuzzy logic and PSO to achieve efficient and robust power allocation, considering energy efficiency and Quality of Service (QoS) requirements. The hybrid approach leverages flexibility of fuzzy logic to handle uncertainties and variations in system parameters, such as channel conditions and QoS requirements. Fuzzy sets and membership functions are defined to represent linguistic variables, and fuzzy rules are formulated based on expert knowledge and system-specific considerations. This allows the approach to adaptively adjust power allocation based on varying channel conditions and QoS requirements, enhancing the robustness of the power allocation strategy. The PSO algorithm is employed for iterative optimization to explore for ideal power allocation solution. The particles in swarm signify possible power allocation configuration, and its position in the search space corresponds to a specific power allocation. The particles update their positions based on local best-known positions and globally best-known positions, allowing the swarm to converge toward the optimal solution. Through extensive simulations and analysis, the proposed hybrid fuzzy-PSO approach is evaluated with regard to energy efficiency and QoS satisfaction. The results demonstrate its effectiveness in achieving energy-efficient power allocation while meeting diverse QoS requirements. The proposed approach outperforms existing methods such as channel- and Orthogonal Multiple Access (OMA)-based power allocation schemes.

Performance analysis of partial NOMA-based layered D2D communications

Conventionally, non-orthogonal multiple access (NOMA) has traditionally been implemented separately from orthogonal multiple access (OMA), aiming to improve the capacity of multi-user systems. However, a recent study has ventured beyond this conventional approach by integrating OMA and NOMA proportionally within the same system. In spite of these advancements, the consideration towards optimizing multi-user systems remains incomplete especially when user service requirements vary significantly. Therefore, this paper explores a novel layered device-to-device (D2D) partial NOMA (P-NOMA) scheme, which introduces a hybrid power-domain access method into multi-user systems. The analysis primarily focuses on evaluating both the system performance and the impact of various parameters on it. In contrast to conventional fully overlapped NOMA signals, P-NOMA signals are partially overlapped with an overlap rate that can be determined based on quality-of-service (QoS) requirements. Simulation results demonstrate that judicious utilization of P-NOMA can effectively enhance overall system performance, particularly in terms of sum rate (SR) metrics, while also flexibly accommodating diverse QoS requirements for multiple users.

Analysis of Wireless Multimedia Big Data Transmission Based on Adaptive Scheduling Algorithm

The increasing demand for wireless multimedia applications has prompted the need for efficient data transmission strategies that can adapt to dynamic network conditions and diverse Quality of Service (QoS) requirements. This paper analyzes wireless multimedia big data transmission using an innovative Adaptive QoS-Aware Scheduling Algorithm (AQOS-ASA). The proposed algorithm aims to optimize the transmission of multimedia data by dynamically adjusting the scheduling based on QoS considerations and real-time network conditions. Through adaptive scheduling, the algorithm ensures that high-priority multimedia data receives optimal transmission treatment, balancing the quality preferences set by users. The algorithm dynamically allocates time slots based on the changing network conditions, allowing for efficient utilization of the available bandwidth. The paper analyses the algorithm's performance in different wireless network scenarios through simulations and evaluations, highlighting its effectiveness in adapting to varying QoS requirements and network conditions. The results demonstrate that the proposed algorithm significantly enhances the quality and efficiency of wireless multimedia big data transmission, making it well-suited for applications with diverse multimedia content and dynamic network environments. The findings contribute valuable insights to wireless multimedia communication and provide a foundation for further advancements in adaptive scheduling algorithms for improved Quality of Service.

An SDN-enabled fog computing framework for wban applications in the healthcare sector

For healthcare systems utilizing Wireless Body Area Networks (WBANs), maintaining the network's diverse Quality of Service (QoS) metrics necessitates effective communication among Fog Computing resources. While fog nodes efficiently handle local requests with substantial processing resources, it is crucial to acknowledge the unpredictable availability of these nodes, potentially resulting in a decline in system performance. This study explores a software-defined fog architecture supporting different healthcare applications in Internet of Things (IoT) environment to ensure consistent specialized medical care amidst evolving health issues. Even minor delays, packet losses, or network overhead could adversely affect patient health. The article establishes a mathematical foundation based on transmitted and sensed data, ensuring each fog node executes an ideal quantity of processes. This study formulates an optimization problem to maximize the utility of fog nodes, leveraging the Lagrangian approach and Karush-Kuhn-Tucker conditions to streamline and resolve the optimization problem. Performance analysis demonstrates a significant reduction in delays by approximately 38 %, 29 %, and 32 %, along with energy savings of roughly 26.89 %, 12.16 %, and 22.50 %, compared to benchmark approaches. This study holds promise in healthcare, cloud-fog simulation, and WBANs, emphasizing the critical need for swift and accurate data processing.

Unlocking QoS Potential: Integrating IoT services and Monte Carlo Control for heterogeneous IoT device management in gateways

The exponential surge in IoT devices has presented significant challenges in managing network traffic and maintaining Quality of Service (QoS) in IoT networks. To tackle these challenges, ongoing efforts are focused on developing innovative solutions that leverage advanced traffic management techniques, robust QoS mechanisms, and enhanced security protocols. Addressing these obstacles is crucial for the continued growth and success of the IoT system, enabling seamless integration of smart devices and revolutionizing multiple industries and sectors by providing unparalleled connectivity and intelligence. This article proposes an innovative approach to enhance the management of heterogeneous IoT devices and ensure Quality of Service (QoS) in IoT networks. Integrating device categories into the QoS policy aims to optimize overall performance while addressing the diverse QoS requirements of different device categories. The benefits of considering device categories include tailored resource allocation, improved user satisfaction, customized QoS handling, and scalability. The proposed approach utilizes the Monte Carlo Control algorithm to learn an optimal QoS policy through simulations of traffic scenarios. Our paper highlights Monte Carlo Control’s (MCC) rapid learning in diverse IoT settings, peaking at 50 episodes, compared to Q-learning’s 100 episodes and dynamic programming’s 200 episodes. Specifically, in audio IoT, MCC achieved 730 packets/second throughput with a 50 ms delay, outperforming QL (723 packets/second, 134 ms) and DP (700 packets/second, 171 ms). These results underscore MCC’s vital role in elevating IoT Quality of Service, making it pivotal for system performance and user experience optimization.

UniPreCIS: A data preprocessing solution for collocated services on shared IoT

Next-generation smart city applications, attributed to the power of the Internet of Things (IoT) and Cyber–Physical Systems (CPS), significantly rely on sensing data quality. With an exponential increase in intelligent applications for urban development and enterprises offering sensing-as-a-service these days, it is imperative that a shared sensing infrastructure could thwart the better utilization of resources. However, a shared sensing infrastructure that leverages low-cost sensing devices for a cost-effective solution remains unexplored territory. A significant research effort is still needed to make edge-based data shaping solutions more reliable, feature-rich, and cost-effective while addressing the associated challenges in sharing the sensing infrastructure among multiple collocated services with diverse Quality of Service (QoS) requirements. Towards this, we propose UniPreCIS, a novel edge-based data preprocessing solution that accounts for the inherent characteristics of low-cost ambient sensors and their exhibited measurement dynamics concerning application-specific QoS. UniPreCISaims to identify and select quality data sources by performing sensor ranking and selection that dynamically adapts to the change in sensor attributes. Finally, multimodal data preprocessing is performed in a unified manner to meet heterogeneous application QoS and, at the same time, reduce the resource consumption footprint for the resource-constrained network edge. We study the effectiveness of UniPreCISon a real-world testbed deployed on our campus. As observed, the processing time and memory utilization of the stakeholder services have been reduced in the proposed approach while achieving up to 90% accuracy, which is arguably significant compared to state-of-the-art sensing techniques.

NPU-Accelerated Imitation Learning for Thermal Optimization of QoS-Constrained Heterogeneous Multi-Cores

Thermal optimization of a heterogeneous clustered multi-core processor under user-defined QoS targets requires application migration and DVFS. However, selecting the core to execute each application and the VF levels of each cluster is a complex problem because (1) the diverse characteristics and QoS targets of applications require different optimizations, and (2) per-cluster DVFS requires a global optimization considering all running applications. State-of-the-art resource management for power or temperature minimization either relies on measurements that are commonly not available (such as power) or fails to consider all the dimensions of the optimization (e.g., by using simplified analytical models). To solve this, ML methods can be employed. In particular, IL leverages the optimality of an oracle policy, yet at low run-time overhead, by training a model from oracle demonstrations. We are the first to employ IL for temperature minimization under QoS targets. We tackle the complexity by training NN at design time and accelerate the run-time NN inference using NPU. While such NN accelerators are becoming increasingly widespread, they are so far only used to accelerate user applications. In contrast, we use for the first time an existing accelerator on a real platform to accelerate NN-based resource management. To show the superiority of IL compared to RL in our targeted problem, we also develop multi-agent RL-based management. Our evaluation on a HiKey 970 board with an Arm big.LITTLE CPU and NPU shows that IL achieves significant temperature reductions at a negligible run-time overhead. We compare TOP-IL against several techniques. Compared to ondemand Linux governor, TOP-IL reduces the average temperature by up to 17 ˆC at minimal QoS violations for both techniques. Compared to the RL policy, our TOP-IL achieves 63 % to 89 % fewer QoS violations while resulting similar average temperatures. Moreover, TOP-IL outperforms the RL policy in terms of stability. We additionally show that our IL-based technique also generalizes to different software (unseen applications) and even hardware (different cooling) than used for training.

A Resource Allocation Scheme for Packet Delay Minimization in Multi-Tier Cellular-Based IoT Networks

With advances in Internet of Things (IoT) technologies, billions of devices are becoming connected, which can result in the unprecedented sensing and control of the physical environments. IoT devices have diverse quality of service (QoS) requirements, including data rate, latency, reliability, and energy consumption. Meeting the diverse QoS requirements presents great challenges to existing fifth-generation (5G) cellular networks, especially in unprecedented scenarios in 5G networks, such as connected vehicle networks, where strict data packet latency may be required. The IoT devices with these scenarios have higher requirements on the packet latency in networking, which is essential to the utilization of 5G networks. In this paper, we propose a multi-tier cellular-based IoT network to address this challenge, with a particular focus on meeting application latency requirements. In the multi-tier network, access points (APs) can relay and forward packets from IoT devices or other APs, which can support higher data rates with multi-hops between IoT devices and cellular base stations. However, as multiple-hop relaying may cause additional delay, which is crucial to delay-sensitive applications, we develop new schemes to mitigate the adverse impact. Firstly, we design a traffic-prioritization scheduling scheme to classify packets with different priorities in each AP based on the age of information (AoI). Then, we design different channel-access protocols for the transmission of packets according to their priorities to ensure the QoS in networking and the effective utilization of the limited network resources. A queuing-theory-based theoretical model is proposed to analyze the packet delay for each type of packet at each tier of the multi-tier IoT networks. An optimal algorithm for the distribution of spectrum and power resources is developed to reduce the overall packet delay in a multi-tier way. The numerical results achieved in a two-tier cellular-based IoT network show that the target packet delay for delay-sensitive applications can be achieved without a large cost in terms of traffic fairness.

Decentralized Configuration of TSCH-Based IoT Networks for Distinctive QoS: A Deep Reinforcement Learning Approach

The IEEE 802.15.4 Time-Slotted Channel Hopping (TSCH) is widely used as a reliable, low-power, and low-cost communication technology for many industrial Internet-of-Things (IoT) networks. In many applications, Quality-of-Service (QoS) requirements are different for heterogeneous nodes, necessitating non-equal parameter settings per node. This results in a very large configuration space making space exploration complex and time-consuming. Moreover, network state and QoS requirements may change over time. Thus, run-time configuration mechanisms are needed for making decisions about proper node settings to consistently satisfy diverse and dynamic QoS requirements. In this paper, we propose a run-time decentralized self-optimization framework based on Deep Reinforcement Learning (DRL) for parameter configuration of a multi-hop TSCH network. DRL adopts neural networks as approximate functions to speed up the process of converging to QoS-satisfying configurations. Simulation results show that our proposed framework enables the network to use the right configuration settings according to the diverse QoS demands of different nodes. Moreover, it is shown that the convergence time of the learning framework is in the order of a few minutes which is acceptable for many IoT applications.

Workload Characterization and Classification: A Step Towards Better Resource Utilization in a Cloud Data Center

Advancements in virtualization technology have led to better utilization of existing infrastructure. It allows numerous virtual machines with different workloads to coexist on the same physical server, resulting in a pool of server resources. It is critical to understand enterprise workloads to correctly create and configure existing and future support in such pools. Managing resources in a cloud data center is one of the most difficult tasks. The dynamic nature of the cloud environment, as well as the high level of uncertainty, has created these challenges. These applications’ diverse Quality of Service (QoS) requirements make data center management difficult. Accurate forecasting of future resource demand is required to meet QoS needs and ensure better resource utilization. Consequently, data center workload modeling and categorization are needed to meet software quality solutions cost-effectively. This paper uses traces of Bitbrain’s data to characterize and categorize workload. Clustering (K Means and Gaussian mixture model) and Classification strategies (K Nearest Neighbors, Logistic Regression, Decision Trees, Random Forest, and Support Vector Machine) characterize and model the workload traces. K Means shows better results as compared to GMM when compared to the Calinski Harabasz index and Davies-Bouldin score. The results showed that the Decision Tree achieves the maximum accuracy of 99.18%, followed by K Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machine (SVM) Logistic Regression (LR), Multi-Layer Perceptron (MLP), and Back Propagation Neural Networks.

Space Information Network With Joint Virtual Network Function Deployment and Flow Routing Strategy With QoS Constraints

Space information network (SIN) can provide global coverage in 6G network. Furthermore, SIN with network function virtualization (NFV) can achieve flexible deployment of network functions and improve the utilization of resources. In SIN with NFV, network functions can be virtualized into virtual network functions (VNFs). However, in SIN with NFV, the mission flow must satisfy the service function chain (SFC) constraint, i.e., the mission flow must be processed by all VNFs in the predefined order. Furthermore, each VNF can be deployed on multiple physical nodes. Moreover, different kinds of services may have the diverse quality of service (QoS) requirements. In this paper, we investigate the joint VNFs deployment and flow routing strategy (VNF-R) to maximize the number of completed missions with the guaranteed end-to-end latency under SFC constraints in time-varying SINs. Specifically, the problem can be formulated as a mixed integer linear programming (MILP) problem, which is proved to be NP-hard. In order to effectively solve the problem, we propose a novel low-complexity near-optimal penalty successive upper bound minimization rounding LP relaxation iterative rounding (PSUM-R-LRIR) algorithm. The simulation results show that the PSUM-R-LRIR algorithm can achieve near-optimal performance, and our proposed VNF-R scheme significantly outperforms the fixed VNF deployment scheme.

Machine Learning for 6G Enhanced Ultra-Reliable and Low-Latency Services

Ultra-reliable and low-latency communications (URLLC), as one of the major communication services of the fifth-generation (5G) and the sixth-generation (6G) cellular networks, is critical to supporting a variety of emerging mission-critical applications. However, the modern mobile networks could not satisfy the latency and reliability requirements, as well as other Quality of Service (QoS) requirements, including spectrum efficiency, energy efficiency, capacity, jitter, round-trip delay, network coverage, etc. To fulfill diverse QoS requirements for various URLLC applications, machine learning (ML) solutions are promising for future 6G networks. In this article, we first categorize the 6G URLLC vision into three connectivity characteristics, including ubiquitous connectivity, deep connectivity, and holographic connectivity, with their corresponding unique QoS requirements. We then identify potential challenges in meeting these connectivity requirements, and investigate promising ML solutions to achieve the intelligent connectivity for the 6G URLLC service. We further discuss how to implement the ML algorithms to guarantee the QoS requirements for different URLLC scenarios, including mobility URLLC, massive URLLC, and broadband URLLC. Finally, we present a case study of downlink URLLC channel access problems, solved by centralized deep reinforcement learning (CDRL) and federated DRL (FDRL), respectively, which validates the effectiveness of machine learning for URLLC services.

Machine learning enabled network and task management in SDN based Fog architecture

Effective communication among Fog Computing resources is crucial concerning the network's diverse Quality of Service (QoS) parameters. However, while Fog nodes may be capable of handling local requests with sufficient computational resources, their availability can be pretty volatile, ultimately degrading overall performance. Therefore, regular link weight revision for such Fog resources is required to realize low latency in communication. Also, the prioritization of tasks and the clustering of resources significantly impact the system's overall performance. In light of this, we have proposed a novel machine learning-enabled Software Defined Networking (SDN)-based Fog Computing system with the ability to manage the network and prioritize jobs while allocating resources. In our proposed model, the SDN controller will continuously update the link weight. With the aid of Dijkstra's Algorithm, our proposed system can find the optimal path for connecting the most appropriate resources for a given task. The Gradient Descent Algorithm has been deployed in the SDN controller to get the optimal weight based on previous experiences and other parameters. Using the Gaussian Naïve Bayes Algorithm, tasks are classified according to priority to schedule them and properly minimize failure. Additionally, the proposed system clusters the resources using the K-Means++ algorithm for easy identification and quick allocation. We have simulated the proposed work using the Python programming tool, and to analyze its performance; various metrics were employed, including waiting time, turnaround time, failure rate, bandwidth utilization and forwarding count for a particular job.

Swarm and Location-Based QoS Routing Algorithm in MEO/LEO Double-Layered Satellite Networks

In this paper, we mainly research on the QoS (Quality of Service) routing algorithms for MEO/LEO (medium Earth orbit/low Earth orbit) double-layered satellite networks. In this type of networks, the rapidly changing network topology due to relative motion of satellites is one of the main challenges when designing an efficient routing algorithm. Specifically, the issues of high rerouting overhead and traffic routing with diverse QoS requirements remain to be resolved. This paper proposed a M-BMDP (modified bandwidth constrained minimum delay path) routing algorithm based on swarm and location for MEO/LEO double-layered satellite networks. This algorithm forms a set of LEO groups according to the footprint of MEO satellites and chooses the relative MEO satellites as its group manager. For delay sensitive traffic, the algorithm can improve the QoS as the cost of packet loss based on hop limit. And for users located in reversed crevice zone, the traffic can route through one MEO satellite to reduce the time delay. The simulation results show that the M-BMDP algorithm performs better in rerouting delay, overhead and pack loss rate compared with existing solutions.

Uncertainty quantification and consideration in ML-aided traffic-driven service provisioning

The network traffic prediction problem has been extensively studied in the literature through the application of statistical linear models and more recently through the application of machine learning (ML). In fact, ML has proven its capabilities on accurately modeling the non-linear nature of network traffic, outperforming conventional statistical linear models. Without doubt, model accuracy constitutes an important evaluation metric. In a network environment, however, where uncertainty may lead to erroneous service provisioning decisions (e.g., violations of the quality-of-service (QoS) requirements), it does not provide any information of how much the traffic predictions can be trusted. Hence, in this work the focus is on addressing traffic prediction uncertainty by leveraging the capabilities of Monte Carlo (MC) dropout inference. The proposed framework is compared with a margin-based technique, traditionally used to compensate for traffic prediction uncertainty, demonstrating that the MC dropout inference framework results in significant spectrum savings, as opposed to the baseline, myopic, margin-based scheme. Even though a small penalty is observed on the unpredictable traffic, this is successfully handled on-line with a reduced operational overhead compared to the case where prediction uncertainty is completely ignored. Importantly, it is shown that, unlike the margin-based framework, the MC dropout inference framework can be used for the provisioning of services with diverse QoS requirements.

An Optimized LTE-Based Technique for Drone Base Station Dynamic 3D Placement and Resource Allocation in Delay-Sensitive M2M Networks

Drones are expected to facilitate extending wireless networks’ access for both human users and smart machine-type-communication devices (MTCDs) with strict and diverse quality of service (QoS) requirements. In this paper, we propose an optimal solution for the dynamic placement of an LTE drone-mounted base station to maximize the coverage of the MTCDs deployed over a large geographical area. The resulting technique jointly optimizes the drone's 3D positioning to maximize coverage and allocates the network resources in such a way that gives high priority to the delay-sensitive machine-to-machine (M2M) traffic. This optimization algorithm determines the optimal bound of the solution. Since it cannot be used in real-time operations due to its computational complexity, we also introduce a heuristic technique that offers a near-optimal solution with much reduced complexity. We conduct several simulation evaluations to assess the proposed techniques. These results are compared to those of other drone placement approaches. The comparisons show that the proposed techniques offer significantly better results in the communication coverage while fulfilling the diverse QoS requirements of the deployed M2M network. Moreover, the heuristic-based technique is shown to succeed in finding solutions close to the optimal bound with a considerable reduction of complexity over the exact algorithm.

SO-RAN: Dynamic RAN Slicing via Joint Functional Splitting and MEC Placement

Next-Generation (NG) mobile networks are expected to revolutionize wireless communication systems, facilitating the deployment of critical applications with stringent Quality of Service (QoS) requirements. Multi-access Edge Computing (MEC) and Radio Access Network (RAN) slicing are considered vital mechanisms of upcoming NG systems as they allow creation of end-to-end isolated RAN slices and increase of serving mobile data traffic on edge. The efficient deployment of RAN functions promises flexible and disaggregated architectures for the diversified needs of NG networks. In this work, we propose a novel optimization approach that minimizes RAN/MEC economic costs and maximizes served traffic. This framework creates isolated RAN slices by optimal placement of RAN and MEC functions per slice, which solves the problem of operating cost-efficient edge networks and increases the served traffic with diverse QoS requirements. We first formulate our framework as a convex and linear problem and then decompose it using a distributed method that accelerates the performance and guarantees the optimal global solution. Our results show that our proposed work better uses the spectrum, and there is a trade-off between the achieved throughput and the cost incurred to the network.