Serverless edge computing enables low-latency Internet of Things (IoT) services but faces scalability challenges due to complex concurrency and resource management. While existing approaches address function initialization and edge-cloud offloading, they often overlook the joint optimization of serverless concurrency and physical-layer resources, leading to potential service degradation and increased costs. To tackle this, we propose OPLA, a novel cross-layer framework for joint latency and concurrency optimization, designed to minimize end-to-end latency while optimizing concurrent serverless functions. OPLA models interactions between physical-layer resources (e.g., bandwidth, transmission power, offloading ratios) and application-layer concurrency decisions. The formulated problem is a highly non-convex mixed-integer nonlinear program (MINLP), which we prove to be at leastNP-complete in certain cases. To approximate its optimal solution efficiently, we propose an iterative exploration-exploitation procedure (EEP). The exploration phase, which is embarrassingly parallelizable, balances solution quality and efficiency with single parameter tuning. The exploitation phase is just a simple successive convex approximation to OPLA. Moreover, we also develop a presolve-postsolve heuristic with deterministic rounding to ensure feasibility for OPLA. Numerical results demonstrate that EEP consistently achieves solutions within a 6% optimality gap relative to a global solver across a wide range of network scales and workloads, confirming its effectiveness and scalability for real-world serverless edge deployments.

Network slicing is a critical feature in 5G and beyond communication systems, enabling the creation of multiple virtual networks(i.e., slices)on a shared physical network infrastructure. This involves efficiently mapping each slice component, including virtual network functions (VNFs) and their interconnections (virtual links), onto the physical network. This paper considers the slice embedding problem in which the order of VNFs can be adjusted. This provides increased flexibility for service deployment, but the selection of the best order of VNFs also complicates embedding. We propose an optimization framework to tackle the challenges of jointly optimizing slice admission control and embedding with flexible VNF ordering. Additionally, we introduce a near-optimal branch-and-bound (BnB) algorithm, combined with the A* search algorithm, to generate embedding solutions efficiently. Extensive simulations on both small and large-scale multi-tiered 5G networks demonstrate that flexible VNF ordering increases the number of deployable slices within a network infrastructure, thereby improving resource utilization and better meeting diverse demands across varied network topologies.

The transition to beyond-fifth-generation (B5G) wireless systems has revolutionized cellular networks, driving unprecedented demand for high-bandwidth, ultra low-latency, and massive connectivity services. The open radio access network (Open RAN) and network slicing provide B5G with greater flexibility and efficiency by enabling tailored virtual networks on shared infrastructure. However, managing resource allocation in these frameworks has become increasingly complex. This paper addresses the challenge of optimizing resource allocation across virtual network functions (VNFs) and network slices, aiming to maximize the total reward for admitted slices while minimizing associated costs. By adhering to the Open RAN architecture, we decompose the formulated problem into two subproblems solved at different timescales. Initially, the successive convex approximation (SCA) method is employed to achieve at least a locally optimal solution. To handle the high complexity of binary variables and adapt to time-varying network conditions, traffic patterns, and service demands, we propose a deep reinforcement learning (DRL) approach for real-time and autonomous optimization of resource allocation. Extensive simulations demonstrate that the DRL framework quickly adapts to evolving network environments, significantly improving slicing performance. The results highlight DRL’s potential to enhance resource allocation in future wireless networks, paving the way for smarter, self-optimizing systems capable of meeting the diverse requirements of modern communication services.

We explore mission assignment and task offloading in Open Radio Access Network (Open RAN)-enabled intelligent transportation systems (ITS), where autonomous vehicles utilize mobile edge computing for efficient processing. Existing studies often overlook mission dependencies and offloading costs, leading to suboptimal decisions. To address this, we formulate a novel optimization problem that integrates these factors and enhances performance through vehicle cooperation. We then develop the chaotic Gaussian-based global artificial rabbit optimization (CG-GARO) algorithm, a new metaheuristic approach, which significantly improves mission assignment efficiency and resource utilization. Simulation results show that our approach surpasses baseline metaheuristics in both system benefits and mission completion rates, demonstrating strong potential for real-world deployment in dynamic ITS environments.

WiFi sensing-based human pose estimation (HPE) has gained significant attention in the academic community due to its advantages over vision- and sensor-based methods, including nonintrusiveness, convenience, and enhanced privacy protection. However, most existing WiFi-based pose Estimators suffer from poor performance and lack robustness in the presence of random noise. To address these challenges, this article presents a novel HPE architecture comprising two key modules: 1) a Denoiser and 2) an Estimator. The Denoiser is based on an autoencoder structure, while the Estimator is based on a new convolutional neural network (CNN) called SDy-CNN, which is designed to dynamically focus on high-information subcarriers of orthogonal frequency division multiplexing signals. Additionally, Bayesian optimization is employed to fine-tune the architecture’s parameters for optimal performance flexibly. Experiments conducted on a comprehensive dataset, MM-Fi, demonstrate that the proposed architecture significantly outperforms existing state-of-the-art Estimators, achieving up to an 8.38% improvement in HPE accuracy in clean data scenarios and up to a 14% improvement in noisy data scenarios. It has also been proven to gain computational efficiency when being much faster than other methods.

Network slicing has appeared a key feature in 5G and beyond communication networks that enables the creation of multiple virtual networks (i.e., slices) over a shared physical network infrastructure. This process involves efficiently embedding (or mapping) each slice element, including virtual network functions (VNFs) and their interconnections, onto the physical network. This paper explores a scenario where the order of VNFs can be adjusted during slice embedding, offering greater flexibility to increase the number of services deployed on the infrastructure. We formulate a novel optimization framework to tackle the challenges of slice admission control and embedding with this flexibility. A heuristic is also introduced to derive embedding solutions in a timely manner. Simulation results demonstrate that allowing flexible VNF ordering significantly increases the number of slices that can be deployed in the network infrastructure.

Datacenter networks commonly facilitate the transmission of data in distributed computing frameworks through coflows, which are collections of parallel flows associated with a common task. Most of the existing research has concentrated on scheduling coflows to minimize the time required for their completion, i.e., to optimize the average dispatch rate of coflows in the network fabric. Nevertheless, modern applications often produce coflows that are specifically intended for online services and mission-crucial computational tasks, necessitating adherence to specific deadlines for their completion. In this paper, we introduce$\mathtt {WDCoflow}$, a new algorithm to maximize the weighted number of coflows that complete before their deadline. By combining a dynamic programming algorithm along with parallel inequalities, our heuristic solution performs at once coflow admission control and coflow prioritization, imposing a$\sigma$-order on the set of coflows. With extensive simulation, we demonstrate the effectiveness of our algorithm in improving up to$3\times$more coflows that meet their deadline in comparison the best SoA solution, namely$\mathtt {CS\rm{-}MHA}$. Furthermore, when weights are used to differentiate coflow classes,$\mathtt {WDCoflow}$is able to improve the admission per class up to$4\times$, while increasing the average weighted coflow admission rate.

With the advent of big data applications, coflow scheduling has become a cornerstone for the engineering of traffic in datacenters. Minimizing the average weighted Coflow Completion Times (CCT) is a crucial step to minimize the execution time of jobs running in distributed computing frameworks. In this paper, we present a new$\sigma $-order coflow scheduling solution, ONE-PARIS, an online semi-clairvoyant and semi-distributed implementation suitable to minimize the weighted CCT in production environments. We achieves this through ONE-PARIS scheduler for ordering coflows and a decentralized resource allocation mechanism, called Sync-Rate, enabling to respect the order of priority of coflows provided by ONE-PARIS and ensuring efficient synchronization between flows of the same coflow in order to free up bandwidth for low-priority flows. Extensive simulations on both synthetic and real traffics show that our proposed coflow scheduler outperforms other state-of-art schemes.

Network slicing has emerged as a key concept in 5G systems, allowing Mobile Network Operators (MNOs) to build isolated logical networks (slices) on top of shared infrastructure networks managed by Infrastructure Providers (InP). Network slicing requires the assignment of infrastructure network resources to virtual network components at slice activation time and the adjustment of resources for slices under operation. Performing these operations just-in-time, on a best-effort basis, comes with no guarantee on the availability of enough infrastructure resources to meet slice requirements. This paper proposes a prioritized admission control mechanism for concurrent slices based on an infrastructure resource reservation approach. The reservation accounts for the dynamic nature of slice requests while being robust to uncertainties in slice resource demands. Adopting the perspective of an InP, reservation schemes are proposed that maximize the number of slices for which infrastructure resources can be granted while minimizing the costs charged to the MNOs. This requires the solution of a max-min optimization problem with a non-linear cost function and non-linear constraints induced by the robustness to uncertainties of demands and the limitation of the impact of reservation on background services. The cost and the constraints are linearized and several reduced-complexity strategies are proposed to solve the slice admission control and resource reservation problem. Simulations show that the proportion of admitted slices of different priority levels can be adjusted by a differentiated selection of the delay between the reception and the processing instants of a slice resource request.

Network slicing has emerged as a pivotal concept in 5G systems, allowing mobile operators to build isolated logical networks (slices) on top of shared infrastructure networks. Within a network slice, several Service Function Chains are usually deployed on a best-effort premise. Nevertheless, this approach does not guarantee the availability of enough infrastructure resources to accommodate the uncertain and time-varying slice resource demands.This paper investigates two adaptive slice resource provisioning methods accounting for the evolution with time of the slice resource demands. A probabilistic guarantee of meeting the slice resource requirements can be obtained, while being robust against uncertainties. The myopic approach accounts for the past demands when provisioning the current demands, while the foresighted approach accounts for both past and future demands. These two methods lead to MILP problems. Their performance is compared with a quasi-static method, where provisioning is agnostic of the past and future demands.

Network slicing allows Mobile Network Operators to split the physical infrastructure into isolated virtual networks (slices), managed by Service Providers to accommodate customized services. The Service Function Chains (SFCs) belonging to a slice are usually deployed on a best-effort premise: nothing guarantees that network infrastructure resources will be sufficient to support a varying number of users, each with uncertain requirements. Taking the perspective of a network Infrastructure Provider (InP), this article proposes a resource provisioning approach for slices, robust to a partly unknown number of users with random usage of the slice resources. The provisioning scheme aims to maximize the total earnings of the InP, while providing a probabilistic guarantee that the amount of provisioned network resources will meet the slice requirements. Moreover, the proposed provisioning approach is performed so as to limit its impact on low-priority background services, which may co-exist with slices in the infrastructure network. Taking all these constraints into account leads to an integer programming problem with many nonlinear constraints. These constraints are first relaxed to get an integer linear programming formulation of the slice resource provisioning problem. This problem is then solved considering the slice resource provisioning demands jointly. A suboptimal approach is finally proposed where slice resource provisioning demands are considered sequentially. Both solutions are compared to provisioning schemes that do not account for best-effort services sharing the common infrastructure network, as well as uncertainties in the slice resource demands.

With network slicing, Mobile Network Operators can accommodate on a common network infrastructure various customized services from Service Providers (SPs). Usually, the Service Function Chains belonging to a slice are deployed on a best-effort basis. Nothing ensures that enough infrastructure resources can be allocated to satisfy the demands of SPs. This paper introduces a radio resources provisioning approach to satisfy the demands of slices with radio coverage constraints. By provisioning, we ensure that enough resources are reserved for further SFC deployment. Numerical results show the effectiveness of the proposed provisioning framework for a slice deployment on a mobile network infrastructure satisfying a minimum data rate for users in the geographical areas where services have to be made available.

With network slicing in 5G networks, Mobile Network Operators can create various slices for Service Providers (SPs) to accommodate customized services. Usually, the various Service Function Chains (SFCs) belonging to a slice are deployed on a best-effort basis. Nothing ensures that the Infrastructure Provider (InP) will be able to allocate enough resources to cope with the increasing demands of some SP. Moreover, in many situations, slices have to be deployed over some geographical area: coverage as well as minimum per-user rate constraints have then to be taken into account. This paper takes the InP perspective and proposes a slice resource provisioning approach to cope with multiple slice demands in terms of computing, storage, coverage, and rate constraints. The resource requirements of the various SFCs within a slice are aggregated within a graph of Slice Resource Demands (SRD). Infrastructure nodes and links have then to be provisioned so as to satisfy all SRDs. This problem leads to a Mixed Integer Linear Programming formulation. A two-step approach is considered, with several variants, depending on whether the constraints of each slice to be provisioned are taken into account sequentially or jointly. Once provisioning has been performed, any slice deployment strategy may be considered on the reduced-size infrastructure graph on which resources have been provisioned. Simulation results demonstrate the effectiveness of the proposed approach compared to a more classical direct slice embedding approach.

Network slicing has recently appeared as a key enabler for the future 5G networks where Mobile Network Operators (MNO) create various slices for Service Providers (SP) to accommodate customized services. As network slices are operated on a common network infrastructure owned by some Infrastructure Provider (InP), sharing the resources across a set of network slices is highly important for future deployment. In this paper, taking the InP perspective, we propose an optimization framework for slice resource provisioning addressing multiple slice demands in terms of computing, storage, and wireless capacity. We assume that the aggregated resource requirements of the various Service Function Chains to be deployed within a slice may be represented by a graph of slice resource demands. Infrastructure nodes and links have then to be provisioned so as to satisfy these resource demands. A Mixed Integer Linear Programming formulation is considered to address this problem. A realistic use case of slices deployment over a mobile access network is then considered. Simulation results demonstrate the effectiveness of the proposed framework for network slice provisioning.