Network slicing enables the deployment of multiple virtual networks over a single infrastructure network, supporting diverse network services. In particular, stringent service requirements motivate End-to-End (E2E) network slicing, where multiple independently administered network domains collectively realize an E2E connectivity. However, because internal resource details of each domain are typically hidden for privacy and administrative reasons, the limited visibility makes accurate E2E performance estimation a significant challenge before its deployment. This paper addresses the problem of truthful information sharing among domain administrators and an E2E slice resource coordinator through a game-theoretic framework. We propose an incentivization strategy based on the Shapley value from cooperative game theory to promote truthful reporting. By quantifying each domain’s contribution to the overall E2E performance, our incentivization strategy adjusts financial compensation to reflect the quality of reported information. Simulations demonstrate that our approach improves E2E performance estimation, reduces performance requirement violations, and lowers slice blocking rates.

We address the problem of establishing an end-to-end network slice across multiple domains and propose a Reinforcement Learning-based framework that enables multiple domains to collaborate on end-to-end network slicing admission and allocation. The objective is to maximize the long-term revenue of the network operator. We employ a Graph Neural Network (GNN) to capture the topology features as the encoder. The simulation results show that our framework improves the profit of the network operator by up to 15% compared to a greedy algorithm.

This paper addresses a resource allocation strategy for network slices, where each network slice supports a different federated learning task. A slice is established when a new federated learning model needs to be trained and is released once the training is complete. The goal is to minimize the average network slice holding time while also providing fairness between slice tenants and improving network efficiency. We propose a reinforcement learning-based strategy to periodically reallocate resources according to the current state of each federated learning task. We offer two reinforcement learning models. The first model achieves more stable performance and considers correlations between tasks, while the second model utilizes fewer parameters and is more robust to varying number of tasks. Both approaches have better performance than baseline heuristic methods. We also propose a method to alleviate the effect of various resources scales to make the training stable.

In this work, we consider the network slice composition problem for Service Function Chains (SFCs), which addresses the issue of allocating bandwidth and VNF resources in a way that guarantees the availability of the SFC while minimizing cost. For the purpose of satisfying the availability requirement of the SFC, we adapt a traffic-weighted availability model which ensures that the long-term fraction of traffic supported by the slice topology remains above a desired threshold. We propose a method for composing a single or multi-path slice topology and for properly dimensioning VNF replicas and bandwidth on the slice paths. Through simulations, we show that our proposed algorithm can reduce the total cost of establishment compared to a dedicated protection approach in 5G networks.

The emergence of diverse network applications demands more flexible and responsive resource allocation for networks. Network slicing is a key enabling technology that provides each network service with a tailored set of network resources to satisfy specific service requirements. The focus of this paper is the network slicing of access networks realized by Passive Optical Networks (PONs). This paper proposes a learning-based Dynamic Bandwidth Allocation (DBA) algorithm for PON access networks, considering slice-awareness, demand-responsiveness, and allocation fairness. Our online convex optimization-based algorithm learns the implicit traffic trend over time and determines the most robust window allocation that reduces the average latency. Our simulation results indicate that the proposed algorithm reduces the average latency by prioritizing delay-sensitive and heavily-loaded ONUs while guaranteeing a minimal window allocation to all ONUs.

This paper addresses the problem of admission control for elastic network slices that may dynamically adjust provisioned bandwidth levels over time. When admitting new slice requests, sufficient spare capacity must be reserved to allow existing elastic slices to dynamically increase their bandwidth allocation when needed. We demonstrate a lightweight deep Reinforcement Learning (RL) model to intelligently make ad-mission control decisions for elastic slice requests and inelastic slice requests. This model achieves higher revenue and higher acceptance rates compared to traditional heuristic methods. Due to the lightness of this model, it can be deployed without GPUs. We can also use a relatively small amount of data to train the model and to achieve stable performance. Also, we introduce a Recurrent Neural Network to encode the variable-size environment and train the encoder with the RL model together.

The increasing demand for diverse network services entails more flexible networks that are realized by virtualized network equipment and functions. When such advanced network systems face a massive failure by natural disasters or attacks, the recovery of the entire system may be conducted progressively due to limited repair resources. The prioritization of network equipment in the recovery phase influences the interim computation and communication capability of systems since the systems are operated under partial functionality. Hence, finding the best recovery order is a critical problem, which is further complicated by virtualization due to the interdependence between virtual network functions and infrastructure elements. This paper deals with a progressive recovery problem under limited resources in networks with VNFs, where some interdependencies exist. We prove the NP-hardness of the progressive recovery problem and approach the optimum solution by introducing DeepPR, a progressive recovery technique based on Deep Reinforcement Learning (Deep RL). Our simulation results indicate that DeepPR can achieve near-optimal solutions in certain networks and is more robust to adversarial failures, compared to a baseline heuristic algorithm.

The increasing reliance upon cloud services entails more flexible networks that are realized by virtualized network equipment and functions. When such advanced network systems face a massive failure by natural disasters or attacks, the recovery of the entire system may be conducted in a progressive way due to limited repair resources. The prioritization of network equipment in the recovery phase influences the interim computation and communication capability of systems, since the systems are operated under partial functionality. Hence, finding the best recovery order is a critical problem, which is further complicated by virtualization due to dependency among network nodes and layers. This paper deals with a progressive recovery problem under limited resources in networks with VNFs, where some dependent network layers exist. We prove the NP-hardness of the progressive recovery problem and approach the optimum solution by introducing DeepPR, a progressive recovery technique based on deep reinforcement learning. Our simulation results indicate that DeepPR can obtain 98.4% of the theoretical optimum in certain networks.

The interdependency between different network layers is commonly observed in cyber physical systems and communication networks adopting the dissociation of logic and hardware implementation, such as software defined networking and network function virtualization. This paper formulates an optimization problem to improve the survivability of interdependent networks by restructuring the provisioning relations. A characteristic of the proposed algorithm is that the continuous availability of the entire system is guaranteed during the restructuring of dependencies by the preservation of certain structures in the original networks. Our simulation results demonstrate that the proposed restructuring algorithm can substantially enhance the survivability of interdependent networks and provide insights into the ideal allocation of dependencies.

This paper studies a network design problem to improve the survivability of interdependent networks by restructuring the dependencies. As different types of networked systems become more integrated, the relation between distinct kinds of network devices has become more intertwined. In order to guarantee the robustness of such systems, survivability problems of interdependent networks must be addressed. A characteristic of the proposed algorithm is that the continuous availability of the entire system is guaranteed by the preservation of certain structures in the original networks during the restructuring process. Simulation results demonstrate that the restructuring heuristic can substantially enhance the survivability of interdependent networks.

With the Internet of Things (IoT) becoming a major component of our daily life, understanding how to improve the quality of service for IoT applications through fog computing is becoming an important problem. In this paper, we introduce a general framework for IoT-fog-cloud applications, and propose a delay-minimizing collaboration and offloading policy for fog-capable devices that aims to reduce the service delay for IoT applications. We then develop an analytical model to evaluate our policy and show how the proposed framework helps to reduce IoT service delay.

We address the problem of survivable path pair routing in multi- domain optical networks with geographically correlated failures. The objective is to minimize the risk of simultaneous failure of both the primary and backup paths. We develop a probabilistic model to calculate the simultaneous failure probability of both the paths and consider a topology aggregation scheme for domains based on calculating the physical vulnerable overlapping area of two paths within a domain. We develop an inter-domain minimum overlapping area routing algorithm based on the aggregated information from each domain. We compare our algorithm to Suurballe's Algorithm and an optimal approach and we show that our heuristic approach is effective in reducing the total probability of simultaneous failure.

This paper discusses the intractability and heuristics of the Cluster Leader Election Problem (CLEP), which is to assign a controller to one of the switches in each cluster of a network. In order to improve scalability in Software Defined Networking (SDN), a network can be clustered into subnetworks and managed by multiple distributed controllers. CLEP deals with the optimal placement of the distributed controllers in the subnetworks considering metrics between the controllers, such as distance and connectivity. Additionally, it is shown that some metrics within each subnetwork, which are discussed in other literature can be guaranteed by selecting specific clustering methods before the placement.