With the rapid advancement of cloud storage and the increasing use of connected devices, uploading data to the cloud results in the loss of physical control by data owners, making confidentiality and integrity entirely dependent on Cloud Service Providers (CSPs). This raises concerns about whether CSPs effectively safeguard outsourced data, as any malicious behavior can lead to data tampering or deviation. Traditional auditing schemes rely on Third Party Authorities (TPAs), which are not always trustworthy. Although various cloud data auditing mechanisms have been proposed, few effectively address the challenge of ensuring data integrity without relying on trusted third parties. To overcome this limitation, we propose a secure and efficient distributed blockchain-based data integrity auditing scheme. Specifically, our approach randomly assigns the audit task to a user selected from among the system participants via blockchain. Blockchain and InterPlanetary File System (IPFS) technologies are leveraged to enforce access control. Furthermore, the proposed scheme supports low-cost batch integrity verification without the need for a TPA. Theoretical analyses confirm that our solution ensures data traceability and auditability, and reduces reliance on third parties. Finally, our simulations show that proof generation remains under 0.7 seconds for 600 data blocks at 256-bit security, while verification costs remain negligible.

Network softwarization makes it easy to quickly deploy various and different network services with the composition of virtual network functions (VNFs) that can be launched, modified and stopped at any time. Nowadays, several VNF servers supporting different types of VNFs exist. Therefore, to minimize the cost of deploying network services, VNFs should not only be placed on the best servers but also chained in an optimal way. In this paper, we propose a new approach to solve the NP-hard VNF placement and chaining problem (VNFPC problem). After proving that VNFPC problem can be transformed to a variant of the multi-constrained routing problem where the number of additive metrics is part of the problem, we proposed efficient heuristic reducing the worst-case time complexity while ensuring high quality solutions. Simulation results show that our constrained shortest paths-based heuristics allows to determine solutions close to the optima by keeping small number of paths on nodes.

During the last decade, we are witnessing the emergence of NFV and SDN to reduce CAPEX and OPEX. Under the SDN paradigm and thanks to NFV, a service can be swiftly deployed by the chaining of several VNFs forming an SFC running on a virtualized infrastructure. Nowadays, there are still quite a number of issues related to SFCs, among them, the optimal placement of SFC components. In this paper, we focused on the variant of the resource allocation cost optimization problem of VNF placement and chaining for limited resources on the servers. After proving that the problem of VNF placement is NP-Hard and equivalent to the multiple knapsack problem, we proposed a genetic algorithm-based meta-heuristic to solve large instance of our VNF placement and chaining problem variant. Simulation results show that our genetic algorithms are efficient since they reduce the SFC mean cost and improve the accepted requests ratio.

Network Function Virtualization (NFV), which decouples network functions from hardware and transforms them into hardware-independent Virtual Network Functions (VNF), is a crucial technology for many emerging networking domains, such as 5G, edge computing and data-center network. Service Function Chaining (SFC) is the ordered set of VNFs. The VNF deployment problem is to find the optimal deployment strategy of VNFs in SFC while guaranteeing the Service-Level Agreements (SLAs). Existing VNF deployment researches mainly focus on sequences of VNFs without energy consideration. However, with the rapid development of application requirement, the SFCs evolve from sequence to dynamic graph and the service providers become more and more sensitive to the energy consumption in NFV. Therefore, in this paper, we identify the Energy-efficient Graph-structured SFC problem (EG-SFC) and formulate it as a Combinatorial Optimization Problem (COP). Benefiting from the recent advances in machine learning for COP, we propose an end-to-end Graph Neural Network (GNN) based on constrained Deep Reinforcement Learning (DRL) method to solve EG-SFC. Our method leverages the Graph Convolutional Network (GCN) to represent the Q-network of Double Deep Q-Network (DDQN) in DRL. The mask mechanism is proposed to deal with the resources constraints in COP. The experimental results show that the proposed method can deal with unseen SFC graphs and achieve better performances than greedy algorithm and traditional DDQN.

Rapidly growing research in the field of Unmanned Aerial Vehicles (UAVs) has redrawn the application map for UAVs that now incorporates personal to public side applications apart from having just military domain within its boundary. Despite numerous practical advantages, UAV s systems are not utilized up to their full potential owing to the characteristics of higher mobility and limited lifetime of on-board batteries. In multi-hop real-time systems, abrupt movement or early depletion of energy resources for some overloaded UAV s may result in the creation of a network hole or even in a breakdown of the whole network. In this article, a new Life-Enhancing recovery Approach for a Multi-UAVs (LEAMU) network is proposed that not only provides a routing solution but also serves as a fail-safe method. The crux of the LEAMU is the identification of the best recovering UAV since the selection of an unhealthy UAV will result in more recovery requests afterward. In LEAMU, network hole creation is avoided beforehand through a distributed election of a suitable candidate keeping distance, remaining energy, neighborhood density, and traffic load factors into consideration. The proposed strategy is simulated and has shown to have a promising future for its integration into the existing UAV systems.

Network virtualization allows the co-existing of logical networks (virtual networks) on physical network (substrate networks). Virtual Network (VN) reliability is a critical problem for end-users and service providers. It aims to ensure service continuity even upon failure. As more and more VNs are created over substrate networks (SN), the failure of a single SN component may lead to the failure of many VNs. Thus, the VN reliability issue is becoming more and more critical. VN reliability can be enhanced in two ways: (1) by failure recovery (post-failure) with protection and/or restoration methods; (2) by failure avoidance with the selection of most reliable components at the network topology setting phase. Traditional virtual network embedding (VNE) methods have mainly focused on bandwidth optimization. In this paper, we focus on the reliability issue. We propose VNE methods which take into account the failure probability of SN components with a failure-avoidance approach, in order to minimize the VN failure probability. Our heuristics are based on the use of Steiner Minimal Tree (SMT). Simulations results confirm that our heuristics provide better reliability against traditional VNE with bandwidth as sole target, and, in case of failure of a SN component, reduce the number of affected VNs.

In this paper, we deal with the Capacitated Network Design Problem (CNDP) with modular link capacities to design minimum cost network while satisfying the flow demands. We propose a two level Genetic Algorithm (GA) based model that can deal with several variations of CNDP. Our proposition defines a new encoding scheme to treat the modular case. Extensive simulation results on Atlanta, France and Germany network instances show that the proposed algorithm is much more efficient than the Iterative Local Search algorithm.

Two strategies of resource sharing are proposed in literature to provide protection while saving resources: (1) restrained sharing which applies the resource sharing to the backup paths only and (2) global sharing which extends the resource sharing to the primary and backup paths. In this paper, we compared the two strategies of resource sharing when the primary paths correspond to the shortest ones according to a strictly positive and static metric. Even when the amount of resources that can be shared between the primary and the backup paths is unbounded, we proved that the maximum number of backup paths is still bounded. Besides, our simulations showed that the resource sharing between the primary and backup paths has very slight impact on the backup path rejection, i.e. the two strategies of resource sharing have very close performances.

The routing and wavelength assignment (RWA) problem for multipoint-to-point communications in all-optical wavelength division multiplexing (WDM) networks is investigated in this paper. Two efficient algorithms, namely Reverse Shortest Path Tree routing (RSPT) and k-Bounded Edge Disjoint Path routing (EDPR), are proposed. The problem of minimizing the total cost while establishing a multipoint-to-point session can be solved by RSPT algorithm in polynomial time. Nevertheless, EDPR algorithm produces a significant reduction in the maximum number of wavelengths required per link (i.e., the link stress) by a multipoint-to-point session. Simulations demonstrate the efficiencies of these two algorithms in supporting multipoint-to-point communications in WDM networks.

With the advent of MPLS, the restoration times of communications is decreased down to 50 ms by the use of preconfigured backup LSPs. To ensure there are enough resources after a failure, the backup LSPs must reserve the resources they need beforehand. However and contrarily to the primary LSPs which really use their resources, the backup LSPs do not use them until a failure of the protected component occurs. Hence, to optimize and maximize resource availability in the network, backup LSPs may share their resource reservation. Indeed, under the hypothesis of single failures in the network, some backup paths are not active at the same time since they protect against the failure of different components. In this article, we propose an efficient Distributed Bandwidth Sharing (DBS) heuristic capable to protect the primary LSPs against all types of failure risks (link, node and SRLG risks) with the transmission of a very small amount of bandwidth information. Our technique is completely distributed; it balances the computations on the different nodes of the topology and is easy to be deployed. Simulations show that with the transmission of a small vector of bandwidth information per link, the rate of rejected backup LSPs is low and close to the ideal.