The densification of Wi-Fi deployments means that fully distributed random channel access is no longer sufficient for high and predictable performance. Therefore, the upcoming IEEE 802.11bn amendment introduces multi-access point coordination (MAPC) methods. This paper addresses a variant of MAPC called coordinated spatial reuse (C-SR), where devices transmit simultaneously on the same channel, with the power adjusted to minimize interference. The C-SR scheduling problem is selecting which devices transmit concurrently and with what settings. We provide a theoretical upper bound model, optimized for either throughput or fairness, which finds the best possible transmission schedule using mixed-integer linear programming. Then, a practical, probing-based approach is proposed which uses multi-armed bandits (MABs), a type of reinforcement learning, to solve the C-SR scheduling problem. We validate both classical (flat) MAB and hierarchical MAB (H-MAB) schemes with simulations and in a testbed. Using H-MABs for C-SR improves aggregate throughput over legacy IEEE 802.11 (on average by 80% in random scenarios), without reducing the number of transmission opportunities per station. Finally, our framework is lightweight and ready for implementation in Wi-Fi devices.

An Intrusion Detection System (IDS) employing machine learning (ML) solutions is crucial for identifying network intrusions. To minimize operational expenses and enhance performance, enterprises have begun outsourcing IDS management to service providers, giving rise to the concept of Intrusion Detection as a Service (IDaS). Earlier research primarily aimed at enhancing the accuracy of ML-based IDS models or expediting their computational process. However, from the service provider's perspective, an optimal architecture ensuring minimal computation cost and processing delay is crucial to increasing revenue. This study evaluates the performance of IDaS in a multi-tier architecture, utilizing traffic split offloading to enhance performance by mapping three in-sequence ML-based IDS tasks (pre-processing, binary detection, multi-class classification) to the architectures as the offloading destinations. We employ a simulated annealing-based traffic offloading and cost allocation (SA-TOCA) algorithm to determine the offloading ratio for each traffic path and the cost requirements for each tier. The results indicate that the edge-cloud architecture is 15% and four times more cost-effective compared to the fog-edge and fog-cloud architectures, respectively, and it demonstrates superior performance in minimizing processing delays. Offloading the majority of traffic to the edge and the remainder to the cloud proves to be an efficient strategy, reducing both computation costs and average delays.

Despite opening attractive perspectives, the concept of Software Defined Networking raises doubts about the performance and practical feasibility. To contradict these concerns, we propose a deployment-ready system aimed at proactive and periodic optimization of flow paths. The modular system consists of modules responsible for traffic prediction, static optimization, measurements, flow management, and validation of optimization results. To make the system efficient, we resolved several scientific issues and proposed novel and valuable solutions, for example methods for efficient proactive flow management and periodic re-optimization of routing policies. Simultaneously, to make the system production-ready and create a reliable research environment, we provide solutions to several technical obstacles. We validate the proposed system with three network topologies, each with three load levels, following real-life traffic models. We consider Equal-Cost Multi-Path Routing as a baseline. The results indicate that the system allows network operators to handle more traffic (packet loss reduced by up to 30%), improve quality of service (less congested links resulted in even 2.5 times lower latency), and reduce operational expenses (energy consumption lowered by up to 10%).

This article is devoted to the problems of static and runtime integrity for cloud deployments. Existing remote attestation solutions for cloud infrastructure do not cover static and dynamic attestation as a whole. They evaluate either the static or dynamic part, not considering the rest. We address this gap by proposing a runtime attestation process based on hardware CET technology, as an enhancement to static attestation enabled by SGX. We show how hardware-assisted protection for control-flow-related attacks can enhance virtual deployment security with minimal tradeoff. Our solution does not significantly increase the processing time. Moreover, a processing time can even be reduced when this mechanism is used as a default protection method against control-flow related attacks.

Emerging applications such as the metaverse, telesurgery or cloud computing require increasingly complex operational demands on networks (e.g., ultra-reliable low latency). Likewise, the ever-faster traffic dynamics will demand network control mechanisms that can operate at short timescales (e.g., sub-minute). In this context, Traffic Engineering (TE) is a key component to efficiently control network traffic according to some performance goals (e.g., minimize network congestion).This paper presents Routing By Backprop (RBB), a novel TE method based on Graph Neural Networks (GNN) and differentiable programming. Thanks to its internal GNN model, RBB builds an end-to-end differentiable function of the target TE problem (MinMaxLoad). This enables fast TE optimization via gradient descent. In our evaluation, we show the potential of RBB to optimize OSPF-based routing (≈25% of improvement with respect to default OSPF configurations). Moreover, we test the potential of RBB as an initializer of computationally-intensive TE solvers. The experimental results show promising prospects for accelerating this type of solvers and achieving efficient online TE optimization.

The constantly growing amount of software in use, accompanied by huge amount of technical debt, gradually raises concern in the industry. New technologies and software development processes become yet another degree of freedom boosting the complexity. As the software development and delivery techniques evolve, technical debt perspective should follow. Taking into account all software artefacts enabling value delivery to customers, embracing DevOps paradigm and its holistic focus on software development lifecycle, the strategy presented in this paper enabled stabilization of a large telecommunication software system after a set of consecutive complex merges. The research question of this paper looks for evidence whether prioritization of technical debt mitigation efforts bring a faster return on investment. A 2-year-long case study focused on technical debt prioritization and mitigation that was conducted on this software system resulted in improved quality and stabilization of feature development efforts (cost and time based). Therefore, the tangible gains from applying this approach comprise over 50% decrease in stability issues, improved screening by over 30%, and 6 times better predictability of delivery time (reducing allocation of stabilization effort and time).

This paper proposes a technical debt prioritization technique to be used throughout software development lifecycle. Our method stresses a broader perspective of the product, so the focus is kept on optimizing the software artifacts supporting the complete flow of the value for the customer (including e.g. test environment or deployment pipelines). Many technical debt approaches are focused on static code analysis and misconsider business aspects as a separate activity. On the contrary, our method, based on the New Solution Selling Process, strongly advocates for a business perspective. It facilitates technical debt valuation against a predicted product roadmap by a close alignment with a structured sales process and the advancement of sales opportunities. Taking into account prediction of sales, progress of conversations with potential clients and bids, probability that a given functionality will be prioritized for the development, this approach may serve as a reference point for measuring business value of technical debt continuously. This valuation serves as a relative comparison of technical debt items enabling prioritization of effort to pay off technical debt. The method was evaluated on a release of a wireless telecommunication system. The results confirm that the suggested priorities may address the most profitable product areas from the company's perspective and ultimately optimize the value for customers.

In this survey paper on the recent research, the authors evaluate consistency of the use of technical debt terminology and its convergence with the agreed-upon conceptual model. This consistency is important from the perspective of decision makers, who may delay or even abandon investment in a given area of the product, unless the benefits from repaying a specific technical debt are clear enough. Additionally, the paper discusses a degree in which the metaphorical origins of the technical debt term are still present and influence the research. The analysis is focused on the selected conference papers and offers a view on the inherent dynamics behind shaping the technical debt concept and its conceptual model. Besides, by discussing aspects related to cognitive linguistics, this study provides the means to understand this dynamics and equip the reader with suggestions to decrease ambiguity in the research. The results of the research lead to the following conclusions: 1. Ambiguity stemming from the origin of metaphorical expression of technical debt still exists in the research and there is a need to decrease it by e.g. framing, clarification, and explicit limitation of the research field. 2. Tool designers, such as SonarQube, are not bound by the research results and lead to more confusion in defining technical debt. 3. Risk Management models should be applied to enable technical debt management by decision makers. Thus, Architecture Tradeoff Analysis Method and other Quality Attribute Models may serve as good references to expand the current technical debt model. Even though some ambiguity is still present, the technical debt concept as a software development artifact and agreed-upon conceptual model provide a consistent approach for interpretation and analysis of technical debt phenomenon. Notably, code smells can be mapped to a list of symptoms of technical debt items.

Local access data network (LADN) is a promising paradigm to reduce latency, enable lowering energy consumption, and improve quality of service (QoS) for the Fifth Generation (5G) radio access network (RAN) supporting vehicle to everything (V2X) communications. To achieve optimum resource allocation and save energy by minimizing the activation of LADN servers in Cloud-RAN, some remote radio heads (RRHs) can be turned on or off depending on the traffic demand. In this paper, we investigate the problem of how to realize effective resource management in 5G RAN supporting V2X communications. More precisely, we first propose a formulation of the resource management problem as an optimization problem with the objective of minimizing the number of RRHs to be turned on subject to the uplink bandwidth constraints. We then use a fully-fledged professional software to solve our optimization problem and propose a solution with heuristic algorithms to deal with the complexity of the problem for large scenarios. Moreover, we analyze the impact of the density of vehicles on the computation time and the influence of the uplink data rate and vehicle densities on the number of active RRHs. Our numerical results show that our proposed model can efficiently utilize the resources and provide optimum vehicles-to-RRHs associations which lead to energy-savings. For instance, to serve 100 vehicles with aggregated uplink data rate equal to 100 [Mbps], the optimal associations save about 70% of the energy comparing to the strongest-signal associations. Furthermore, we obtain optimal results for the small size problem in reasonable computation times, which are around 50 [ms].

Modern network services are constantly increasing their requirements in terms of bandwidth, latency and cost efficiency. To satisfy these requirements, the concept of network slicing has been introduced in the context of next-generation 5G networks. However, to successfully provision resources to slices, a complex optimization problem must be addressed to allocate resources over a cloud network, i.e., a distributed computing infrastructure interconnected through high-capacity network links. In this study, we propose two new latency and energy-aware optimization models for provisioning 5G slices in cloud networks comprising both distributed computing and network resources. The proposed approaches differ from other existing solutions since we conduct our studies with respect to the end-to-end latency. Relevant models of latency and energy consumption are proposed based on a comprehensive review of the state-of-the-art. To effectively solve those optimization problems, a configurable heuristic is also proposed and investigated over different network topologies. Performance of the proposed heuristic is compared against near-optimal solutions. Moreover, we assess the importance of matching between resource provisioning algorithms and architectural assumptions related to 5G network slices and a proper problem modeling.

In this paper, an innovative and adaptive BitTorrent-based peer-to-peer television (P2PTV) system is proposed and investigated. It is shown how end-users of such systems and Internet Service Providers (ISPs) may benefit from simultaneous usage of both (a) the recently standardized ALTO Protocol, and (b) Scalable Video Coding (SVC). The system description is followed by a thorough evaluation of the results gathered during the simulation experiments.

A current view on the design of networks resilient to non-malicious failures supported by risk engineering is presented in this keynote. The aspect of risk response is emphasized.

Information technology is widely used in processes vital to enterprises.Therefore, IT systems must meet at least the same level of security as required from the business processes supported by these systems.In this paper, we present a view on cybersecurity management as an enterprise-centered process, and we advocate the use of enterprise architecture in security management.Activities such as risk assessment, selection of security controls, as well as their deployment and monitoring should be carried out as a part of enterprise architecture activity.A set of useful frameworks and tools is presented and discussed.

Researchers who address the problem of application-level traffic optimization often consider only the simplest comparison scenario: (a) the case before the option to cooperate and optimize the service is enabled, and (b) the case in which all clients try to optimize the traffic. In this paper, the situations that a user may not wish to cooperate or that a user does not perceive the optimization possibility to be valuable enough to cooperate are taken into account. An analytical model for estimating the popularity of the cooperate-to-optimize option is derived. A method for calculating the time-dependent value of the predicted popularity is presented. The model is verified by simulation experiments.

The paper presents a concept of a rating algorithm aiming at overall peer-to-peer traffic reduction (and locality awareness) and decrease of load on costly links, thus diminishing operator's costs. The rating algorithm is not only performed by a sole Oracle, as is typically proposed, but its operation is also supported by a similar entity located in other domains. Due to this concept, a usually omitted fact that the inter-domain routes are asymmetrical is properly dealt with. Simulations performed on a real topology show the usefulness of the presented approach.

The paper proves that it is possible to optimize application-level multicast operation from the viewpoint of traffic flows. A modification of the FreePastry/Scribe application is proposed to enable cooperation with the IETF ALTO (Application- Layer Traffic Optimization) protocol. Consequently, the overlay topology is constructed taking into account the underlying network topology. The presented results show that costly traffic types can be reduced while the increase of the delay is not harmful.

The paper presents a concept of the peer-to-peer related traffic optimization. The mechanism is based on the Oracle idea, where a peer is informed on closely located P2P nodes suggesting connection to them. The Oracle gains the relevant information on the basis of BGP databases, where the important extension is related to using a cooperation mechanism between different operators' domains.

Applications based on peer-to-peer systems are oblivious to the underlying Internet topology. Chaotic connections between peers generate a large amount of traffic crossing carriers' borders, causing growth of operators' costs and have negative influence on performance (increased download time). In this paper, Yukka method is proposed to improve performance by localizing traffic on the basis of the operator and autonomous system related information. YukkaPlugin, an implementation of the Yukka method in one of the BitTorrent clients, uses for illustration RIPE NCC database responses mapping each IP address within Europe into an ISP network and country. The YukkaPlugin favors peers which are localized in an area close to a user. Applying the Yukka method results in shorter completion times achieved by an end-user.

Due to the development of Next Generation Networks, leading to a multiservice transport layer with a multidomain environment, the importance of interconnection issues keeps growing. As the number of possible partners increases, operators face different routing options with regard to service quality and cost. We present the extension of the Least Cost Routing (LCR) concept which takes into account the reliability issues. As for a large number of variables the LCR problem is unsolvable, some heuristic algorithms to find the problem solution are proposed and evaluated. The solution helps to optimize connections among telecommunication operators by minimizing costs for served demands and protecting against network failures.

Carriers conveying long-distance traffic face different routing options with regard to service quality and cost. Thus, least cost routing (LCR) solutions supporting optimal decisions become a very important issue in the inter-carrier operations. In this paper, we present this problem in the context of the long-distance traffic partner-carrier choice. The mathematical optimization problem is formulated. Especially, constraints (e.g., capacity, volume) related to the techniques used are considered. Different tariff formulations are taken into account as well.

A new concept Quality of Resilience (QoR) presented in this paper is based on the distinction in the reliability-related Quality of Service (QoS) parameters of the short-term quality factors and the long-term quality factors. The former parameters are called availability parameters and the latter are called QoR parameters. In one hand by dividing the service duration time into intervals, the service is considered available during a time interval, if the Service Level Agreement (SLA) between the user and the network operator is satisfied. In the other hand the long-term characteristics of the service are derived from the service downtime distribution. With the downtime histograms the asymptotic characteristics of the service can be represented both at the transport and service layers. Since the resilience mechanism implemented into the network match the transport layer downtime histograms, this new characterization of the QoS helps to measure the impacts of a given recovery scheme on the next generation services.

In the paper, theoretical formulas describing the reliability function as well as the mean time to failure for static p-cycles are derived. The practical conclusions concerning the design of p-cycles are drawn: p-cycles do not always have good properties from the reliability viewpoint; if an operator decides to use them, it has to ensure that the trade-off between sharing of resources and the availability is reasonable. An appropriate function on which such a choice can be based is introduced.