Strategic parameter-driven routing models for multidestination traffic in telecommunication networks

The term traffic-carrying performance, as applied to any telecommunications network, is used to describe how effectively that network is able to transfer information between users. Earlier chapters have shown that traffic-carrying performance is inversely related to the percentage of calls lost and call set-up delay experienced with a circuit-switched telephone network, and to information-transfer delay across all types of data network. Call losses or delays can only be reduced by providing more or, with data, faster circuits and more equipment, at greater expense; thus traffic-carrying performance is also directly related to overall network cost. By matching the traffic-carrying performance of a private telecommunications network as closely as possible to the needs of the business concerned, it is possible to ensure that network costs are no greater than absolutely necessary. This objective can be achieved by designing the network to meet a performance target.

Aimed at the characteristic of the software defined network (SDN), several green routing algorithms are proposed. However, there are many drawbacks consisted in the existing algorithms. Therefore, we propose a self-adaptive energy saving routing algorithm (LAR) which is based on residual bandwidth of links and SDN. The proposed algorithm makes the link utilization which is changing in real time as the link cost. It would obtain the topology information and link status to optimize and prune the topology for reducing the computing time of routing algorithm before selecting routing path. After a period of time, the incoming flows will automatically be gathered in heavily-loaded links. The links without traffic will be switched off while the whole network connectivity and QoS are guaranteed. Simulation results show that it is possible to reduce considerable energy consumption during off-peak hours and link energy saving can be up to 55%. And, the algorithm has the distinct advantage in terms of complexity and network performance comparing related schemes.

The authors describe the preliminary results of a rule-based and hypertext system developed to aid the telecommunication analyst to more advantageously use MIND-Data in network design and cost analysis processes. The software package MIND-Data is an interactive tool designed to help network designers experiment with different alternatives to achieve the best compromise between time-response requirements and network cost. The objectives pursued in the present work include providing intelligent online help support for the MIND-Data user evaluating the general requirements for the network, and providing critic cost analysis for specific parts of the network. >

The subject matter of the article is the processes of synthesis of the information and telecommunication network (ITN) for solving applied problems of safety-critical systems (SCS). The goalis to develop a mathematical model for the optimal distribution of applied tasks of safety-critical systems over the ITN nodes. The tasks to be solved are: to formalize the procedure of distribution of applied tasks and SCS software over the ITN nodes; to develop a mathematical model of optimal distribution in order to minimize the cost of network resources; to select an effective algorithm for solving it. The methods used are: alternative-graph approach, mathematical optimization models, methods for solving nonlinear integer programming problems with Boolean variables. The following results were obtained: the task of selecting the ITN optimal structure was formulated according to the alternative-graph model of information processing; in addition to structural characteristics, the requirements for the parameters necessary for performing applied tasks were taken into account while constructing a mathematical model; when minimizing the cost of a computing resource, constraints related to the capabilities for financing the development and operation of the network are taken into account; the costs for organizing additional connections among the network nodes are considered as well. As a result, a mathematical model of distributing SCS applied problems over the ITN environment was obtained in order to minimize the total costs of computing resource, data transmission, network setting and maintenance. This model is a non-linear integer programming problem with Boolean variables. Taking into account the specific nature of the objective function and model constraints with the use of pseudo-Boolean functions, the original task is reduced to a linear form. The obtained model represents the canonical form of the linear optimizing problem of large-dimensional Boolean programming, for which the method of the recession vector is effective. Conclusions. The scientific novelty of the results obtained is as follows: 1) the optimization model of distributing applied tasks over the nodes of the computer network was improved by defining the objective function in order to minimize the costs of both computational and data transmission and the constraints caused by the requirements for the technical and information struct ure of the network; 2) methods for solving the problems of optimizing the ITN structure on the basis of models of nonlinear Boolean programming by transforming the initial task into a linear form and applying the recession vector method was further developed, which makes obtaining a quasi-optimal solution of the problem in the context of large dimension possible.

The integration of multiple resources conservation networks is necessary to attain the ever-stringent sustainable goals. This work takes initiatives to develop a heat integrated water network via a proposed P-graph-based sequential methodology. In the first step, a set of feasible water regeneration networks is generated using the conventional P-graph framework. Then, the obtained feasible networks will be used as the inputs in the second stage which aims to generate various sets of feasible heat exchanger networks. It is worth noting that the second model is solved by an extended P-graph framework (P-HENS) for combinatorial process network optimization. The solutions are then ranked based on the total network cost. To demonstrate the effectiveness of the proposed method, a typical water regeneration network (three sources and three sinks) with multi-contaminants is used. The results show a total of 103 feasible water network structures (water network cost ranging from 0.76 M$/y to 1.18 M$/y). Thereafter, a list of feasible HIWRN can be determined using P-HENS. The top four HIWRNs which offer similar total network cost (~1.639 M$/y) are demonstrated. This proposed method provides valuable insights that allow decision-makers to further select the optimal solution which may be more beneficial as compared to the one obtained via conventional methods.

Computer networks fundamentally changed the way we communicate and interact with each other. In fact, they now form the backbone of our modern societies. While early networks were merely a mechanism for exchanging data between end-hosts, current computer and telecommunication networks are way more than that. Compared to the early days of the networking not only the hardware of networks has changed fundamentally, but with it also the software deployed to control and manage these networks. To reduce the significant management cost of large networks, automated management, including autonomic computing and communications evolved.

QNoC: QoS architecture and design process for network on chip

The unsanctioned growth of the encrypted network traffic is a two-sided problem for the cybersecurity, on one hand, it preserves the privacy of the users, and, on the other hand, it obscures the malicious motive of the traditional intrusion detection systems. The current paper presents this challenge by the construction of a model of the encrypted traffic data anomaly that can be explained in data analytics. The solution proposed includes the classical machine learning (Random Forests, Support Vector Machines), deep learning (Autoencoders, LSTMs) algorithms, and explainability (SHAP, LIME, counterfactual analysis). This framework was tested and trained with several benchmark networks (CICIDS2017, ISCX VPN/Tor, UNSW-NB15) and guarantees the universality of the framework in different network settings. The findings show that the accuracy and recall of deep learning models can outperform those of hybrids, but hybrid ensembles (e.g., RF + Autoencoder) can be more accurate because they do not weaken the performance identified by them, but on the contrary, enhance their interpretability. Explainability profiling revealed that time spent in a flow, packets inter-arrival variance, and bytes distribution are the critical characteristics of traffic that are relevant in differentiating a deviant behavior and an ordinary encrypted traffic. The system has already been found to be practically applicable in case study of enterprise and IoT and telecom networks. In addition, explainable AI implementation will lead to improved trust in the analyst, regulatory bodies, and reduce ethical issues regarding black-box detection systems. The results show that accuracy and transparency ought to be an element of cybersecurity. Directions Future Future Future directions involve the application of federated learning to carry out privacy-preserving detection, real time explainability dashboards, standard controlled encrypted traffic benchmarks, and graph-based anomaly detection. The given work is a viable and efficient solution for anomaly detection in an encrypted space that contributes to the development of both technical and ethical components of the cybersecurity sector.

The co-existence of heterogeneous access technologies with largely different characteristics results in handoff asymmetry. As the convergence of dissimilar networks has been envisioned to characterize the next generation wireless networks, the seamless and efficient handoff between different access technologies (vertical handoff) has become essential. As the "best" network has to be chosen, handoff decision should be carefully executed based on network characteristics and dynamics such as link cost, link capacity and power consumption of each available network. In this study, we have proposed dynamic weight factors for explicit handoff notification scheme for mobile systems equipped with an intelligent handoff manager to decide the "best" network interface and "best" time moment to handoff. We have simulated the smart decision model using dynamic weight factors to evaluate various scenarios. Results show that using dynamic weight factor, usage cost and charge consumption can be saved by approximately by 6.44% and 5.50% respectively.

Monitoring and evaluating policies and programs in low- and middle-income countries are often difficult because of the lack of routine data. High mobile phone ownership in these countries presents an opportunity for efficient data collection through telephone interviews. This study examined the feasibility of collecting data on medicines through telephone interviews in Kenya. Data on the availability and prices of medicines at 137 health facilities and 639 patients were collected in September 2016 via in-person interviews. Between December 2016 and December 2017, monthly telephone interviews were conducted with health facilities and patients. An unannounced in-person interview was conducted with respondents to validate the telephone interview within 24 h. A bottom-up itemization costing approach was used to estimate the costs of telephone and in-person data collection. In-depth interviews were conducted with data collectors and respondents to explore their perceptions on both modes of data collection. The level of agreement between data on medicines availability collected through phone and in-person interviews was strong at the health facility level [kappa = 0.90; confidence interval (CI) 0.88–0.92] and moderate at the household level (kappa = 0.50, CI 0.39–0.60). Price data from telephone and in-person interviews showed strong intra-class correlation at health facilities [intra-class correlation coefficient (ICC) = 0.96] and moderate intra-class correlation at households (ICC = 0.47). The cost per phone interview at health facilities and households were $19.73 and $16.86, respectively, compared to $186.20 for a baseline in-person interview. Participants considered telephone interviews to be more convenient. In countries with high cell phone penetration, telephone data collection should be considered in monitoring and evaluating public health programs especially at health facilities. Additional strategies may be needed to optimize this mode of data collection at the household level. Variations in cell phone ownership, telecommunication network and data collection costs across different settings may limit the generalizability of the findings from this study.

Modern telecommunication networks are very complex and they should be able to deal with rapid and unpredictable changes in traffic flows. Virtual Network Topology is used to carry IP traffic over Wavelength-division multiplexing optical network. To use network resources in the most optimal way, there is a need for an algorithm, which will dynamically re-share resources among all devices in the particular network segment, based on links utilization between routers. Attractor selection mechanism could be used to dynamically control such Virtual Network Topology. The advantage of this algorithm is that it can adopt to very rapid, unknown and unpredictable changes in traffic flows. This mechanism was adopted from biology - gene regulatory networks in cells use it to adopt to unknown changes in the environment. The dynamical system modeling the gene regulatory network is considered. The relations between genes are described by the regulatory matrix W with the entries from the set {−1, 0, 1} meaning inhibition, no relation and activation respectively. The structure of the attracting set in some specific case is studied. It is remarkable that the attracting set contains a focus-type critical point.

In WDM networks with limited drop-offs, the route of a multicast connection consists of a set of light-trees. Each of the light-tree is rooted at the source node and contains no more than a limited number, say k, destination nodes due to the power loss of dropping optical signals off at destination nodes. We call such a light-tree k-drop light-tree. In this paper we study the multicast routing problem of constructing a set of k-drop light-trees that have the minimal network cost. The network cost of a set of light-trees is defined as the summation of the link cost of all the light-trees. We first prove that this problem is polynomial-time solvable for k=2 and NP-hard for k/spl ges/3. We then propose a 4-approximation algorithm for the problem for k /spl ges/3. A wavelength assignment algorithm is also proposed to assign wavelengths to the light-trees of a multicast connection. In the end we give simulation results showing that k-drop multitree muting can significantly save not only the network cost but also wavelengths used. Moreover, when k/spl ges/5 its performance is very close to the case where k is infinite (i.e., the case of using a single tree for a multicast connection).

This paper deals with the minimization of the total system cost in the PCS network using CDMA cellular system standard. The total system cost includes the installation costs of base stations (BS), base station controllers (BSC) and PCS exchanges (PCX), the weighted-distance connection costs between BS and BSC and between BSC and PCX and the hard handoff cost. Our objective is to keep each of these costs at a minimum with guaranteed service performance. However, there exists a tradeoff property between these costs. Therefore, we use an efficient and effective searching technique, hierarchical genetic algorithms (HGA), which has the ability to handle multiobjective functions and various constraints and find multiple different solutions for decision making.

Due to mobility in wireless networks, the state information of all outgoing links (link delays, available resources and costs) of nodes is imprecise. Fuzzy logic has proven to be a powerful tool when dealing with imprecise information. The implemented distributed protocol incorporates fuzzy inference systems to guide the process of finding routes with sufficient resources to satisfy a certain delay and other requirements with least end-to-end cost in a dynamic network. Multi-path parallel search is used to increase the probability of finding a feasible path. When there are such multiple paths, the one with least cost is selected. Results show how this protocol can tolerate a high degree of information imprecision. Success ratios for delay resource constrained routing reach values over 92%.

In principle, sparse neural networks should be significantly more efficient than traditional dense networks. Neurons in the brain exhibit two types of sparsity; they are sparsely interconnected and sparsely active. These two types of sparsity, called weight sparsity and activation sparsity, when combined, offer the potential to reduce the computational cost of neural networks by two orders of magnitude. Despite this potential, today’s neural networks deliver only modest performance benefits using just weight sparsity, because traditional computing hardware cannot efficiently process sparse networks. In this article we introduce Complementary Sparsity, a novel technique that significantly improves the performance of dual sparse networks on existing hardware. We demonstrate that we can achieve high performance running weight-sparse networks, and we can multiply those speedups by incorporating activation sparsity. Using Complementary Sparsity, we show up to 100× improvement in throughput and energy efficiency performing inference on FPGAs. We analyze scalability and resource tradeoffs for a variety of kernels typical of commercial convolutional networks such as ResNet-50 and MobileNetV2. Our results with Complementary Sparsity suggest that weight plus activation sparsity can be a potent combination for efficiently scaling future AI models.