Incentives and Pricing in Communications Networks

To study the underlying organizing principles of various complex systems, designing an efficient graph-based model for data representation, is a fundamental aspect. As the topological structure of the network changes over time, it is a challenging task to design a communication system having ability to respond to randomly changing traffic. We are interested to find out the suitable and fair traffic flow rates to each system for getting optimal system utility using dynamic complex network model. In this context, we design and simulate a growth model of the data communication network based on the dynamics of in-flowing links which is motivated by the concept that newly added node will connect to the most influential nodes already present in the system. The connectivity distribution of the evolved communication networks follows power law form, free from network scale. We analyze Kelly’s optimization framework for a rate allocation problem in communication networks at different time instants, and optimal rates are obtained with the consideration of arbitrary communication delays.

This paper extends previous research efforts related to the simulation performance modelling and analysis of satellite communication networks. Specifically, the use of low earth orbit (LEO) satellite networks for personal communications is examined. Six different satellite constellation configurations are investigated in a packet-switched operating environment. Performance metrics examined are the end-to-end packet delay and the utilization of satellite channels in the dynamic environment. Realistic and accurate models of the physical satellite network and its terrestrial transmitters require that numerous operating characteristics and assumptions be specified. These are based on proposed design requirements of commercial systems, such as Iridium. Via the use of simulation, we show the relative delay and utilization performance of differing satellite network architectures. From these simulation models, mathematical metamodels are derived for the system delays. These innovative models are used to predict the delay performance of different network architectures not previously simulated. Comparison of these metamodels with simulation results show that metamodels provide an accurate means for performance prediction. Copyright © 1999 John Wiley & Sons, Ltd.

In order to improve the security of low-latency cluster dispatching communication network nodes, this paper proposes a security situation assessment method of low-latency cluster dispatching communication network nodes based on grey relational model. The security situation assessment model of low-delay cluster scheduling communication network nodes is constructed, and the comprehensive risk index is introduced as the three-layer assessment index of low-delay cluster scheduling communication network nodes. Based on the analysis of load loss probability and expected response characteristics of insufficient packet loss load in communication transmission, the reliability index calculation system of low-delay cluster scheduling communication network node scheduling is established. By introducing expected load reduction parameters, the frequency characteristics of node scheduling security in low-delay cluster scheduling communication network are monitored. Based on the average sustained risk index evaluation of load reduction, the security permission conditions of node scheduling in low-delay cluster scheduling communication network are established. The network transmission load control is carried out by using grey correlation scheduling model. The node control model of low-delay cluster scheduling communication network is constructed by combining risk assessment and load parameter detection. The learning control of node security situation assessment is realized by using Cyber-net and unsupervised learning methods. The node security situation assessment of low-delay cluster scheduling communication network nodes is realized through access and decentralized scheduling. Tests show that this method has high accuracy in evaluating the security situation of nodes in low-delay cluster dispatching communication networks, and has strong completeness and reliability in grid-connected control of communication networks.

Communication networks are complex information systems influenced by a vast amount of factors. It is critically important to forecast the paths that data take to verify the network, check its security and plan its updates. Model allows exploring processes that take place in the network without affecting performance and availability of a real network itself. With modelling it becomes possible to investigate the results of infrastructural changes introduced to the network before actually implementing them. It is important to be able to formally convert real network description into the model definition which preserves all data that is significant for network operation and skip data which is not. Outlining the rules for such conversion and using a limited set of basic functional components provide the ground for automatic model creation for the network of different levels of complexity.Proposed approach to modelling of communication networks is based on decomposition of the overall function of every particular real network component into a set of functions that belong to some predefined basic set. Functions of the basic set include L3 routing, L2 switching, packet filtering, NAT, etc. Model of a real network component is defined as a group of functional nodes each of which implements some function from the basic set.Configuration and current state of network components that influence its operation are also decomposed into elements each of which relates to some particular functional node. Configuration of network components is modelled as a set of configuration storage elements and current state is modelled as a set of current state storage elements.Links that connect real network components and links that connect functional nodes in the model are presented as singledirection channels that implement propagation of L2 frames thus simplifying the model due to excluding physical layer (L1) from the scope.Using the proposed approach to modelling may allow to formalize conversion of a real network descrip- tion to a model thus making automated modelling possible. By using a sufficient basic set of functional nodes it is possible to model the network containing components of any complexity level.

The future integrated sensing and communication (ISAC) networks is expected to equip with sufficient computation resources. However, current research focuses on single-domain resource allocation in ISAC and computing force networks, leaving the joint optimization of sensing, communication, and computation resource allocation unexplored. In this paper, we propose a novel approach to this problem by deep incorporating computation resources, combined with a federated learning framework, while considering sensing precision and power consumption. Firstly, a multi-objective optimization is designed, involving Cramer-Rao Bound, sum rate of ISAC networks, and power consumption of computing force networks. Subsequently, the multi-objective optimization is transformed into a multi-task learning model. We aim to obtain joint optimization of sensing, communication, and computation resource allocation via deep learning techniques. Towards the multi-task learning model, the multiple-gradient descent algorithm is utilized to obtain the multi-objective optimization. Furthermore, a practical low-complexity the multiple-gradient descent algorithm is developed to reduce the computational cost. Finally, the effectiveness of the proposed deep learning algorithms is verified by simulations results.

The Block Oriented Network Simulator (BONeS) DESIGNER provides a graphical environment for modeling and discrete-event simulation of computer and communication networks, distributed computer systems, and computer architectures. DESIGNER uses a hierarchical data flow block diagram modeling paradigm which provides for definition of data structures that flow between blocks. This modeling paradigm provides sufficient flexibility to model and simulate a broad range discrete-event systems, including computer network and distributed processing systems. >

Queuing networks models are widely used as important tools for the performance modeling and evaluation of both computer systems and communication networks. Exact solutions are generally not attainable except for some special cases. Consequently, numerical techniques and analytic approximations are needed for the study of queuing networks with bursty traffic. This paper presents the performance modeling and evaluation of general packet radio service (GPRS) for wireless global system for mobile telecommunication (GSM) cells with bursty multi-class traffic under a partial sharing scheme (PSS). The model has been developed using a new powerful tool, MOSEL (Modeling, specification and evaluation language), which is based on numerical methods and the Petri-nets. The proposed model has been validated against simulation and those developed using maximum entry (ME), a well established methodology for approximate solutions. Numerical results demonstrate the credibility of our models.

MOGUL (model generator with user leadthrough) and REGAL (report generator and lister) are easy-to-use, powerful tools for simulation modeling of computer systems and communication networks. Using MOGUL and REGAL along with Wolverine Software's GPSS/H and PROOF, the user gets a complete environment for building computer system and network models, running simulation experiments, and evaluating results. Animation of the graphical output is supported. Graphical input specification of the processors, devices, and networks in a model will also be possible in an upcoming release. >

State of the art management products have until now focused on monitoring of resources such as networks, systems, servers and applications. Management usually does not deal with handling the complete resource life-cycle, i.e., of providing resources on-demand to application providers, matching and allocating resources to users so that application requirements are best met, providing guarantees through service level agreements, and monitoring and assuring these SLAs. Grid aims to provide seamless access to computational resources. In Grid based systems and the latest initiative of Open Grid Service Infrastructure (OGSI) [3] concepts related to discovery of network topology, allocation based on farms, monitoring towards certain goal (as specified in SLAs), and control to assure these goals are missing. However, there are parallels between grid and traditional management systems. A grid just like traditional management systems is comprised of grid nodes each of which manages a group of resources. The management+ [1] system that we propose, will be able to manage the complete life-cycle of resources assigned to applications, provided, Grid technologies are used for reserving, allocating, and handing over resources to applications and traditional management functionalities are incorporated that deal with monitoring and control.A management+ system will be comprised of grid nodes that communicate with each other to coordinate the tasks of resource allocation and management. The management+ system may be used for managing a resource pool inside the enterprise or spanning multiple enterprises. In order to do so, the management+ system needs capabilities to discover the resources so as to create a resource pool, maintain & model the resource information, be able to provide resources to application providers on receiving requests, be able to provide guarantees as agreed upon through Service-Level Agreements, and by monitoring and assuring them. This poses the following requirements for the management+ system: Resource Discovery: Under the realm of each grid node are collections of resources that are allocatable and manageable by that grid node, that need to be registered and discovered. The registration information may be maintained in a central repository that may be LDAP based (as in traditional Grid) or in a UDDI based registry. The other approach is to let each of the grid nodes manage their own repository, but to have protocols similar to p-2-p systems for searching resources amongst grid nodes. Resource Modeling: Resources that are managed by the grid or those even within a grid node are heterogeneous in nature. They are of different types, potentially distributed in different geographic locations and administrative domains (e.g., consolidated in racks vs. located on desktops). Modeling is therefore an important task for the management+ system. A relevant resource model is CIM. Requesting Resources and Guarantees: Application providers need to specify their requests to the management+ system in some form. The specification of the request may be done in terms of application level metrics (e.g. throughput, transactions/sec) or at a low-level, an enhanced RSL may be used. The guarantees may be provided in terms of reliability, availability, security, timeliness through SLA[5]. Resource Allocation and Deployment: Resource requests are sent to the management+ system that then undertakes resource reservation, allocation and deployment. Resource reservation is done when the resources are not immediately required. The management+ system has to keep a note of all the competing reservations and of ensuring that the resource pool is being properly utilized. As the reservations become current, requests for resources are satisfied through match-making [3]. Monitoring Guarantees: Traditionally, applications are installed and operated on a fixed set of associated, physical hardware. In management+ system, resources virtualize the physical hardware. This virtualization provides the capability of transparently switching the physical hardware (in case of degradations/failures) while maintaining the transparency of a continually running application. The metrics specified on the resources in SLAs have to be monitored and aggregated into higher-level metrics that business manager(s) may relate to. Assuring Guarantees: Once the SLA violations are detected, it is important for the management+ system to take corrective actions. Analysis tools are employed to analyze the violation data so as to decide on corrective action(s). The corrective action may range from taking one of the control actions like fail-over, reboot/rejuvenation of the resources to transparently switching the failed resources with new set of resources out of the resource pool. These new resources are obtained through the same resource allocation and deployment mechanisms as discussed earlier. This may enable closed-loop management.KeywordsCorrective ActionGrid NodeService Level AgreementResource PoolResource ReservationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Loss-cognizant pricing in networks with greedy users

Localization of information on communication networks of an open-source online community

Collaborative optimization of distribution network and 5G base stations considering its communication load migration and energy storage dynamic backup flexibility

Based on the data in real combat games, the combat System-of-Systems is usually composed of a large number of armed equipment platforms (or systems) and a reasonable communication network to connect mutually independent weapons and equipment platforms to achieve tasks such as information collection, sharing, and collaborative processing. However, the generation algorithm of the combat system in the existing research is too simple and not suitable for reality. To overcome this problem, this paper proposes a communication network generation algorithm by adopting the joint distribution strategy of power law distribution and Poisson distribution to model the communication network. The simulation method is used to study the operation under continuous attack on communication nodes. The comprehensive experimental results of the dynamic evolution of the combat network in the battle scene verify the rationality and effectiveness of the communication network construction.

We study the problem of admission control and resource (subcarriers, power and bit-loading) allocation in a heterogeneous OFDMA wireless network serving both QoS-constrained high-priority users and best-effort users. By clustering subcarriers, efficient algorithms for cluster and power allocation have been proposed. Our strategy maximizes the total network utility of the best-effort users while satisfying the QoS request for maximum number of high-priority users. We approximate best-effort user utility function as a piece-wise linear function and propose a linear programming based cluster allocation algorithm. The feasibility of the resource allocation problem depends on the number of HP users in the network. Since a large number of demanding HP users would render the resource allocation problem infeasible, a joint admission control and resource allocation scheme could be an efficient way of tackling both problems with less overhead. By incorporating admission control, we propose an efficient and optimal joint admission control and resource allocation algorithm.

Presently the demand from users for communication networks with high bandwidth, high data rate, high security, and reliable connections has suddenly surged due to increased internet applications over the last five years. This is making communication networks complex. Further use of optical fiber cables as the backbone because of their high bandwidth and capacity, which can carry Tera bits of information, has further increased the complexity of accessing and analyzing the optical networks due to their non-linear behavior. In this regard, the availability of advanced machine learning algorithms based on mathematical tools, which are in a mature stage, has been looked upon. Machine learning is becoming an important tool because of the unexpected growth of network complexity. In the optical network, resource efficiency and capacity of the network is determined by routing and resource allocation. Since optical networks are the backbone, making them energy efficient will reduce carbon footprints and hence can reduce global warming. Hence, mathematical tools of machine learning algorithms are explored for optical communication systems and networks. Applications such as nonlinear equalization, optical performance monitoring, signal detection, mitigating various noise effects etc., and corresponding to their machine learning tools are discussed in detail. Researchers can explore further using machine learning tools in an optical domain and hence can propose and enhance flexibility and reliability.