Network Management Solutions Research Articles

Online reconfiguration scheme of self-sufficient distribution network based on a reinforcement learning approach

With increasing number of distributed renewable energy sources integrated in power distribution networks, network security issues such as line overloading or bus voltage violations are becoming increasingly common. Traditional capital-intensive system reinforcements could lead to overinvestment. Moreover, active network management solutions, which have emerged as important alternatives, may become a financial burden for distribution system operators or reduce profits for owners of distributed renewable energy sources, or both. To address these limitations, this paper proposes an online network reconfiguration scheme based on a deep reinforcement learning approach. In this scheme, the distribution network operator modifies the network topology to change the power flow when the reliability of network is threatened. Because the variability of distributed renewable energy is large in self-sufficient distribution networks, the reconfiguration process needs to be performed online within short time intervals, which involves the use of conventional algorithms. To solve this problem efficiently, a deep q-learning model is utilized to determine the optimal network topology. Performances of proposed and other algorithms were compared in modified CIGRE 14-bus and IEEE 123-bus test network, as well as varying penalties for frequent switching operation in consideration of physical characteristic of the network. Simulation results demonstrated that the proposed algorithm showed almost identical performances with brute-force search algorithm in both test networks, satisfying network constraints over almost all timespans. Further, the proposed method required very small computation times - under a second per each state and its scalability was verified by comparing the computation time between two test networks.

CogITS: cognition‐enabled network management for 5G V2X communication

The 5G promise for ubiquitous communications is expected to be a key enabler for transportation efficiency. However, the consequent increase of both data payload and number of users derived from new Intelligent Transport Systems makes network management even more challenging; an ideal network management will need to be capable of self-managing fast-moving nodes that sit in the 5G data plane. Platooning applications, for instance need a highly flexible and high efficient infrastructure for optimal road capacity. Network management solutions have, then, to accommodate more intelligence in its decision-making process due to the network complexity of ITS. This study proposes this envisioned architecture, namely cognition-enabled network management, for 5G V2X communication (CogITS). It is empowered by machine learning to dynamically allocate resources in the network based on traffic prediction and adaptable physical layer settings. Preliminary proof-of-concept validation results, in a platooning scenario, show that the proposed architecture can improve the overall network latency over time with a minimum increase of control message traffic.

Message-Based Communication for Heterogeneous Internet of Things Systems.

The Internet of Things (IoT) domain presents a wide spectrum of technologies for building IoT applications. The requirements are varying from one application to another granting uniqueness to each IoT system. Each application demands custom implementations to achieve efficient, secure and cost-effective environments. They pose a set of properties that cannot be addressed by a single-based protocol IoT network. Such properties are achievable by designing a heterogeneous IoT system, which integrates diverse IoT protocols and provides a network management solution to efficiently manage the system components. This paper proposes an IoT message-based communication model applied atop the IoT protocols in order to achieve functional scalability and network management transparency agnostic to the employed communication protocol. The paper evaluates the proposed communication model and proves its functional scalability in a heterogeneous IoT system. The experimental assessment compares the payload size of the proposed system with respect to the LwM2M standard, a protocol designed specifically for IoT applications. In addition, the paper discusses the energy consumption introduced by the proposed model as well as the options available to reduce such impact.

Capacity and Frequency Optimization of Wireless Backhaul Network Using Traffic Forecasting

Telecom operators are aiming to provide high-grade data, multimedia applications and low latency videos for smart devices. As today’s mobile data is experiencing rapid growth and the usage of smart devices are fabricating unparalleled challenges for telecom operators to meet the global bandwidth requirement. From the first generation to fourth generation, the technology evolution is predominantly governed by the hardware side but now it is moving towards the concept of cognitive network management, resource orchestration and machine learning-based solutions. In this paper, we propose the adaptive capacity and frequency optimization (ACFO) method for adaptive optimization based on time series forecasting approach. The daily capacity utilization of microwave (MW) links is analyzed to use forecasted demand. Based on the projected demand, the capacity and frequency optimization will be executed. The two main forecasting models 1) SARIMA and 2) MLP are used and for performance evaluation, we used RMSE and MAPE criterion. The analytic outcomes show that MLP with two layers and six hidden nodes (6/6) are good enough to achieve the desired results. In some cases, we need to exceed the hidden nodes up to fifteen (15/15). By using the forecasting approach, the reactive optimization will successfully shift to the predicted/proactive optimization, will balance the resource distribution and can condense the wastage of resources. The outcome of the study will be a contribution to the dynamic resource optimization in wireless backhaul network.

A Scalable and Error-Tolerant Solution for Traffic Matrix Assessment in Hybrid IP/SDN Networks

The advent of the Software Defined Networking (SDN) paradigm represents a great opportunity for the definition of new network management solutions. In this work, we focus on the definition and implementation of a novel technique to solve the Traffic Matrix Assessment (TMA) problem from the perspective of an Internet Service Provider. Since the migration from legacy IP networks to fully-deployed SDN ones needs to be incremental due to budget and technical constraints, this paper proposes a mixed measurement and estimation scalable solution for hybrid IP/SDN networks to accurately solve the TMA problem by exploiting the availability of flow rule counters in SDN switches. The performance evaluation shows that our error-tolerant solution is able to assess the TM with a negligible estimation error by only measuring a small percentage of traffic flows, overcoming other state-of-the-art algorithms proposed in the literature. Moreover, the performance analysis of the proposed implementation using the OpenDaylight controller over an emulated network environment, shows that a trade-off between the quality of the assessed TM and its impact on the network in terms of control messages can be found by properly tuning the number of measured flows.

The State of the Art of Software Defined Networking (SDN) Issues in Current Network Architecture and a Solution for Network Management Using the SDN

Software defined networking (SDN) architecture has been verified to make the current network architecture management simpler, and flexible. The key idea of SDN is to simplify network management by introducing a centralized control, through which dynamic updates of forwarding rules, simplification of the network devices task, and flow abstractions can be realized. In this article, the researchers discuss the complex design of the current network architecture, which has inevitably resulted in poor network resources management, such as bandwidth management. SDN-based network model has been proposed to simplify the management of the limited bandwidth of a network. The proposed network model utilizes the limited network bandwidth systematically by giving real-time traffics higher priority than non-real-time traffics to access the limited resource. The experimental results showed that the proposed model helped ensure real-time traffics would be given greater priority to access the limited bandwidth, where major portion of the limited bandwidth being allocated to the real-time traffics.

Management Platforms and Protocols for Internet of Things: A Survey.

Internet of Things (IoT) management systems require scalability, standardized communication, and context-awareness to achieve the management of connected devices with security and accuracy in real environments. Interoperability and heterogeneity between hardware and application layers are also critical issues. To attend to the network requirements and different functionalities, a dynamic and context-sensitive configuration management system is required. Thus, reference architectures (RAs) represent a basic architecture and the definition of key characteristics for the construction of IoT environments. Therefore, choosing the best technologies of the IoT management platforms and protocols through comparison and evaluation is a hard task, since they are difficult to compare due to their lack of standardization. However, in the literature, there are no management platforms focused on addressing all IoT issues. For this purpose, this paper surveys the available policies and solutions for IoT Network Management and devices. Among the available technologies, an evaluation was performed using features such as heterogeneity, scalability, supported technologies, and security. Based on this evaluation, the most promising technologies were chosen for a detailed performance evaluation study (through simulation and deployment in real environments). In terms of contributions, these protocols and platforms were studied in detail, the main features of each approach are highlighted and discussed, open research issues are identified as well as the lessons learned on the topic.

Securing the Precision Time Protocol (PTP) Against Fake Timestamps

Time distribution mechanisms favored for use in the smart grid, such as precision time protocol (PTP), were not designed with security in mind, and thus suffer several security vulnerabilities. PTP is vulnerable to fake timestamp attacks through master impersonation and sync message injection. Such an attack will synchronize clocks to a false time reference. In this letter, we consider an IEC 61850 substation and propose an approach to detect fake timestamps communicated through false PTP sync messages. This approach builds on top of existing network and system management solutions. We introduce a new simple network management protocol data objects to monitor PTP functionality and detect the existence of fake timestamps in a PTP synchronized network. The approach is implemented on a testbed. The collected results demonstrate its ability to protect against fake timestamp attacks.

Dynamic Control Plane for SDN at Scale

As SDN migrates to wide area networks and 5G core networks, a scalable, highly reliable, low latency distributed control plane becomes a key factor that differentiates operator solutions for network control and management. In order to meet the high reliability and low latency requirements under time-varying volume of control traffic, the distributed control plane, consisting of multiple controllers and a combination of out-of-band and in-band control channels, needs to be managed dynamically. To this effect, we propose a novel programmable distributed control plane architecture with a dynamically managed in-band control network, where in-band mode switches communicate with their controllers over a virtual overlay to the data plane with dynamic topology. We dynamically manage the number of controllers, switches, and control flows assigned to each controller as well as traffic over control channels achieving both controller and control traffic load-balancing. We introduce “control flow table” (rules embedded in the flow table of a switch to manage in-band control flows) in order to implement the proposed distributed dynamic control plane. We propose methods for off-loading congested controllers and congested in-band control channels using control flow tables. A validation test-bed and experimental results over multiple topologies are presented to demonstrate the scalability and performance improvements achieved by the proposed dynamic control plane management procedures when the controller CPU and/or availability or throughput of in-band control channels becomes bottlenecks.

Self-Adaptive Decentralized Monitoring in Software-Defined Networks

The software-defined networking (SDN) paradigm can allow network management solutions to automatically and frequently reconfigure network resources. When developing SDN-based management architectures, it is of paramount importance to design a monitoring system that can provide timely and consistent updates to heterogeneous management applications. To support such applications operating with low latency requirements, the monitoring system should scale with increasing network size and provide precise network views with minimum overhead on the available resources. In this paper, we present a novel, self-adaptive, decentralized framework for resource monitoring in SDN. Our framework enables accurate statistics to be collected with limited burden on the network resources. This is realized through a self-tuning, adaptive monitoring mechanism that automatically adjusts its settings based on the traffic dynamics. We evaluate our proposal based on a realistic use case scenario, where a content distribution service and an on-demand gaming platform are deployed within an ISP network. The results show that reduced monitoring latencies are obtained with the proposed framework, thus enabling shorter reconfiguration control loops. In addition, the proposed adaptive monitoring method achieves significant gain in terms of monitoring overhead, while preserving the performance of the services considered.

ORCHESTRA: Enabling Inter-Technology Network Management in Heterogeneous Wireless Networks

Modern connected devices are equipped with the ability to connect to the Internet using a variety of different wireless network technologies. Current network management solutions fail to provide a fine-grained, coordinated, and transparent answer to this heterogeneity, while the lower layers of the OSI stack simply ignore it by providing full separation of layers. To address this, we propose the ORCHESTRA framework to manage the different devices in heterogeneous wireless networks and introduce capabilities such as packet-level dynamic and intelligent handovers (both inter- and intra-technology), load balancing, replication, and scheduling. The framework is the first of its kind in providing a fine-grained packet-level control across different technologies by introducing a fully transparent virtual medium access control layer and an software-defined networking-like controller with global intelligence. Furthermore, we present a novel optimization problem formulation that can be solved to optimally configure the network. We provide a thorough evaluation through simulations and a prototype implementation. We show that our framework enables, in a real-life setting, transparent and real-time inter-technology handovers and that coordinated load balancing can double the network-wide throughput across different scenarios.

IntelliMon: An intelligent Polling Engine for Network Management

Background: Traditional network management solutions suffer from scalability issues and delayed fault reporting when monitoring large network topologies, as described in various patents. IntelliMon is an intelligent polling engine which takes into account network baselining statistics, indicative of the past behavior of the network and utilizes this information to dynamically formulate its polling strategies to achieve faster detection of network faults, while regulating the amount of network management traffic that it generates. This leads to lower Mean-Time-to-Detect (MTTD) for network faults compared to traditional network management solutions, which employ static pre-configured polling strategies. Method: Network baselining statistics are used to formulate dynamic polling strategies for nodes or areas of the network which are prone to faults. Results: Early simulation results of up to 5000 node network show that IntelliMon achieves significantly lower MTTD, specifically 14-32% lower than traditional polling engines under different scenarios. Conclusion: Heuristics combined with hard-statistics derived from network baselining are effective in formulating adaptive polling strategies that are able to detect network faults quicker especially for large topologies. Keywords: Mean-time-to-detect, polling strategies, network management, large network topologies, baselining statistics, network management traffic.

Migration-Based Dynamic and Practical Virtual Streaming Agent Placement for Mobile Adaptive Live Streaming

Software defined networking (SDN) and network function virtualization have emerged as a promising solution for elastic, dynamic, and scalable network management. A collection of works have investigated how virtualization and cloud technology can ameliorate the infrastructure management for the fifth generation mobile network. However, employing these techniques in mobile network for live streaming has not received enough attention. By leveraging the development of SDN and virtualization techniques, mobile live streaming service providers can efficiently manage their system to cope with network dynamics incurred by the variation of the environment. Specifically, we dynamically instantiate network entities at appropriate locations in response to the user live streaming demands. These network entities are in charge of transcoding and transmitting the live videos to the mobile end users, which we name as virtual live streaming agent (vLA). In this paper, we investigate the dynamic virtualization and migration of vLAs for mobile live streaming services. Typically, rate adaptive streaming is adopted to improve spectrum efficiency and user quality of experience. We formulate an integer linear program for the optimal vLA placement problem. Furthermore, we design a practical SDN-based live vLA migration process. Then, by designing and integrating migration cost functions, we develop a dynamic vLA placement mechanism for dynamic environments with the consideration of migration cost. Both heuristic and on-line algorithms are designed for the vLA placement and migration problems. Finally, a large scale real trace-based simulation is conducted to demonstrate the performance of our vLA placement and migration algorithms.

Software-Defined Next-Generation Satellite Networks: Architecture, Challenges, and Solutions

Traditional satellite networks depend on the closed and planned architecture. Thus, there are many challenges such as configuration update, new communication and networking technologies introduction, truly-differentiated services provision, satellite network device interoperability, and the integration of satellite and terrestrial networks. Software-defined networking (SDN) has the features of flexibility, programmability, and logical centralization, which increases network resource utilization, simplifies network management, reduces operating cost, and promotes the evolution and innovation. In this paper, a new software-defined architecture for next-generation satellite networks, called SoftSpace, is presented. The concepts of network function virtualization, network virtualization, and software-defined radio are exploited in the SoftSpace to facilitate the incorporation of new applications, services, and satellite communication technologies. This can not only reduce the capital expenditures and operational expenditures but also integrate satellite networks with terrestrial networks seamlessly, as well as can improve the interoperability of satellite network devices. In addition, we discuss the challenges and solutions for network management. The necessary network management instruments including multi-layer controller architecture, cooperative traffic classification, and utility-optimal network virtualization are presented. Finally, we discuss the challenges and solutions for space networking. The software-defined space networking solutions including quality of experience-aware space routing, SDN-enabled hybrid fault recovery mechanism, and software-defined space mobility management are developed.

Design and implementation of an automated network monitoring and reporting back system

Abstract The task of network monitoring becomes tedious with increase in the network size, heterogeneity and complexity. The available network monitoring and management solutions are not only expensive but also difficult to use, configure and maintain. Manually pin pointing a faulty device in the large complex network is very tricky and time consuming for network administrators. Thus, an automated system is needed that immediately reports to network administrator about fault type and location as soon as it arises. This paper presents design and implementation of an intelligent network monitoring and reporting back system for large size organizations/industries. It is based on programming open-source tools (such as Nagios and RT) and intelligently integrating them to monitor network devices such as switches and routers. To monitor end-user devices in the network, a software package has been developed using Universal Plug & Play (UPnP) technology. The developed monitoring system immediately notifies network administrator as soon as a network problem arises. The notification message clearly pin points the fault location in network topology, its type and impact on rest of the network. If the network problem is not resolved within the pre-specified deadline, a second notification is sent out to the next responsible person. Thus, all people in the priority list are notified one by one with pre-specified deadlines until the problem is resolved.

A Survey on the Use of P2P Technology for Network Management

Network management has steadily evolved over recent years. Along with the growing need for advanced features in network management solutions, several distribution models were investigated, varying ...

Photonic Networks and Devices Feature: Introduction

This feature issue, based on the OSA Photonic Networks and Devices (NETWORKS) meeting, part of the Advanced Photonics Congress, showcases the latest progress across the photonic network ecosystem from physical-layer devices to network management solutions.

COVE: Co-operative Virtual Network Embedding for Network Virtualization

Network virtualization provides a promising solution for next-generation network management by allowing multiple isolated and heterogeneous virtual networks to coexist and run on a shared substrate network. A long-standing challenge in network virtualization is how to effectively and efficiently map these virtual nodes and links of heterogeneous virtual networks onto specific nodes and links of the shared substrate network, known as the Virtual Network Embedding (VNE) problem. Existing centralized VNE algorithms and distributed VNE algorithms both have advantages and disadvantages. In this paper, a novel cooperative VNE algorithm is proposed to coordinate centralized and distributed algorithms and unite their respective advantages and specialties. By leveraging the learning technology and topology decomposition, autonomous substrate nodes entrusted with detailed mapping solutions cooperate closely with the central controller with a global view and in charge of general management to achieve a successful embedding process. Besides a topology-aware resource evaluation mechanism and customized mapping management policies, Bloom filter is elaborately introduced to synchronize the mapping information within the substrate network, instead of flooding which generates massive communication overhead. Extensive simulations demonstrate that the proposed cooperative algorithm has acceptable and even better performance in terms of long-term average revenue and acceptance ratio than previous algorithms.

EC-CENTRIC: An Energy- and Context-Centric Perspective on IoT Systems and Protocol Design

The radio transceiver of an Internet of Things (IoT) device is often where most of the energy is consumed. For this reason, most research so far has focused on low-power circuit and energy-efficient physical layer designs, with the goal of reducing the average energy per information bit required for communication. While these efforts are valuable per se, their actual effectiveness can be partially neutralized by ill-designed network, processing, and resource management solutions, which can become a primary factor of performance degradation, in terms of throughput, responsiveness, and energy efficiency. The objective of this paper is to describe an energy-centric and context-aware optimization framework that accounts for the energy impact of the fundamental functionalities of an IoT system and that proceeds along three main technical thrusts: 1) balancing signal-dependent processing techniques (compression and feature extraction) and communication tasks; 2) jointly designing channel access and routing protocols to maximize the network lifetime; and 3) providing self-adaptability to different operating conditions through the adoption of suitable learning architectures and of flexible/reconfigurable algorithms and protocols. After discussing this framework, we present some preliminary results that validate the effectiveness of our proposed line of action, and show how the use of adaptive signal processing and channel access techniques allows an IoT network to dynamically tune lifetime for signal distortion, according to the requirements dictated by the application.

An Energy Efficient Ethernet Strategy Based on Traffic Prediction and Shaping

Recently, different communities in computer science, telecommunication, and control systems have devoted a huge effort towards the design of energy efficient solutions for data transmission and network management. This paper collocates along this research line and presents a novel energy efficient strategy conceived for Ethernet networks. The proposed strategy, which exploits the opportunities offered by the IEEE 802.3az amendment to the Ethernet standard (known as energy efficient Ethernet) is based on the possibility of predicting the future traffic from the analysis of the current data flow. In agreement with the results of such a dynamic prediction, Ethernet links can be forced into a low power consumption state for variable intervals. Theoretical bounds are derived to detail how the performance figures depend on the parameters of the designed strategy and scale with respect to traffic load. Furthermore, simulation results carried out with both real and synthetic traffic traces are presented to prove the effectiveness of the strategy, which leads to considerable energy savings at the cost of only a limited bounded delay in data delivery.