Journal of Network and Systems Management最新文献

In the fog computing-based Internet of Things (IoT) architecture, the sensor devices represent the basic elements needed to sense the surrounding environment. They gather and send a huge amount of data to the fog gateway and then to the cloud due to their use in various real-world IoT applications. This would lead to high data traffic, increased energy consumption, and slow decisions at the fog gateway. Therefore, it is important to reduce the transmitted data to save energy and provide an accurate decision regarding the safety and health of the building’s environment. This paper suggests an energy-aware data transmission approach with decision-making (EDaTAD) for Fog Computing-based IoT applications. It works on two-level nodes in the fog computing-based TI architecture: sensor devices and fog gateways. The EDaTAD implements a Lightweight Redundant Data Removing (LiReDaR) algorithm at the sensor device level to lower the gathered data before sending it to the fog gateway. In the fog gateway, a decision-making model is proposed to provide suitable decisions to the monitoring staff in remote monitoring applications. Finally, it executes a Data Set Redundancy Elimination (DaSeRE) approach to discard the repetitive data sets before sending them to the cloud for archiving and further analysis. EDaTAD outperforms other methods in terms of transmitted data, energy consumption, and data accuracy. Furthermore, it assesses the risk efficiently and provides suitable decisions while decreasing the latency time.

Yearly, the rates of Internet penetration are on the rise, surpassing 80% in developed nations. Despite this progress, over two billion individuals in rural and low-income regions face a complete absence of Internet access. This lack of connectivity hinders the implementation of vital services like remote healthcare, emergency assistance, distance learning, and personal communications. To bridge this gap and bring essential services to rural populations, this paper leverages Unmanned Aerial Vehicles (UAVs). The proposal introduces a UAV-based network architecture and an energy-efficient algorithm to deploy Internet of Things (IoT) applications. These applications are broken down into microservices, strategically distributed among a subset of UAVs. This approach addresses the limitations associated with running an entire IoT application on a single UAV, which could lead to suboptimal outcomes due to battery and computational constraints. Simulation results conducted in a realistic scenario underscore the effectiveness of the proposed solution. The evaluation includes assessing the percentage of IoT requests successfully served to users in the designated area and reducing the energy consumption required by UAVs during the handling of such requests.

With the improvement of service delay and quality requirements for new applications such as unmanned driving, internet of vehicles, and virtual reality, the deployment of network services is gradually moving from the cloud to the edge. This transition has led to the emergence of multi-access edge computing (MEC) architectures such as distributed micro data center and fog computing. In the MEC environment, network infrastructure is distributed around users, allowing them to access the network nearby and move between different service coverage locations. However, the high mobility of users can significantly affect service orchestration and quality, and even cause service interruption. How to respond to user mobility, dynamically migrate user services, and provide users with a continuous and seamless service experience has become a huge challenge. This paper studies the dynamic migration of service function chain (SFC) caused by user mobility in MEC environments. First, we model the SFC dynamic migration problem in mobile scenarios as an integer programming problem with the goal of optimizing service delay, migration success rate, and migration time. Based on the above model, we propose a deep reinforcement learning-driven SFC adaptive dynamic migration optimization algorithm (DRL-ADMO). DRL-ADMO can perceive the underlying network resources and SFC migration requests, intelligently decide on the migration paths of multiple network functions, and adaptively allocate bandwidth, achieving parallel and seamless SFC migration. Performance evaluation results show that compared with existing algorithms, the proposed algorithm can optimize 7% service delay and 20% migration success rate at the cost of sacrificing a small amount of migration time.

Intent-Based Networking (IBN) is an important step towards achieving network automation. Many challenges of today’s complex network management systems can be tackled by the solutions proposed by IBN. However, although IBN has gained a lot of attention from the academic and industrial community in the second half of the last decade leading to many scientific publications and research papers, there has been little effort made on proposing a comprehensive framework for IBN, which converts system-level IBN concepts and theories into a fully featured software implementation. This paper presents such framework. Its implementation is standards-based and open-source. The framework can be used to facilitate and validate novel research ideas and test cases. The paper discusses relevant IBN design concepts and theories, how the framework’s software architecture is derived from those concepts, and the technical and implementation details on key IBN aspects and features including Intent life-cycle, Intent definition and translation, Intent orchestration, and Intent assurance using closed-loops. We also demonstrate a real intent-based use case realized by the framework in order to show and validate the proof-of-concept (PoC). The Future work of this project is also discussed.

Virtual network embedding (VNE) is the process of allocating resources in a substrate (i.e. physical) network to support virtual networks optimally. The VNE problem is an NP-hard problem that has been studied for more than a decade in the continuous seek to maximize the revenue of physical infrastructures with more efficient VNE solutions. Metaheuristics have been widely used in online VNE as they incorporate mechanisms to avoid local optimum solutions, explore larger search spaces, and keep acceptable execution times. All metaheuristic optimization algorithms require initialization for which the vast majority of online VNE solutions implement random initialization. This paper proposes three novel initialization functions namely, Initialization Based on Node Selection (IFNS), Initialization Function Based on Community Detection (IFCD), and Initialization Function Based on Previous Solutions (IFPS), intending to enhance the performance of the online VNE process. Through simulation, our initialization functions have been proven to enhance the acceptance rate, revenue, and revenue-to-cost metrics of the VNE process. The enhancements achieved by our initialization functions are statistically significant and their implementation does not add computational overhead to the classic VNE approaches.

The storage requirement for distributed tracing can be reduced significantly by sampling only the anomalous or interesting traces that occur rarely at runtime. In this paper, we introduce an unsupervised sampling pipeline for distributed tracing that ensures high sampling accuracy while reducing the storage requirement. The proposed method, SampleHST-X, extends our recent work SampleHST. It operates based on a budget which limits the percentage of traces to be sampled while adjusting the storage quota of normal and anomalous traces depending on the size of this budget. The sampling process relies on accurately defining clusters of normal and anomalous traces by leveraging the distribution of mass scores, which characterize the probability of observing different traces, obtained from a forest of Half Space Trees (HST). In our experiments, using traces from a cloud data center, SampleHST yields 2.3(times) to 9.5(times) better sampling performance. SampleHST-X further extends the SampleHST approach by incorporating a novel class of Half Space Trees, namely Approximate HST, that uses approximate counters to update the mass scores. These counters significantly reduces the space requirement for HST while the sampling performance remains similar. In addition to this extension, SampleHST-X includes a Family of Graph Spectral Distances (FGSD) based trace characterization component, which, in addition to point anomalies, enables it to sample traces with collective anomalies. For such traces, we observe that the SampleHST-X approach can yield 1.2(times) to 19(times) better sampling performance.