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In the era of the Internet of Everything, the burst of traffic will bring many problems. Traffic shaping, as a common means to limit burst traffic rate, achieves “peak and valley reduction” to smooth the output rate, avoid network congestion, guarantee the quality of service (QoS) and improve the overall network transmission efficiency. This paper first explains the basic concept of traffic shaping, the related algorithms in traffic shaping, and compares the differences between traffic shaping and traffic policing. It then describes the research on traffic shaping in software-defined networks and the results from the combination of the two. This is followed by an introduction to relatively new technology for industry, time-sensitive networks, and an exploration of the functions of various types of traffic shapers in time-sensitive networks. This is followed by a description of case studies of traffic shaping in IoT scenarios and an overview of these traffic shaping schemes, summarizing the usefulness of the shaping schemes. Finally, the future of traffic shaping is explored.

It is challenging to deal with the Internet congestion problem because of several factors such as ever-growing traffic and distributed network architecture. The congestion problem can be solved or alleviated by various methods, including rate control, bandwidth-guarantee routing and bandwidth reservation. We use the term broad-sensed Internet congestion control and avoidance (BICC&A) to generally denote all of the above methods. Most BICC&A solutions depend on or benefit from the knowledge of network conditions, including traffic status (type and volume), available bandwidth and topology. In this paper, we present a comprehensive survey of the applications of machine learning to network condition acquirement methods for BICC&A and specific BICC&A methods. First, we provide an overview of the background knowledge of BICC&A and machine learning. Then, we provide detailed reviews on the applications of machine learning techniques to network condition acquirement methods for BICC&A and to specific BICC&A methods. Finally, we outline important research opportunities.

Speed profile design and tracking control are two important issues to ensure the performance of the automatic train operation system. Aiming to realize the maximum optimization probability of train running, this paper constructs an integrated optimal method through speed profile design and fuzzy PID controller design. In the recommended speed profile design, three typical running strategies are included in the proposed comprehensive scheme to extend solution space, and the constructed train performance simulation is combined with NSGA-II algorithm to solve such multiobjective problem. As for recommended speed profile tracking, in order to improve the tracking performance of the train, which is a nonlinear system, a fuzzy PID controller is designed to adaptively adjust PID gains. Note that multiple indicators of punctually, energy consumption, stopping accuracy and comfort are all considered in the integrated design. Taking Beijing Subway Line 8 as the numerical example, the results show that the design of speed profile and fuzzy PID controller are both effective, and energy consumption saves about 10.40% with other better indicators compared with the original data.

Fuzzy Petri nets (FPNs) are an important tool for knowledge representation and reasoning in the rule-based expert system. Recently, various fuzzy sets and linguistic models have been introduced into FPNs to improve its ability in handling imprecise, fuzzy, and linguistic information. However, the existing FPN models still have the following two deficiencies: The first one is the incompatibility of the knowledge representation parameters in modeling the membership degrees of linguistic variables and hesitancy of experts when experts provide linguistic evaluations. Another one is that the reasoning operators in existing reasoning algorithm considering the local weights and ordered weights of propositions fail to guarantee the reasoning result satisfies the monotonicity, boundary, and idempotency. In this paper, we propose the q-rung orthopair fuzzy linguistic reasoning Petri nets (q-ROFLRPNs) by using the q-rung orthopair fuzzy linguistic sets (q-ROFLSs) to enhance the capability of conventional FPNs in dealing with fuzzy and linguistic knowledge. We define new closed operational laws of q-ROFLSs by linguistic scale functions (LSFs), which not only guarantee the validity and reliability of reasoning results but also handle different semantic situations of the linguistic term set. In addition, an enhanced reasoning algorithm based on weighted ordered weighted averaging (WOWA) operator is proposed by considering the weights of propositions themselves and their ordered weights, and the monotonicity, boundary, and idempotency of the results are satisfied. At last, a case study on fault diagnosis for metro door system is provided to demonstrate the effectiveness and advantages of the proposed model.

Clouds are becoming a very important platform for hybrid real-time tasks. To enhance the reliability of cloud, fault tolerance of cloud becomes a critical issue. However, the complexities and specialties of traditional fault-tolerant mechanisms cannot meet the fault-tolerant requirements of clouds. To address this issue, we propose a novel fault-tolerant scheduling algorithm named ARCHER for hybrid tasks in cloud. ARCHER has three significant characteristics: 1) it integrates the traditional primary/backup model and checkpoint technology which can flexibly determine the execution time of the backup copies of tasks, so it greatly enhances the resource utilization and produces more time slots to execute tasks as many as possible; 2) it employs task classification mechanism to realize precise scheduling for different types of tasks and virtual machines, which reduces the response time of clouds; and 3) it uses time slot exploiting mechanism, task forward mechanism, and task transform mechanism to achieve high-resource utilization. We conduct extensive simulations to evaluate the performance of ARCHER by comparing it with four baseline algorithms. The experimental results show that ARCHER can effectively improve the resource utilization of cloud while guaranteeing fault tolerance.

Server consolidation is a useful solution aiming at cost-efficiency and high resource utilization of data centers and clusters. Nowadays, as the data-intensive and I/O intensive applications are widely used, more attention is paid to the local storage-based clouds which can offer much better I/O performance at relatively low price compared with the shared storage. However, it will obviously increase the migration cost (e.g., energy and time). Meanwhile, there is few suitable resource demand estimation method for local storage-based clouds at present, which plays an important role in system's migration efficiency. And we find out that in this specific storage architecture, almost all the existing server consolidation algorithms do not have a suitable resource demand estimation method and a live migration scheme. To solve this problems, this paper designs and implements Combining Three (C3), a cloud architecture for local storage, C3 significant modules: prediction, consolidation, and migration. It was proved in statistical analysis that ARIMA may be the most suitable prediction model for the server workload, which motivates us to propose the resource estimation predictor. Also, we improve the existing consolidation method by adjusting the sorting index and fit degree during migration. Then, we propose a live migration scheme for local storage environment as the third module of C3. We conduct extensive experiments using real-world traces from Google to validate the effectiveness and superiority of our proposed algorithm.

Cloud-integrated cyber-physical system (CCPS) is playing an increasingly important role in our daily life. Unfortunately, how to dynamically make the reserved bandwidth allocation for CCPS is still a great challenge. To address this issue, we propose a novel strategy that systematically and dynamically allocates reserved bandwidth for multiple CPS services in CCPS with a nearly optimal method. By making a balance between the three key factors of crash cost, QoS loss and operating cost, the proposed strategy is able to minimize the operating cost, the influence of the discrepancy between the service-level agreement and the bandwidth actually delivered, while preserving essential QoS level. Then we develop an online algorithm based on the proposed strategy using the Lyapunov optimization theory. The online algorithm can approximate the optimal solution within provable bounds and is capable of processing the tasks within a preset delay. Theoretical analysis of performance proves advantages and shows that the algorithm has the ability to solve the complicated dynamic bandwidth allocation problems for multiple CPS services in practical CCPS. Extensive experiments validate its effectiveness as well as its superiority to five existing strategies (FM, MS + LB, MS + EF, DS + LB, DS + EF) in overall cost, crash cost, QoS loss, and operating cost.