In this paper, a cloud-edge model predictive control (MPC) framework is proposed for cyber-physical systems in the presence of deception attacks and Denial-of-Service (DoS) attacks. In the proposed framework, the original MPC optimization problem is decomposed into cloud and edge layers by using an efficient parameterized control input sequence. Then, a novel controller updating mechanism is developed by discontinuously comparing the optimal value functions of the modified optimization problem and the original optimization problem, which saves the communicational and computational resources. Specifically, the control performance is optimized over all possible uncertainties and deception attack realizations using a min-max optimization technique, while the DoS attacks can be tackled with the parameterization feature of the control input sequence. Besides, the closed-loop system is guaranteed to be input-to-state practical stable (ISpS) under the proposed MPC strategy. Simulation studies and comparisons are performed to verify effectiveness of the proposed method.

Real-time communication (RTC) applications demand consistent low latency to ensure a smooth and interactive user experience. However, wireless networks, including WiFi and cellular, although they provide satisfactory median latency, often suffer from significant tail latency due to the highly variable network bandwidth. We observe that the control loop for managing the sending rate of RTC applications becomes inflated when congestion occurs at the wireless access point (AP), leading to untimely rate adaptation in response to wireless dynamics. Existing solutions fail to quickly adapt to bandwidth fluctuations due to the inflated control loop. In this paper, we propose Zhuge, a purely wireless AP-based solution that addresses these issues by separating congestion feedback from congested queues. Our approach involves the design of a Fortune Teller, which accurately estimates the wireless latency for each packet upon its arrival at the wireless AP. To ensure scalability, we also develop a Feedback Updater that translates the estimated latency into understandable feedback messages for various end-to-end protocols, delivering them back to the senders immediately for rate adaptation. Our evaluation, based on both trace-driven simulations and real-world scenarios, demonstrates that Zhuge significantly reduces the occurrence of large tail latency and alleviates RTC performance degradation by 22% to 95%.

Active queue management (AQM) algorithms, widely deployed in the internet, are designed to signal end hosts with network conditions in the format of packet losses. However, real-time communication (RTC) applications adopt delay-sensitive congestion control algorithms (CCAs), which are no longer responsive to losses or explicit notifications from AQMs. Moreover, packet losses introduced by different AQMs will further degrade the performance of RTC applications due to unexpected and unnecessary loss recovery. We are therefore motivated to understand the behaviors of AQMs and take necessary countermeasures for RTC applications proactively. For example, with the help of AQM inference, RTC applications will benefit by using loss recovery mechanisms that adapt to various kinds of AQMs to deal with packet losses. However, it is challenging to infer the AQM from end hosts since numerous AQMs have different configurations after decades of evolution. We analyze the temporal behaviors of loss series, extract the inherent invariant features of different AQMs, and categorize them into three types. Our simulation shows that AQM inference can classify AQMs with an accuracy of 96%. We also evaluate a use case on using the AQM inference to improve the loss recovery mechanism (forward error correction, FEC). Our FEC method based on AQM inference improves the recovery rate by at least 56%, and finally reduces the end-to-end delay by 13%.

The second-generation digital video broadcasting via satellite (DVB-S2) and the second-generation DVB interactive satellite system (DVB-RCS2) standards published by European Telecommunications Standards Institute (ETSI) have been dominating the global high-throughput satellite (HTS) communications market for recent five years. ETSI provisioned a technical report (TR) ETSI TR 101 545-4 as rudimentary guidelines for implementing a DVB-S2/DVB-RCS2 satellite system with security functions. Based on the ETSI guidelines, this paper depicts security implementations for a scenario of several distributed hub gateway stations which was unincorporated in the TR. Moreover, several optimizations and modifications were made to improve correctness and efficiency. A top-level star network security architecture, a hub security architecture, a return channel satellite terminal (RCST) security architecture, encrypted frame structures, authentication, and key distribution are described in this paper. The test results shew a less than 9% payload data rate decrease for the given security system in comparison to the unencrypted, proving the same speed performance level as the insecure commercial HTS ground infrastructure.

Real-time communication (RTC) applications like video conferencing or cloud gaming require consistent low latency to provide a seamless interactive experience. However, wireless networks including WiFi and cellular, albeit providing a satisfactory median latency, drastically degrade at the tail due to frequent and substantial wireless bandwidth fluctuations. We observe that the control loop for the sending rate of RTC applications is inflated when congestion happens at the wireless access point (AP), resulting in untimely rate adaption to wireless dynamics. Existing solutions, however, suffer from the inflated control loop and fail to quickly adapt to bandwidth fluctuations. In this paper, we propose Zhuge, a pure wireless AP based solution that reduces the control loop of RTC applications by separating congestion feedback from congested queues. We design a Fortune Teller to precisely estimate per-packet wireless latency upon its arrival at the wireless AP. To make Zhuge deployable at scale, we also design a Feedback Updater that translates the estimated latency to comprehensible feedback messages for various protocols and immediately delivers them back to senders for rate adaption. Trace-driven and real-world evaluation shows that Zhuge reduces the ratio of large tail latency and RTC performance degradation by 17% to 95%.

No abstract available.

Dynamic networks have enabled dynamically adapting the network topology to meet the need of real-time traffic demands. However, due to the complexity of topology optimization, existing solutions suffer from a trade-off between performance and efficiency, which either have large optimality gaps or excessive optimization overhead. To break through this trade-off, our key observation is that we could offload the optimization procedure to every network node to handle the complexity. Thus, we propose HierTopo, a hierarchical topology optimization method for dynamic networks that achieves both high performance and efficiency. HierTopo firstly runs a local policy on each network node to aggregate network information into low-dimension features, then uses these features to make global topology decisions. Evaluation on real-world network traces shows that HierTopo outperforms the state-of-the-art solutions by 11.52-38.91% with only milliseconds of decision latency, and is also superior in generalization ability.

Major commercial client-side video players employ adaptive bitrate (ABR) algorithms to improve the user quality of experience (QoE). With the evolvement of ABR algorithms, increasingly complex methods such as neural networks have been adopted to pursue better performance. However, these complex methods are too heavyweight to be directly deployed in client devices with limited resources, such as mobile phones. Existing solutions suffer from a trade-off between algorithm performance and deployment overhead. To make the deployment of sophisticated ABR algorithms practical, we propose PiTree, a general, high-performance, and scalable framework that can faithfully convert sophisticated ABR algorithms into decision trees with teacher-student learning. In this way, network operators can train complex models offline and deploy converted lightweight decision trees online. We also present theoretical analysis on the conversion and provide two upper bounds of the prediction error during the conversion and the generalization loss after conversion. Evaluation on three representative ABR algorithms with both trace-driven emulation and real-world experiments demonstrates that PiTree could convert ABR algorithms into decision trees with <; 3% average performance degradation. Moreover, compared to original deployment solutions, PiTree could save considerable operating expenses for content providers.

Major commercial client-side video players employ adaptive bitrate (ABR) algorithms to improve user quality of experience (QoE). With the evolvement of ABR algorithms, increasingly complex methods such as neural networks have been adopted to pursue better performance. However, these complex methods are too heavyweight to be directly implemented in client devices, especially mobile phones with very limited resources. Existing solutions suffer from a trade-off between algorithm performance and deployment overhead. To make the implementation of sophisticated ABR algorithms practical, we propose PiTree, a general, high-performance and scalable framework that can faithfully convert sophisticated ABR algorithms into lightweight decision trees to reduce deployment overhead. We also provide a theoretical upper bound on the optimization loss during the conversion. Evaluation results on three representative ABR algorithms demonstrate that PiTree could faithfully convert ABR algorithms into decision trees with <3% average performance degradation. Moreover, comparing to original implementation solutions, PiTree could save operating expenses for large content providers.