This paper investigates a resilient model predictive control scheme for switched systems governed by sojourn probabilities under multi-channel Denial-of-Service (DoS) attacks. Considering that control signals transmitted through different channels may be subject to DoS attacks from distinct adversaries, multiple Markov chains are employed to characterize these attack patterns. To address the complexity introduced by multiple Markov chains in controller design, a mapping technique is proposed to aggregate them into a single Markov chain that simultaneously reflects the status of individual DoS attacks. Furthermore, a dynamic resilient controller is designed to counteract random perturbations in controller gains, enabling online fine-tuning of system control performance. The introduced decision variable effectively enlarges the initial feasible region. Finally, a series of optimization problems are formulated to achieve desired system performance. The effectiveness of the proposed algorithm is validated through an application to an F-404 engine system.

The efficient and secure management of resources within flying ad-hoc networks (FANETs) poses formidable challenges. FANETs constitute a pivotal element of the space-air–ground-integrated network (SAGIN), employing network virtualization (NV) technology in tandem with service function chain (SFC) to facilitate end-to-end network services, akin to terrestrial networks. Nonetheless, the transient, dynamic nature of FANETs coupled with their susceptibility to network attacks engenders considerable complexity in the placement of SFCs within these networks. To address the rationality and security of resource allocation for SFC placement, this article proposes a reinforcement learning algorithm that sets strict security-level restrictions on the placement process and fully extracts the key features in FANETs. Additionally, a multilayer policy network is devised to dynamically perceive alterations in the FANET environment and compute an optimal SFC placement strategy. The proposed algorithm exhibits real-time adaptability to the dynamic environment, quantifies influential factors during placement, and achieves dynamic SFC placement. To assess the efficacy of the algorithm, three evaluation metrics—namely, SFC placement success rate, long-term average revenue, and long-term revenue cost ratio—are formulated and extensively evaluated through a plethora of experiments. Comparative analysis against alternative algorithms demonstrates enhancements of 20.6%, 15.3%, and 12.1% in the aforementioned metrics, respectively. The experimental findings substantiate both the convergence and efficiency of the proposed algorithm.

With the increasing penetration of Internet of Vehicles (IoV) in people’s lives, the safety and reliability of its communication has to attract our attention. Frequency hopping orthogonal frequency division multiplexing (FH-OFDM) is applied to vehicle networking communication with its excellent communication performance. But the sub-carrier frequency hopping pattern in the traditional FH-OFDM information transmission technology has the risk of being easily detected, which seriously affects the security, concealment and anti-jamming of its information transmission. However, existing anti-jamming methods cannot solve this problem. This paper presents a data driven frequency hopping OFDM (DDFH-OFDM) information transmission method for IoV. This method combines message-driven frequency-hopping technology with frequency-hopping OFDM system. The selection of subcarriers is controlled by using the encrypted partial transmission data instead of the pseudorandom sequence. At the same time, the frequency transmission block mapping rule is designed to make the distribution of frequency-hopping subcarriers more uniform. Through simulation, under the same bit error rate (BER), compared with the FH-OFDM information transmission method, the proposed method has good anti-noise, anti-broadband interference and anti-narrowband interference performance, which further improves the communication performance of IoV. It provides a reference technology for anti-jamming communication applications in IoV scenes.

The security of network information in the Internet of Things faces enormous challenges. The traditional security defense mechanism is passive and certain loopholes. Intrusion detection can carry out network security monitoring and take corresponding measures actively. The neural network-based intrusion detection technology has specific adaptive capabilities, which can adapt to complex network environments and provide high intrusion detection rate. For the sake of solving the problem that the farmland Internet of Things is very vulnerable to invasion, we use a neural network to construct the farmland Internet of Things intrusion detection system to detect anomalous intrusion. In this study, the temperature of the IoT acquisition system is taken as the research object. It has divided which into different time granularities for feature analysis. We provide the detection standard for the data training detection module by comparing the traditional ARIMA and neural network methods. Its results show that the information on the temperature series is abundant. In addition, the neural network can predict the temperature sequence of varying time granularities better and ensure a small prediction error. It provides the testing standard for the construction of an intrusion detection system of the Internet of Things.

Based on the symbol-flipping algorithm with multiple-votes (MV-SF), this paper presents two simplified algorithms, symbol flipping with truncated and multiple votes (T-MV-SF) and symbol flipping with simplified and multiple votes (S-MV-SF), for nonbinary low-density parity-check(NB-LDPC) codes. Unlike previous symbol-flipping based algorithms (SFBAs), the T(S)-MV-SF uses the truncated voting information in each check and variable node update. The proposed algorithms also approximate the sorting function in each check update. Therefore, their complexity and memory consumption are remarkably reduced as compared with existing SFBAs. Furthermore, we introduce several parameters to compensate the information loss caused by the truncated voting information. After optimizing all the parameters, the proposed algorithms even have better performance than the MV-SF. As compared with the MV-SF, the maximum performance gain among all numerical examples is about 0.11 dB for the S-MV-SF on the (63, 37) code. The T-MV-SF has the largest reduction of the computational complexity on the (255, 175) code, which is about 80% of the complexity of the MV-SF. Moreover, the performance of the T-MV-SF is better than the MV-SF on the (255, 175) code. As a result, the proposed algorithms can effectively balance the error performance and complexity for decoding of NB-LDPCs.

I/O consolidation is a growing trend in production environments due to increasing complexity in tuning and managing storage systems. A consequence of this trend is the need to serve multiple users and/or workloads simultaneously. It is imperative to ensure that these users are insulated from each other by virtualization in order to meet any service-level objective (SLO). Previous proposals for performance virtualization suffer from one or more of the following drawbacks: (1) They rely on a fairly detailed performance model of the underlying storage system; (2) couple rate and latency allocation in a single scheduler, making them less flexible; or (3) may not always exploit the full bandwidth offered by the storage system.This article presents a two-level scheduling framework that can be built on top of an existing storage utility. This framework uses a low-level feedback-driven request scheduler, called AVATAR, that is intended to meet the latency bounds determined by the SLO. The load imposed on AVATAR is regulated by a high-level rate controller, called SARC, to insulate the users from each other. In addition, SARC is work-conserving and tries to fairly distribute any spare bandwidth in the storage system to the different users. This framework naturally decouples rate and latency allocation. Using extensive I/O traces and a detailed storage simulator, we demonstrate that this two-level framework can simultaneously meet the latency and throughput requirements imposed by an SLO, without requiring extensive knowledge of the underlying storage system.

I/O consolidation is a growing trend in production environments due to the increasing complexity in tuning and managing storage systems. A consequence of this trend is the need to serve multiple users/workloads simultaneously. It is imperative to make sure that these users are insulated from each other by visualization in order to meet any service level objective (SLO). This paper presents a 2-level scheduling framework that can be built on top of an existing storage utility. This framework uses a low-level feedback-driven request scheduler, called AVATAR, that is intended to meet the latency bounds determined by the SLO. The load imposed on AVATAR is regulated by a high-level rate controller, called SARC, to insulate the users from each other. In addition, SARC is work-conserving and tries to fairly distribute any spare bandwidth in the storage system to the different users. This framework naturally decouples rate and latency allocation. Using extensive I/O traces and a detailed storage simulator, we demonstrate that this 2-level framework can simultaneously meet the latency and throughput requirements imposed by an SLO, without requiring extensive knowledge of the underlying storage system.

No abstract available.

Synthesizing I/O requests that can accurately capture workload behavior is extremely valuable for the design, implementation and optimization of disk subsystems. This paper presents a synthetic workload generator for TPC-H, an important decision-support commercial workload, by completely characterizing the arrival and access patterns of its queries. We present a novel approach for parameterizing the behavior of inter-mingling streams of sequential requests, and exploit correlations between multiple attributes of these requests, to generate disk block-level traces that are shown to accurately mimic the behavior of a real trace in terms of response time characteristics for each TPC-H query.

A range of database services are being offered on clusters of workstations today to meet the demanding needs of applications with voluminous datasets, high computational and I/O requirements and a large number of users. The underlying database engine runs on cost-effective off-the-shelf hardware and software components that may not really be tailored/tuned for these applications. At the same time, many of these databases have legacy codes that may not be easy to modulate based on the evolving capabilities and limitations of clusters. An indepth understanding of the interaction between these database engines and the underlying operating system (OS) can identify a set of characteristics that would be extremely valuable for future research on systems support for these environments. To our knowledge, there is no prior work that has embarked on such a characterization for a clustered database server. Using IBM DB2 Universal Database (UDB) Extended Enterprise Edition (EEE) V7.2 Trial version and TPC-H like/sup 1/ decision support queries, this paper studies numerous issues by evaluating performance on an off-the-shelf Pentium/Linux cluster connected by Myrinet. These include detailed performance profiles of all kernel activities, as well as qualitative and quantitative insights on the interaction between the database engine and the operating system.

The growth of business enterprises and the emergence of the Internet as a medium for data processing has led to a proliferation of applications that are server-centric. The power dissipation of such servers has a major consequence not only on the costs and environmental concerns of power generation and delivery, but also on their reliability and on the design of cooling and packaging mechanisms for these systems. This paper examines the energy and performance ramifications in the design of disk arrays which consume a major portion of the power in transaction processing environments. Using traces of TPC-C and TPC-H running on commercial servers, we conduct in-depth simulations of energy and performance behavior of disk arrays with different RAID configurations. Our results demonstrate that conventional disk power optimizations that have been previously proposed and evaluated for single disk systems' (laptops/workstations) are not very effective in server environments, even if we can design disks than have extremely fast spinup/spindown latencies and predict the idle periods accurately. On the other hand, tuning RAID parameters (RAID type, number of disks, stripe size etc.) has more impact on the power and performance behavior of these systems, sometimes having opposite effects on these two criteria.

Using a public domain version of a commercial clustered database server and TPC-H like decision support queries, this paper studies the performance and scalability issues of a Pentium/Linux cluster and an 8-way Linux SMP. The execution profile demonstrates the dominance of the I/O subsystem in the execution, and the importance of the communication subsystem for cluster scalability. In addition to quantifying their importance, this paper provides further details on how these subsystems are exercised by the database engine. These 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.