This study addresses the optimal control problem for discrete-time nonlinear systems with fixed initial state. A fast policy iteration$(\text{PI})$algorithm is developed to compute the numerical solution of the optimal control law based on adaptive dynamic programming, where dynamic programming, artificial neural network and reinforcement learning are used. The highlight of this algorithm lies in the fact that during each updating iteration, the dynamic sparse sampling, i.e., only a small number of key and sensitive points along the iterative suboptimal trajectory are sampled, is used to replace the traditional global and local spatial sampling, thus the computational burden can be greatly relaxed compared with the previous global and local PI algorithms. Numerical experiments demonstrate the effectiveness and rapidity of the proposed algorithm.

This paper presents a second-order passive noise-shaping SAR ADC for low-power receivers, aimed at reducing the power consumption of the anti-aliasing filter and input buffer to enhance energy efficiency. A self-synchronized asynchronous logic with system control and standby mode is proposed, allowing the ADC to maintain high energy efficiency across a wide sampling range of 80kHz to 20MHz. Additionally, a dynamic control logic for the splitting monotonic switching DAC is proposed. Fabricated using a 55nm CMOS process, the ADC occupies only 0.012mm2. Measurement results demonstrate that, at a supply voltage of 0.8V and an oversampling ratio (OSR) of 5, the ADC achieves an effective number of bits (ENOB) between 7.6 and 8 bits across the specified sampling rates. Power consumption scales linearly from 105nW to 18.47µW with frequency, offering an energy-efficient solution for low-power receivers.

The battery-free and crystal-less nodes require energy-efficient radio, leading to the design of the ultra-low-power crystal-less receiver. The paper proposes a crystal-less receiver classification method for state-of-the-art works and presents a sub-1mW Class-Ab crystal-less receiver architecture with a carrier-auxiliary intermediate frequency (IF) negative feedback local oscillator (LO) generator and approximate low-IF mode. The proposed LO generator and receiver are implemented using 55nm CMOS technology with a total power of 492.4µW. Without any crystals, the LO frequency based on the bulk acoustic wave (BAW) oscillator maintains a ±6ppm error range from 0°C to 70°C. After locking the LO calibration loop, the phase noise at the 1MHz offset is better than -137dBc/Hz and long-term frequency drift is less than ±0.06ppm in two seconds.

Backscatter sensing with battery-less tag sensors has gained popularity in low-cost wireless sensing for IoT applications. This paper proposes a backscatter sensing method with a single tag for canceling the effect of path variation (PV), such as unknown reader-sensor distance and orientation. A reference/sensing (Ref/Sen) mode selection in the tag sensor is applied to generate the differential backscattered signals, which selects a reference/sensing capacitor by a mode-selection switch and causes the reflection coefficient variation. A Ref-Sen differential (RSD) variable ∆r is defined and calculated by the interrogator based on two signal parameters: power and phase. ∆r is a PV-independent variable corresponding to the sensing information. An RFID sensing system with a liquid-level tag sensor has been designed and evaluated. Experimental results demonstrate a 0.5-mm resolution in liquid-level sensing. The robustness-test results under different PVs, including 18 dBm power variation and 0.6 m distance variation, have proved the proposed PV-cancelling method’s validity. The standard deviations for the amplitude and phase variations are 0.00344 and 1.65°.

Apsara vSwitch (AVS) is a per-host deployed forwarding component for instance network connectivity in the Alibaba Cloud. To meet the growing performance demands, we accelerated AVS by adopting the most widely used "Sep-path" offloading architecture, which introduces a separate hardware data path to speed up popular traffic. However, the deployment results prove that it is difficult to bridge the gap in performance and programming flexibility of the software and hardware data paths, resulting in unpredictable performance and low iteration velocity.

The vSwitch, as a critical component for Virtual Machine (VM) network connectivity in cloud environments, has prompted increasing attention towards its forwarding performance. While software optimization schemes have limitations in meeting the expanding network capacity demands [11, 12, 15, 17, 18], hardware offloading architectures leveraging SoC, FPGA, and ASIC have been proposed to transfer the match-action workload [1, 3, 6, 7, 13, 16], addressing the growing need for network capacity.

Cloud computing has witnessed tremendous growth, prompting enterprises to migrate to the cloud for reliable and on-demand computing. Within a single Virtual Private Cloud (VPC), the number of instances (such as VMs, bare metals, and containers) has reached millions, posing challenges related to supporting millions of instances with network location decoupling from the underlying hardware, high elastic performance, and high reliability. However, academic studies have primarily focused on specific issues like high-speed data plane and virtualized routing infrastructure, while existing industrial network technologies fail to adequately address these challenges.

With the fast development of various computing paradigms, the amount of data is rapidly increasing that brings the huge storage overhead. However, the existing data deduplication techniques do not make full use of similarity detection to improve the storage efficiency and data transmission rate. In this paper, we study the problem of utilising the duplicate and resemblance detection techniques to further compress data. We first present a framework of FIDCS-ERD, a feature-based intelligent deduplication compression system with extreme resemblance detection. We also introduce the main components and the detailed workflow of our compression system. We propose a content-defined chunking algorithm for duplicate detection and a Bloom filter-based resemblance detection algorithm. FIDCS-ERD implements the intelligent file chunking and the fast duplicate and resemblance detection. By extensive experiments over the real datasets, we demonstrate that FIDCS-ERD has better compression effect and more accurate resemblance detection compared to the existing approaches.

Privacy preservation is one of the greatest concerns in big data. As one of extensive applications in big data, privacy preserving data publication (PPDP) has been an important research field. One of the fundamental challenges in PPDP is the trade-off problem between privacy and utility of the single and independent data set. However, recent research has shown that the advanced privacy mechanism, i.e., differential privacy, is vulnerable when multiple data sets are correlated. In this case, the trade-off problem between privacy and utility is evolved into a game problem, in which payoff of each player is dependent on his and his neighbors' privacy parameters. In this paper, we first present the definition of correlated differential privacy to evaluate the real privacy level of a single data set influenced by the other data sets. Then, we construct a game model of multiple players, in which each publishes data set sanitized by differential privacy. Next, we analyze the existence and uniqueness of the pure Nash Equilibrium. We refer to a notion, i.e., the price of anarchy, to evaluate efficiency of the pure Nash Equilibrium. Finally, we show the correctness of our game analysis via simulation experiments.

Nowadays, people can enjoy a rich real-time sensing cognition of what they are interested in anytime and anywhere by leveraging powerful mobile devices such as smartphones. As a key support for the propagation of these richer live media contents, cellular-based access technologies play a vital role to provide reliable and ubiquitous Internet access to mobile devices. However, these limited wireless network channel conditions vary and fluctuate depending on weather, building shields, congestion, etc., which degrade the quality of live video streaming dramatically. To address this challenge, we propose to use crowdsourcing brokerage in future networks which can improve each mobile user's bandwidth condition and reduce the fluctuation of network condition. Further, to serve mobile users better in this crowdsourcing style, we study the brokerage scheduling problem which aims at maximizing the user's quality of experience satisfaction degree cost effectively. Both offline and online algorithms are proposed to solve this problem. The results of extensive evaluations demonstrate that by leveraging crowdsourcing technique, our solution can cost-effectively guarantee a higher quality view experience.

Mobile cloud computing provides powerful computing and storage capacity on managing GPS big data by offloading vast workloads to remote clouds. For the mobile applications with urgent computing or communication deadline, it is necessary to reduce the workload transmission latency between mobile devices and clouds. This can be technically achieved by expanding mobile cloudlets that are moving co-located with Access Points (APs). However, it is not-trivial to place such movable cloudlets efficiently to enhance the cloud service for dynamic context-aware mobile applications. In view of this challenge, an adaptive cloudlet placement method for mobile applications over GPS big data is proposed in this paper. Specifically, the gathering regions of the mobile devices are identified based on position clustering, and the cloudlet destination locations are confirmed accordingly. Besides, the traces between the origin and destination locations of these mobile cloudlets are also achieved. Finally, the experimental results demonstrate that the proposed method is both effective and efficient.

Attracted by the advantages of cloud computing, more and more services and applications are migrated to this new paradigm. As promising as it is, cloud computing also brings new challenges to many research issues, such as service scheduling. Most existing scheduling methods are offline and could not deal with the uncertainties and dynamics during the execution, especially in the dynamic cloud environment. In view of this challenge, in this paper, we propose an uncertainty-aware evolutionary scheduling method for cloud service provisioning. It aims at dealing with uncertainties during execution and updating the scheduling so as to meet the deadline and optimize the execution cost of cloud applications. Our method consists of two phases, baseline scheduling and evolutionary scheduling during execution. In baseline scheduling, we suggest a reverse-auction-based pricing mechanism for service provisioning. In evolutionary scheduling, an uncertain model with three types of uncertainties is considered and four uncertain events are discussed. Accordingly, the evolutionary scheduling policy is presented based on intermediate workflow to get a global optimal schedule, so as to improve the success rate for the execution of the cloud applications. Finally, experiments are designed and performed to demonstrate the effectiveness of our method.

With the prosperity of media streaming applications over the Internet in the past decades, multimedia data has sharply increased (categorized as multimedia big data), which exerts more pressure on the infrastructure, such as networking of the application provider. In order to move this hurdle, an increasing number of traditional media streaming applications have migrated from a private server cluster onto the cloud. With the elastic resource provisioning and centralized management of the cloud, the operational costs of media streaming application providers can decrease dramatically. However, to the best of our knowledge, existing migration solutions do not fully take viewer information such as hardware condition into consideration. In this article, we consider the deployment optimization problem named ODP by leveraging local memories at each viewer. Considering the NP-hardness of calculating the optimal solution, we turn to propose computationally tractable algorithms. Specifically, we unfold the original problem into two interactive subproblems: coarse-grained migration subproblem and fine-grained scheduling subproblem. Then, the corresponding offline approximation algorithms with performance guarantee and computational efficiency are given. The results of extensive evaluation show that compared with the baseline algorithm without leveraging local memories at viewers, our proposed algorithms and their online versions can decrease total bandwidth reservation and enhance the utilization of bandwidth reservation dramatically.