Emulation Experiments Research Articles

Dynamic Voltage and Frequency Scaling for Intermittent Computing

We present hardware/software techniques to intelligently regulate supply voltage and clock frequency of intermittently-computing devices. These devices rely on ambient energy harvesting to power their operation and small capacitors as energy buffers. Statically setting their clock frequency fails to capture the unique relations these devices expose between capacitor voltage, energy efficiency at a given operating frequency, and the corresponding operating range. Existing dynamic voltage and frequency scaling techniques are also largely inapplicable due to extreme energy scarcity and peculiar hardware features. We introduce two hardware/software co-designs that accommodate the distinct hardware features and function within a constrained energy envelope, offering varied trade-offs and functionalities. Our experimental evaluation combines tests on custom-manufactured hardware and detailed emulation experiments. The data gathered indicate that our approaches result in up to 3.75 × reduced energy consumption and 12 × swifter execution times compared to the considered baselines, all while utilizing smaller capacitors to accomplish identical workloads.

Modeling and analysis of transmission dynamics of tuberculosis with preventive treatment and vaccination strategies in China

In China, approximately 350 million people with latent tuberculosis infection (LTBI) are the major source of new TB cases, meanwhile, BCG vaccination for newborns and children under five fails to fully arrest the TB epidemic due to the mechanisms of vaccine failure. It is expected that widespread TB preventive treatment (TPT) and new vaccination strategies may be solutions to end TB. This work allows for TPT and vaccination strategies in a nonlinear age-structured SVEIR model with nonexponential residence times to exploit the interventions for ending TB in China. Global stability analysis reveals that the target model preserves the threshold-value dynamics in terms of the basic reproduction number so that some existing results are extended, moreover, the model is applied to fit the data of TB cases in China. Furthermore, sensitivity analysis for endemic steady state suggests that four potentially feasible interventions (i.e., amplifying the inhibition effect of infectious individuals by limiting range of their mobility, shortening the treatment duration for patients, adopting BCG revaccination and enhancing the rate of TPT) can lower the steady level of infectious individuals at endemic steady state and delay its arrival time. Finally, several multifaceted strategies composed of these interventions are compared via emulational experiments. We conclude that the target of End TB Strategy by 2035 in China would be reached just by the multifaceted strategy with moderate intensity if BCG revaccination, the duration of TPT for 2 months and a 40% completion rate are realized. This investigation sheds light on the fantastic potential applications of new TPT and vaccination strategies.

Color router-based long-wave infrared multispectral imaging.

In the field of long-wave infrared multispectral imaging, traditional snapshot techniques often deploy broadband filters in front of the sensor to encode spectral information about the scene. However, this approach causes a significant loss of precious optical energy, especially for the limited radiation energy of the long-wave infrared region. To address this issue, we first propose an imaging strategy that replaces conventional filters with specially designed diffractive elements, which are optimized by a gradient descent algorithm. The diffractive elements enable effective steering of diverse wavelengths to their designated pixels, significantly minimizing the reflection losses throughout light transmission and thereby augmenting the system's optical energy efficiency. Secondly, we use the MST neural network to reconstruct the spectral information and realize the snapshot computational multispectral imaging. In the experiments, we concentrate the wavelength band within 8-12 μm, simulating and optimizing the design of the diffractive elements. We also discuss how this innovative design can adapt to the field change of image plane that may be encountered in the actual imaging system. Emulation experiments show that our proposed method ensures excellent spectral separation and high imaging quality under different field conditions. This study provides new ideas and practical guidance for the lightweight and efficient development of long-wave infrared multispectral imaging technology.

Polling Sanitization to Balance I/O Latency and Data Security of High-density SSDs

Sanitization is an effective approach for ensuring data security through scrubbing invalid but sensitive data pages, with the cost of impacts on storage performance due to moving out valid pages from the sanitization-required wordline, which is a logical read/write unit and consists of multiple pages in high-density SSDs. To minimize the impacts on I/O latency and data security, this article proposes a polling-based scheduling approach for data sanitization in high-density SSDs. Our method polls a specific SSD channel for completing data sanitization at the block granularity, meanwhile other channels can still service I/O requests. Furthermore, our method assigns a low priority to the blocks that are more likely to have future adjacent page invalidations inside sanitization-required wordlines, while selecting the sanitization block, to minimize the negative impacts of moving valid pages. Through a series of emulation experiments on several disk traces of real-world applications, we show that our proposal can decrease the negative effects of data sanitization in terms of the risk-performance index, which is a united time metric of I/O responsiveness and the unsafe time interval, by 16.34% , on average, compared to related sanitization methods.

A machine learning approach for real-time cortical state estimation

Objective. Cortical function is under constant modulation by internally-driven, latent variables that regulate excitability, collectively known as ‘cortical state’. Despite a vast literature in this area, the estimation of cortical state remains relatively ad hoc, and not amenable to real-time implementation. Here, we implement robust, data-driven, and fast algorithms that address several technical challenges for online cortical state estimation. Approach. We use unsupervised Gaussian mixture models to identify discrete, emergent clusters in spontaneous local field potential signals in cortex. We then extend our approach to a temporally-informed hidden semi-Markov model (HSMM) with Gaussian observations to better model and infer cortical state transitions. Finally, we implement our HSMM cortical state inference algorithms in a real-time system, evaluating their performance in emulation experiments. Main results. Unsupervised clustering approaches reveal emergent state-like structure in spontaneous electrophysiological data that recapitulate arousal-related cortical states as indexed by behavioral indicators. HSMMs enable cortical state inferences in a real-time context by modeling the temporal dynamics of cortical state switching. Using HSMMs provides robustness to state estimates arising from noisy, sequential electrophysiological data. Significance. To our knowledge, this work represents the first implementation of a real-time software tool for continuously decoding cortical states with high temporal resolution (40 ms). The software tools that we provide can facilitate our understanding of how cortical states dynamically modulate cortical function on a moment-by-moment basis and provide a basis for state-aware brain machine interfaces across health and disease.

Modeling the Tax potential of Regions based on an Agent-based approach and the Experience of China's Fiscal policy

The article examines China's experience in applying fiscal contracts to stimulate the economic growth of its provinces and analyzes their potential to stimulate regional development in Russia. Using agent-based modeling implemented in the NetLogo digital environment, various fiscal schemes are considered, their impact on tax revenues and budget obligations of regions. Despite the fact that the model itself is imitative in nature, the agent-based approach used in it allows us to assess the impact of these schemes on tax revenues and budget obligations of regions. Based on the results of emulation experiments, optimal schemes are identified to maximize regional budget revenues, while maintaining a certain share of central budget revenues in consolidated budget revenues. The work highlights the importance of balancing the interests of central and regional governments and offers recommendations for the development of fiscal strategies in Russia aimed at stimulating economic growth and sustainable development.

Inertia compensation for wind turbine emulator based on anticipation deviation suppression

The wind turbine emulator (WTE) is an important tool for dynamic emulation experiments of wind turbines. This paper studies the problem of high-frequency fluctuation of the rotating speed of the dynamic emulation platform when emulating the mechanical dynamics of large inertia wind turbine under fluctuating wind speed. Through the time domain analysis of the discrete mathematical model of the WTE, it is found that the torque compensation loop of the transmission system will incur the acceleration deviation because of the influence of the time delay, and this anticipatory deviation increases with the increase of the compensation multiple. As a result, the acceleration of the transmission chain oscillates, resulting in high-frequency fluctuations in the rotational speed. To this end, this paper proposes an inertia compensation scheme for the WTE based on the anticipation deviation suppression. Based on the principle of zero-pole configuration, a feedforward suppressor is designed to offset the zero-pole of the closed-loop transfer function of the transmission chain system, in order to eliminate the high-frequency fluctuation of the rotating speed caused by the torque deviation. The effectiveness of the proposed strategy is validated using the maximum power point tracking control experiment.

Influence of Tissue Thermophysical Characteristics and Situ-Cooling on the Detection of Breast Cancer

This article presents a numerical simulation model using COMSOL software to study breast thermophysical properties. It analyzes tumor heat at different locations within the breast, records breast surface temperatures, investigates the effects of factors such as blood perfusion, size, depth, and thermal conductivity on breast size, and applies Pennes’ bioheat formula to illustrate thermal distribution on the breast skin surface. An analysis was conducted to examine how changes in tumor location depth, size, metabolism, blood flow, and heat conductivity affect breast skin surface temperature. The simulation model results showed that the highest variations in skin temperatures for breasts with tumors and without tumors can range from 2.58 °C to 0.274 °C. Further, large breast size with a large surface area consistently reduces the temperature variations on the skin and might have difficulty in yielding observable temperature contrast. For small breast sizes, however, heat from tumor sizes below 0.5 cm might be quite difficult to detect, while tumors located deep within the breast layers could not produce observable temperature variations. Motivated by the above interesting results, an emulation experiment was conducted to enhance the observable heat and temperature background contrast, using situ-cooling gel applied to silicon breasts, while the tumor source was emulated using LEDs. The experiment was used to evaluate the effectiveness of adding situ-cooling to the breast surface area, and to study the modulated effect of tumor size and depth. Experimental results showed that situ-cooling enhances thermal contrast in breast thermal images. For example, for a tumor location at a depth of 10 cm, a difference of 6 °C can still be achieved with situ-cooling gel applied, a feat that was not possible in the simulation model. Furthermore, changes in tumor size and location depth significantly impacted surface temperature distribution.

Emulation Framework for Haptic Data Transmission Using Real-Time Transport Protocol

The Tactile Internet (TI) can be regarded as the next evolution in the world of communication. With its envisioned purpose and potential in shaping up the economy, industry and society, this paradigm aims to bring a new dimension to life by enabling humans to interact with machines remotely and in real-time with haptic and kinesthetic feedback. However, to translate this into reality, Tactile Internet will need to meet the stringent requirements of extremely low latency in conjunction with ultra-high reliability, availability, and security. This poses a challenge on the available communication systems to achieve a round-trip delay within 1 to 10 milliseconds time bound that enables the timely delivery of critical tactile and haptic sensations.
 This paper aims to evaluate the Real-Time Transport Protocol (RTP) through an emulation framework. It integrates containerization using Linux-based Docker Containers with NS-3 Network Simulator to conceptualize a haptic teleoperation system. The framework is then used to test the protocol’s feasibility for delivering texture haptic data between master and slave domains in accordance with the end-to-end delay requirements specified by IEEE 1918.1 standards. The results have shown that the timely provision of haptic data is achievable by obtaining an average round-trip delay of 17.8493 ms from the emulation experiment. As such, the results satisfy the expected IEEE 1918.1 standards constraints for medium-dynamic environment use cases.

No apparent trade-offs associated with heat tolerance in a reef-building coral

As marine species adapt to climate change, their heat tolerance will likely be under strong selection. Yet trade-offs between heat tolerance and other life history traits could compromise natural adaptation or assisted evolution. This is particularly important for ecosystem engineers, such as reef-building corals, which support biodiversity yet are vulnerable to heatwave-induced mass bleaching and mortality. Here, we exposed 70 colonies of the reef-building coral Acropora digitifera to a long-term marine heatwave emulation experiment. We tested for trade-offs between heat tolerance and three traits measured from the colonies in situ – colony growth, fecundity, and symbiont community composition. Despite observing remarkable within-population variability in heat tolerance, all colonies were dominated by Cladocopium C40 symbionts. We found no evidence for trade-offs between heat tolerance and fecundity or growth. Contrary to expectations, positive associations emerged with growth, such that faster-growing colonies tended to bleach and die at higher levels of heat stress. Collectively, our results suggest that these corals exist on an energetic continuum where some high-performing individuals excel across multiple traits. Within populations, trade-offs between heat tolerance and growth or fecundity may not be major barriers to natural adaptation or the success of assisted evolution interventions.

Suppressing Decoherence in Quantum State Transfer with Unitary Operations.

Decoherence is the fundamental obstacle limiting the performance of quantum information processing devices. The problem of transmitting a quantum state (known or unknown) from one place to another is of great interest in this context. In this work, by following the recent theoretical proposal, we study an application of quantum state-dependent pre- and post-processing unitary operations for protecting the given (multi-qubit) quantum state against the effect of decoherence acting on all qubits. We observe the increase in the fidelity of the output quantum state both in a quantum emulation experiment, where all protecting unitaries are perfect, and in a real experiment with a cloud-accessible quantum processor, where protecting unitaries themselves are affected by the noise. We expect the considered approach to be useful for analyzing capabilities of quantum information processing devices in transmitting known quantum states. We also demonstrate the applicability of the developed approach for suppressing decoherence in the process of distributing a two-qubit state over remote physical qubits of a quantum processor.

Adaptive Switch on Wear Leveling for Enhancing I/O Latency and Lifetime of High-Density SSDs

NAND flash-based high-density solid-state drives (SSDs) commonly have limited write endurance, as a single block becomes unavailable after a finite number of program/erase cycles. The (static) wear leveling algorithms migrate cold data to more worn blocks for yielding an even distribution of wears in SSDs. However, migrating cold data must delay normal I/O processing, and unnecessary wear leveling operations may damage SSD endurance as each operation causes a program/erase cycle. This article proposes an adaptive switch on wear leveling (called ASWL), to boost I/O performance and lifetime for high-density SSDs. The basic idea of ASWL is to adaptively switch the threshold function for intelligently triggering wear leveling operations at different stages of SSD lifetime. To this end, we build a mathematical model determining the wear leveling threshold condition with a dynamic manner, by considering the real-time factors of I/O intensity and the wear difference of SSD blocks. Through a series of emulation experiments on several realistic disk traces, we show that the proposed ASWL mechanism can not only greatly improve I/O performance but also noticeably extend the lifetime of high-density SSDs, in contrast to existing wear leveling methods.

Machine Learning Techniques Based on Primary User Emulation Detection in Mobile Cognitive Radio Networks.

Mobile cognitive radio networks (MCRNs) have arisen as an alternative mobile communication because of the spectrum scarcity in actual mobile technologies such as 4G and 5G networks. MCRN uses the spectral holes of a primary user (PU) to transmit its signals. It is essential to detect the use of a radio spectrum frequency, which is where the spectrum sensing is used to detect the PU presence and avoid interferences. In this part of cognitive radio, a third user can affect the network by making an attack called primary user emulation (PUE), which can mimic the PU signal and obtain access to the frequency. In this paper, we applied machine learning techniques to the classification process. A support vector machine (SVM), random forest, and K-nearest neighbors (KNN) were used to detect the PUE in simulation and emulation experiments implemented on a software-defined radio (SDR) testbed, showing that the SVM technique detected the PUE and increased the probability of detection by 8% above the energy detector in low values of signal-to-noise ratio (SNR), being 5% above the KNN and random forest techniques in the experiments.

Xatu

The performance of Adaptive Bitrate (ABR) algorithms for video streaming depends on accurately predicting the download time of video chunks. Existing prediction approaches (i) assume chunk download times are dominated by network throughput; and (ii) apriori cluster sessions (e.g., based on ISP and CDN) and only learn from sessions in the same cluster. We make three contributions. First, through analysis of data from real-world video streaming sessions, we show (i) apriori clustering prevents learning from related clusters; and (ii) factors such as the Time to First Byte (TTFB) are key components of chunk download times but not easily incorporated into existing prediction approaches. Second, we propose Xatu, a new prediction approach that jointly learns a neural network sequence model with an interpretable automatic session clustering method. Xatu learns clustering rules across all sessions it deems relevant, and models sequences with multiple chunk-dependent features (e.g., TTFB) rather than just throughput. Third, evaluations using the above datasets and emulation experiments show that Xatu significantly improves prediction accuracies by 23.8% relative to CS2P (a state-of-the-art predictor). We show Xatu provides substantial performance benefits when integrated with multiple ABR algorithms including MPC (a well studied ABR algorithm), and FuguABR (a recent algorithm using stochastic control) relative to their default predictors (CS2P and a fully connected neural network respectively). Further, Xatu combined with MPC outperforms Pensieve, an ABR based on deep reinforcement learning.

Design and Implementation of Educational Emulation Experiment Systems Based on Discrete Logic

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Contribution:</i> The scheme for virtual emulation experiments based on discrete logic which appropriately lowers students’ cognitive burden and possesses manipulatable interactive logic in tune with real experiments is put forward. The relationship between students’ experiential learning and the influence of cognitive burden is also discussed. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Background:</i> Many kinds of research prove that a great deal of time expense and complex experiment setup strategies are a must if one needs adequate exploration experience and results through real experiments. These outputs are also limited to lab resources, leading to students’ less experience gaining. Moreover, conventional virtual emulation experiments are possible solutions but the lowering of cognitive burden is not adjustable. Students still lack practical experience due to the less challenging and more mechanic interaction. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Intended Outcomes:</i> A scheme for virtual emulation experiments based on discrete logic which appropriately lowers students’ cognitive burden and possesses manipulatable interactive logic in tune with real experiments and an experiment system based on this scheme. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Application Design:</i> Students will deal with different experiment missions and prompts according to their cognitive level in virtual experiment scenarios instead of complex real ones. Students’ operation strategies and parameter settings are analyzed and presented for more proper designs. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Findings:</i> The analysis of the experiment data is exciting: students who have accomplished virtual emulation experiments take up the absolute majority of successors when it comes to transfer questions, which means they have got more practical experience.

Xatu: Richer Neural Network Based Prediction for Video Streaming

The performance of Adaptive Bitrate (ABR) algorithms for video streaming depends on accurately predicting the download time of video chunks. Existing prediction approaches (i) assume chunk download times are dominated by network throughput; and (ii) apriori cluster sessions (e.g., based on ISP and CDN) and only learn from sessions in the same cluster. We make three contributions. First, through analysis of data from real-world video streaming sessions, we show (i) apriori clustering prevents learning from related clusters; and (ii) factors such as the Time to First Byte (TTFB) are key components of chunk download times but not easily incorporated into existing prediction approaches. Second, we propose Xatu, a new prediction approach that jointly learns a neural network sequence model with an interpretable automatic session clustering method. Xatu learns clustering rules across all sessions it deems relevant, and models sequences with multiple chunk-dependent features (e.g., TTFB) rather than just throughput. Third, evaluations using the above datasets and emulation experiments show that Xatu significantly improves prediction accuracies by 23.8% relative to CS2P (a state-of-the-art predictor). We show Xatu provides substantial performance benefits when integrated with multiple ABR algorithms including MPC (a well studied ABR algorithm), and FuguABR (a recent algorithm using stochastic control) relative to their default predictors (CS2P and a fully connected neural network respectively). Further, Xatu combined with MPC outperforms Pensieve, an ABR based on deep reinforcement learning.

Securing Relational Databases against Security Vulnerabilities: A Case of Microsoft SQL Server and PostgreSQL

This study evaluates mechanisms to secure relational databases against security vulnerabilities and utilized PostgreSQL and Microsoft SQL Server due to data breach incidences reported across the world. Emulation experiments with documentary review were employed to collect necessary study data. Identification of security features and vulnerabilities that are found to affect the data tier of the web applications were examined. The findings from the study have shown that Microsoft SQL Server is more security feature-rich in terms of Confidentiality, Integrity, and Availability compared to PostgreSQL and Microsoft SQL Server is more resilient to security attacks in its default behavior compared to PostgreSQL.

BA-MPCUBIC: Bottleneck-Aware Multipath CUBIC for Multipath-TCP.

The Congestion Control Algorithm (CCA) in the Multipath Transmission Control Protocol (MPTCP) is fundamental to controlling the flow of data through multiple subflows (SF) simultaneously. The MPTCP CCA has two design goals: first, always ensure better throughput than single path TCP (SPTCP) flows, and second, collectively, MPTCP SFs going through a shared bottleneck (SB) should occupy bandwidth fairly, i.e., close to the bandwidth occupied by an SPTCP flow. Although several MPTCP CCAs exist, they primarily focus on specific scenarios and could not satisfy the design goals in diverse and dynamic scenarios. Recently, CUBIC has become a widely used CCA for SPTCP for its better compatibility with high-speed internet. CUBIC’s effective implementation in the MPTCP is expected to provide improved throughput and fairer behavior, thus satisfying the design goals. However, although the current multipath CUBIC (MPCUBIC) implementation ensures better fairness, it fails to ensure better throughput. We believe the application of same rule for SFs going through an SB and non-shared bottleneck (NSB) makes it difficult for MPCUBIC to adapt to diverse and dynamically changing network scenarios, thus resulting in poor throughput. Therefore, we present an improved version of MPCUBIC, namely bottleneck-aware MPCUBIC (BA-MPCUBIC), to resolve the throughput issue. First, we deploy an innovative bottleneck detection method that successfully differentiates between an SB and NSB based on round-trip-time, enhanced congestion notification, and packet loss. Then, we implement SPTCP CUBIC and MPCUBIC as the CCAs for SFs going through NSBs and SBs, respectively. Extensive emulation experiments demonstrate that the BA-MPCUBIC successfully detects SBs and NSBs with the highest detection accuracy and the lowest detection time compared with other approaches. Moreover, BA-MPCUBIC successfully satisfies the MPTCP design goals in the considered diverse and dynamic scenarios by ensuring both better throughput and fairness.

A new solution based on optimal link-state routing for named data MANET

NDN is an important instance of Information-Centric Networking. When integrating NDN into MANET, exploring new routing is a necessary task for this research area. The LSAs flooding is a common method to obtain network topology during route establishment. However, the LSAs flooding often results in a broadcast storm in high-density MANET. Using the MPR set proposed in the OLSR can effectively reduce the number of LSAs in the process of route establishment and can further solve the broadcast storm. In this paper, an enhanced neighbor discovery protocol firstly is designed to establish a MPR set. The new protocol can effectively avoid the problem incurred by unidirectional links that impact the network performance in a wireless environment. And then a new and proactive routing NOLSR based on OLSR for NDN-MANET is proposed to support NDN in MANET. And another important work is that NOLSR is implemented on top of NDN Forwarding Daemon NFD. Finally, we make a comparative analysis between NOLSR and the two most relative schemes such as traditional LSA-flooding and the scheme [1] by emulation experiments in the NDN emulator mini-NDN.

Adaptive Decrease Window for BALIA (ADW-BALIA): Congestion Control Algorithm for Throughput Improvement in Nonshared Bottlenecks

The main design goals of the multipath transmission control protocol (MPTCP) are to improve the throughput and share a common bottleneck link fairly with a single-path transmission control protocol (TCP). The existing MPTCP congestion control algorithms achieve the goal of fairness with single-path TCP flows in a shared bottleneck, but they cannot maximize the throughput in nonshared bottlenecks, where multiple subflows traverse different bottleneck links. This is because the MPTCP is designed not to exceed the throughput of a single-path TCP competing in the bottleneck. Therefore, we believe that MPTCP congestion control should have different congestion window control mechanisms, depending on the bottleneck type. In this paper, we propose an adaptive decrease window (ADW) balanced linked adaptation (BALIA) congestion control algorithm that adaptively adjusts the congestion window decrease in order to achieve better throughput in nonshared bottlenecks while maintaining fairness with the single-path TCP flows in shared bottlenecks. The ADW-BALIA algorithm detects shared and nonshared bottlenecks based on delay fluctuations and it uses different congestion window decrease methods for the two types of bottleneck. When the delay fluctuations of the MPTCP subflows are similar, the ADW-BALIA algorithm behaves the same as the existing BALIA congestion control algorithm. If the delay fluctuations are dissimilar, then the ADW-BALIA algorithm adaptively modulates the congestion window reduction. We implement the ADW-BALIA algorithm in the Linux kernel and perform an emulation experiment that is based on various topologies. ADW-BALIA improves the aggregate MPTCP throughput by 20% in the nonshared bottleneck scenario, while maintaining fairness with the single-path TCP in the shared bottleneck scenario. Even in a triple bottleneck topology, where both types of bottlenecks exist together, the throughput increases significantly. We confirmed that the ADW-BALIA algorithm works stably for different delay paths, in competition with CUBIC flows, and with lossy links.