This study focuses on developing a deep reinforcement learning (DRL) flow control policy to mitigate aerodynamic loads of a tall building at high Reynolds number Re=7.53×104. Multiple jets are placed at the four corners and the free end of the building, aiming to suppress wind loading of the building under varying incoming wind conditions. Pressure probes on the building surface are used as feedback observers. The soft actor-critic (SAC) algorithm is deployed to train an effective DRL control policy. The DRL agent can optimize the jet velocities, resulting in reductions of 39.1%, 53.7%, and 38.4% in the fluctuations of the drag, lift, and moment coefficients, respectively. Furthermore, the mean drag coefficient is reduced by 27.3%. This study investigates the behavior of multiple jets and their effects on the wind force and flow field. It was found that the multiple jets can reduce crosswind force fluctuations in tall buildings, enhance downwash flow, and mitigate the shedding of wake vortices. These results highlight the potential of DRL in active flow control and lay the foundation for the efficient, robust, and practical implementation of this control technique in real-world engineering applications.

In this paper, we aim to improve the carbon efficiency (CE) of hybrid energy-supplied cellular networks by jointly optimizing communication and energy resources. The network is powered by both renewable and conventional grid energy. However, the stochastic and intermittent nature of renewable energy causes spatiotemporal mismatches between energy supply and traffic demand, thereby posing a challenge to CE improvement. Moreover, due to the nonlinearity of power amplifiers (PAs) at base stations (BSs), energy dissipation nonlinearly increases with transmit power. As a result, the existing static PA efficiency-based resource allocation may lead to energy inefficiency and CE degradation. On this basis, we formulate a stochastic long-term CE optimization problem that considers PA nonlinearity. Aided by Lyapunov optimization theory, the problem is equivalently transformed into three short-term deterministic subproblems, i.e., traffic flow control, resource allocation, and energy sharing. Leveraging this insight, we propose a queue-aware traffic flow control policy and a second-order cone programming-based resource allocation method to align traffic with PA characteristics. Additionally, a many-to-many stable matching-based energy sharing scheme is developed, where energy-deficient BSs are matched with energy-excessive BSs based on energy loss coefficients. Consequently, energy waste due to energy sharing is reduced, thereby improving CE.

Active flow control based on reinforcement learning has received much attention in recent years. Indeed, the requirement for substantial data for trial-and-error in reinforcement learning policies has posed a significant impediment to their practical application, which also serves as a limiting factor in the training of cross-case agents. This study proposes an in-context active flow control policy learning framework grounded in reinforcement learning data. A transformer-based policy improvement operator is set up to model the process of reinforcement learning as a causal sequence and autoregressively give actions with sufficiently long context on new unseen cases. In flow separation problems, this framework demonstrates the capability to successfully learn and apply efficient flow control strategies across various airfoil configurations. Compared with general reinforcement learning, this learning mode without the need for updating the network parameter has even higher efficiency. This study presents an effective novel technique in using a single transformer model to address the flow separation active flow control problem on different airfoils. Additionally, the study provides an innovative demonstration of incorporating reinforcement-learning-based flow control with aerodynamic shape optimization, leading to collective enhancement in performance. This method efficiently lessens the training burden of the new flow control policy during shape optimization, and opens up a promising avenue for interdisciplinary intelligent co-design of future vehicles.

Increasing the number of processing cores is currently a common way to boost processor performance. However, the load on the memory subsystem consequently increases as the number of its agents grows. Hardware data compression is an unconventional approach to improving memory subsystem performance by reducing, firstly, the main memory access rate by increasing the cache capacity and, secondly, data traffic by packing the data more densely. The paper describes the implementation of hardware data compression in the on-chip network and interprocessor links of a configuration with wide data transmission channels and a wormhole flow control policy. The existing solutions cannot be applied to such configurations because they are essentially based on using narrow data channels and flow control policies implying uninterrupted packet transmission, which is not maintained with the wormhole flow control. The method proposed in this paper enables the use of hardware compression in the aforementioned configuration by moving data compression and decompression from networks to the connected devices, as well as by using a number of optimizations to hide the data processing delays. Optimizations of some specific cases, such as the transmission of large data packets with several cache lines or the transmission of zero data, are considered. Special attention is given to data transmission via interprocessor links, where, due to their lower bandwidth compared to the on-chip network, data compression can be the most beneficial. The increase in memory subsystem bandwidth from using hardware data compression was confirmed in the experiments showing the relative IPC increase in SPEC CPU2017 benchmarks up to 14 percent.

Abstract In recent years, with the rapid growth of Internet-related services, the traditional software-defined network architecture has gradually failed to adapt to user demands and services. This paper proposes an ant colony algorithm (ACO)-based data flow control policy optimization scheme specifically designed for software-defined networks (SDNs). It has been found that the traditional ACO algorithm is prone to overfitting during the optimization process of data flow control policies for SDN, and a pheromone updating strategy has been introduced to optimize this phenomenon. After solving this phenomenon, the optimization scheme of data flow control policy for software-defined networks based on the ACO algorithm will be formally formulated, and simulation experiments will be used to confirm the effectiveness of the optimization scheme in this paper. The results show that this paper’s algorithm has a higher priority than the control algorithm in terms of four evaluation metrics: average link throughput, link utilization, average round-trip delay, and data packet loss rate. This study enables the optimization of data flow control strategies under software-defined network architecture and also improves the utilization of network data flow to bring about a better network experience.

This paper presents a new distributed slot reservation frame-work for joint resource allocation and flow control in mmWave IAB networks. We derive the Dynamic Slot Reservation (DSR) algorithm from a novel approach to solve a minimum clearing time linear program in a completely distributed manner. The algorithm to solve this problem, the Static Slot Reservation (SSR) algorithm, is also a contribution of the paper. We compare the delay performance of the DSR algorithm with a well known optimal, centralized algorithm, the joint-MWM algorithm, for a realistic IAB network scenario of multi-hop flows. We show that flows that traverse several links have significantly lower delays under DSR than under the joint-MWM algorithm. This paper also provides an instantaneous rate control policy for IAB networks which changes flow rates based on the number of flows at each node in the network. The flow rates under this policy are the same as the steady-state flow rates achieved by the DSR algorithm. We prove that the proposed flow control policy provides stability for all flow arrival rate vectors that are achievable by any flow control policy. This paper provides distributed admission control policies to provide rate and/or latency guarantees to flows under dynamic scenarios with stochastic flow arrivals and changing access link rates.

SummarySoftware‐defined network (SDN) technology is widely used for computer networks, especially in enterprise data centers and virtualized networking. However, SDN networks encounter severe challenges to security. One such challenge comes from third‐party applications that contain malicious logic and security vulnerabilities, resulting in controller integrity attacks. In this paper, we propose a defensive mechanism of object integrity for SDN (DMoiSDN) to mitigate the issue known as Cross‐App Poisoning (CAP). Our results contribute to increasing the integrity level of the controller's resources by conducting a potential risk analysis, which showed a decrease of 57% in the risk factor for potential attacks. We further examined the results of comparing DMoiSDN's performance with related work that uses information flow control (IFC) policies. The best results among the three conducted scenarios were as follows: we found that decreased latency in our system ranged from 12% to 90%, with an average of 59%, when encountering an increase in requests. It ranged from 78% to 49%, with an average of 63%, when receiving a variable number of total permissions for each application. DMoiSDN is expected to show a perceptible but reasonable latency and, to some extent, be able to avoid a critical impact on performance.

Reducing passenger waiting time in oversaturated metro lines with passenger flow control policy

Reinforcement Learning (RL) is a popular approach for deciding on an optimum traffic signal control policy to alleviate congestion in a road network. However, the traffic signal control policy can also be optimized in conjunction with the design of vehicular flow directions to further improve traffic performance. The design of vehicular flow directions refers to the right of way or directional restriction imposed in a road network. Here, a new RL-based technique is presented for co-optimization of the design of vehicular flow directions and control policy for traffic signals. This technique consists of a two-step iterative process, wherein a set of vehicular flow directions for a road network is generated, then a RL-based approach is used to train the traffic signal control policy over the given set of vehicular flow directions. Following the proposed technique, the vehicular flow directions with poor traffic performance are iteratively eliminated, while new vehicular flow directions are generated to achieve better traffic performance and realize convergence to a maximum possible expected traffic performance. The proposed RL-based technique is evaluated by using two examples under rush hour and non-rush hour traffic conditions. It is found that, compared to a RL-based approach in which only traffic signal control policy is considered, the proposed approach can be used to obtain a better traffic performance in terms of vehicular queue length and throughput.

Sensitive resources in Trusted Execution Environment (TEE) have suffered serious security threats in recent years. Previous protection approaches either lack a strong assurance of TEE security properties or are limited to a single platform. We propose a compatible verified TEE architecture, called <monospace>CVTEE</monospace> , which delegates a security monitor to manage TEE resources securely. This architecture has two key advantages: i) its functional correctness and security are guaranteed by a machine-checkable proof of security objectives of Trusted Application (TA) isolation, runtime confidentiality, and runtime integrity, and ii) it is applicable to different TEE platforms and implementation-independent due to its high level of abstraction and non-determinism of data types. Note that access control policy and information flow control policy are the core for security management of resources. After formally specifying the security attributes of TEE resources, we develop these policies based on Common Criteria (CC) in the security monitor and provide atomic interfaces. <monospace>CVTEE</monospace> is formally verified with 386 lemmas/theorems and <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 10,000 LOC of Isabelle/HOL. In addition, we implement a proof of concept for the access control module of Teaclave, and prove that the constructed access control model meets the security requirements through 5 theorems.

Joint optimization of modular vehicle schedule and fair passenger flow control under heterogeneous passenger demand in a rail transit system

Influence of an R&D lot on productivity in semiconductor manufacturing

Introduction. The article analyzes the changes in approaches to the small economies financial openness improvement. It takes into account the new challenges of global economic instability and the contradictions of the European integration requirements of financial openness in the conditions of global economic destabilization and highlights possible ways of overcome them. Materials and methods. To disclose the theoretical model of the interconnection between financial openness, financial stability and economic growth has been used system-functional and comparative economic-statistical analysis methods based on World Bank and IMF statistic data. Results and discussion. It is emphasized that there is a trend towards strengthening control over capital flows at the global level and the removal of restrictions on the movement of international capital flows is one of the key requirements for the approximation of regulatory norms in the field of financial sector for countries going to join the EU with the regulatory framework of the latter. Given the current conditions of global uncertainty and the threat of increasing financial instability, the EU's position on the free movement of capital flows at this stage may be significantly revised. Due to the current conditions, the requirement of free movement of capital flows contradicts with a list of requirements for the approximation of the characteristics of the financial sector of the candidate countries and the EU. The need for a capital flow control policy for small open economies wishing to join the EU at this stage is not just only the need of ensuring macroeconomic stability, but also to the convergence criteria of systems, in particular in terms of joining to ERM II and BEPS. Conclusions. In overall case of small open economies, in the context of the implementation of European norms and the approximation of regulatory framework in the financial sector, it is necessary to talk about the feasibility of controlling the international capital flows at the stage of building adequate institutional environment as a support measure for further transition to free exchange rate and capital flows liberalization. As a proposal, it is stated that the elimination of identified contradictions and the transition to the free capital movement in the interests of Ukraine's European integration requires proper development of the country's financial sector and, above all, its institutional environment. The implementation of appropriate practical measures for the institutional provision of financial openness should include a policy of control over the movement of capital flows, which combines the potential of fiscal, monetary and regulatory policies in different segments of financial markets as part of the macroprudential policy.

VCS: A method of in-order packet delivery for adaptive NoC routing

Flow-control policies that balance server loads are well known for improving performance of queueing systems with multiple nodes. However, although load balancing benefits the system overall, it may negatively impact some of the queueing nodes. For example, it may reduce throughput rates or engender unfairness with respect to some performance measures. For queueing systems with multiple single-server nodes, we propose a set of constrained load-balancing policies that ensures the expected arrival rate to each queueing node is not reduced, and we show that such policies provide multiple benefits for each queueing node: stochastically fewer customers and lower variance of the number of customers at each queueing node. These results imply performance improvement as measured by multiple general objective functions, including but not limited to the expected number of customers at a queueing node, probability of having a high number of customers, variance of the number of customers, and expected number of customers conditional on exceeding a threshold defined by a fixed service level. We demonstrate numerically that our proposed policies capture a large portion of the potential maximal improvement. This paper was accepted by Noah Gans, stochastic models and simulation.

Study of the optimization model for traffic flow

Recent advances in cloud data centers toward fog data centers

In recent years, imbalanced utilization of medical resources is widely concerned within the tiered Chinese hospital system. Reverse referral, as a measure of promoting patient flows from upper level hospitals (ULHs) to lower level hospitals (LLHs), has demonstrated its advantages on alleviating ULH workload and balancing resource utilization between ULHs and LLHs. Nevertheless, it remains unclear on how to control the reverse referral decision process at the operational level. In this paper, we consider an ULH-dominant setting at which the LLH must accept patient referrals from the ULH whenever it has available beds. We focus our attention on an easy-to-implement threshold policy for the ULH to make the reverse referral decision. To investigate, we first formulate an analytically tractable queueing model for a simplified reverse referral process. We then investigate a more general patient flow control model, for which we analyze the patient population dynamics with a Markov chain process, and apply the concept of state-dependent Markovian arrival process to generate an infinitesimal generator of the system. We use RG factorization to compute the system performance measures. We next formulate a threshold optimization problem with the objective of maximizing the ULH profit. Simulation experiments are performed, which conclude that the threshold control policy is insensitive to the service time distribution. Finally, we report real-world inspired numerical studies, from which we generate insights into effective adjustment of the control threshold in response to the system parameters and discuss potential hindrance from the LLH incorrectly informing its real-time resource availability to the ULH. Our work is the first that applies systems engineering to the real-time reverse referral decision problem in China. It provides the novel perspective of resource balancing to patient flow control studies in the care transition management literature.

The negative perception of irregular migration and fearmongering have been exacerbated since the global financial crisis of 2008, leading the national security paradigm to prevail worldwide in the political discourse of current times, in detriment of the human rights discourse. As a result, the securitization rhetoric has taken hold of political debate to such an extent that it is leaving the issue of how migrations can contribute to the development of countries in the background. As discussed below, the abandonment of the humanitarian focus on migration policy results in the criminalization of poverty and immigration, stemming from the perception of the foreigner as a potential threat to the stability of public order in nations. In light of this, the interpretative framework of the Securitization Theory of the Copenhagen School and the new focuses of the Critical Security Studies will be useful to understand how the militarization of the migration flow control policy has gained a greater importance in the international landscape, also in part due to the globalization of risks.

SummaryRecent developments in the cloud technologies have motivated the migration of distributed large systems, specifically the Internet of Things to the cloud architecture. Since Internet of Things consist of a vast network and variety of objects, the cloud platform proves to be an ideal option. It is essential for the proper functioning of the Internet of Things to be able to share data among the system processes. The biggest problem faced during the transition of the IoTs to the cloud is the security of data especially while data sharing within the cloud and among its tenants. Information Flow Control mechanisms are one of the many solutions to enable a controlled sharing of data. Integration of Information Flow Control Systems to the existing architecture requires various levels of re‐engineering efforts. Moreover, most of the Information Flow Control systems focus on data flow within the cloud and neglect the security and integrity of data while it is being transferred to the cloud from various devices. This research focuses on securing the entire process of data migration to cloud from devices while the in‐cloud data flow is monitored by the Information Flow Control policies specified by the users. We have developed a prototype for the proposed model, and results are evaluated on the basis of energy consumption and execution time. As proposed model provides security services such as privacy, integrity, and authentication, hence it takes more execution time and consumes more energy as compared with the existing model.