Learning Constrained Resource Allocation Policies in Wireless Control Systems

Resource capacity plans and allocation policies have a significant impact on the performance of business projects. This is particularly true in situations where multiple projects compete concurrently for scarce resources. Project management tools have limited ability to analyze the impact of resource allocation policies in systems with variability. Simulation tools are designed for this type of analysis. This paper focuses on simulation analyses of the relation between changes to resource capacity, resource allocation policies, variability, and project performance. Scenarios are simulated for different combinations of changes to resource quantities, work schedule durations, allocation policies, and task duration variability. Each scenario's performance is measured based on total project cycle-times and costs. The results demonstrate how increasing the flexibility of resource allocation policies can increase the effectiveness of resource capacity and significantly reduce project cycle-times without increasing project costs.

Resource allocation and admission control algorithm based on non-cooperation game in wireless mesh networks

This article provides a framework for understanding the nature, role, functioning, design, and effects of organizational oversight systems. Using a case study with elements recognizable to an academic audience, the authors explore how a dean of a fictitious School of Medicine might use organizational control structures to develop effective solutions to global disarray within the academic medical center. Organizational control systems are intended to help influence the behavior of people as members of a formal organization. They are necessary to motivate people toward organizational goals, to coordinate diverse efforts, and to provide feedback about problems. The authors present a model of control to make this process more visible within organizations. They explore the overlap among academic medical centers and large businesses-for instance, each is a billion-dollar enterprise with complex internal and external demands and multiple audiences. The authors identify and describe how to use the key components of an organization's control system: environment, culture, structure, and core control system. Elements of the core control system are identified, described, and explored. These closely articulating elements include planning, operations, measurement, evaluation, and feedback systems. Use of control portfolios is explored to achieve goal-outcome congruence. Additionally, the authors describe how the components of the control system can be used synergistically by academic leadership to create organizational change, congruent with larger organizational goals. The enterprise of medicine is quickly learning from the enterprise of business. Achieving goal-action congruence will better position academic medicine to meet its multiple missions.

Unmanned aerial vehicles (UAVs) have been conceived as an available solution to substitute terrestrial base stations (TBSs) to provide downloading services for user equipment (i.e. mobile devices) that have difficulty communicating directly with TBSs. However, the mobility of user equipment (UE) and the random nature of the number of UE will cause several challenges including 1) the hardness of determining optimal user association and UAV resource (i.e., bandwidth and transmit power) allocation decision, 2) the burden of network function maintenance owing to the necessity of shutting down the entire system. Therefore, in this article, joint user association and resource allocation are designed for a software-defined network (SDN)-adopted leaderless softwarized UAV network, where each UAV is regarded as a flying SDN controller to enhance the control ability of the considered network. The purpose is to maximize energy efficiency (EE) with satisfying the quality of service (QoS). To this end, a joint method based on hierarchical agglomerative clustering (HAGC) and multi-agent deep deterministic policy gradient (MADDPG) is proposed. Specifically, the HAGC approach is utilized to determine the optimal MDs association with UAVs. Afterward, MADDPG approach is leveraged to obtain the best policy for resource allocation, aiming to achieve the maximum EE. Finally, the effectiveness of the proposed method is confirmed by the evaluation results.

There is a strong trend towards wireless networked Real-Time Control Systems. However, a variety of factors such as the variability of wireless channel capacities, multi-path fading, and ambient interference make it extraordinarily difficult to design and implement Wireless Control Systems (WCSs). To attack the uncertainty in available communication resource in WCSs, this chapter presents an Adaptive Feedback Scheduling scheme that takes advantage of the methodology of cross-layer design. The proposed approach exchanges information across the physical layer and the application layer in the presence of noise interference and link transmission rate variations. The overall QoC is guaranteed through controlling the deadline miss ratio of control loops. Design parameters of the feedback scheduler are adapted to dynamic changes in wireless channel capacity. To further improve the feedback scheduling performance, an event-driven invocation mechanism for feedback schedulers is suggested. Simulation results demonstrate that this approach is efficient to deal with channel capacity variations and noise interference, thus providing an enabling technology for wireless control in dynamic environments. It is also argued that in this context event-driven invocation mechanism improves the performance of the feedback scheduling system.KeywordsSampling PeriodTransmission RateMedium Access ControlControl PerformanceMedium Access Control ProtocolThese 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.

Mobile Edge Computing (MEC) is a promising computing paradigm, which can provide mobile devices with powerful computing capability and low delay by offloading resource-intensive tasks to the MEC Server. However, due to the varying wireless channel conditions and different task delay requirements, the resource allocation decision made by MEC Server cannot always achieve the lowest system energy consumption. In this paper, we consider resource allocation problem in a multi-user MEC system. The objective is to achieve minimum system energy consumption under delay constraint by suitably allocating communication and computing resources. We solve the communication resources (subcarrier) allocation problem through regression algorithm, make use of three regression algorithms to predict subcarrier allocation. And then calculate the computing resources (CPU frequency) allocation and system energy consumption. Simulation results show that machine learning can achieve near-optimal system energy consumption, where the system energy consumption of the ANN(Artificial Neural Network), Xgboost (Extreme Gradient Boosting), and RF(Random Forest) algorithms is 1.30%, 7.49%, and 23.29% higher than the optimal system energy consumption respectively. In the view of prediction loss, ANN and Xgboost perform better than RF.

The coexistence of Ultra-Reliable and Low-Latency Communications (URLLC) and enhanced Mobile BroadBand (eMBB) brings significant challenges for service pairing and resource allocation in beyond fifth-generation (B5G) wireless networks. To meet the reliability requirement of URLLC services and improve the fairness of eMBB services, we first develop a supervised learning-based resource allocation policy for eMBB services. Then, a two-phase resource allocation framework is proposed for URLLC services: 1) eMBB/URLLC service pairing, and 2) URLLC resource allocation. In the first phase, matching theory pairs eMBB and URLLC services for better fairness. In the second phase, URLLC resource allocation policy is optimized by a constrained unsupervised learning algorithm. Simulation results show that our proposed framework can achieve better trade-offs among fairness, throughput, and reliability compared with two existing baselines. For example, a dynamic proportional fairness algorithm can meet the reliability requirement of URLLC when its average packet arrival rate is below 0.7 packets/mini-slot. The proposed algorithm can support URLLC services with an average packet arrival rate of 1.6 packets/mini-slot

Wireless sensor-actuator networks (WSANs) are being adopted in process industries because of their advantages in lowering deployment and maintenance costs. While there has been significant theoretical advancement in networked control design, only limited empirical results that combine control design with realistic WSAN standards exist. This paper presents a cyber-physical case study on a wireless process control system that integrates state-of-the-art network control design and a WSAN based on the WirelessHART standard. The case study systematically explores the interactions between wireless routing and control design in the process control plant. The network supports alternative routing strategies, including single-path source routing and multi-path graph routing. To mitigate the effect of data loss in the WSAN, the control design integrates an observer based on an Extended Kalman Filter with a model predictive controller and an actuator buffer of recent control inputs. We observe that sensing and actuation can have different levels of resilience to packet loss under this network control design. We then propose a flexible routing approach where the routing strategy for sensing and actuation can be configured separately. Finally, we show that an asymmetric routing configuration with different routing strategies for sensing and actuation can effectively improve control performance under significant packet loss. Our results highlight the importance of co- joining the design of wireless network protocols and control in wireless control systems.

Cloud systems are inefficient in processing delay-sensitive applications due to the WAN latency associated. To augment the processing of cloud services and provide delay-free computation, fog computing is used. The delay sensitivity of the tasks and heterogeneity of the fog-cloud hybrid architecture calls for efficient resource allocation policies. The decision making must be precise and also multiple criteria must be considered while deciding which resources to allocate. In this article, we propose two variants of analytic hierarchy process (AHP)-based resource allocation policies for fog-cloud hybrid systems. The proposed resource allocation policies consider network load, in addition, to the compute load during decision making. The overall aim of the resource allocation policies is to reduce the delay incurred by each task. The allocation policies differ in the way they assign weights to each criterion of optimization. One of the resource allocation policies uses predetermined weights for compute and network while the second method finds the weights dynamically from the overall data. The experimental results show that the proposed approach outperforms existing resource allocation approaches thereby showing the usefulness of AHP-based optimization in fog-cloud hybrid systems.

Wireless is now a pervasive aspect of everyday life; from the Bluetooth module in a mobile handset to broadband wireless networks covering scores of square kilometres. No longer is wireless networking an experimental technology, it has become ubiquitous, and wireless closed-loop control is now being seriously considered for industrial applications. Wireless networking enables the vision of a security enterprise - ease of expansion with minimal implementation and maintenance costs. The bottom line is that wireless networking is cheaper. Computer-based simulation can help develop a thorough understanding of wireless control systems; the network and control system can be configured quickly and the results from modifications to either the protocol or the control strategy can be implemented and tested scientifically, without time-consuming and difficult hardware implementation.

Considering the proliferation in the number of cloud users on an everyday basis, the task of resource provisioning in order to support all these users becomes a challenging problem. When resource allocation is non-optimal, users may face high costs or performance issues. So, in order to maximize profit and resource utilization while satisfying all client requests, it is essential for Cloud Service Providers to come up with ways to allocate resources adaptively for diverse conditions. This is a constrained optimization problem. Each client that submits a request to the cloud has its own best interests in mind. But each of these clients competes with other clients in the quest to obtain required quantum of resources. Hence, every client is a participant in this competition. So, a preliminary analysis of the problem reveals that it can be modelled as a game between clients. A game theoretic modelling of this problem provides us an ability to find an optimal resource allocation by employing game theoretic concepts. Resource allocation problems are NP-Hard, involving VM allocation and migration within and possibly, among data centres. Owing to the dynamic nature and number of requests, static methods fail to surmount race conditions. Using a Min-Max Game approach, we propose an algorithm that can overcome the problems mentioned. We propose to employ a utility maximization approach to solve the resource provisioning and allocation problem. We implement a new factor into the game called the utility factor which considers the time and budget constraints of every user. Resources are provisioned for tasks having the highest utility for the corresponding resource.

Software-Defined Wireless Network (SDWN) aims to build a flexible wireless network infrastructure that can support future Internet services. In this paper, we present WLAN architecture to take advantage of OpenFlow that provides the global view of the entire network including wireless network configuration, resource allocation, and flow control policies to make the load balanced network environment. We build simulation environment through Mininet-WiFi to analysis throughput and jitter values of associated stations. The results demonstrate that proposed architecture can divide the load between APs that increase the average throughput of associated stations.

Reinforcement Learning for Resource Constrained Project Scheduling Problem with Activity Iterations and Crashing

The chapter presents a case study demonstrating how security requirements of an Industrial Automation and Control System (IACS) component can be represented in a form of Protection Profile that is based on IEC 62443 standards and how compliance assessment of such component can be supported by explicitly representing a conformity argument in a form based on the OMG SACM metamodel. It is also demonstrated how an advanced argument assessment mechanism based on Dempster-Shafer belief function theory can be used to support assessors while analyzing and assessing the conformity argument related to an IACS component. These demonstrations use a NOR-STA tool for representing, managing and assessment of evidence-based arguments, which have been developed in our research group.

1. Introduction. The generic dynamic resource allocation problem involves a number of locations containing resources. The dynamic aspect of the problem is the arrival of consumers, each of which requires a certain amount of service from the resources, and the control variable of the problem is the allocation policy, which species at which location each consumer is to be served. Oftentimes in applications, locations contain nitely many resources, hence the network can get congested and may eventually overow in the sense that consumers may be lost. The purpose of this work is to provide estimates on the network overow time under certain resource allocation policies. In particular we concentrate on three allocation policies, namely optimal repacking (OR), least load routing (LLR), and Bernoulli splitting (BS). The OR policy continuously repacks the consumers in the network so as to minimize the maximum load over the locations. LLR and BS are nonrepacking policies, under which the assignment decisions are irrevocable. The LLR policy assigns each arriving consumer to an admissible location with the least load, whereas the BS policy assigns each arriving consumer to one of the admissible locations randomly and independently, so as to minimize the maximum mean load over the locations. The policies investigated in this paper have been considered by several authors and in various contexts. In particular, Gibbens, Kelly and Turner (1993) considered the LLR policy for dynamic routing in circuit switched networks. Azar, Broder, and Karlin (1992) compared the LLR and the optimal nonrepacking policies based on a worst case analysis, whereas Alanyali and Hajek (1997) compared the three policies based on certain properties implied by uid limit approximations.