Real-time Resource Management Research Articles

Resource Management for Multiplexing eMBB and URLLC Services Over RIS-Aided THz Communication

Integrating the multitude of emerging internet of things (IoT) applications with diverse requirements in beyond fifth generation (B5G) networks necessitates the coexistence of enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) services. However, bandwidth limited and congested sub-6GHz bands are incapable of fulfilling this coexistence. In this paper, we consider a reconfigurable intelligent surface (RIS)-aided wideband terahertz (THz) communication system to this end. In specific, we formulate a resource management problem, aiming at jointly optimizing the reflection coefficient of the RIS elements and the transmit power of the base station, as well as the wideband THz resource block allocation. To solve this problem, we adopt a supervised learning approach relying on optimization, deep learning and ensemble learning methods. Simulation results show that for an RIS of size <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$11\times 11$ </tex-math></inline-formula> , up to 49% spectral efficiency gain is achieved for the eMBB service compared to the counterparts, while ensuring the reliability and latency requirements of the URLLC service. Further, the ensemble learning model can perform real-time resource management at the expense of up to 1% performance loss, compared to the optimization approach.

A novel approach to energy-aware resource management: Toward green NOMA heterogeneous networks

The dense deployment of small cell networks is a key feature of next generation mobile networks aimed at providing the necessary capacity increase. In order to reach an acceptable performance in such ultra-dense networks, real-time resource management is of great importance. Therefore, self-optimization networking is proposed as the only viable solution to increase the networks’ utility. This paper proposed a self-optimizing model to enhance network performance and guarantee the users’ QoS requirements by considering limited resources and using effective user association, carrier scheduling and handover optimization algorithms. In order to maximize the network performance, we applied the smart backhauling technique in order to analyze the signaling to increase the validity of the decision making process. Based on the semantic information extracted from the access layer, the network decision-making center is able to adjust the network parameters and resource allocation effectively. The goal function is defined as maximizing the total energy efficiency by considering the transmission power, energy harvesting capability and the user QoS constraints so that the idle small cells are considered turned off temporarily to boost the power efficiency. Although the optimization problem is non-convex, a quadratic mixed-integer function is solved to obtain a global optimal solution. Since the actual implementation of the real-time algorithm has high computational complexity, two algorithms with different complexity levels are proposed. These algorithms use the carrier matching feature and optimal transmission power for problem-solving. The simulation results prove that, despite the increased computational complexity, effective resource allocation and optimal HO relations made the proposed approach capable to increase performance indices such as network throughput by up to 30%.

Resource management of GEO relays for real-time remote sensing

Remote sensing is one of the main applications of satellite communication. With limited communication time between low earth orbit (LEO) sensing satellites and the ground, geostationary orbit (GEO) satellite relaying has become an effective solution. In practical applications, one GEO communication satellite usually needs to provide data forwarding services for multiple satellites or even multiple satellite constellations. Therefore, communication resource management of GEO satellites has become crucial. Existing methods only focus on resource allocation without taking data forwarding task scheduling into consideration, which limits their applicability in real-time applications. In this paper, we propose a real-time resource management approach for GEO relaying, aiming to maximize network throughputs as well as to reduce transmission delays. In this approach, resource allocation is modeled as a Stackelberg game while task scheduling is modeled as a real-time queuing problem. Furthermore, we propose two real-time algorithms, including an adaptive gradient descent method to find optimal price for data forwarding services with low computational complexity, and a queue-jumping algorithm for data forwarding scheduling to minimize transmission delays. With comparison with existing methods, simulations verify the effectiveness of our proposed method with respect to convergence speed of the algorithm, network throughputs, and transmission delays.

MC-SDN: Supporting Mixed-Criticality Real-Time Communication Using Software-Defined Networking

Despite recent advances, there still remain many problems to design reliable cyber-physical systems. One of the typical problems is to achieve a seemingly conflicting goal, which is to support timely delivery of real-time flows while improving resource efficiency. Recently, the concept of mixed-criticality (MC) has been widely accepted as useful in addressing the goal for real-time resource management. However, it has not been yet studied well for real-time communication. In this paper, we present the first approach to support MC flow scheduling on switched Ethernet networks leveraging an emerging network architecture, software-defined networking (SDN). Though SDN provides flexible and programmatic ways to control packet forwarding and scheduling, it yet raises several challenges to enable real-time MC flow scheduling on SDN, including: 1) how to handle (i.e., drop or re-prioritize) out-of-mode packets in the middle of the network when the criticality mode changes and 2) how the mode change affects end-to-end transmission delays. Addressing such challenges, we develop MC-SDN that supports real-time MC flow scheduling by extending SDN-enabled switches and OpenFlow protocols. It manages and schedules MC packets in different ways depending on the system criticality mode. To this end, we carefully design the mode change protocol that provides analytic mode change delay bound, and then resolve implementation issues for system architecture. For evaluation, we implement a prototype of MC-SDN on top of Open vSwitch, and integrate it into a real world network testbed as well as a 1/10 autonomous vehicle. Our extensive evaluations with the network testbed and vehicle deployment show that MC-SDN supports MC flow scheduling with minimal delays on forwarding rule updates and it brings a significant improvement in safety in a real-world application scenario.

Adaptive resource management strategies in paper and pulp industry: a discrete-event simulation approach

In Paper and Pulp (P&P) industry resource management scenarios, such as processing cell(s) efficiency reduction, equipment break-down or failures, alternative energy source (biomass and solar energy) utilization/ synchronization with regular energy sources and management decided change in production rate or product quality often occur in industrial processes causing material flow disruption, energy and production losses. In such scenarios, immediate and appropriate actions are required to be taken to minimize energy and production losses. Emerging resource management options tackle these issues through four different ways i.e., by mixing of readymade pulp at various processing stages, by varying the percentage of input raw material, by adopting a parallel utilization of alternative energy sources and by switching on/off some processing units or parallel working segments. These ways are implementable with immediate effect, accurately monitorable and their degree of application can be regulated within a wide range, thus denoted as adaptive solutions.This research work proposes a discrete-event simulation based real-time resource management technique for paper and pulp industry, wherein some resource management challenges are addressed and solved strategically with adaptive solutions. The presented examples validate that using the developed approach, the effective adaptive strategy can be timely planned to resolve occasionally or permanently happening resource management challenges ensuring an un-interrupted material flow throughout the plant along with the minimization of productivity and energy losses.

Sparse Beamforming for Real-Time Resource Management and Energy Trading in Green C-RAN

This paper considers cloud radio access network with simultaneous wireless information and power transfer and finite capacity fronthaul, where the remote radio heads are equipped with renewable energy resources and can trade energy with the grid. Due to uneven distribution of mobile radio traffic and inherent intermittent nature of renewable energy resources, the remote radio heads may need real-time energy provisioning to meet the users’ demands. Given the amount of available energy resources at remote radio heads, this paper introduces two provisioning strategies to strike an optimum balance among the total power consumption in the fronthaul, through adjusting the degree of partial cooperation among the remote radio heads, the total transmit power and the maximum or the overall real-time energy demand. More specifically, this paper formulates two sparse optimization problems and applies reweighted $ {\ell _{1}}$ -norm approximation for $ {\ell _{0}}$ -norm and semidefinite relaxation to develop two iterative algorithms for the proposed strategies. Simulation results confirm that both of the proposed strategies outperform two other recently proposed schemes in terms of improving energy efficiency and reducing overall energy cost of the network.

Toward thermodynamics of real-time scheduling

The modern problem of real-time resource management to increase enterprise efficiency is considered. A new look at the dynamic self-organizing processes based on multi-agent technologies in building and revising schedules by events in real time is suggested. Schedule is considered as a flexible network of opera tions of demand and resource agents. This schedule is formed during the interactions of basic agent classes that set and break the dynamic links between each other, depending on the events and changing situation in the real world. A thermodynamic model of demand–resource network (DRN) dynamics is introduced. There is a similarity to Ilya Prigogine’s non-linear thermodynamics theory which allows us to explain the phenomenon of unstable equilibrium emergence, order and chaos, catastrophes, bifurcations and other non-linear events that are signifi cant to the self-organizing processes control in multi-agent systems (MASs).

Simultaneous Multibeam Resource Allocation Scheme for Multiple Target Tracking

A colocated multiple-input multiple-output (MIMO) radar system has the ability to address multiple beam information. However, the simultaneous multibeam working mode has two finite working resources: the number of beams and the total transmit power of the multiple beams. In this scenario, a resource allocation strategy for the multibeam working mode with the task of tracking multiple targets is developed in this paper. The basis of our technique is to adjust the number of beams and their directions and the transmit power of each beam through feedback, with the purpose of improving the worst tracking performance among the multiple targets. The Bayesian Cramer–Rao lower bound (BCRLB) provides us with a lower bound on the estimated mean square error (MSE) of the target state. Hence, it is derived and utilized as an optimization criterion for the resource allocation scheme. We prove that the resulting resource optimization problem is nonconvex but can be reformulated as a set of convex problems. Therefore, optimal solutions can be obtained easily, which greatly aids real-time resource management. Numerical results show that the worst case tracking accuracy can be efficiently improved by the proposed simultaneous multibeam resource allocation (SMRA) algorithm.

Multi-agent system “Smart Factory” for real-time workshop management in aircraft jet engines production

The paper presents design principles of multi-agent system “Smart Factory” for real time resource management in manufacturing workshops. This objective is achieved by flexible event-driven reaction on changes in factory environment, resource allocation, scheduling, optimization and controlling in real time. The developed method of adaptive resource scheduling based on multi-agent technology, main functionality and architecture of the system are discussed. The results of application and measured benefits for aircraft jet engines manufacturer are presented.

Dynamic hinting: Collaborative real-time resource management for reactive embedded systems

The increasing complexity of today's reactive embedded applications can rapidly result in reduced real-time capabilities of the underlying hard and software. As an example for this paper we'll refer to the specific and growing demands on the severely resource constrained sensor nodes in sensor/actuator networks (SANet). While preemptive operating systems are one way to retain acceptable reactivity within highly dynamic environments, their concurrency paradigm commonly leads to severe resource management problems, caused by the coexistence of tasks with interfering and even varying requirements. To counteract these problems, we present the novel Dynamic Hinting approach for maintaining good reactivity in typically resource constrained sensor/actuator systems by efficient combination of preemptive task scheduling and collaborative resource allocation. With respect to task priorities, our technique significantly improves classical methods for handling priority inversions (and deadlocks where required) under both short- and long-term resource allocations. Furthermore, we facilitate compositional software design by providing independently developed tasks with runtime information for yet collaborative and reflective resource sharing - e.g. by means of time-utility-functions. In many cases this even allows to reduce blocking delays as otherwise imposed by bounded priority inversion.

Development of a large basin rainfall-runoff modeling system using the object-oriented hydrologic modeling system (OHyMoS)

Development of basin-scale rainfall-runoff modeling systems is essential for integrated water resources management in terms of both assessing management alternatives and real-time resource management. This study developed a distributed rainfall-runoff modeling system based on the Object-oriented Hydrologic Modeling System (OHyMoS) for a large river basin (the Daechung Dam basin, South Korea; 3,994 km2) that is highly regulated by multipurpose dams. We applied three hydrologic element modules to simulate hillslope runoff, channel runoff, and dam reservoir outflow and then linked these modules together under OHyMoS to simultaneously predict discharges at eight specific outlets. This newly developed rainfall-runoff modeling system generally provided acceptable hydrographs during the typical Korean rainy period from 1 June to 30 September, although the simulated hydrographs for extreme flood events during typhoons and local heavy rainfall were underestimated compared to measured hydrographs. The developed modeling system can be used for water resources planning and management in the Daechung Dam basin and also readily extended to other large basins, such as the whole Geum River basin (9,835 km2) that includes the Daechung Dam basin, by incorporating sub-basin models into the system.

A case study on the feasibility and performance of an UWB-AoA real time location system for resources management of civil construction projects

The application of integrated satellite and modern wireless positioning technologies for ubiquitous real-time resources management in large scale civil engineering projects can greatly optimize the time and cost in the construction process, and is now the trend for modern construction project man- agement. As the outdoor conditions of most civil construction sites are open to sky, satellite position- ing with the popularly used Global Positioning System (GPS) has been proved to be very e‰cient and eective. However, the condition in indoor and underground construction site is very complicated due to the fact that dierent construction activities would be carried out in dierent congested areas, involving heavy construction plant, equipment, pro- fessionals and technical personnel. Nowadays dier- ent emerging technologies such as Wi-Fi and ZigBee can be adopted for position and tracking in indoor environments. Nevertheless, under the very com- plicated construction site conditions these technolo- gies may fail due to movement of human resources and construction plant, variation of metrological conditions, and serious multipath eects of signals. It is considered that Ultra Wide Band (UWB) tech- nology is more suitable for indoor construction site environments. In this paper, a case study on the attempt of integrating GPS with Ubisense Real- time Location System (RTLS) for resources man- agement in an underground railway construction site is discussed. Laboratory and field tests have shown that the RTLS can provide better resources management capability in terms of positioning accuracy and stability than Wi-Fi and ZigBee tech- nologies under complicated construction environ- ments. The test results show that the system can normally achieve better than 15 cm accuracy, and better than 1 m under adverse geometrical site condition. However, the high instrumental set up cost and the requirement for high quality data trans- mission cable for high precision time synchroni- zation between sensors may deter wide application of similar system for resources management in con- struction sites.

Layered connection management for dynamic all-optical networks

Traditionally, optical channels are manually provisioned and exist for months or years. New automated means of managing network resources, together with demands for greater flexibility from the customers, have led to the concept of dynamic optical connections. However, over time, the establishment and removal of these connections in the presence of changing traffic demands can lead to inefficient exploitation of the underlying resources. The authors propose a novel dual-layered connection management system. Distributed, fast and simplified control plane functionalities are used at the lower layer to provide efficient real-time per-connection resource management. The time-consuming optimisation functions are located in a centralised upper layer where they are able to achieve more efficient solutions that can improve the overall network utilisation. The control of the lower layer by the upper level is carried out via two means, namely: influence-based path calculation for short-lived connections and intrusive redeployment for long-lived connections. Simulation results, under a variety of conditions, show an improvement in performance of over 10% in each of the metrics considered for both long-lived and short-lived connections for two commonly cited test networks. We therefore conclude that the proposed system can offer worthwhile improvements in network performance.

Real-time adaptive resource management

Distributed mission-critical environments employ a mixture of hard and soft real-time applications that usually expect a guaranteed range of quality of service (QoS). These applications have different levels of criticality and varied structures ranging from periodic independent tasks to distributed pipelines or event-driven modules. The underlying distributed system must evolve and adapt to the high variability in resource demands that competing applications impose. The current industry trend is to use commercial off-the-shelf (COTS) hardware and software components to build distributed environments for mission-critical applications. The paper considers how adding a middleware layer above the COTS components facilitates consistent management of system resources, decreases system complexity, and reduces development costs.

Intelligent image management in a distributed PACS and telemedicine environment

Advances in information technology in the past decade have resulted in a proliferation of clinical information systems dedicated to individual user groups and clinical functional areas. This, in turn, has led to the need for hospital-wide management and integration of information, and has triggered major efforts towards the development of integrated hospital information systems. A framework for developing an integrated regional health telematics system is presented, based on the functional and data integration of federated autonomous information systems. The concept of the patient meta-record is introduced as a central element in achieving integration in terms of content, structure, and access to information resources. Intelligent image management at the level of either a single health care provider (hospital) or a regional health telematics network is addressed and is achieved through event and model-driven strategies enabled by a distributed hierarchical storage management system. Intelligence, in this context, refers to methods of efficient and effective real-time resource management in the timely delivery of health care.

Real-time database — similarity and resource scheduling

While much recent work has focussed on the performance of transaction systems where individual transactions have deadlines, our research addresses the semantics of data usage in real-time applications and its integration with real-time resource management, in particular, the timeless value of real-time data and the inherent path and not state-based constraints on concurrency control. Central to our research is the idea of similarity which is a reflexive, symmetric relation over the domain of a data object. By exploiting the similarity relation, we propose a class of efficient data-access policies for real-time data objects. We shall also discuss the design of a distributed real-time data-access interface. Our goal is to build a database facility which can support predictable real-time applications involving high-speed communication, information access, and multimedia.

Rate-monotonic analysis for real-time industrial computing

Issues of real-time resource management are pervasive throughout industrial computing. The underlying physical processes of many industrial computing applications impose explicit timing requirements on the tasks processed by the computer system. These timing requirements are an integral part of the correctness and safety of a real-time system. It is tempting to think that speed (for example, processor speeds or higher communication bandwidths) is the sole ingredient in meeting system timing requirements, but speed alone is not enough. Proper resource-management techniques also must be used to prevent, for example, situations in which long, low priority tasks block higher priority tasks with short deadlines. One guiding principle in real-time system resource management is predictability, the ability to determine for a given set of tasks whether the system will be able to meet all of the timing requirements of those tasks. Predictability calls for the development of scheduling models and analytic techniques to determine whether or not a real-time system can meet its timing requirements. The author illustrates an analysis methodology, rate monotonic analysis, for managing real-time requirements in a distributed industrial computing situation. The illustration is based on a comprehensive robotics example drawn from a typical industrial application.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Parallel approaches to the solution of the assignment problem

This paper discusses efforts to develop parallel algorithms that can be used to solve large-scale assignment problems typical of military battle management systems. Attempts to parallellze the classical Hungarian method are explored. Drawing on experimental data, a regresslon model is derived that relates the Hungarian method's run time to a cubic function of the number of parallel processors used in the solution. Four parallel heuristics for solving the assignment problem are developed and analysed. Two of them perform well in a parallel environment. The first, based on Vagel's approximation, can be used to identify a feasible, near-optimal assignment. The second algorithm partitions the assignment problem into independent subproblems across the parallel array. The resulting solution, although infeasible in the strict definition of the assignment problem, is seen to be reasonably good and can be obtained very rapidly. Either of these two heuristics are of potential use in the stringent computing environment of a real-time resource management system.