Resource Management Algorithms Research Articles

HUNTER: AI based holistic resource management for sustainable cloud computing

The worldwide adoption of cloud data centers (CDCs) has given rise to the ubiquitous demand for hosting application services on the cloud. Further, contemporary data-intensive industries have seen a sharp upsurge in the resource requirements of modern applications. This has led to the provisioning of an increased number of cloud servers, giving rise to higher energy consumption and, consequently, sustainability concerns. Traditional heuristics and reinforcement learning based algorithms for energy-efficient cloud resource management address the scalability and adaptability related challenges to a limited extent. Existing work often fails to capture dependencies across thermal characteristics of hosts, resource consumption of tasks and the corresponding scheduling decisions. This leads to poor scalability and an increase in the compute resource requirements, particularly in environments with non-stationary resource demands. To address these limitations, we propose an artificial intelligence (AI) based holistic resource management technique for sustainable cloud computing called HUNTER. The proposed model formulates the goal of optimizing energy efficiency in data centers as a multi-objective scheduling problem, considering three important models: energy, thermal and cooling. HUNTER utilizes a Gated Graph Convolution Network as a surrogate model for approximating the Quality of Service (QoS) for a system state and generating optimal scheduling decisions. Experiments on simulated and physical cloud environments using the CloudSim toolkit and the COSCO framework show that HUNTER outperforms state-of-the-art baselines in terms of energy consumption, SLA violation, scheduling time, cost and temperature by up to 12, 35, 43, 54 and 3 percent respectively.

Privacy-Preserving Federated Learning for UAV-Enabled Networks: Learning-Based Joint Scheduling and Resource Management

Unmanned aerial vehicles (UAVs) are capable of serving as flying base stations (BSs) for supporting data collection, machine learning (ML) model training, and wireless communications. However, due to the privacy concerns of devices and limited computation or communication resource of UAVs, it is impractical to send raw data of devices to UAV servers for model training. Moreover, due to the dynamic channel condition and heterogeneous computing capacity of devices in UAV-enabled networks, the reliability and efficiency of data sharing require to be further improved. In this paper, we develop an asynchronous federated learning (AFL) framework for multi-UAV-enabled networks, which can provide asynchronous distributed computing by enabling model training locally without transmitting raw sensitive data to UAV servers. The device selection strategy is also introduced into the AFL framework to keep the low-quality devices from affecting the learning efficiency and accuracy. Moreover, we propose an asynchronous advantage actor-critic (A3C) based joint device selection, UAVs placement, and resource management algorithm to enhance the federated convergence speed and accuracy. Simulation results demonstrate that our proposed framework and algorithm achieve higher learning accuracy and faster federated execution time compared to other existing solutions.

A Coupled Spring Forced Bat Searching Algorithm for Energy Resource Management

Abstract Swarm intelligence-based optimization algorithms show great success to solve complex problems with high efficiency. Recently, a novel and heuristic algorithm, Bat searching algorithm (BA) has been proposed. Moreover, numerical evaluation has already demonstrated the better performance of BA compared with other algorithms variations. In this paper, we propose a coupled spring forced BA (SFBA) algorithm by considering that each particle is a spring and is coupled with the optimal solution found so far as the second abstract spring. The synergistic integration of the coupled springs, the bat’s behavior, and swarm intelligence governs and navigates the new algorithm in the searching process. Moreover, the convergence of the SFBA is studied via Jury’s Test. Numerical evaluation is provided for the proposed SFBA algorithm by conducting comparison with other variations of BA in the literature, which indicates that the performance of SFBA surpasses all the listed variations of BA significantly. Moreover, the proposed SFBA is applied so solve a large-scale energy resource management in uncertain environments, and the results are numerically compared with other BA algorithms.

Knapsack Problem-based Algorithm for Time Resource Management of Multiple Phased Array Radars for Multiple Targets Tracking

Knapsack Problem-based Algorithm for Time Resource Management of Multiple Phased Array Radars for Multiple Targets Tracking

Energy-efficient resource management for OFDM modulated bidirectional relaying with quality-of-service constraint

This paper addressed the energy-efficient resource management problem of an amplify-and-forward relay-assisted bidirectional relay system under a quality-of-service (QoS) constraint. The objective is to develop a holistic resource management algorithm for joint implementation of relay selection, power adaptation and bit-rate management for optimal energy efficiency (EE). A three-stage approach is proposed to solve the energy-efficient resource management problem. At stage 1 (stage of power management), a per-subcarrier energy-efficient problem is investigated, leading to a power adaptation algorithm for maximizing the system EE and ensuring the required level of the system QoS. Within the framework of the power adaptation algorithm and by exploiting spatial diversity of multiple-relay channels, distributed relay selection is investigated at stage 2 (stage of relay management). Next, the bit-rate management problem is tackled at stage 3, namely assigning bit rate to different subcarriers to maximize the system EE further. Finally, summarizing the results achieved at the three stages, a novel EE technology combined with relay selection, bit-rate management and power adaptation is developed. Simulation results validated the correctness and the efficiency of the proposed algorithm. It is shown that the proposed algorithm can significantly reduce the total transmit power of the system while ensuring the required system QoS.

Guest Editorial Introduction to the Special Section on Vehicular Networks in the Era of 6G: End-Edge-Cloud Orchestrated Intelligence

The articles in this special section focus on vehicular networks in the era of 6G mobile communication. With the growth of the vehicle population, vehicular networks play a key role in building safe, efficient, and intelligent transport systems and has been attracting a lot of attention from both academic and industrial communities around the world. The rise of autonomous driving technology and the prosperity of mobile applications, e.g., real-time video analytic, image-aided navigation, natural language processing, and etc, have brought tremendous pressure on current vehicular networks, e.g., high bandwidth, ultra-low latency, high reliability, high security, powerful computation capability, and massive connections. It is necessary to continue to develop vehicular networks by combining the latest research intends in other fields to meet quickly rising communication and computation demands. The upcoming 6G technology, which provides Holographic and Artificial Intelligence (AI) enabled communications, together with the increasing implementation of artificial intelligence in mobile devices, will lead to a new research trend to end-edge-cloud orchestrated computing with intelligence. It means that, not only the intelligent communication protocols, but also the intelligent computing resource management and machine learning algorithms among the mobile vehicles, the edge and the cloud, should be redesigned to support the development of vehicular networks.

Stackelberg Game-Based Radio Resource Management Algorithm in an Urban Rail Transit Communication System

Train-to-wayside (T2W) and train-to-train (T2T) communication modes may coexist in future train-centric communication-based train control (CBTC) systems. The feasibility of T2T communication in urban rail transit is analyzed first. Referring to the device-to-device (D2D) communication scenario in the general cellular network, this paper establishes a radio resource optimization model for the coexistence of train-to-train communication and train-to-wayside communication. With the aim of more efficient scheduling of radio time-frequency resources in the dedicated frequency band, we propose a Stackelberg game-based radio resource management algorithm based on the consideration of different service priorities of trains. The analysis and simulation results show that the proposed algorithm can effectively guarantee the performance of the system and improve the reliability of the CBTC system.

Optimization-Based Resource Management Algorithms with Considerations of Client Satisfaction and High Availability in Elastic 5G Network Slices.

A combined edge and core cloud computing environment is a novel solution in 5G network slices. The clients’ high availability requirement is a challenge because it limits the possible admission control in front of the edge cloud. This work proposes an orchestrator with a mathematical programming model in a global viewpoint to solve resource management problems and satisfying the clients’ high availability requirements. The proposed Lagrangian relaxation-based approach is adopted to solve the problems at a near-optimal level for increasing the system revenue. A promising and straightforward resource management approach and several experimental cases are used to evaluate the efficiency and effectiveness. Preliminary results are presented as performance evaluations to verify the proposed approach’s suitability for edge and core cloud computing environments. The proposed orchestrator significantly enables the network slicing services and efficiently enhances the clients’ satisfaction of high availability.

Resource Management Techniques for Cloud/Fog and Edge Computing: An Evaluation Framework and Classification

Processing IoT applications directly in the cloud may not be the most efficient solution for each IoT scenario, especially for time-sensitive applications. A promising alternative is to use fog and edge computing, which address the issue of managing the large data bandwidth needed by end devices. These paradigms impose to process the large amounts of generated data close to the data sources rather than in the cloud. One of the considerations of cloud-based IoT environments is resource management, which typically revolves around resource allocation, workload balance, resource provisioning, task scheduling, and QoS to achieve performance improvements. In this paper, we review resource management techniques that can be applied for cloud, fog, and edge computing. The goal of this review is to provide an evaluation framework of metrics for resource management algorithms aiming at the cloud/fog and edge environments. To this end, we first address research challenges on resource management techniques in that domain. Consequently, we classify current research contributions to support in conducting an evaluation framework. One of the main contributions is an overview and analysis of research papers addressing resource management techniques. Concluding, this review highlights opportunities of using resource management techniques within the cloud/fog/edge paradigm. This practice is still at early development and barriers need to be overcome.

Trajectory Mining‐Based City‐Level Mobility Model for 5G NB‐IoT Networks

Due to the large coverage of 5G NB‐IoT networks, a more realistic mobility model for a macroscopic scene will greatly facilitate the development of optimal radio resource management algorithms. However, models devised for a random motion scene are no longer applicable in circumstances. Therefore, in this paper, a city‐level mobility model is proposed based on the feature mining of the real trajectory of vehicles in the city of Shenzhen. The proposed model is separately designed in the motion trajectory to reduce the mutual influence between the time and spatial sequence. Simulation results show that it can better present specific node motions with the physical constraints of the city layout, which are motivated with a high degree of fit in terms of self‐similarity, hotspots, and long‐tail features.

Enabling Device‐to‐Device (D2D) Communication for the Next Generation WLAN

Device‐to‐device (D2D) communication technology is widely acknowledged as an emerging candidate to alleviate the wireless traffic explosion problem for the next generation wireless local area network (WLAN) IEEE 802.11ax. In this paper, we integrate D2D communication into IEEE 802.11ax for maximizing the spectrum efficiency. Due to the spectrum scarcity, the number of available resource units (RUs) is typically less than D2D pairs and stations (STAs), which makes the management of spectrum resources more complex. To tackle this issue, we design an efficient resource management algorithm for uplink random access and resource allocation in D2D‐enabled 802.11ax, which provides more channel access and resource reuse opportunities for STAs and D2D pairs. Specifically, we propose an enhanced back‐off mechanism and derive the optimal contention window (CW) by a theoretical model, which improves the uplink random access efficiency. To tackle the complex interference problem in RU scheduling phase, we develop an efficient and low‐complexity resource allocation algorithm based on the maximal independent set (MIS), which can schedule multiple D2D pairs to share the same RU with the specific STA. Simulation results demonstrate that the proposed algorithm significantly improves the network performance in terms of system throughput, collision rate, complete time, and channel utilization.

Robust Time Resource Management in Cognitive Radar Using Adaptive Waveform Design

The Cognitive Radar is almost a new research topic, in which most efforts have been done for its conceptual description and the adaptive waveform design feature of these radars. While other aspect of adaptive action for optimum performance of cognitive radars has been ignored. In this paper, a framework for adaptive time resource management in Cognitive Radars is proposed. The main purpose of this paper is proposing an algorithm for time resource management, with incorporation of adaptive waveform design capability of cognitive radars, to enhance the radar performance for an efficient time resource usage. After developing the equations for radar time resource management using adaptive waveform design for the first time, an implementable algorithm is proposed and its performance is simulated and analysed. The results show that using the proposed algorithm resulted in a more efficient time resource management compared to the existing ones.

Long-term optimization for MEC-enabled HetNets with device–edge–cloud collaboration

For effective computation offloading with multi-access edge computing (MEC), both communication and computation resources should be properly managed, considering the dynamics of mobile users such as the time-varying demands and user mobility. Most existing works regard the remote cloud server as a special edge server. However, service quality cannot be met when some of the edge servers cannot be connected. Besides, the computation capability of the cloud has not been fully exploited especially when edge servers are congested. We develop an on-line offloading decision and computational resource management algorithm with joint consideration of collaborations between device–cloud, edge–edge and edge–cloud. The objective is to minimize the total energy consumption of the system, subject to computational capability and task buffer stability constraints. Lyapunov optimization technique is used to jointly deal with the delay-energy trade-off optimization and load balancing. The optimal CPU-cycle frequencies, best transmission powers and offloading scheduling policies are jointly handled in the three-layer system. Extensive simulation results demonstrate that, with V varies in [0.1,5]×109, the proposed algorithm can save more than 50% energy and over 120% task processing time than three existing benchmark algorithms averagely.

Joint Radio Resource Allocation and Cooperative Caching in PD-NOMA-Based HetNets

In this paper, we propose a novel joint resource allocation and cooperative caching scheme for power-domain non-orthogonal multiple access (PD-NOMA)-based heterogeneous networks (HetNets). In our scheme, the requested content is fetched directly from the edge if it is cached in the storage of one of the base stations (BSs), and otherwise is fetched via the backhaul. Our scheme consists of two phases: 1) Caching phase where the contents are saved in the storage of the BSs; and 2) Delivery phase where the requested contents are delivered to users. We formulate a novel optimization problem over radio resources and content placement variables. We aim to minimize the network cost subject to quality-of-service (QoS), caching, subcarrier assignment, and power allocation constraints. By exploiting advanced optimization methods, such as alternative search method (ASM), Hungarian algorithm, successive convex approximation (SCA), we obtain an efficient sub-optimal solution of the optimization problem. Numerical results illustrate that our ergodic caching policy via the proposed resource management algorithm can achieve a considerable reduction on the total cost on average compared to the most popular caching and random caching policy. Moreover, our cooperative NOMA scheme outperforms orthogonal multiple access (OMA) in terms of the delivery cost in general with an acceptable complexity increase.

Deep Reinforcement Learning-Based Dynamic Resource Management for Mobile Edge Computing in Industrial Internet of Things

Nowadays, driven by the rapid development of smart mobile equipments and 5G network technologies, the application scenarios of Internet of Things (IoT) technology are becoming increasingly widespread. The integration of IoT and industrial manufacturing systems forms the industrial IoT (IIoT). Because of the limitation of resources, such as the computation unit and battery capacity in the IIoT equipments (IIEs), computation-intensive tasks need to be executed in the mobile edge computing (MEC) server. However, the dynamics and continuity of task generation lead to a severe challenge to the management of limited resources in IIoT. In this article, we investigate the dynamic resource management problem of joint power control and computing resource allocation for MEC in IIoT. In order to minimize the long-term average delay of the tasks, the original problem is transformed into a Markov decision process (MDP). Considering the dynamics and continuity of task generation, we propose a deep reinforcement learning-based dynamic resource management (DDRM) algorithm to solve the formulated MDP problem. Our DDRM algorithm exploits the deep deterministic policy gradient and can deal with the high-dimensional continuity of the action and state spaces. Extensive simulation results demonstrate that the DDRM can reduce the long-term average delay of the tasks effectively.

Radio resource management for large constellations in a spectrum sharing environment

SummaryIn the last five years, the satellite community has witnessed growing interest in the delivery of Broadband Services from non‐geostationary orbit (NGSO) systems. However, accessing the entire spectrum available without having to share it among the different operators is key for these systems to have a chance of breaking even, which makes for a challenging business case. This paper focuses on the challenges of spectrum coexistence of multiple satellite constellations for broadband communications and proposes a radio resource management algorithm to enable such coexistence.

Adaptive resource management for multi-target tracking in co-located MIMO radar based on time-space joint allocation

Compared with the traditional phased array radar, the co-located multiple-input multiple-output (MIMO) radar is able to transmit orthogonal waveforms to form different illuminating modes, providing a larger freedom degree in radar resource management. In order to implement the effective resource management for the co-located MIMO radar in multi-target tracking, this paper proposes a resource management optimization model, where the system resource consumption and the tracking accuracy requirements are considered comprehensively. An adaptive resource management algorithm for the co-located MIMO radar is obtained based on the proposed model, where the sub-array number, sampling period, transmitting energy, beam direction and working mode are adaptively controlled to realize the time-space resource joint allocation. Simulation results demonstrate the superiority of the proposed algorithm. Furthermore, the co-located MIMO radar using the proposed algorithm can satisfy the predetermined tracking accuracy requirements with less comprehensive cost compared with the phased array radar.

A genetic algorithm for energy efficient fog layer resource management in context-aware smart cities

The development of novel Information and Communication Technology (ICT) based solutions becomes essential to meet the ever increasing rate of global urbanization in order to satiate the constraint in resources. The popular ‘smart city paradigm is characterized by ubiquitous cyber provisions for the monitoring and control of such city's critical infrastructures, encompassing healthcare, environment, transportation and utilities among others. In order to manage the numerous services keeping their Quality of Service (QoS) demands upright, it is imperative to employ context aware computing as well as fog computing simultaneously. This paper investigates the feasibility of energy minimization at the fog layer through intelligent sleep and wake-up cycles of the fog nodes which are context-aware. It proposes a virtual machine management approach for effectively allocating service requests with a minimal number of active fog nodes using a genetic algorithm (GA); and thereafter, a reinforcement learning (RL) approach is incorporated to optimize the period of fog nodes’ duty cycle. Simulations are carried out using MATLAB and the results demonstrate that the proposed scheme improves energy consumption of the fog layer by approximately 11–21% when compared to existing context sharing based algorithms.

Meta-heuristic algorithms for resource Management in Crisis Based on OWA approach

In crisis management, Threat Evaluation (TE) and Resource Allocation (RA) are two key components. To build an automated system in this area after modelling Threat Evaluation and Resource Allocation processes, solving these models and finding the optimal solution are further important issues. In this paper, Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Strength Pareto Evolutionary Algorithms (SPEA-II) are employed to solve a multi-objective multi-stage Resource Allocation problem. These Algorithms have been compared using normalized values of the objectives by generational distance, spread, hyper-volume, cardinality and actual computational times. It is found that the non-dominated solutions obtained by SPEA-II are better than NSGA-II both in terms of convergence and diversity but at the expense of computational time. Here, the fuzzy inference systems and the decision tree have been used to conduct threat evaluation process. Finally, Ordered Weighted Averaging (OWA) with maximum Bayesian entropy method for determining the operator weights has been used to pick the final choice among optimal options. We plan to use the proposed method in this paper for crisis management in Iranian Red Crescent organization during fire fighting. Two real studies have been done and results have been presented.

Resource Allocation in Full-Duplex UAV Enabled Multismall Cell Networks

Flying platforms, such as unmanned aerial vehicles (UAVs) are a promising solution for future small cell networks. UAVs can be used as aerial base stations (BSs) to enhance coverage, capacity and reliability of wireless networks. Also, with recent advances of self interference cancellation (SIC) techniques in full-duplex (FD) systems, practical implementation of FD BSs is feasible. In this paper, we investigate the problem of resource allocation for multi-small cell networks with FD-UAVs as aerial BSs with imperfect SIC. We consider three different scenarios: a) maximizing the DL sum-rate; b) maximizing the UL sum-rate; and finally c) maximizing the sum of UL and DL sum-rates. The aforementioned problems result in non-convex optimization problems, therefore, successive convex approximation algorithms are developed by leveraging D.C. (Difference of Convex functions) programming to find sub-optimal solutions. Simulation results illustrated validity and effectiveness of the proposed radio resource management algorithms in comparison with ground BSs, in both FD mode and its half-duplex (HD) counterpart. The results also indicate those situations where using aerial BS is advantageous over ground BS and reveal how FD transmission enhances the network performance in comparison with HD one.