Resource Management Algorithms Research Articles

Predicting Device-to-Device Channels From Cellular Channel Measurements: A Learning Approach

Device-to-device (D2D) communication, which ena- bles a direct connection between users while bypassing the cellular channels to base stations (BSs), is a promising way to offload the traffic from conventional cellular networks. In D2D communication, optimizing the resource allocation requires the knowledge of D2D channel gains. However, such knowledge is hard to obtain at reasonable signaling costs. In this paper, we show this problem can be circumvented by tapping into the information provided by the estimated cellular channels between the users and surrounding BSs as these channels are estimated anyway for a normal operation of the network. While the cellular and D2D channel gains exhibit independent fast fading behavior, we show that average gains of the cellular and D2D channels share a non-explicit relation, which is rooted into the network topology, terrain, and buildings setup. We propose a deep learning approach to predict the D2D channel gains from seemingly independent cellular channels. Our results show a high degree of convergence between the true and predicted D2D channel gains. Moreover, we demonstrate the robustness of the proposed scheme against environment changes and inaccuracies during the offline training. The predicted gains allow to reach a near-optimal capacity in many radio resource management algorithms.

Multipoint channel charting-based radio resource management for V2V communications

We consider a multipoint channel charting (MPCC) algorithm for radio resource management (RRM) in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication systems. A massive MIMO (mMIMO) infrastructure network performs logical localization of vehicles to a MPCC, based on V2I communication signals. Combining logical distances given by channel charting with V2V measurements, the network trains a function to predict the quality of a direct V2V communication link from observed V2I communication signals. In MPCC, the network uses machine learning techniques to learn a logical radio map from V2I channel state information (CSI) samples transmitted from unknown locations. The network extracts CSI features, constructs a dissimilarity matrix between CSI samples, and performs dimensional reduction of the CSI feature space. Here, we use Laplacian Eigenmaps (LE) for dimensional reduction. The resulting MPCC is a two-dimensional map where the spatial distance between a pair of vehicles is closely approximated by the distance in the MPCC. In addition to V2I CSI, the network acquires V2V channel quality information for vehicles in the training set and develops a link quality predictor. MPCC provides a mapping for any vehicle location in the training set. To use MPCC for cognitive RRM of V2I and V2V communications, network management has to find logical MPCC locations for vehicles not in the training set, based on newly acquired V2I CSI measurements. For this, we develop an extension of LE-based MPCC to out-of-sample CSI samples. We evaluate the performance of link quality prediction for V2V communications in a mMIMO millimeter-wave scenario, in terms of the relative error of the predicted outage probability.

The application of genetic algorithm and data analytics for total resource management at the firm level.

Total Resource Management (TRM) in industry 3.5 relates to the evaluation and improvement of performance through the use of intelligent tools or methods in enhancing efficiency and effectiveness of operations. This paper illustrates a study of TRM in the rubberwood processing industry to prepare it towards a sustainable transition to industry 4.0. The rubberwood processing industry operates using massive production data. Such big data emerge from production using multi-processes because the various products come in different sizes and quality levels (grades). The rubberwood processing company in this study faces significant problems of inaccurate and delayed data. These problems introduce mistakes in inventory management and wage payment. The company also faces an economic issue due to confirmation which is labour-intensive. This study applied the Genetic Algorithm (GA) technique to verify accepted material or woodpiece and use data analytics to improve the efficiency of the verification system. Furthermore, a Web-Based Application (WBA) is developed for production data management. The results show a significant drop in the percentage of data inaccuracy when the GA confirmation method is applied and also a decrease in the percentage of production data discrepancy among processes. This successful sustainable transition is attributed to TRM because the achieved performance improvement enriches effectiveness in production, material, labour and service resources.

Coordinated management of DVFS and cache partitioning under QoS constraints to save energy in multi-core systems

Reducing the energy expended to carry out a computational task is important. In this work, we explore the prospects of meeting Quality-of-Service requirements of tasks on a multi-core system while adjusting resources to expend a minimum of energy. This paper considers, for the first time, a QoS-driven coordinated resource management algorithm (RMA) that dynamically adjusts the size of the per-core last-level cache partitions and the per-core voltage–frequency settings to save energy while respecting QoS requirements of every application in multi-programmed workloads run on multi-core systems. It does so by doing configuration-space exploration across the spectrum of LLC partition sizes and Dynamic Voltage–Frequency Scaling (DVFS) settings at runtime at negligible overhead. We show that the energy of 4-core and 8-core systems can be reduced by up to 18% and 14%, respectively, compared to a baseline with even distribution of cache resources and a fixed mid-range core voltage–frequency setting. The energy savings can potentially reach 29% if the QoS targets are relaxed to 40% longer execution time.

A Framework for Sharing Staff between Outbound and Inbound Airport Processes

This paper proposes an advanced simulation-optimization approach to evaluate and optimize the passenger flows within international airports. This approach allocates resources intelligently during the simulation process and balances demand and service quality. The resource allocation performed by our Advanced Resource Management (ARM) algorithm was used to develop an integrated system for arranging resources, identifying the proper resources, and allocating them throughout the model. It was used to investigate the influences of different staff allocation techniques on the inbound and outbound processes of an airport terminal. The purpose of the proposed simulation-optimization approach is to enhance passenger satisfaction through ensuring reasonable wait times during processing at the lowest cost possible (minimal staff hours).

Resource Management for Hybrid RF/VLC V2I Wireless Communication System

In this letter, resource management (RM) for hybrid wireless communication systems between vehicles and infrastructures is investigated, where radio frequency and visible light communications coexist. Specifically, the RM issue is formulated as an optimization problem of the lower bound of energy efficiency over multiple consecutive data transmission slots, which considers the variations of link availability in high-mobility scenarios and requires merely incomplete channel state information. An RM algorithm is proposed to solve this problem and simulations results demonstrate the energy efficiency enhancement of our proposed algorithm.

Security information transmission algorithms for IoT based on cloud computing

Due to the traditional cloud computing-based information transmission mechanisms and IOT’s problems of large errors and low security, a heterogeneous integrated network resource management algorithm based on information security transmission is proposed. The algorithm adopts the advantages of information security transmission technology to collect resources in heterogeneous integrated network, then improves resource management algorithms and finally establishes a resource management algorithm model based on information security transmission, thereby implement the management process of heterogeneous integrated network resources. Through experimental demonstration and analysis methods, the effectiveness of the resource management algorithm is determined, which can reduce resource management errors and can improve security performance and management accuracy in the resource management process. In addition, the paper also introduces main data encryption technology and discusses the intelligent collection process of the Internet of Things(IoT), which is the technology background of the presented algorithm.

Revenue-optimal task scheduling and resource management for IoT batch jobs in mobile edge computing

With the growing prevalence of Internet of Things (IoT) devices and technology, a burgeoning computing paradigm namely mobile edge computing (MEC) is delicately proposed and designed to accommodate the application requirements of IoT scenario. In this paper, we focus on the problems of dynamic task scheduling and resource management in MEC environment, with the specific objective of achieving the optimal revenue earned by edge service providers. While the majority of task scheduling and resource management algorithms are formulated by an integer programming (IP) problem and solved in a dispreferred NP-hard manner, we innovatively investigate the problem structure and identify a favorable property namely totally unimodular constraints. The totally unimodular property further helps to design an equivalent linear programming (LP) problem which can be efficiently and elegantly solved at polynomial computational complexity. In order to evaluate our proposed approach, we conduct simulations based on real-life IoT dataset to verify the effectiveness and efficiency of our approach.

Energy Efficiency for MISO-OFDMA-Based User-Relay Assisted Cellular Networks

The concept of improving energy efficiency (EE) without sacrificing the service quality has become important nowadays. The combination of orthogonal frequency-division multiple-access (OFDMA) multiantenna transmission technology and relaying is one of the key technologies to deliver the promise of reliable and high data rate coverage in the most cost-effective manner. In this article, EE is studied for the downlink multiple-input single-output OFDMA-based user-relay assisted cellular networks. EE maximization is formulated for the decode and forward relaying scheme with the consideration of both transmit and circuit power consumption as well as the data rate requirements for the mobile users. The quality of service constrained EE maximization, which is defined for multicarrier, multiuser, multirelay, and multiantenna networks, is a nonconvex and combinatorial problem so it is hard to tackle. To solve this difficult problem, a radio resource management algorithm that solves the subcarrier allocation, mode selection, and power allocation separately is proposed. The efficiency of the proposed algorithm is demonstrated by numerical results for different system parameters.

Adaptive simultaneous multibeam resource management for colocated MIMO radar in multiple targets tracking

Different from conventional phased array radar, the colocated multiple-input multiple-output (MIMO) radar can form multiple beams simultaneously through transmitting orthogonal waveforms by its sub-arrays. The illuminating mode of multiple beams can be changed with the sub-array number, providing greater flexibility in system resource management. In this paper, a novel resource management optimization model for the colocated MIMO radar in multiple targets tracking is proposed, where the average time and energy resource consumption of the system are minimized under the guarantee that the illuminated targets can be effectively detected. When solving the proposed optimization problem, the adaptive simultaneous multibeam resource management (ASMRM) algorithm for the colocated MIMO radar is obtained, where the sub-array number, the beam directions, the system sampling period, the transmit waveform energy and the working mode can be controlled adaptively. Simulation results demonstrate that the working parameters of the colocated MIMO radar can be changed effectively. Furthermore, in terms of the average system resource consumption, the proposed algorithm is superior to the algorithms with fixed parameters while realizing the effective multiple targets tracking.

Severity-Based Prioritized Processing of Packets with Application in VANETs

To fully realize the potential of vehicular networks, several obstacles and challenges need to be addressed. Chief among the obstacles are strict QoS requirements of applications and differentiated service requirements in different situations. Although DSRC and WAVE have been adopted as the de facto standards, they do not address all the problems and there is room for improvements. In this study, we propose a generic prioritization and resource management algorithm that can be used to prioritize processing of received packets in vehicular networks. We formulate the generic severity-based prioritized packet processing problem as Penalized Multiple Knapsack Problem (PMKP) and prove that it is an NP-Hard problem. We thus develop a real-time heuristic that utilizes a relaxed version of the formulation. The relaxed formulation executes in polynomial time and guarantees a minimum delay per severity-level while respecting the processing rate constraint. To measure the performance of the proposed heuristic, real traffic data is used in a small-scale experiment. The proposed heuristic is tested against the PMKP solution and results show a small degradation of up to 4 percent in profit for the heuristic compared to the PMKP solution. Also, the proposed heuristic is tested against a non-prioritized processing algorithm that works using first come first served policy. Results show that the proposed heuristic gains 9 to 67 percent more profit than the non-prioritized processing algorithm in moderate and high congestion scenarios.

A Hybrid Bio-Inspired Algorithm for Scheduling and Resource Management in Cloud Environment

In this paper, we propose a novel HYBRID Bio-Inspired algorithm for task scheduling and resource management, since it plays an important role in the cloud computing environment. Conventional scheduling algorithms such as Round Robin, First Come First Serve, Ant Colony Optimization etc. have been widely used in many cloud computing systems. Cloud receives clients tasks in a rapid rate and allocation of resources to these tasks should be handled in an intelligent manner. In this proposed work, we allocate the tasks to the virtual machines in an efficient manner using Modified Particle Swarm Optimization algorithm and then allocation / management of resources (CPU and Memory), as demanded by the tasks, is handled by proposed HYBRID Bio-Inspired algorithm (Modified PSO + Modified CSO). Experimental results demonstrate that our proposed HYBRID algorithm outperforms peer research and benchmark algorithms (ACO, MPSO, CSO, RR and Exact algorithm based on branch-and-bound technique) in terms of efficient utilization of the cloud resources, improved reliability and reduced average response time.

Аналіз алгоритмів планування ресурсами в розподіленому обчислювальному середовищі

The article analyzes resource scheduling algorithms in a distributed computing environment. The main task that distributed computing technologies solve is providing access to globally distributed resources using special tools. The complexity of managing global resources is due to the fact that access to the necessary data can occur on different computers. In addition, global distributed computing networks formed from autonomous resources can change their configuration dynamically. Resource management in heterogeneous distributed computing systems requires the search for new models of computation and resource management. Analysis of recent research and publications has shown that currently the implementation of resource planning in a distributed computing environment requires the search for new approaches and new algorithms. Most of the work on resource planning methods in a distributed computing environment is used to solve specific tasks related to specific applications and therefore cannot be universal. The aim of the work is to analyze resource planning algorithms in a distributed computing environment in order to find methods and algorithms for resource management in a problem-oriented distributed environment, taking into account the specifics of individual tasks and use the possibility of parallel execution of different tasks. The analysis of resource planning algorithms in a distributed computing environment shows that to date, a large number of planning algorithms focused on use in a distributed computing environment. But often such algorithms do not take into account the problem-oriented specifics of the environment, and this affects the efficiency of planning. In this regard, a promising area is related to the development of resource planning algorithms in a distributed computing environment, which would create an efficient and effective resource planning system.

Ecosystems’ economic assessment in the context of different climatic zones

The failure of the key approach to assessing the value of natural goods, based only on the assessment of providing value, provoked the emergence of the ecosystem services’ theory. In addition to providing services of nature this theory includes supporting, regulating and social or cultural services. This approach provoked further adjustment of the state regulation system in the way of developing the market of ecosystem services, considering the full range of services provided by nature to society, for the organization of a transparent mechanism for the use of natural resources and ecosystem services. However, to date, in the Russian Federation this approach has not received legislation support, but the academic community is actively working on this aspect and even suggests adding the value of ecosystem services in the methodology for assessing the national wealth. The economic assessment of real and potential damages from ecosystems’ degradation using the theory of ecosystem services becomes extremely important for the development of an algorithm for optimal natural resource management at various levels of state regulation. The results of the study are the identified boundaries of economic assessment of the value of ecosystems (soil and forest) in the context of 1) valuation methods and 2) climatic zones in order to identify the most valuable ecosystems for simplifying the processes of natural resource management.

An Efficient Cluster Based Resource Management Scheme and its Performance Analysis for V2X Networks

As the demand for VANETs data transmission continues to increase, the defined cellular band becomes a bottleneck to meet the demands for all vehicle-to-everything (V2X) users. To deal with this problem, an efficient cluster based resource management scheme and its performance analysis for V2X networks is suggested to discover exceptional needs for various types of VANETs connections, namely vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) connections, and to enhance the efficiency of cellular user with respect to sum ratio, packet received ratio and average throughput for V2I connections whereas maintaining constancy for each V2V link. To deal with the fast channel deviations because of high mobility, we developed an efficient cluster based resource management technique to attain spectrum sharing and power control that relies on large scale fading. In addition, we have also examined the resource management problem of VANETs and V2X users to minimize data communication effects. Primarily, the total cellular sum ratio of every V2I connections is employed as an analysis target to enhance the throughput and to minimize end-to-end latency of the whole V2I link. Moreover, efficient resource management and cluster head selection algorithms are developed which grant the optimum resource distribution. According to our results, the proposed scheme with efficient resource management improves cellular user sum rate, average packet received ratio and throughput in comparison with existing schemes.

Uplink Low Power Based Radio Resource Management in Wireless Heterogeneous Networks

Due to the rapid rise of energy consumption and limited battery power of wireless user equipment (UE), low power communication becomes very important. Heterogeneous network deployment can significantly reduce regional power consumption. For heterogeneous network scenarios, this paper proposes an uplink low-power resource allocation scheme considering battery power limitation and user QoS requirements. Based on the assumption that each user has access to at most one base station (BS), the scheme is modeled as a mixed integer nonlinear programming (MINLP) problem. Since each user has access to one BS at most, the transmitting power matrix from the UE to the BSs (UE-to-BS matrix) is a sparse matrix. The MINLP problem can be transformed into an optimization problem searching sparse solution. In this paper, the reweighted L1 norm is introduced as a penalty function to remove the integer constraint and to regulate the sparsity of the UE-to-BS transmitting power matrix. In order to obtain the optimal resource allocation scheme, a reweighted L1 norm penalty based radio resource management (RRM) algorithm is proposed. Simulation results show that the reweighted L1 norm penalty based RRM algorithm has good convergence performance and the results are very close to optimal solutions. The proposed resource management scheme can prolong the working time of low-energy users and guarantee the QoS requirements of UE in the system.

Unified Scheduling Framework for Dual Connectivity in Multi-RAT NSA 5G Networks

Supporting the dual connectivity technology in non-stand-alone 5G scenarios involves three main functionalities: a user device (UD)-base station (BS) association (UBA) mechanism, a flow control algorithm (FCA) to determine the data split transmitted by each BS and radio resource management (RRM) algorithms. However, previous works only focused on the design of at most two of these functionalities. In thisletter, we go beyond and design a unified scheduling framework including the three aforementioned functionalities. The proposed framework targets the user satisfaction maximization and runs in a completely decentralized fashion, where the UBA algorithm runs independently at each UD, the FCA is executed at the master LTE BS and the RRM is performed individually by each BS. Simulations show the incremental gains of each proposed functionality as well as the total gain of the unified framework.

Resource Allocation for Hybrid Visible Light Communications (VLC)-WiFi Networks

Visible Light Communication (VLC) is becoming a promising technology of wireless communication, due to high data rate, low cost, and immunity to Radio Frequency (RF) interference. However, the VLC systems often suffer from service disruptions because of the limited coverage of light. Therefore, the heterogeneous networks of VLC and WiFi are proposed to achieve high-speed indoor communication and seamless coverage. This paper investigates an efficient heterogeneous network resource management algorithm, which is not only significant to integrate the current network resources but also has practical significance to improve the performance of heterogeneous networks. We establish a distributed joint resource management system model and propose two kinds of resource management allocation strategies to allocate bandwidth among the users with maximum fairness. The simulation results show the feasibility of the heterogeneous network and the effectiveness of the algorithm.

Resource Management of Maritime Edge Nodes for Collected Data Feedback

With the development of marine economy, more marine applications emerge leading to more deployed sensors and more generated data. However, current marine Internet only provides low transmission rate constrained by limited communication resources and bad transmission environment. How to efficiently use these resources and guarantee quality-of-service (QoS) of applications need to be addressed. This paper proposes an online optimization resource management algorithm to improve the communication resource efficiency with guaranteed QoSs for different applications. This algorithm needs neither of the resource cost function nor QoS constraint function at the resource scheduling node. Furthermore, the gradient information, which is usually needed in learning strategies, is not required either. Instead, this algorithm performs resource management of both computation and communication resources in each time slot only based on the observation of the last time slot. With slot-by-slot resource allocation, the communication cost can be minimized to be suitable for maritime scenarios. In the meantime, a long-term delay constraint can be satisfied. Results show that the proposed algorithm achieves the goal of reducing communication cost while guaranteeing the delay-constraint of different applications. Although more computation resources will improve its performance, this algorithm still can obtain the minimized cost given a low computation resource.

Resource and replica management strategy for optimizing financial cost and user experience in edge cloud computing system

Edge cloud computing can provide resources that are close to users and reduce response time. However, the edge cloud computing system still faces many challenges on addressing the overload problem due to its limited capacity. In this paper, a resource management strategy is proposed to satisfy the workloads of the edge cloud, while minimizing the financial cost of the rented nodes. Furthermore, the replica management strategy, which consists of the replica allocation and consistency preservation strategy, is studied. A dynamic replica allocation strategy is proposed to satisfy the user experience while reducing the storage overheads. In addition, the replica consistency preservation strategy is proposed for guaranteeing the data consistency and correctness. Finally, extensive experiments are conducted based on a real-world dataset. With the increase in the time, the proposed resource management algorithm can significantly reduce the total financial cost of the rented nodes and SLA default rate and improve the CPU utilization. For instance, the total financial cost of the proposed algorithm averagely achieves up to 32.27% and 53.65% reduction over that of CAAS algorithm and DRM algorithm, respectively. In addition, the proposed replica allocation algorithm can effectively reduce the data transmission time and the storage overhead.