Multi-dimensional Resource Research Articles

Advancing African Psychology: An Exploration of African American College Students’ Definitions and Use of Spirit in Times of Stress

Although religion and spirituality are understood as multidimensional resources in times of stress, less is known about the culturally relevant concept of spirit in African psychology. Eighteen African American college students completed written open-ended questionnaires about how—in relation to religion and spirituality—they define and use spirit in response to stress. Qualitative constant comparison analysis of the data yielded themes defining spirit and its use in response to stress. The definition of spirit was described distinctly as energy, while it overlapped with religion and spirituality as a connection with or a search for God. Spirit also overlapped with religion as faith and spirituality as a feeling/sense/guiding consciousness or inner thought within oneself, and soul. Themes for the use of spirit in response to stress were descriptive of optimal functioning and included (a) thoughts of asking God for help, (b) prayer, (c) positive emotions, (d) self-affirming thoughts, and (e) productive activity. The findings are discussed regarding spirit as a distinct yet overlapping concept with religion and spirituality that has implications for research and practice within African psychology.

Research on Vertical Search Method of Multidimensional Resources in English Discipline Based on Edge Computing

The traditional vertical search method only considers the content of the webpage, and the global master node is not enough, which will lead to premature convergence and fall into the local optimum, resulting in insufficient multi-dimensional search of resources. Therefore, this paper proposes a multidimensional resource vertical edge based on the calculation of English subject search method. This paper analyzes the architecture of search engine firstly and then introduces the multiaccess edge computing architecture. At last, it constructs the vertical search task computing model of multidimensional resources in English discipline. By associating and traversing the attributes of multidimensional resources of English discipline, the vertical search of attribute information is realized offline, and the vertical search method of multidimensional resources of English discipline based on edge calculation is designed. In order to verify the effectiveness of the proposed method, a comparative experiment is designed. Experimental results show that the method can improve the resource search ratio and recall ratio, and it can also effectively improve the search efficiency. For an English subject resource data of 50 MB, the calculation methods of edge multidimensional resource data search recall rate can reach 97% and multidimensional resource data search time consumption is only 39 ms. The experimental results show that the performance of English subject multidimensional resources vertical search is much better.

Cooperatively Improving Data Center Energy Efficiency Based on Multi-Agent Deep Reinforcement Learning

The problem of high power consumption in data centers is becoming more and more prominent. In order to improve the energy efficiency of data centers, cooperatively optimizing the energy of IT systems and cooling systems has become an effective way. In this paper, a model-free deep reinforcement learning (DRL)-based joint optimization method MAD3C is developed to overcome the high-dimensional state and action space problems of the data center energy optimization. A hybrid AC-DDPG cooperative multi-agent framework is devised for the improvement of the cooperation between the IT and cooling systems for further energy efficiency improvement. In the framework, a scheduling baseline comparison method is presented to enhance the stability of the framework. Meanwhile, an adaptive score is designed for the architecture in consideration of multi-dimensional resources and resource utilization improvement. Experiments show that our proposed approach can effectively reduce energy for data centers through the cooperative optimization while guaranteeing training stability and improving resource utilization.

Optimal Job Scheduling With Resource Packing for Heterogeneous Servers

Jobs in modern computing clusters have highly diverse processing duration and heterogeneous resource requirements. In this paper, we consider the problem of job scheduling for a computing cluster comprised of multiple servers with heterogeneous computation resources, while taking the different resource demands of the jobs into account. Our focus is to achieve a low overall job response time for the system (which is also referred to as the job flowtime) while providing fairness between small and large jobs. Since the job flowtime minimization problem under multiple (even homogeneous) servers are known to be NP-hard, we propose an approximation algorithm to tackle the original online scheduling problem by adopting the recently-proposed notion of fractional job flowtime as a surrogate objective for minimization. For the general online job arrival case with multi-dimensional resource requirements, we apply Online Convex Optimization (OCO) techniques to design the corresponding scheduling algorithm with performance guarantees. In the single-dimensional resource setting, we show that the dynamic fit of the online version of our approximate algorithm grows only sublinearly with respect to time and derive a bound for its dynamic regret when comparing to its offline counterpart. While the baseline version of our proposed scheduling algorithm assumes the possibilities of job preemption and job migration across different servers, we show that the extent of job preemption and migration can be well controlled by augmenting the objective function with the corresponding switching costs.

Wireless Virtual Reality in Beyond 5G Systems with the Internet of Intelligence

Virtual reality (VR) over wireless has promising applications in healthcare, education, entertainment, and industrial production. However, it is difficult for the existing wireless systems to meet the needs of massive content transmission, ultra-low latency, and high computation in wireless VR. In this article, with the recent advances of edge intelligence and the Internet of Intelligence, we propose a novel framework that can jointly provide computation, storage, and communication resources for wireless VR in beyond 5G systems. In this framework, intelligence can be fully exploited to coordinate the computing, caching, and transmission systems to enable ubiquitous deployments of wireless VR. We present some key techniques and propose specific methods to support wireless VR. In addition, we propose a novel quantum-inspired RL reinforcement learning (QRL) algorithm for the multidimensional resource provisioning issue in wireless VR. In the simulations, some essential performance metrics are evaluated and some interesting results are presented, showing the effectiveness of the proposed strategy.

Deep Reinforcement Learning Based Resource Management for DNN Inference in Industrial IoT

Performing deep neural network (DNN) inference in real time requires excessive network resources, which poses a big challenge to the resource-limited industrial Internet of things (IIoT) networks. To address the challenge, in this paper, we introduce an end-edge-cloud orchestration architecture, in which the inference task assignment and DNN model placement are flexibly coordinated. Specifically, the DNN models, trained and pre-stored in the cloud, are properly placed at the end and edge to perform DNN inference. To achieve efficient DNN inference, a multi-dimensional resource management problem is formulated to maximize the average inference accuracy while satisfying the strict delay requirements of inference tasks. Due to the mix-integer decision variables, it is difficult to solve the formulated problem directly. Thus, we transform the formulated problem into a Markov decision process which can be solved efficiently. Furthermore, a deep reinforcement learning based resource management scheme is proposed to make real-time optimal resource allocation decisions. Simulation results are provided to demonstrate that the proposed scheme can efficiently allocate the available spectrum, caching, and computing resources, and improve average inference accuracy by 31.4$\%$ compared with the deep deterministic policy gradient benchmark.

VON embedding in elastic optical networks (EON) integrated with quantum key distribution (QKD)

The explosive growth of users’ requirements promotes the development of optical network function virtualization (ONFV) over elastic optical networks (EON). Through optical network virtualization, network services can be provided by sharing physical resources in terms of virtual optical network (VON), which can simplify optical-layer resource management and promote the flexibility of multi-dimensional resource allocation. However, few studies have paid attention to the security of data transmission in VON. Eavesdropping against VON could compromise not just virtualized services, but the whole infrastructure. Quantum key distribution (QKD) integrated with traditional optical network is a promising technology to solve this problem. In this paper, we first demonstrate the concept of VON embedding (VONE) in the EON integrated with QKD. Based on this concept, a dynamic VONE algorithm is proposed to allocate physical network resources to VON requests, which considers not only bandwidth requirements but also key demands. The performance of the proposed VONE algorithm is evaluated via simulation in terms of blocking probability, key resource utilization as well as average path length. Results show that in order to achieve a tradeoff between blocking probability and key resource utilization, the values of adjustment factor and key generation rate should be appropriately set.

Dynamic User Allocation in Stochastic Mobile Edge Computing Systems

Mobile edge computing (MEC) is a new distributed computing paradigm where edge servers are deployed at, or near cellular base stations in close proximity to end-users. This offers computing resources at the edge of the network, facilitating a highly accessible platform for real-time, latency-sensitive services. A typical MEC environment is highly stochastic with random user arrivals and departures over time. Here, we address the user allocation problem from a service provider's perspective, who needs to allocate its users to the cloud or edge servers in a specific area. A user, who has a multi-dimensional resource requirement, can be allocated to either the remote cloud, which incurs a high latency, or an edge server, which results in a low latency but might require the user to wait in a queue. This article aims to achieve a controllable trade-off between performance (throughput) and several associated costs such as queuing delay and latency costs. We model this problem as a stochastic optimization problem, propose SUAC (Stochastic User AlloCation) – an online Lyapunov optimization-based algorithm, and prove its performance bounds. The experimental results demonstrate that SUAC outperforms existing approaches, effectively allocating users with a desired trade-off while keeping the system strongly stable.

Customized Slicing for 6G: Enforcing Artificial Intelligence on Resource Management

Next generation wireless networks are expected to support diverse vertical industries and offer countless emerging use cases. To satisfy stringent requirements of diversified services, network slicing is developed, which enables service-oriented resource allocation by tailoring the infrastructure network into multiple logical networks. However, there are still some challenges in cross-domain multi-dimensional resource management for end-to-end (E2E) slices under the dynamic and uncertain environment. Trading off the revenue and cost of resource allocation while guaranteeing service quality is significant to tenants. Therefore, this article introduces a hierarchical resource management framework, utilizing deep reinforcement learning in admission control of resource requests from different tenants and resource adjustment within admitted slices for each tenant. In particular, we first discuss the challenges in customized resource management of 6G. Second, the motivation and background are presented to explain why artificial intelligence (AI) is applied in resource customization of multi-tenant slicing. Third, E2E resource management is decomposed into two problems, multi-dimensional resource allocation decision based on slice-level feedback, and real-time slice adaption aimed at avoiding service quality degradation. Simulation results demonstrate the effectiveness of AI-based customized slicing. Finally, several significant challenges that need to be addressed in practical implementation are investigated.

Joint Resource Allocation for Device-to-Device Communication Assisted Fog Computing

In this paper, joint resource management for device-to-device (D2D) communication assisted multi-tier fog computing is studied. In the considered system model, each subscribed mobile end user can choose to offload its computation task to either an edge server deployed at the base station via the cellular connection or one nearby third-party fog node via the direct D2D connection. After receiving offloading requests from all end users, the network operator determines the optimal management of the fog computing system, including both computation and communication resource allocations, according to its service agreements with end users, energy cost of edge-server processing and total expense in renting third-party fog nodes. With the objective of maximizing the network management profit, a joint multi-dimensional resource optimization problem, integrating link scheduling, channel assignment and power control, is formulated. An optimal solution algorithm is proposed based on the idea of branch-and-price for addressing this complicated mixed integer nonlinear programming problem. To facilitate the practical implementation in large-scale systems, a suboptimal greedy algorithm with significantly reduced computational complexity is also developed. Simulation results examine the efficiency of the proposed D2D-assisted fog computing framework, and demonstrate the superiority of the proposed resource allocation algorithm over the counterparts.

Lane and speed allocation mechanism for autonomous vehicle agents on a multi-lane highway

Following the arc of automation, it is expected that many driverless vehicles will soon be on the road. The need for autonomous traffic to self-organize necessitates the development of advanced algorithms for unsupervised machine-to-machine negotiation.Mechanisms rooted in game theory detect and resolve contradictions among agents with differing objectives and enable automated collaboration with a socially beneficial outcome. A case study of cyber-enabled vehicles following private objectives and using private ranked preferences to bid for lane and speed changes on a multi-lane highway illustrates interactions that rely on mechanism design. The presented approach constitutes a strategyproof mechanism and thus induces the truthfullness of bids. A lane position and speed setting allocation rule detects and resolves conflict and provides an option for monetization. A NetLogo simulation implements the methodology described. The model allows for additional resources or resource dimensions. Minor domain specific alterations to the distance operator can translate to many application areas and scenarios providing multiple or multi-dimensional resource allocation for a fee.

On Throughput Optimization in Software-Defined Multi-Dimensional Space Division Multiplexing Optical Networks

The capacity expansion of a single time division multiplexing (TDM) or wavelength division multiplexing (WDM) domain is difficult to cope with long-term capacity pressure. The emergence of space division multiplexing (SDM) technology provides a new dimension for the expansion of optical network capacity, namely the spatial dimension. However, this also poses a challenge to both network control and resource optimization. Therefore, to improve the flexibility of network control, we propose an architecture of software-defined multi-dimensional SDM optical networks based on architecture on demand (AoD) nodes and multi-core fiber (MCF) links in this paper. Next, we mathematically describe the system model including a model of the SDM network, a model of multi-type service requests, and a MCF model. Then, an integer linear programming (ILP) is developed to minimize the index of multi-dimensional resources occupied by multiple types of services in the time, frequency, and space domains. To reduce inter-core crosstalk between homogeneous cores, the core classification algorithm is designed to build multi-granular network layers. Also, for adjacent heterogeneous cores, we utilize the AoD to customize different functions and use different spectrums to transmit different types of services. Finally, we propose routing and multi-dimensional resource allocation algorithms to optimize network throughput. The simulation results show that the proposed algorithms can effectively improve the throughput of the software-defined multi-dimensional SDM optical networks compared with the benchmark algorithms.

Quantum Learning Based Nonrandom Superimposed Coding for Secure Wireless Access in 5G URLLC

Secure wireless access in ultra-reliable low-latency communications (URLLC), which is a critical aspect of 5G security, has become increasingly important due to its potential support of grant-free configuration. In grant-free URLLC, precise allocation of different pilot resources to different users that share the same time-frequency resource is essential for the next generation NodeB (gNB) to exactly identify those users under access collision and to maintain precise channel estimation required for reliable data transmission. However, this process easily suffers from attacks on pilots. We in this article propose a quantum learning based nonrandom superimposed coding method to encode and decode pilots on multidimensional resources, such that the uncertainty of attacks can be learned quickly and eliminated precisely. Particularly, multiuser pilots for uplink access are encoded as distinguishable subcarrier activation patterns (SAPs) and gNB decodes pilots of interest from observed SAPs, a superposition of SAPs from access users, by joint design of attack mode detection and user activity detection though a quantum learning network (QLN). We found that the uncertainty lies in the identification process of codeword digits from the attacker, which can be always modelled as a black-box model, resolved by a quantum learning algorithm and quantum circuit. Novel analytical closed-form expressions of failure probability are derived to characterize the reliability of this URLLC system with short packet transmission. Simulations how that our method can bring ultra-high reliability and low latency despite attacks on pilots.

Distributed Slice Selection-Based Computation Offloading for Intelligent Vehicular Networks

Distributed artificial intelligence (AI) is becoming an efficient approach to fulfill the high and diverse requirements for future vehicular networks. However, distributed intelligence tasks generated by vehicles often require diverse resources. A customized resource provision scheme is required to improve the utilization of multi-dimensional resources. In this work, a slice selection-based online offloading (SSOO) algorithm is proposed for distributed intelligence in future vehicular networks. First, the response time and energy consumption are reduced for processing tasks locally on the vehicles. Then, the offloading overheads, including latency and energy consumption, are calculated by considering the available resource amount, wireless channel states and vehicle conditions. The slice selection results is obtained by the deep reinforcement learning (DRL)-based method. Based on the selection solution, resource allocation results are achieved by KKT conditions and bisection method. Finally, the experimental results depict that the proposed SSOO algorithm outperforms other comparing algorithms in terms of energy consumption and task completion rate.

Model of multidimensional resource configuration in production scheduling: proactive and reactive approach

Abstract Modern manufacturing systems are characterized by a high degree of automation and robotization as well as saturation with IT technologies such as IoT, Cloud Computing, RFID, etc. This results in shortening both production cycles and supply chains. It also means that the classic approach to resources as well as planning and scheduling processes has to be changed. The multidimensionality of resources, their high configurability and a proactive and reactive approach to planning and scheduling are the main elements of these changes. The paper considers the problem of schedule robustness in relation to the availability of multidimensional resources. A model of multidimensional resource configuration was proposed, which is the basis of a proactive-reactive robust approach to planning/scheduling in the context of manufacturing problems. There is also presented implementation of the model using mathematical programming and a proprietary hybrid approach.

Joint Optical and Wireless Resource Allocation for Cooperative Transmission in C-RAN.

Cooperative multipoint transmission (CoMP) is one of the most promising paradigms for mitigating interference in cloud radio access networks (C-RAN). It allows multiple remote radio units (RRUs) to transmit the same data flow to a user to further improve the signal quality. However, CoMP may incur redundant data transmission over fronthaul network in the C-RAN. In a C-RAN employing CoMP, a key problem is how to coordinate heterogeneous resource allocation to maximize the cooperation gain while reducing the fronthaul load. In this paper, the cooperation transmission based on a multi-dimensional resource schedule (MRSCT) scheme, jointly considering user association, spectrum resource allocation, and wavelength resource allocation, is firstly envisioned in the underlying C-RAN integrating time and wavelength division multiplexing passive optical network (TWDM-PON) to maximize fronthaul efficiency. Then a two-timescale resource allocation framework including two sub-approaches is established. More specially, the first sub-approach mainly focuses on exploiting reinforcement learning to obtain a wavelength resource allocation strategy to relieve fronthaul traffic load. Moreover, the second sub-approach adopts the overlapping coalition formation game to establish a user-centric cooperative set, where spectrum resources are dynamically allocated to further alleviate the interference issue. The theoretical analysis and simulation results validate the performance of MRSCT scheme on the fronthaul efficiency, user experience, and system service capability.

Multi-Dimensional Routing, Wavelength, and Timeslot Allocation (RWTA) in Quantum Key Distribution Optical Networks (QKD-ON)

Currently, with the continuous advancement of network and communication technology, the amount of data carried by the optical network is very huge. The security of high-speed and large-capacity information in optical networks has attracted more and more attention. Quantum key distribution (QKD) provides information-theoretic security based on the laws of quantum mechanics. Introducing QKD into an optical network can greatly improve the security of the optical network. In order to reduce the cost of deployment on QKD infrastructure, quantum signals in QKD and classical signals in optical networks are multiplexed in the same fiber by wavelength-division manner. Moreover, due to the limited wavelength resources in an optical fiber, time-division technology is adopted to construct different kinds of channels in QKD system for efficient utilization of wavelength resources. Under such situation, how to satisfy the security requirements of service requests and complete the efficient scheduling of multi-dimensional resources, i.e., wavelengths and timeslots, is a challenging problem. This paper addresses this problem by considering multi-dimensional routing, wavelength, and timeslot allocation (RWTA) in short-distance quantum key distribution optical networks (QKD-ON), in which any two nodes can directly establish a quantum channel, and the maximum distance between any two nodes is less than the distance that can carry out point-to-point quantum key distribution process. While accommodating services with security requirements in QKD optical networks, to avoid the wavelength time-slot fragmentation caused by the constraints of wavelength consistency and time-slot continuity, we propose a time-window-based security orchestration strategy as well as relative-loss of time continuous compactness based RWTA strategy. We conducted the simulations under various scenarios, e.g., different key updating periods and different distributions on wavelength resources, etc., and the results show that the proposed strategy can achieve better performance compared with the baselines in terms of key success rate, key-updating delay, and blocking probability.

Indigenous Environmental Knowledge of Borana Pastoralists

This paper investigated the indigenous environmental knowledge (IEK) of Borana pastoralists in Dhas district of Borana zone in Southern Ethiopia. To select participants for key informant interview and focus group discussion (FGD), snowball sampling was used to pinpoint people who have IEK of Borana pastoralists. Results show that the customary practices of Borana pastoralists that linked to multi-dimensional natural resources management (NRM) include the taxonomy of pastureland and water resources instituted on the season of utility and gazing dimensions, range scouts, herd splitting, cattle mobility, herd diversification, and bush burning. However, currently owing to various factors the IEKs of Borana pastoralists have been facing threatening challenges that question their existence. The study incorporated the capitalization on the customary institutions that advance the IEK and cattle productivity finally improving the livelihoods of the Borana pastoralists. This research intends to help various stakeholders, predominantly pastoralist development office, ecologists and other development partners in a bid to develop Borana pastoralists, to boost their sustainability, and to promote sustainable NRM.

Resource Management in Space-Air-Ground Integrated Vehicular Networks: SDN Control and AI Algorithm Design

With its potential versatility and reliability, the space-air-ground integrated vehicular network (SAGVN) is envisioned as a promising solution to deliver quality vehicular services anywhere at any time. This article proposes a software defined framework for SAGVN to achieve flexible, reliable, and scalable network resource management. First, key applications and research challenges in resource management are identified. Then we propose a hybrid and hierarchical SAGVN control architecture to balance the trade-off between system status acquisition and signaling overhead in different scenarios. Considering the dynamic networking environment with multi-dimensional resources and diverse services, it is challenging to make optimal resource management decisions in real time; thus, artificial intelligence (AI)-based engineering solutions are investigated to facilitate efficient network slicing, mobility management, and cooperative content caching and delivery. A trace-driven case study is presented to demonstrate the effectiveness of the proposed SAGVN framework with AI-based methods in increasing the SAGVN throughput performance.

A problem of long distances in the educational environment

A process of the specialist’s professional development involve two aspects of the decision-making support: choosing a field of study (by an applicant) and choosing an applicant for a job position and training (by a company). The correctness of solving this task depends greatly on developing methods allowing to identify potential of an applicant. The goal of this research is developing a formalized approach to create the metrics in the educational environment, which would allow comparing applicants to each other in terms of compliance with the requirements of a certain job. The methods: the research uses system analysis approaches; models of characteristics changing during the training; scaling of heterogeneous values; integrating particular estimations (getting integral estimation) based on expert opinions. The results: the analysis allowed identify a set of dependencies related both to a study subject and job requirements, distinctive features and resource characteristics of learning technologies. Several models and an approach to assessing a prospective applicant were suggested taking into account a multi-dimensional resource, which is required to train a certain applicant up to the required level. The practical relevance: the suggested approach allows to make formalized setting and further addressing a set of evaluation and optimization tasks related both to the best choice of future job (for an applicant), and a choice of most promising applicants and learning techniques (for a company).