Research on adaptive distributed optimal resource allocation consistency algorithm in WAN environment

We develop two distributed downlink resource allocation algorithms for user-centric, cell-free, spatially-distributed, multiple-input multiple-output (MIMO) networks. In such networks, each user is served by a subset of nearby transmitters that we call distributed units or DUs. The operation of the DUs in a region is controlled by a central unit (CU). Our first scheme is implemented at the DUs, while the second is implemented at the CUs controlling these DUs. We define a hybrid quality of service metric that enables distributed optimization of system resources in a proportional fair manner. Specifically, each of our algorithms performs user scheduling, beamforming, and power control while accounting for channel estimation errors. Importantly, our algorithm does not require information exchange amongst DUs (CUs) for the DU-distributed (CU-distributed) system, while also smoothly converging. Our results show that our CU-distributed system provides 1.3- to 1.8-fold network throughput compared to the DU-distributed system, with minor increases in complexity and front-haul load - and substantial gains over benchmark schemes like local zero-forcing. We also analyze the trade-offs provided by the CU-distributed system, hence highlighting the significance of deploying multiple CUs in user-centric cell-free networks.

In this paper, we propose a joint power control and resource allocation approach for co-channel deployed high dense small-cells in macro-cell networks, denoted as Two-tier Optimized Resource and Power Allocation approach (TORPA). The ultimate goal of our contribution is to mitigate the co-tier and cross-tier interference faced by indoor and outdoor users in downlink communications and efficiently satisfy their resource requirements. With this in mind, we formulate the resulting optimization problem as a Mixed-Integer Linear Program (MILP) that aims to minimize the total transmit power and achieve an efficient two-tier physical resource allocation. Then, we propose a semi-centralized scheme to solve it. Extensive simulations show that TORPA can efficiently allocate power and distribute spectrum resources to both Small-cell Users (SUs) and Macro-Users (MUs) compared to the distributed resource allocation scheme and a prominent state of the art gametheoretic approach. In particular, TORPA improves the SU’s throughput by up to 9 times and 22% in comparison to these both schemes.

Edge video analytics enables agile responses of machine-centric applications by streaming videos from end devices to edge servers (ESs) for resource-intensive deep neural network (DNN) inference. Quality of Inference (QoI), reflected by end-to-end analytics delay and inference accuracy, is crucial for the high-quality real-time decision-making. Due to the limited ES resources and dynamic network conditions, video configuration adaptation and fine-grained resource allocation are necessary to enhance the QoI of video streams, which involves a delicate balance between delay and accuracy. In this article, we propose an edge-enabled multivideo analytics framework, in which performing DNN inference relies on heterogeneous resources, including CPU, GPU, and memory. Considering the different impacts of heterogeneous resources on QoI and double-queue backlogs, we formulate the joint video configuration adaptation, CPU-GPU resource allocation, and batch size selection (JCCGB) problem based on semi-Markov decision process to maximize the long-term average QoI. Then, Lyapunov optimization is applied to transform the original problem into one that minimizes the Lyapunov drift-plus-penalty upper bound, thereby ensuring the stability of both the transmission and computation queues. To tackle the potential multimodality of the optimal configuration adaptation and resource allocation policy, we propose the diffusion-deep-reinforcement-learning-based JCCGB (DD-JCCGB) scheme. This approach effectively enhances the policy performance and accelerates the training process. Experiments driven by real-world network traces demonstrate that the DD-JCCGB algorithm improves the long-term average QoI and outperforms baseline schemes.

: Project goals were to develop techniques of continual reallocation of resources to maintain application performance despite statically unpredictable change in resource demands. Research was targeted to multiple application systems executing on HPC (High Performance Computing) platforms. This project built on the results of a previous program, called Adaptive Resource Allocation (ARA). In ARA, Honeywell developed techniques for dynamic reallocation of resources to single parallel applications, structured as multi-pipelines, executing on a high performance parallel machine. They extended ARA results to systems with multiple applications and multiple machines connected over a network. In October 1997 DARPA merged the technical effort on this project with the RTARM project funded under Quorum. This did not affect the core statement of work for ARM, but led to extension of its completion date. ARM focused on developing an approach based on adaption models, and addressed best-effort resource allocation in an environment with partitionable rather than shared resources. Parallel HPG platforms were de-emphasized in favor of general distributed computing platforms. Results from ARM are being integrated into RTARM. The layered architecture of ARM has given way to a hierarchical architecture characterized by uniformity across different levels. The MPI-based communication infrastructure in ARM has given way to a CORBA ORB infrastructure. While ARM implementation was targeted to Unix machine connected over Ethernet, the target platform for PTARM consists of Windows NT machines networked over ATM.

Resource allocation has been a very significant topic for both research and development over the last two decades. Given the increasing volume of data, the proliferation of connected devices, and the demand for seamless service delivery, optimal resource allocation has become a vital factor that influences cloud performance. Recently, deep learning-a subcategory of machine learning-seems to possess a great potential to answer this challenge by enabling predictive, adaptive, and self-organized resource allocation. For the first time, this review embraces all the major milestones achieved in dynamic resource allocation with a discussion on over 25+ peer-reviewed articles published from the year 2000 to 2024. This review has emphasized the use of CNNs, RNNs, and other variants of deep learning approaches. Such a review provides a better view of the potential benefits of the different methodologies by highlighting the pros and cons of each. It also covers the use cases, computational methodologies that discuss algorithmic novelty and challenges in scalability, latency, and energy efficiency. A summary of the development in tech was made by comparison in a table to give a meta-view for the top-ten studies. These findings have important implications for cloud service delivery in applications ranging from industrial automation to consumer-oriented applications. They showcase the vast possibilities of deep learning for changing cloud network operations through advanced optimization and point out several open issues, including the integration of federated and edge learning models that will be necessary to achieve improved decentralization and preservation of network information privacy.

We propose an evolutionary game theory (EGT)-based distributed resource allocation scheme for small cells underlaying a macro cellular network. EGT is a suitable tool to address the problem of resource allocation in self-organizing small cells since it allows the players with bounded-rationality to learn from the environment and take individual decisions for attaining the equilibrium with minimum information exchange. EGT-based resource allocation can also provide fairness among users. We show how EGT can be used for distributed subcarrier and power allocation in orthogonal frequency-division multiple access (OFDMA)-based small cell networks while limiting interference to the macrocell users below given thresholds. Two game models are considered, where the utility of each small cell depends on average achievable signal-to-interference-plus-noise ratio (SINR) and data rate, respectively. Forthe proposed distributed resource allocation method, the average SINR and data rate are obtained based on a stochastic geometry analysis. Replicator dynamics is used to model the strategy adaptation process of the small cell base stations and an evolutionary equilibrium is obtained as the solution. Based on the results obtained using stochastic geometry, the stability of the equilibrium is analyzed. We also extend the formulation by considering information exchange delay and investigate its impact on the convergence of the algorithm. Numerical results are presented to validate ourtheoretical findings and to show the effectiveness of the proposed scheme in comparison to a centralized resource allocation scheme.

System identification extracts information from a system's operational data to derive a representative model for the system. Studies of system identification have been concentrated on estimation algorithms and their convergence. Focusing on optimal resource allocation under a given reliability requirement, this paper studies identification complexity and its relations to decision making. Dynamic resource assignments are investigated. Resource allocation algorithms are developed and their convergence properties are established. Illustrative examples demonstrate better resource management than worst-case strategies when our algorithms are applied.

Teaching resources in higher education are an important part of the talent cultivation system of higher education institutions. Dynamic management and optimal allocation of teaching resources are the focus of the construction of each school, which reflects the teaching, research, and management level of the school to a certain extent. In this paper, after combining the current situation and management problems of teaching resources in six schools in City A and putting forward constructive opinions, we further use data envelopment analysis to study the efficiency of teaching resource allocation in School H in City A and put forward the method of resource optimization.The input redundancy rate of School X1 in School H during the three years of 2021-2023 is 30.25%, 44.15%, and 55.26%, respectively. The data shows that the school has an excess of teacher inputs and there is a shortage of resources for teachers. For the current dynamic management and resource allocation problems of teaching resources in A city school, this paper argues that the management of school teaching resources is a long-term and complex organic whole, and needs to be reformed in terms of structure, resource integration and management, and to construct a high-quality and efficient management model. In terms of resource optimization and allocation, schools can optimize the talent resource allocation mechanism and other aspects to promote the full allocation of teaching resources in higher education.

Chatbot technologies are increasingly integral to higher education, offering significant potential to enhance teaching effectiveness and student engagement. This study explores the strategic integration of chatbot technologies, focusing on optimal selection and resource allocation to maximize educational efficacy. We developed a mathematical optimization model and introduced the “Relative Net Utility Differential” (RNUD) as a pivotal metric to facilitate the decision-making process for chatbot technology selection and resource distribution. Our findings reveal that when RNUD values for all chatbots fall within a specified range, the model efficiently determines the optimal resource allocations, providing clear analytical solutions for each technology. Additionally, we devised an algorithm for scenarios where RNUD values vary significantly, effectively isolating optimal solutions by excluding suboptimal choices. Numerical analysis confirms that this algorithm maintains robust performance and high computational efficiency, particularly when the chatbot count does not exceed 20. This efficiency persists across various distributions of chatbots’ technical attributes, and increasing the number of candidate chatbots generally enhances overall educational utility. This suggests that a diverse technological environment can lead to more tailored and effective educational interventions. Moreover, our study highlights a competitive interaction among chatbots: those with superior technical attributes tend to receive more resources. However, when the technical attributes of one chatbot improve, the resources allocated to others decrease, and vice versa. These insights underscore the importance of not only providing more sophisticated chatbots but also carefully managing their interactions to maximize overall educational utility.

Migration is the major factor that affects the change in the population of the given territory and its distribution of natural resources along with fertility and mortality. It also affects almost all areas of life i.e. social, economic, demographic, political, and environmental. The main cause of migration in Nepal is poverty, destitution, unequal allocation and distribution of resources, geographical variation of labor demand (Pull and push factors). The main objective of this study is to analyze the general patterns of migration in Nepal. This study is based on secondary data obtained from the census 1952/54 to 2011. The absentees and Labor statistics obtained from Nepal Labor Migration Report are also used. Regression analysis is applied to measure the trend values. Student’s t-statistic is used to measure the significance between migration to India and other countries. The percentage of absentees is threefold (2.3% to 7.3%) within the given period. The trend of absentees is steady up to 2001 and then increases rapidly. The forecasted absentees are 2.1 million for 2021 and a 2.6 million for 2031. The labor migration to India increases gradually while the migration to other countries increases rapidly. The t-test shows that there is no significant difference between trends of absentees of India and other countries. The main destination of labor migrants in Malaysia followed by Qatar and Saudi Arabia besides India. Though remittance contributes a major part of Nepal’s economy more than seventy percent of out-migrants are involved in the private job and the majority of the migrants are unskilled.

As higher education moves to blended learning environments, a divide is emerging in the Australian higher education sector. This divide is predicated on differing skills and usage patterns, not access to devices. Access is not perceived to be the issue as numerous Australian secondary schools offer a school-issued laptop scheme. Yet many students transitioning to university are grappling with the necessary skills required to participate in a setting. Referred to as digital natives, these young people were expected to be digitally proficient. This thesis challenges the existence of Mark Prensky's (2001) Digital Native and provides an analysis of how differing fluency stages influence perceived preparedness for university study. Conceptualising the growing inequalities arising from a widening divide, the thesis investigates impacts on the student experience, fluency and secondary schooling opportunities. The thesis reports on three studies drawn from three research questions. Using a mixed-mode approach centred on Critical Theory and Ajzen's Theory of Planned Behaviour (TPB), the thesis provides an analysis of the divide in Australian higher education. Study 1 reports on RQ1: What is the relationship between socioeconomic, sociocultural/ geographic indicators and the divide? Four hundred and nine first-year business students were surveyed at regional and urban Australian universities. This study provides empirical data on the divide and determines a link between fluency, socioeconomic status, sociocultural capital, identity and student self-reported preparedness and skills. Study 2 reports on RQ2: Is fluency a precursor to preparedness for university study? Fifteen of the surveyed respondents completed a test with usability testing software prior to an in-depth interview. Study 2 provides a link between access and application of environments in schooling and the development of fluency. This study presents data showing disadvantage indicators can be alleviated through access to learning environments during schooling. Study 3 reports on RQ3: What enhances and develops fluencies? and examines the divide from a student's perceptive. Case studies were developed from in-depth interviews and presented as techno-biographies to determine respondents' fluency stage. These techno-biographies outline differing experiences and opportunities for skills development between secondary schools. Study 3 explores prior experience to identify influences, skills, knowledge, attitude and mindset. The study suggests that influences and prior experiences contribute to fluency and perceived preparedness for university study. The three studies are intertwined in their investigation of an association between disadvantage indicators, prior experience and stages of fluency. Particular attention is placed on examining the distribution and allocation of digitally resourcing in secondary schools. The three studies culminate in a concept model to illustrate the link between the distribution of resources, fluency and preparedness for university study. The thesis demonstrates a link between access to a learning management system (LMS) or curriculum during secondary school and disadvantage indicators. Access to a school LMS consistently produced higher self-reported skills than those without, even when disadvantage indicators were present. The issue of perceived preparedness for university study and/or a learning environment was also linked to participants who had access to a school LMS. Rural, regional, low socioeconomic, low sociocultural capital and state-school participants were less likely to have had access to a curriculum during secondary schooling and therefore less likely to report preparedness for university study. Conversely, these disadvantage indicators were overcome if participants had access to an LMS or curriculum. The thesis identifies a divide in higher education emanating from the distribution, use and allocation of secondary schooling resources and prior experience. The resourcing of secondary schools with school-issued laptops did not increase fluency or perceived preparedness for university study. However, the implementation of a curriculum or LMS produced significant outcomes in the development of fluency. These findings illustrate the influence of immersion in the formation of fluency. Resourcing schools without a clear curriculum does not increase fluency. If the divide is to be conquered, the appropriate application of resources in secondary schools must be implemented to enable the development of fluency.

In this article, a distributed resource allocation problem for second‐order multi‐agent systems over undirected switching topologies is investigated. For the distributed resource allocation problem, most of the existing algorithms depend on the gradient of cost function with specific expression, which may be unknown to agents. To deal with unknown cost functions, the extremum seeking control is introduced in the designed distributed resource allocation algorithm to estimate the gradient of the cost function only by the value of the cost. Besides, an estimator is proposed to estimate the optimal Lagrange multiplier associated with the equality constraint. The communication graph is supposed to be switched among a set of undirected subgraphs, which may be connected or unconnected. For undirected and connected switching graphs, the average dwell‐time constraint is used to describe the switching frequency. For the undirected graphs, containing connected and unconnected ones, both the average dwell‐time and the time‐ratio constraints are used to model the switching signal. For the two cases, the semi‐globally practically asymptotical stability of the designed algorithm is established. The obtained results are verified by an example.

The Third Generation Partnership Project (3GPP) introduced cellular vehicle-to-everything (C-V2X) for vehicular communications. In the standard, C-V2X Mode 4 is defined for the distributed resource selection. Subsequently, in 3GPP Release 16, NR-V2X is introduced with Mode 1 and Mode 2 for vehicular communications. Likewise C-V2X Mode 4, NR-V2X Mode 2 is used for decentralized resource scheduling. The vehicles select the resources based on their local observations by utilizing the semi-persistent scheduling (SPS). Since, the vehicles select the resources based on the local observation, sensing nature of SPS is challenged by the hidden node problem that lead to resource conflict. To resolve the contention, 3GPP also introduced the physical sidelink feedback channel (PSFCH) to assist the distributive resource scheduling based on the receiver feedback. However, this incurred a signaling overhead. In this work, federated learning is exploited for distributive training via offline method and distributive multiagent-based resource scheduling is performed following the principles of NR-V2X Mode 2. Distributed training favors the model accuracy by accommodating the varying affect of the environment due to the high mobile dynamics. Simulation is conducted by integrating SUMO in conjunction with 3GPP NR-V2X standard. Performance results demonstrate a substantial improvement compared to other deep learning methods, where centralized training and random resource selection procedures are employed. This research marks a significant stride toward efficient and conflict-resilient resource allocation in vehicular communications.

Efficient transmission resource allocation in medium access control (MAC) is essential for ensuring high performance in vehicular ad hoc networks (VANETs). However, existing solutions cannot satisfy the requirements of high throughput and low delay in high-density environments. In this article, we propose a timeslot-reserved MAC protocol to meet such demands for VANETs. This protocol provides distributed resource allocation and has two striking features: the high efficiency of handshake negotiation and the different solutions to disparate delay requirements of packets. In this protocol, a novel neighbor information table adds a new field of the needed number of frames in the future multiple frames. This reflects the reservation idea and reduces the number of negotiations between neighbor vehicles. The service applicant without enough timeslot resources is excluded from resource allocation negotiation, to decrease the invalid resource allocation. The resource-insufficient service channel and the resource-insufficient timeslot segments are also excluded from packet transmission, to further reduce the invalid resource allocation. Delay-sensitive packets and delay-tolerant packets select different kinds of timeslot segments, to satisfy different delay demands of different packets. Simulation results demonstrate the advantages of the proposed protocol in terms of throughput, delay, and packet loss rate.

This paper presents a number of key radio resource management strategies across physical (PHY) and medium access control (MAC) layers for reducing base station energy consumption including power efficient link adaptation (LA), exploitation of multi-user diversity and trading bandwidth for energy efficiency. By collectively taking advantage of these radio resource management strategies, a multi-user adaptive power and resource allocation algorithm is proposed to ease the power requirements of a base station, whilst maintaining the same levels of service. The scheduling algorithm is evaluated within a LTE downlink simulator for various traffic load conditions. The results show that the proposed algorithm achieves a significant energy saving over a conventional non-energy aware resource allocation scheme.