Adaptive Scheduling Algorithm Research Articles

Analysis of Wireless Multimedia Big Data Transmission Based on Adaptive Scheduling Algorithm

The increasing demand for wireless multimedia applications has prompted the need for efficient data transmission strategies that can adapt to dynamic network conditions and diverse Quality of Service (QoS) requirements. This paper analyzes wireless multimedia big data transmission using an innovative Adaptive QoS-Aware Scheduling Algorithm (AQOS-ASA). The proposed algorithm aims to optimize the transmission of multimedia data by dynamically adjusting the scheduling based on QoS considerations and real-time network conditions. Through adaptive scheduling, the algorithm ensures that high-priority multimedia data receives optimal transmission treatment, balancing the quality preferences set by users. The algorithm dynamically allocates time slots based on the changing network conditions, allowing for efficient utilization of the available bandwidth. The paper analyses the algorithm's performance in different wireless network scenarios through simulations and evaluations, highlighting its effectiveness in adapting to varying QoS requirements and network conditions. The results demonstrate that the proposed algorithm significantly enhances the quality and efficiency of wireless multimedia big data transmission, making it well-suited for applications with diverse multimedia content and dynamic network environments. The findings contribute valuable insights to wireless multimedia communication and provide a foundation for further advancements in adaptive scheduling algorithms for improved Quality of Service.

Research on Wireless Multimedia Big Data Transmission Model Based on Non-linear Algebraic Equation with the Adaptive Scheduling Algorithm

With the rapid development of computer technology, image processing and automatic book recognition has become a very important and practical field, and this discipline has a broad prospect and great potential. The digital vision system is a multi-plane information analysis method based on the human brain, which is applied to library identification problems by studying several regular changes in the distribution of objects themselves in two-dimensional space and three-dimensional position, in order to improve book quality and reduce costs. In addition, digital reading devices have become an essential part of people's daily learning life, and multimedia retrieval methods have also undergone radical changes, image processing is becoming more and more complex, and algorithms must become more efficient and reliable as the amount of computing increases to a certain level, so as to adapt to this fast and powerful data storage and query needs. Therefore, the trend is to improve the speed and reliability of computer systems. In this research, we provide a nonlinear model of optimal allocation employing an ant colony management strategy, a genetic operator, and an adaptive scheduling algorithm based on topology (dB) to discover the best scheduling scheme and test its feasibility via simulation. It proposes a viable idea for tackling the practical wireless multimedia massive data transmission model problem by applying the method to the real-time processing process in other sectors with high importance and resilience.

Research on wireless multimedia big data transmission model based on non-linear algebraic equation with the adaptive scheduling algorithm

With the rapid development of computer technology, image processing and automatic book recognition has become a very important and practical field, and this discipline has a broad prospect and great potential. The digital visión system is a multi-plane information análisis method basad on the human brain, which is applied to library identification problems by studying several regular changes in the distribution of objects themselves in two-dimensional space and three-dimensional position, in order to improve book quality and reduce costs. In addition, digital Reading devices have become an essential part of people’s daily learning life, and multimedia retrieval methods have also undergone radical changes, image processing is becoming more and more complex, and algorithms must become more efficient and reliable as the amount of computing increases to a certain level, so as to adapt to this fast and powerful data storage and query needs. Therefore, the tren is to improve the speed and reliability of computer systems. This paper discusses and proposes measures for the analysis and extraction, classification and sorting of library images.

Performance-driven scheduling for malleable workloads

The development of adaptive scheduling algorithms that take advantage of malleability has become a crucial area of research in many large-scale projects. Malleable workloads can improve the system’s performance but, at the same time, provide an extra dimension to the scheduling problem. This paper proposes an adaptive, performance-based job scheduling method that emphasizes the backfilling concept with malleability. The proposed method performs the malleability operations only when the estimated execution time of the involved applications is better than or equal to the execution time on the allocated resources without reconfiguration. The reconfiguration feasibility is determined by performance models considering the application scalability and reconfiguration overheads. Different policies for implementing malleability are presented, each targeting a specific workload in terms of job size and scalability. The comprehensive evaluation shows an improvement in the slowdown up to 49% compared to the non-adaptive baseline scheduling algorithm.

Speaking to remember: Model-based adaptive vocabulary learning using automatic speech recognition

Memorizing vocabulary is a crucial aspect of learning a new language. While personalized learning- or intelligent tutoring systems can assist learners in memorizing vocabulary, the majority of such systems are limited to typing-based learning and do not allow for speech practice. Here, we aim to compare the efficiency of typing- and speech based vocabulary learning. Furthermore, we explore the possibilities of improving such speech-based learning using an adaptive algorithm based on a cognitive model of memory retrieval. We combined a response time-based algorithm for adaptive item scheduling that was originally developed for typing-based learning with automatic speech recognition technology and tested the system with 50 participants. We show that typing- and speech-based learning result in similar learning outcomes and that using a model-based, adaptive scheduling algorithm improves recall performance relative to traditional learning in both modalities, both immediately after learning and on follow-up tests. These results can inform the development of vocabulary learning applications that–unlike traditional systems–allow for speech-based input.

Hierarchical reinforcement learning with adaptive scheduling for robot control

Conventional hierarchical reinforcement learning (HRL) relies on discrete options to represent explicitly distinguishable knowledge, which may lead to severe performance bottlenecks. It is possible to represent richer knowledge through continuous options, but reliable scheduling methods are lacking. To design an available scheduling method for continuous options, in this paper, the hierarchical reinforcement learning with adaptive scheduling (HAS) algorithm is proposed. Its low-level controller learns diverse options, while the high-level controller schedules options to learn solutions. It achieves an adaptive balance between exploration and exploitation during the frequent scheduling of continuous options, maximizing the representation potential of continuous options. It builds on multi-step static scheduling and makes switching decisions according to the relative advantages of the previous and the estimated continuous options, enabling the agent to focus on different behaviors at different phases of the task. The expected t-step distance is applied to demonstrate the superiority of adaptive scheduling in terms of exploration. Furthermore, an interruption incentive based on annealing is proposed to alleviate excessive exploration during the early training phase, accelerating the convergence rate. Finally, we apply HAS to robot control with sparse rewards in continuous spaces, and develop a comprehensive experimental analysis scheme. The experimental results not only demonstrate the high performance and robustness of HAS, but also provide evidence that the adaptive scheduling method has a positive effect both on the representation and option policies.

Adaptive scheduling algorithm for phased array fire control radar based on comprehensive priority

Aiming at the problem of resource allocation in multi-task scheduling of phased array fire control radar, this paper proposes an adaptive scheduling algorithm based on comprehensive priority, that is, the shooting advantage degree of phased array fire control radar to different targets is calculated by using Analytic Hierarchy Process, and then combined with the priority of the work mode and the task deadline to carry out the comprehensive priority planning of the task. In the performance evaluation algorithm, the index of shooting value rate is proposed to evaluate the performance of the scheduling algorithm for high shooting advantage degree tasks. The simulation results show that, compared with the traditional scheduling algorithm, the proposed scheduling algorithm significantly improves the shooting value rate and the comprehensive scheduling ability of phased array fire control radar.

Parallel Optimization of BLAS on a New-Generation Sunway Supercomputer

The new-generation Sunway supercomputer has ultra-high computing capacity. But due to the unique heterogeneous architecture of the supercomputer, the open-source versions of basic linear algebra subprograms (BLAS) are insufficient for performance or compatibility. In addition, due to the update of the architecture, BLAS based on the previous Sunway could not fully exploit the performance of the successor. To address the challenges, we propose an optimized BLAS on the new-generation Sunway supercomputer in this paper. Specially, for achieving efficient computation, a parallel optimization method based on the new-generation Sunway for the Level-1 BLAS computing between vectors and the Level-2 BLAS computing between vectors and matrices is first proposed. Then, an adaptive scheduling algorithm for various data sizes is proposed, which is used to balance the tasks of core groups. Finally, to achieve highly efficient general matrix multiplication (GEMM) kernels, a parallel optimization method based on the new-generation Sunway for the Level-3 BLAS computing between matrices is proposed, which includes source-level optimization as well as assembly-level optimization. Experimental results show that the memory bandwidth utilization of the optimized Level-1/2 BLAS exceeds 95%, and the computational efficiency of the optimized GEMM kernel exceeds 94%.

The Scheduling Research of a Wind-Solar-Hydro Hybrid System Based on a Sand-Table Deduction Model at Ultra-Short-Term Scales

Establishing a wind-solar-hydro hybrid generation system is an effective way of ensuring the smooth passage of clean energy into the grid, and its related scheduling research is a complex and real-time optimization problem. Compared with the traditional scheduling method, this research investigates and improves the accuracy of the scheduling model and the flexibility of the scheduling strategy. The paper innovatively introduces a sand-table deduction model and designs a real-time adaptive scheduling algorithm to evaluate the source-load matching capability of the hybrid wind-solar-hydro system at ultra-short-term scales, and verifies it through arithmetic examples. The results show that the proposed adaptive sand-table scheduling model can reflect the actual output characteristics of the hybrid wind-solar-hydro system, track the load curve, and suppress the fluctuation of wind and solar energy, with good source-load matching capability.

GNN at the Edge: Cost-Efficient Graph Neural Network Processing Over Distributed Edge Servers

Edge intelligence has arisen as a promising computing paradigm for supporting miscellaneous smart applications that rely on machine learning techniques. While the community has extensively investigated multi-tier edge deployment for traditional deep learning models (e.g. CNNs, RNNs), the emerging Graph Neural Networks (GNNs) are still under exploration, presenting a stark disparity to its broad edge adoptions such as traffic flow forecasting and location-based social recommendation. To bridge this gap, this paper formally studies the cost optimization for distributed GNN processing over a multi-tier heterogeneous edge network. We build a comprehensive modeling framework that can capture a variety of different cost factors, based on which we formulate a cost-efficient graph layout optimization problem that is proved to be NP-hard. Instead of trivially applying traditional data placement wisdom, we theoretically reveal the structural property of quadratic submodularity implicated in GNN’s unique computing pattern, which motivates our design of an efficient iterative solution exploiting graph cuts. Rigorous analysis shows that it provides parameterized constant approximation ratio, guaranteed convergence, and exact feasibility. To tackle potential graph topological evolution in GNN processing, we further devise an incremental update strategy and an adaptive scheduling algorithm for lightweight dynamic layout optimization. Evaluations with real-world datasets and various GNN benchmarks demonstrate that our approach achieves superior performance over de facto baselines with more than 95.8% cost reduction in a fast convergence speed.

L‐RUBI: An efficient load‐based resource utilization algorithm for bi‐partite scatternet in wireless personal area networks

SummaryRecently, much of the wireless personal area network (WPAN) research concerns network protocols, scheduling, and security challenges but the major issue of resource utilization has been very rarely investigated. The design of resource sharing in a network gets more attention when the number of users increases. While optimizing performance, resource utilization plays a critical role. In this paper, the numerical performance of a wireless resource utilization algorithm for a bi‐partite scatternet is presented. This algorithm is focused to enhance the bandwidth allocation and power utilization of wireless scatternets. Every node can communicate with a single neighbor at a time with minimum resources. Finally, the performances of the RUBI algorithm are shown. This algorithm is compared with the existing algorithms such as the load adaptive scheduling algorithm and pseudorandom coordinated scheduling scheme in terms of various parametric metrics like reliability, throughput, collision probability, transmission probability, and signal‐to‐noise ratio (SINR). The proposed L‐RUBI achieves 93.4% of reliability, 93.6% of transmission probability, 91.4% of throughput, 76.8% of collision performance, and 72.2% SINR.

Delay Optimization in LoRaWAN by Employing Adaptive Scheduling Algorithm With Unsupervised Learning

Low Power Wide Area Network (LPWAN) technologies have been exponentially growing because of the tremendous growth of the Internet of Things (IoT) devices across the globe. Several LPWAN technologies have been utilized by the researchers to address certain issues like increased number of collisions, retransmissions, delay, and energy consumption. However, Long Range Wide Area Network (LoRaWAN) is the most suitable and attractive technology in terms of delay optimization, low cost and efficient energy consumption. The main issue which arises in LoRaWAN is because of its high packet drop rate due to collision. The reason behind this packet drop rate is the MAC scheme known as Pure Aloha used by LoRaWAN for the transmission of the frames. Long Range (LoRa) End Devices (EDs) initiate communication with Pure Aloha that leads to a high number of retransmissions. These retransmissions further enhance the delay in LoRa networks. This paper aims to optimize the delay in LoRaWAN by using an Adaptive Scheduling Algorithm (ASA) with an unsupervised probabilistic approach called Gaussian Mixture Model (GMM). By using ASA with GMM, the retransmissions are reduced which optimizes the delay in LoRaWAN. The results show that in our approach, Packet Collision Rate (PCR) is reduced by 39% as compared to conventional LoRaWAN. In addition, the Packet Success Ratio (PSR) is also increased by 39% as compared to the conventional LoRaWAN and Dynamic Priority Scheduling Technique (PST). Further, the delay is optimized by 91% and 79%. This research could be effective for the environments where the critical data of patients need to be sent with optimised retransmissions and minimum delay towards gateways.

A Comparative Analysis of Dynamic Scheduling Algorithms for Enhanced Resource Management in Homogeneous and Heterogeneous Fog Computing Environments

Fog computing, an emerging paradigm, extends cloud capabilities to the network's edge, enhancing real-time data processing and analysis. This study focuses on optimizing fog computing systems using an adaptive scheduling algorithm. It efficiently categorizes incoming tasks based on their security requirements into private, semi-private, and public groups, distributing them among Mobile Data Centers (MDCs) and Cloud Data Centers (CDCs). By considering various parameters like job quantities, MDCs, CDCs, their capacities, and communication delays, the algorithm ensures tasks are allocated optimally. The performance of three scheduling algorithms (FCFS, SJF, EDF) is analyzed in both homogeneous and heterogeneous fog environments, shedding light on system efficiency, workload distribution, and resource management. This study provides valuable guidance for the design and operation of efficient fog computing infrastructures tailored to the diverse requirements of IoT applications while optimizing resource utilization.

DQN-Based Adaptive MCS and SDM for 5G Massive MIMO-OFDM Downlink

In this letter, we propose a deep Q-network (DQN)-based joint adaptive scheduling algorithm of modulation and coding scheme (MCS) and space division multiplexing (SDM) in 5G massive MIMO-OFDM, that enables the next-generation NodeB (gNB) to choose optimal MCS and SDM for downlink scheduling that maximizes the spectral efficiency (SE) and throughput while in a low block error rate (BLER). The simulation results show that in the typical high-speed mobile and uplink interference scenarios, the proposed DQN-based algorithm has a 10% to 40% improvement in SE and a 10% to 20% improvement in throughput compared to representative algorithms, and can effectively compensate for the channel state information (CSI) feedback delay.

TDMA policy to optimize resource utilization in Wireless Sensor Networks using reinforcement learning for ambient environment

Data packet reaches from the end node to sink in a multihop fashion in the internet of things (IoTs) and sensor networks. Usually, a head node (among neighboring or special purpose nodes) can collect data packets from the nodes and forward them further to sink or other head nodes. In Time-division multiple access (TDMA) driven scheduling, nodes often own slots in a time frame and are scheduled for data forwarding in the allotted time slot (owner node) in each time frame. A time frame in which the owner node does not have data to forward goes into sleep mode. Though the supposed owner node is in sleep mode, the corresponding head node is active throughout the time frame. This active period of a head node can cause an increase in energy consumption. Besides, because the head node in an active state does not receive a data packet, it is causing significantly to the throughput, ultimately leading to low channel utilization. We propose the Markov design policy (MDP) for such head nodes to reduce the number of time slots wasted in the time frame in our work. The proposal is the first such kind of MDP-based modeling for node scheduling in TDMA. The simulation results show that the proposed method outperforms existing adaptive scheduling algorithms for channel utilization, end-to-end delay, system utilization, and balance factor.

A Big Data Analysis Algorithm Designed for the Interactive Platform of the Intelligent Sensor Information System

In order to improve the interactive sharing ability of the medical information system and improve the adaptability of the medical file access and management, a big data analysis algorithm designed for the intelligent sensor information system interaction platform is proposed. Using the information collection, statistics, and summary in the archives, adopt the statistical method analysis, optimize the medical archives intelligent sensor information adaptive scheduling and access algorithm, establish the medical information interactive sharing database, design the medical information system interactive sharing platform under the B/S structure system, query and update the database management of the sharing platform, and complete the software development of the medical archives intelligent sensor information embedded interactive sharing platform. Simulation results show the recall rate of 0.82 to 1, much higher than the other two comparison methods, with better data mining performance. Therefore, the intelligent sensor medical information system interaction platform based on big data analysis can improve the science and standardization of the medical business.

Education Teaching Evaluation Method Aided by Adaptive Genetic Programming and Robust Scheduling

Process mining technology aims to automatically generate process models by analyzing events, thereby assisting the design and redesign of process models. Although many process mining methods have appeared, they all have deficiencies. These methods focus on mining from the behavioral aspects described by the log, while ignoring the structural nature of the process model itself. The complexity of the process describes the simplicity and ease of understanding of the process. Higher process complexity affects the readability of the process. Genetic programming has strong robustness. Its individual representation based on tree structure can describe the special structure of the process. The introduction of process complexity fitness enables it to consider the complexity of the process model itself while mining log behavior, so as to achieve nonlinear mining of complex processes. This paper analyzes the process mining based on genetic programming and proposes process individuals based on tree structure. By realizing the combination of process complexity measurement and process mining technology, noncomplex process mining can be realized. In this paper, the process complexity is combined with the process mining algorithm based on genetic programming, and a measure of process structural complexity is proposed, which is converted into complexity fitness and introduced into the fitness function of genetic programming, to realize the use of genetic programming. The research results show that the improved new adaptive genetic programming robust scheduling algorithm provides a new idea and method for the evaluation of college sports under different sports conditions. This makes the college sports evaluation management system more intelligent and improves the rational allocation of physical education teaching.

Optimizing Off-Chip Memory Access for Deep Neural Network Accelerator

Off-chip memory, such as DRAM, its access energy cost is orders of magnitude higher than other operations such as multiply and accumulate, thereby dominating the system energy consumption. Therefore, optimizing the access of the off-chip memory is crucial to further improve the energy efficiency of the deep neural network (DNN) accelerator. Towards this, this brief proposed an adaptive scheduling algorithm to minimize the DRAM access. Compared with the previous works, it can not only dynamically determine the data partition and the data type that will be reused, but also considered the constraints between adjacent layer, that is, if the output feature map of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$i$ </tex-math></inline-formula> th layer is divided into N parts, the output feature map of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$i+1$ </tex-math></inline-formula> th layer can only be divided into N parts or write back to the off-chip memory. Therefore, a minimize and realizable memory access solution can be obtained. Choosing 3 popular networks UNet, VGG-16 and MobileNet as benchmarks, the experiment results show that our scheduling algorithm can achieve a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$34.46\% \sim 93.42\%$ </tex-math></inline-formula> reduction in energy consumption of DRAM access and a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$34.34\% \sim 93.37\%$ </tex-math></inline-formula> reduction in DRAM access latency when compared to previous works.

Design of Multichannel Resource Adaptive Scheduling Algorithm for English Online Translation Teaching

In order to optimize the multichannel resource scheduling of English online translation teaching and realize the goal of adaptive scheduling algorithm, a multichannel resource adaptive scheduling algorithm for English online translation teaching is designed. The clustering algorithm is used to sort out various English online translation teaching multichannel resources and remove redundancy. Build a unified standard specification; establish an English online translation teaching resource scheduling model with three modules of foundation layer, environment layer, and application layer; form an easy-to-find data set; build an adaptive scheduling algorithm function module of system management, standard management, media materials, online courses, courseware library, and test question library; and optimize the integration speed under multiobjective scheduling algorithm; this paper analyzes the multichannel resource scheduling process of English online translation teaching and realizes the research on the designed adaptive scheduling algorithm. The experimental results show that the test results of each functional module of the scheduling platform under the designed algorithm meet the functional requirements, and the update amount of resources is close to the update upper limit, which can reasonably manage teaching resources and improve students’ interest and learning ability.

Energy Economy and Energy Structure Optimization Countermeasures Based on Big Data Mining

The impact of energy on the economy must be examined not only from an energy structure standpoint but also from an economic structure standpoint, as there is a close relationship between energy structure and energy economy. To avoid the negative effects of energy consumption on the economy, it is also necessary to assess the constraints to the development of the energy economy and then optimize the energy economy and energy structure to achieve rapid economic growth. The research object in this paper is a data center powered by renewable energy, and the resource and energy consumption management model and algorithm are examined. Different adaptive scheduling algorithms are designed for different emphases, and an approximate application scheduling algorithm for renewable energy utilization based on DM is proposed. When building a data center, researching the resource management and energy consumption management strategies can help determine the best energy access scheme. It can provide the necessary reference for specific planning, such as software and hardware configuration, equipment parameter determination, and power access, resulting in lower construction and operation costs.