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2025 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Chakravarty, Sujit Kumar Batra, Amul Singh, Neeti Kumar, Gajendra Ministry of Earth Science India Meteorological Department New Delhi India

ISBN: (纸本)9798331529833

Adaptive protocols that react to shifting network and ambient conditions have been developed in response to the Internet of Things' (IoT) increasing need for effective communication. The MQTT protocol is used in this study's adaptive scheduling strategy to maximize IoT communication by modifying transmission intervals in response to network congestion and environmental variables like pressure, temperature, and humidity. Using a mathematical model Adaptive scheduling enhances throughput, lowers latency, and improves energy economy in contrast to fixed scheduling which has trouble adapting. According to experimental data, adaptive scheduling performs better than fixed techniques, offering notable energy savings, decreased latency, and increased throughput. This opens the door for more effective and sustainable IoT communication. © 2025 IEEE.

关键词： scheduling algorithms

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Wireless Resource scheduling for High Mobility Scenarios: A Combined Traffic and Channel Quality Prediction Approach

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IEEE TRANSACTIONS ON BROADCASTING 2022年第3期68卷 712-722页

作者： Xiong, Jian Hu, Hengrui Cheng, Peng Yang, Can Shi, Zhiping Gui, Lin Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China Natl Key Lab Sci & Technol Commun Chengdu Peoples R China La Trobe Univ Dept Comp Sci & Informat Technol Melbourne Vic 3086 Australia Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia Yangtze Delta Reg Inst Huzhou Huzhou Peoples R China Univ Elect Sci & Technol China Natl Key Lab Commun Huzhou 313001 Peoples R China

The rapid growth of mobile data traffic in a high-speed scenario imposes great challenges on wireless system design and optimization, where the inherent Doppler shift, frequent cell switching, and large penetration loss will significantly degrade the quality of transceiver signals. Wireless resource scheduling dynamically allocates wireless resources according to the user channel conditions and quality of service requirement, which is an enabling approach to maximize the resource utilization. In this paper, we develop a new wireless resource scheduling method in high mobility scenario to uplift the system performance. We first propose a feature extraction algorithm to extract the spatial traffic characteristics. On this basis, several time-series prediction algorithms are leveraged to predict the traffic volume. Then, we introduce cell switching indicator (CSI) into channel quality prediction to improve the signal-to-noise ratio (SNR) prediction performance. Based on the predicted results of service traffic and user channel quality variations, a resource scheduling strategy is proposed, which compromises the total system throughput and user satisfaction. Simulation results validate the effectiveness of the proposed scheduling algorithm.

关键词： Hidden Markov models Wireless communication Predictive models scheduling algorithms Recurrent neural networks Prediction algorithms Feature extraction Channel quality prediction high-speed railway communication traffic flow prediction wireless resource scheduling

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Energy-Aware Non-Preemptive Task scheduling With Deadline Constraint in DVFS-Enabled Heterogeneous Clusters

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2022年第12期33卷 4083-4099页

作者： Wang, Qiang Mei, Xinxin Liu, Hai Leung, Yiu-Wing Li, Zongpeng Chu, Xiaowen Harbin Inst Technol Shenzhen 518055 Peoples R China Hong Kong Baptist Univ Dept Comp Sci Kowloon Tong Hong Kong Peoples R China Jefferson Lab Newport News VA 23606 USA Hang Seng Univ Hong Kong Dept Comp Siu Lek Yuen Hong Kong Peoples R China Tsinghua Univ Inst Network Sci & Cyberspace Beijing 100190 Peoples R China Hong Kong Univ Sci & Technol Guangzhou Data Sci & Analyt Thrust Hong Kong Peoples R China

Energy conservation of large data centers for high performance computing workloads, such as deep learning with Big Data, is of critical significance, where cutting down a few percent of electricity translates into million-dollar savings. This work studies energy conservation on emerging CPU-GPU hybrid clusters through dynamic voltage and frequency scaling (DVFS). We aim at minimizing the total energy consumption of processing a batch of offline tasks or a sequence of real-time tasks under deadline constraints. We derive a fast and accurate analytical model to compute the appropriate voltage/frequency setting for each task, and assign multiple tasks to the cluster with heuristic scheduling algorithms. In particular, our model stresses the nonlinear relationship between task execution time and processor speed for GPU-accelerated applications, for more accurately capturing real-world GPU energy consumption. In performance evaluation driven by real-world power measurement traces, our scheduling algorithm shows comparable energy savings to the theoretical upper bound. With a GPU scaling interval where analytically at most 36% of energy can be saved, we record 33-35% of energy savings. Our results are applicable to energy management on modern heterogeneous clusters.

关键词： Graphics processing units Task analysis Energy consumption Voltage scheduling algorithms Voltage control Kernel Graphics processing units dynamic voltage and frequency scaling task scheduling

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Contention Cognizant scheduling of Task Graphs on Shared Bus-Based Heterogeneous Platforms

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2022年第2期41卷 281-293页

作者： Roy, Sanjit Kumar Devaraj, Rajesh Sarkar, Arnab Indian Inst Technol Guwahati Dept Comp Sci & Engn Gauhati 781039 India Nvidia Graph SW TEGRA Bangalore 560045 Karnataka India Indian Inst Technol Kharagpur Adv Technol Dev Ctr Kharagpur 721302 W Bengal India

Demands for high performance as well as reliability within stringent resource budgets are driving a shift from homogeneous to heterogeneous processing platforms for the implementation of today's cyber-physical systems (CPSs). These CPSs are typically represented as directed-acyclic task graphs (DTGs) due to the complex interactions between their functional components which are often distributed in nature. This work deals with the problem of scheduling a CPS modeled as DTG. First, we present an optimal solution using integer linear programming (ILP) for the DTGs, to be executed on distributed heterogeneous processors which are interconnected via shared buses. However, this ILP-based optimal solution exhibits high computational complexity and does not scale for moderately large problem sizes. Hence, we propose a low-overhead heuristic algorithm called the contention cognizant task and message scheduler (CC-TMS), which is able to produce satisfactorily efficient as well as fast solutions within a reasonable time. The efficiency of the proposed scheme has been extensively evaluated through simulation-based experiments using benchmark DTGs. Through the case study of a real-world automotive traction controller, we demonstrate the practical applicability of our proposed scheme.

关键词： Task analysis Program processors Schedules Computational modeling Optimal scheduling scheduling algorithms Job shop scheduling Cyber-physical systems (CPSs) heterogeneous platform integer linear programming (ILP) scheduling task graphs

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AoI-Guaranteed Bandit: Information Gathering Over Unreliable Channels

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第10期23卷 9469-9486页

作者： Huang, Ziyao Wu, Weiwei Fu, Chenchen Chau, Vincent Liu, Xiang Wang, Jianping Luo, Junzhou Southeast Univ Sch Comp Sci Engn Nanjing 210018 Jiangsu Peoples R China City Univ Hong Kong Dept Comp Sci Kowloon Tong Hong Kong Peoples R China

In many IoT applications, information needs to be gathered from multiple heterogeneous sources to the base station for real-time processing and follow-up actions. Undoubtedly, information freshness, measured by age of information (AoI), is critical in taking responsive actions. Recent studies have taken AoI into the consideration of transmission scheduling over wireless channels. However, existing studies on guaranteeing AoI either assume error-free wireless channels or priorly known link reliability, which is unrealistic. In this article, we tackle the AoI-guaranteed transmission scheduling problem over an unreliable channel with the aim of throughput maximization, which is modelled as an AoI-Guaranteed Multi-Armed Bandit (AG-MAB) problem. Since the problem has not been studied in the literature even for the oracle case with given link reliability, we first propose an optimal stationary randomized sampling (SRS) policy for the oracle case. For the AG-MAB problem with unknown link reliability, we propose learning algorithms that meet the AoI requirements with probability 1 and incur sublinear regret compared to Oracle SRS, which can also detect the unsatisfiability of the AoI constraint and switch to the fallback policy promptly with guaranteed accuracy. Numerical results show that our algorithm outperforms the AoI-constraint-aware baselines on throughput with per-source AoI requirement guaranteed.

关键词： Reliability Throughput Program processors scheduling algorithms Wireless communication Upper bound Reliability theory AoI information freshness multi-armed bandits transmission scheduling channel unreliability

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FlexTAS: Flexible Gating Control for Enhanced Time-Sensitive Networking Deployment

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年

作者： Lin, Jiashuo Li, Weichao Feng, Xingbo Zhan, Shuangping Ning, Lewei Wang, Yi Wang, Tao Wan, Hai Tang, Bo Tao, Xiaofeng Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China Pengcheng Lab Shenzhen 518055 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Tsinghua Univ Sch Software Beijing 100084 Peoples R China

Time-sensitive networking (TSN), essential in industrial networks for its promise of reliable and deterministic data transmission, faces deployment challenges due to the limitations of existing time-aware shaper (TAS)-based scheduling algorithms. Specifically, the size of the generated gate control lists (GCLs) is usually too large to be deployed in actual devices. To bridge the gap between theory and practice, we propose FlexTAS, a flexible and practical solution for TSN. The key insight behind FlexTAS is that relaxing gating does not introduce uncertainty, as long as nonoverlap reserved time slots are guaranteed. FlexTAS is comprised of two main components: first, a novel gating model deviates from the conventional TAS model by incorporating selective relaxation of gating at certain nodes;and second, a deep reinforcement learning-based engine to rapidly generate valid schedules. We build a real testbed and validate the effectiveness of our proposed solution. Our evaluation demonstrates that FlexTAS effectively controls the number of gate entries within the GCL capacity of devices, while simultaneously meeting the Quality of Service(QoS) requirements of time-triggered streams. It significantly reduces the number of GCL entries by 60% to 80%, and facilitates deployment in heterogeneous networks, thus offering a practical solution for TSN.

关键词： Streams Logic gates Jitter Job shop scheduling Delays Quality of service Engines Analytical models Synchronization scheduling algorithms Deterministic network flexible gating control network scheduling time-sensitive networking (TSN)

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Maximizing Entanglement Rates via Efficient Memory Management in Flexible Quantum Switches

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2024年第7期42卷 1749-1762页

作者： Promponas, Panagiotis Valls, Victor Guha, Saikat Tassiulas, Leandros Yale Univ Dept Elect Engn New Haven CT 06520 USA IBM Res Europe Dublin Dublin D15HN66 Ireland Univ Arizona Wyant Coll Opt Sci Tucson AZ 85721 USA

We study the problem of operating a quantum switch with memory constraints. In particular, the switch has to allocate quantum memories to clients to generate link-level entanglements (LLEs), and then use these to serve end-to-end entanglements requests. The paper's main contributions are (i) to characterize the switch's capacity region and study how it scales with respect to the number of quantum memories and probability of successful LLEs and (ii) to propose a memory allocation policy that is throughput optimal. In addition, when the requests are bipartite and the LLE attempts are always successful, we show that the proposed policy has polynomial time complexity. We evaluate the proposed policy numerically and illustrate its performance depending on the requests arrivals characteristics and the time available to obtain a memory allocation.

关键词： Quantum entanglement Optical switches Electrons Photonics Resource management Memory management Mathematical models scheduling algorithms quantum networks quantum entanglement

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A V2X-Based Distributed Virtual Traffic Light scheduling Algorithm 24

A V2X-Based Distributed Virtual Traffic Light Scheduling Alg...

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5th International Conference on Computer Science and Management Technology, ICCSMT 2024

作者： Li, Zhaohui Fu, Qi Hunan University of Science and Technology Hunan Xiangtan China

ISBN: (纸本)9798400709999

Urban traffic congestion not only increases fuel consumption and pollution but also causes inconvenience to residents. Although traditional physical traffic light systems provide a certain solution, their reliance on complex road infrastructure results in high maintenance and installation costs. In contrast, virtual traffic light systems, as a self-organizing traffic control system, do not rely on road infrastructure. A V2X-based distributed virtual traffic light scheduling algorithm has been designed and implemented, aiming to optimize vehicle traffic efficiency and reduce CO2 emissions. The research adopts hierarchical and clustering methods, combined with a priority scheduling strategy and Look-Ahead decision tree algorithm, to improve the overall efficiency of traffic flow. Simulation results show that this method performs excellently under different traffic densities. Under balanced traffic conditions, the scheduling algorithm increases the number of vehicles passing per minute by approximately 7.31% compared to the uncontrolled system and by approximately 8.28% compared to the traditional traffic light system;it also reduces the stop-start ratio per vehicle by approximately 9.90% and 20.50%, respectively. Under unbalanced traffic conditions, the number of vehicles passing increases by approximately 9.44% and 2.91%, and the stop-start ratio decreases by approximately 7.68% and 15.10%. By reducing the stop-start ratio per vehicle, fuel consumption can be effectively lowered, thereby reducing CO2 emissions. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： scheduling algorithms

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A Cache scheduling Method Based on Adaptive Expiration for Data Process System 7th

A Cache Scheduling Method Based on Adaptive Expiration for D...

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7th International Conference on Wireless Communications, Networking and Applications, WCNA 2023

作者： Fang, Bin Zhong, Yibo Sun, Yizhen Tu, Jinjin Jiang, Guang Xiao, Yao Information and Communication Branch of State Grid Hunan Electric Power Company Limited Changsha410007 China Nanjing NARI Information and Communication Technology Nanjing City210000 China School of Software Xinjiang University Urumqi830091 China

ISBN: (纸本)9789819624089

Cache data plays a crucial role in the operation of the power system. In practical applications, the demand for retrieval and queries has surged due to the vast amount of terminal data collected throughout the province. Placing data in the cache effectively improves query speed [1]. However, cache space is limited, and the amount of storable data is small [2]. Without proper control over the data placed in the cache, the hit rate may decrease [3]. Thus this paper proposes a new control method through adaptively setting the expiration time of cached data, which considers the historical traffic distribution and real-time query distribution. The simulation results show that the proposed method can significantly reduce the cache hit rate, which leads to the improvement of cache utilization. In addition, it avoids frequent cache replacements and reduces I/O overhead. © The Author(s) 2025.

关键词： scheduling algorithms

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Leveraging AI for Enhancing Robustness and Optimization in Constraint-Based Job-Shop scheduling with Operators

Leveraging AI for Enhancing Robustness and Optimization in C...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Mahfuri, Mahmoud Ghwanmeh, Sameh Khishe, Mohammad Saeed, Ali International University for Science and Technology Computer and Information Technology Department Syria College of Computer Information Technology American University in the Emirates Computer Science Dept United Arab Emirates Innovation Center for Artificial Intelligence Applications Yuan Ze University Taiwan Jadara Research Center Jadara University Jordan Computer Center Northern Technical University Ninevah Iraq

ISBN: (纸本)9798350369106

in complex scheduling scenarios, such as the Job scheduling Problem (JSP) faced by human operators, the challenge is to optimize the total time mandatory to complete all jobs and the reliability of the schedule. This paper presents a unified approach that combines constraint satisfaction (CSP) techniques with heuristic algorithms to resolve JSP using operators called JSO(n, p), where n is the number of tasks and p is the number of data. The main aims are to minimize the total time required to complete all tasks and to maximize system consistency under uncertainties such as equipment failure or operator unavailability. We analysis branch-and-bound (BB) methods, neural networks, and hybrid algorithms to achieve optimal software solutions. This paper also assesses the impact of bus constraints on traditional scheduling models and suggests solutions for reliability. The evaluation of various algorithmic strategies and extensive experiments on many sampling problems demonstrate the balance between robustness and optimality. © 2024 IEEE.

关键词： scheduling algorithms

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