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A Survey of scheduling in 5G URLLC and Outlook for Emerging 6G Systems

IEEE ACCESS 2023年 11卷 34372-34396页

作者： Haque, Md. Emdadul Tariq, Faisal Khandaker, Muhammad R. A. Wong, Kai-Kit Zhang, Yangyang Univ Rajshahi Dept Informat & Commun Engn Rajshahi 6205 Bangladesh Univ Glasgow James Watt Sch Engn Glasgow G12 8QQ Scotland Heriot Watt Univ Sch Engn & Phys Sci Edinburgh EH14 4AS Scotland UCL Dept Elect & Elect Engn London WC1E 6BT England Kuang Chi Inst Adv Technol Shenzhen 518057 Peoples R China

Future wireless communication is expected to be a paradigm shift from three basic service requirements of 5th Generation (5G) including enhanced Mobile Broadband (eMBB), Ultra Reliable and Low Latency communication (URLLC) and the massive Machine Type Communication (mMTC). Integration of the three heterogeneous services into a single system is a challenging task. The integration includes several design issues including scheduling network resources with various services. Specially, scheduling the URLLC packets with eMBB and mMTC packets need more attention as it is a promising service of 5G and beyond systems. It needs to meet stringent Quality of Service (QoS) requirements and is used in time-critical applications. Thus through understanding of packet scheduling issues in existing system and potential future challenges is necessary. This paper surveys the potential works that addresses the packet scheduling algorithms for 5G and beyond systems in recent years. It provides state of the art review covering three main perspectives such as decentralised, centralised and joint scheduling techniques. The conventional decentralised algorithms are discussed first followed by the centralised algorithms with specific focus on single and multi-connected network perspective. Joint scheduling algorithms are also discussed in details. In order to provide an in-depth understanding of the key scheduling approaches, the performances of some prominent scheduling algorithms are evaluated and analysed. This paper also provides an insight into the potential challenges and future research directions from the scheduling perspective.

关键词： scheduling algorithms Ultra reliable low latency communication 5G mobile communication 6G mobile communication scheduling Long Term Evolution Resource management 5G 6G packet scheduling joint scheduling URLLC eMBB mMTC

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Randomized scheduling of Real-Time Traffic in Wireless Networks Over Fading Channels

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IEEE-ACM TRANSACTIONS ON NETWORKING 2023年第4期31卷 1688-1701页

作者： Tsanikidis, Christos Ghaderi, Javad Columbia Univ Dept Elect Engn New York NY 10027 USA

Despite the rich literature on scheduling algorithms for wireless networks, algorithms that can provide deadline guarantees on packet delivery for general traffic and interference models are very limited. In this paper, we study the problem of scheduling real-time traffic under a conflict-graph interference model with unreliable links due to channel fading. Packets that are not successfully delivered within their deadlines are of no value. We consider traffic (packet arrival and deadline) and fading (link reliability) processes that evolve as an unknown finite-state Markov chain. The performance metric is efficiency ratio which is the fraction of packets of each link which are delivered within their deadlines compared to that under the optimal (unknown) policy. We first show a conversion result that shows classical non-real-time scheduling algorithms can be ported to the real-time setting and yield a constant efficiency ratio. In particular, Max-Weight scheduling (MWS) yields an efficiency ratio of 1/2. We then propose randomized algorithms that achieve efficiency ratios strictly higher than 1/2, by carefully randomizing over the maximal schedules. Further, we propose low-complexity and myopic distributed randomized algorithms, and characterize their efficiency ratio. Simulation results are presented that verify that the randomized algorithms outperform classical ones such as and for scheduling real-time traffic over fading channels.

关键词： Fading channels Interference scheduling Real-time systems Schedules Optimal scheduling scheduling algorithms real-time traffic Markov processes stability wireless networks

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A Memory-Constraint-Aware List scheduling Algorithm for Memory-Constraint Heterogeneous Muti-Processor System

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2023年第4期34卷 1082-1099页

作者： Yao, Yu Song, Yukun Huang, Ying Ni, Wei Zhang, Duoli Hefei Univ Technol Sch Microelect Hefei Anhui 230601 Peoples R China

An effective scheduling algorithm is vital for the execution efficiency of applications on Heterogeneous Muti-Processor System (HMPS), especially Memory-Constraint Heterogeneous Muti-Processor System (MCHMPS). Stringent local and external memory constraints have significant impact on the execution performance of applications executed on MCHMPS, predictability is also a critical factor for task scheduling on MCHMPS. Therefore, a novel list scheduling algorithm termed Memory-constraint-aware Improved Predict Priority and Optimistic Processor Selection scheduling (MIPPOSS), essentially a heuristic search optimization algorithm, is proposed in this paper. In MIPPOSS, a predictive approach is applied for task prioritization and processor selection, and a novel memory-constraint-aware approach is employed in the processor selection phase. MIPPOSS has polynomial complexity and produces better results for application scheduling on target architecture. Randomly generated DAGs and 3 real-world applications experiments, including Cybershake, LIGO, and Montage, show that MIPPOSS outperforms the other five competing algorithms by a large margin.

关键词： Task analysis scheduling Memory management Costs Computational modeling scheduling algorithms Time complexity Heterogeneous systems memory constraints parallel computing list scheduling

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Workload-Aware scheduling Using Markov Decision Process for Infrastructure-Assisted Learning-Based Multi-UAV Surveillance Networks

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IEEE ACCESS 2023年 11卷 16533-16548页

作者： Park, Soohyun Park, Chanyoung Jung, Soyi Kim, Jae-Hyun Kim, Joongheon Korea Univ Dept Elect & Comp Engn Seoul 02841 South Korea Ajou Univ Dept Elect & Comp Engn Suwon 16499 South Korea

In modern networking research, infrastructure-assisted unmanned autonomous vehicles (UAVs) are actively considered for real-time learning-based surveillance and aerial data-delivery under unexpected 3D free mobility and coordination. In this system model, it is essential to consider the power limitation in UAVs and autonomous object recognition (for abnormal behavior detection) deep learning performance in infrastructure/towers. To overcome the power limitation of UAVs, this paper proposes a novel aerial scheduling algorithm between multi-UAVs and multi-towers where the towers conduct wireless power transfer toward UAVs. In addition, to take care of the high-performance learning model training in towers, we also propose a data delivery scheme which makes UAVs deliver the training data to the towers fairly to prevent problems due to data imbalance (e.g., huge computation overhead caused by larger data delivery or overfitting from less data delivery). Therefore, this paper proposes a novel workload-aware scheduling algorithm between multi-towers and multi-UAVs for joint power-charging from towers to their associated UAVs and training data delivery from UAVs to their associated towers. To compute the workload-aware optimal scheduling decisions in each unit time, our solution approach for the given scheduling problem is designed based on Markov decision process (MDP) to deal with (i) time-varying low-complexity computation and (ii) pseudo-polynomial optimality. As shown in performance evaluation results, our proposed algorithm ensures (i) sufficient times for resource exchanges between towers and UAVs, (ii) the most even and uniform data collection during the processes compared to the other algorithms, and (iii) the performance of all towers convergence to optimal levels.

关键词： Poles and towers Surveillance Data models scheduling algorithms Training data Computational modeling Autonomous aerial vehicles Markov processes Unmanned aerial networks scheduling learning systems surveillance Markov decision process (MDP)

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Burst-Aware Time-Triggered Flow scheduling With Enhanced Multi-CQF in Time-Sensitive Networks

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IEEE-ACM TRANSACTIONS ON NETWORKING 2023年第6期31卷 2809-2824页

作者： Yang, Dong Cheng, Zongrong Zhang, Weiting Zhang, Hongke Shen, Xuemin Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 0B5 Canada

Deterministic transmission guarantee in time-sensitive networks (TSN) relies on queue models (such as CQF, TAS, ATS) and resource scheduling algorithms. Thanks to its ease of use, the CQF queue model has been widely adopted. However, the existing resource scheduling algorithms of CQF model only focus on periodic time-triggered (TT) flows without consideration of bursting flows. Considering that the bursting flows often carry high-priority data in real systems, in this paper we investigate the mixed-flow (i.e., TT and bursting flows) scheduling problem in CQF-based TSN aiming to maximize the number of schedulable flows and system load balance while satisfying the deterministic demands of delay, jitter, and reliability for both TT and bursting flows. Unfortunately, it is challenging to schedule the mixed flows with the original CQF model because of the huge difference between TT and bursting flows. To resolve this problem, we firstly design an enhanced Multi-CQF model to satisfy the basic demands of bursting flows sent at any time without affecting the deterministic transmission of TT flows. Given the complexity of mixed-flow scheduling and the proposed queue model, it is difficult for traditional algorithms to fully utilize network resources. Thus, we further propose a time-correlated DRL resource scheduling (TimeDRS) algorithm to optimize the resource allocation. TimeDRS can be extended to other time-related resource scheduling scenarios, such as TDMA-based scheduling. Experimental results demonstrate that our proposed approaches can greatly reduce frame loss and end-to-end latency for bursting flows, and well balance runtime and schedulability compared with state-of-the-art benchmarks.

关键词： Jitter Job shop scheduling scheduling algorithms Computational modeling Load modeling Heuristic algorithms Delays Time-sensitive networks resource scheduling enhanced Multi-CQF deep reinforcement learning

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Multiuser scheduling Algorithm for 5G IoT Systems Based on Reinforcement Learning

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第4期72卷 4643-4653页

作者： Li, Zibo Pan, Su Qin, Yuxi Nanjing Univ Posts & Telecommun Coll Telecommun & Informat Engn Nanjing 210003 Peoples R China Nanjing Univ Posts & Telecommun Coll Internet Things Nanjing 210003 Peoples R China

MU-MIMO technology is adopted in 5G to support the increasing number of user terminals accessing the 5G IoT systems. The algorithms adopted in the existing literatures for user scheduling in MIMO system are greedy algorithm essentially, which needs to repeatedly calculate the achievable data rate (or its low complexity characterization) of each user during the user selection. Due to the large number of IoT terminals, the existing methods will generate huge computational load. In this paper, we propose a multiuser scheduling algorithm for 5G IoT systems based on reinforcement learning. The user terminal's action-value, which denotes the expectation of user terminal's achievable data rate, is obtained through Q-learning. We define the Q-value as the upper bound of the confidence interval of the user terminal's action-value and the proposed algorithm selects users on the basis of the Q-value. The proposed algorithm does not need to try different user combinations to maximize the throughput, and it is unnecessary to repeatedly calculate user's achievable data rate, so that the computational load is reduced. Simulation and numerical results show that the computational complexity of the proposed algorithm is lower than that of existing algorithms. At the same time, the system throughput achieved by this algorithm is not lower than that of greedy algorithms.

关键词： Base stations Throughput Computational complexity scheduling algorithms 5G mobile communication Optimal scheduling Greedy algorithms 5G IoT systems multiuser scheduling reinforcement learning computational complexity

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Combination of Throughput-Optimal scheduling and Network Utility Maximization in NOMA Systems With Flow-Level Dynamics

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第11期72卷 14573-14588页

作者： Chen, Yunpei Zhu, Qi Guo, Caili Yuan, Yifei Feng, Chunyan Nanjing Univ Posts & Telecommun Engn Res Ctr Hlth Serv Syst Based Ubiquitous Wire Jiangsu Key Lab Wireless Commun Minist Educ Nanjing 210003 Peoples R China Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing Key Lab Network Syst Architecture & Conve Beijing 100876 Peoples R China China Mobile Res Inst Future Mobile Technol Lab Beijing 100053 Peoples R China

In wireless networks, the network queue stability (throughput-optimality) and the network utility maximization are two crucial tasks for network operators. Besides, new techniques of mobile communications, e.g. the non-orthogonal multiple access (NOMA), have emerged endlessly to meet coming challenges. In this article, we for the first time, investigate a downlink NOMA system with flow-level dynamics, where long-lived flows and short-lived flows exist simultaneously, and formulate an optimization problem of user selection and power allocation. We aim to maximize the network utility at each time slot and simultaneously guarantee the throughput-optimality, under the quality of service (QoS) constraints. Considering the practicability of algorithm, the suboptimal algorithm containing two stages, is proposed. And we analyze the complexity of proposed algorithm. In the performance evaluation, the benchmark algorithm is provided for comparison. And we take the energy efficiency (EE) as an example of the network utility and define the scheduling delay (SD). Simulation results show that the suboptimal algorithm is throughput-optimal, and understandably, its performances could be better than those in the benchmark algorithm in terms of EE and SD, as long as the appropriate parameter is set.

关键词： NOMA Wireless networks Dynamic scheduling scheduling algorithms Heuristic algorithms Quality of service Downlink Throughput-optimality network utility maximi- zation non-orthogonal multiple access suboptimal algorithm

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Collaborative Learning-Based scheduling for Kubernetes-Oriented Edge-Cloud Network

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IEEE-ACM TRANSACTIONS ON NETWORKING 2023年第6期31卷 2950-2964页

作者： Shen, Shihao Han, Yiwen Wang, Xiaofei Wang, Shiqiang Leung, Victor C. M. Tianjin Univ Coll Intelligence & Comp Tianjin 300350 Peoples R China IBM T J Watson Res Ctr Yorktown Hts NY 10598 USA Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518052 Peoples R China Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada

Kubernetes (k8s) has the potential to coordinate distributed edge resources and centralized cloud resources, but currently lacks a specialized scheduling framework for edge-cloud networks. Besides, the hierarchical distribution of heterogeneous resources makes the modeling and scheduling of k8s-oriented edge-cloud network particularly challenging. In this paper, we introduce KaiS, a learning-based scheduling framework for such edge-cloud network to improve the long-term throughput rate of request processing. First, we design a coordinated multi agent actor-critic algorithm to cater to decentralized request dispatch and dynamic dispatch spaces within the edge cluster. Second, for diverse system scales and structures, we use graph neural networks to embed system state information, and combine the embedding results with multiple policy networks to reduce the orchestration dimensionality by stepwise scheduling. Finally, we adopt a two-time-scale scheduling mechanism to harmonize request dispatch and service orchestration, and present the implementation design of deploying the above algorithms compatible with native k8s components. Experiments using real workload traces show that KaiS can successfully learn appropriate scheduling policies, irrespective of request arrival patterns and system scales. Moreover, KaiS can enhance the average system throughput rate by 15.9% while reducing scheduling cost by 38.4% compared to baselines.

关键词： Cloud computing Heuristic algorithms Throughput IEEE transactions Clustering algorithms Dynamic scheduling Computational modeling Edge computing kubernetes reinforcement learning scheduling algorithms

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A group scheduling algorithm for massive heterogeneous data in the "dual carbon" digital intelligence monitoring center considering time-varying characteristics and priorities

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Sustainable Energy Research 2025年第1期12卷 1-14页

作者： Kang, Wenni Zhu, Dongge Zhang, Shuang Liu, Jia Ma, Rui State Grid Ningxia Electric Power Co. Ltd. Technical Research Institute Yinchuan750002 China

In view of the fact that the data of the "double carbon" digital intelligent monitoring center has the characteristics of constantly changing with time and that there are key tasks with high real-time requirements in the massive heterogeneous data, a "double carbon" that takes into account time-varying characteristics and priorities is proposed. The massive heterogeneous data grouping scheduling algorithm of the digital monitoring center schedules data according to the urgency of the task. The functional data analysis (FDA) method is used to convert the massive multi-source heterogeneous data of the "double carbon" digital intelligence monitoring center into continuous functions to solve the problem of frequency inconsistency and unify the data format;through the CNN–LSTM based on the Attention mechanism. The model extracts time-varying features from the data that eliminates heterogeneous characteristics, and implements data grouping in the "dual carbon" digital intelligence monitoring center;by setting differentiated priorities for different groups of data, it combines the data scheduling demand estimation model and delayed response time (RTT) factor and congestion factor, calculate the data priority-oriented data scheduling link similarity (DPLS), allocate the data to be scheduled to the scheduling link with the highest DPLS value for transmission, and realize the "double carbon" digital intelligence monitoring center data group scheduling. Experimental results show that this algorithm can unify heterogeneous data to the form of functional expression and improve data consistency. The absolute value of the Pearson correlation coefficient of data grouping reaches 0.953, and the grouping effect is good. High-priority data can be scheduled to the best transmission link to improve the efficiency and reliability of data transmission and realize the optimal allocation of resources. © The Author(s) 2025.

关键词： scheduling algorithms

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RLPTO: A Reinforcement Learning-Based Performance-Time Optimized Task and Resource scheduling Mechanism for Distributed Machine Learning

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2023年第12期34卷 3266-3279页

作者： Lu, Xiaofeng Liu, Chao Zhu, Senhao Mao, Yilu Lio, Pietro Hui, Pan Beijing Univ Post & Telecommun Natl Engn Ctr Mobile Internet Secur Technol Beijing 100876 Peoples R China Alibaba Cloud Comp Co Ltd Beijing 100102 Peoples R China Univ Cambridge Comp Lab Cambridge CB2 1TN England Hong Kong Univ Sci & Technol Computat Media & Arts Thrust Guangzhou 510000 Peoples R China

With the wide application of deep learning, the amount of data required to train deep learning models is becoming increasingly larger, resulting in an increased training time and higher requirements for computing resources. To improve the throughput of a distributed learning system, task scheduling and resource scheduling are required. This article proposes to combine ARIMA and GRU models to predict the future task volume. In terms of task scheduling, multi-priority task queues are used to divide tasks into different queues according to their priorities to ensure that high-priority tasks can be completed in advance. In terms of resource scheduling, the reinforcement learning method is adopted to manage limited computing resources. The reward function of reinforcement learning is constructed based on the resources occupied by the task, the training time, the accuracy of the model. When a distributed learning model tends to converge, the computing resources of the task are gradually reduced so that they can be allocated to other learning tasks. The results of experiments demonstrate that RLPTO tends to use more compu-ting nodes when facing tasks with large data scale and has good scalability. The distributed learning system reward experiment shows that RLPTO can make the computing cluster get the largest reward.

关键词： Cloud computing scheduling algorithms dynamic scheduling resource management distributed computing reinforcement learning

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