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OLMS: A Flexible Online Learning Multi-Path scheduling Framework

IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2025年第3期12卷 2277-2291页

作者： Cai, Kechao Chen, Zhuoyue Zhang, Jinbei Lui, John C. S. Shenzhen Campus Sun Yat Sen Univ Sch Elect & Commun Engn Shenzhen Campus Shenzhen 510275 Peoples R China Chinese Univ Hong Kong Dept Comp Sci & Engn Shatin Hong Kong Peoples R China

Over the past decade, there has been a tremendous surge in the inter-connectivity among hosts in networks. Many multi-path transport protocols, such as MPTCP, MPQUIC, and MPRDMA, have emerged to facilitate multi-path data transmissions between pairs of hosts. However, existing packet schedulers in these protocols are quite limited as they neglect the stochastic nature inherent in heterogeneous paths, such as, round-trip time and available bandwidth. Moreover, users have diverse requirements;for instance, some prioritize low latency, while others consistently seek to achieve high bandwidth. In this paper, we propose a flexible Online Learning Multi-path scheduling (OLMS) framework to schedule packets to multiple paths and meet various user-defined requirements by learning the dynamic characteristics of paths in various applications. Specifically, we consider two types of applications, which are 1) maxRTT constrained and 2) bandwidth constrained, and use OLMS to schedule packets to satisfy the distinct user-defined requirements. Our theoretical analysis demonstrates that OLMS achieves guarantees with sublinear regret and sublinear violation. Furthermore, we implement a prototype of OLMS in MPQUIC and conduct experiments across different scenarios. Our experiments on Mininet show that OLMS enables an 8.42%-18.71% increase in bandwidth utilization in the maxRTT constrained application and negligible violations of user-defined requirements in both applications compared to other schedulers. Additionally, OLMS reduces flow completion times by 4.22%-10.26% compared to other schedulers, all without incurring large overhead.

关键词： scheduling Bandwidth Schedules Transport protocols scheduling algorithms Data communication Receivers Monitoring Estimation Prototypes Dynamic scheduling multi-armed bandit problem multi-path data transmission protocols online learning

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Earth Observation Satellite Downlink scheduling With Satellite-Ground Optical Communication Links

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2025年第2期61卷 2281-2294页

作者： Li, Hai Li, Yongjun Liu, Yuanhao Deng, Boyu Li, Yu Li, Xin Zhao, Shanghong Air Force Engn Univ Coll Informat & Nav Xian 710077 Peoples R China Chinese Acad Mil Sci Beijing 100071 Peoples R China Natl Univ Def Technol Coll Informat & Commun Wuhan 430000 Peoples R China

Consistent growth in the number of high-resolution Earth observation satellites (EOSs) poses a significant challenge to the downlink scheduling problem for observation data. Satellite-to-ground optical communication is considered promising for downloading massive observation data due to its large communication capacity. To improve the downlink transmission capacity of the EOS observation system, this article focuses on the EOS downlink scheduling problem with satellite-ground optical communication links (EDSPO). First, we establish a mathematical model considering many practical constraints for EDSPO to maximize the profit of downloaded observation data. To tackle this complex scheduling problem, a downlink scheduling algorithm combining with the adaptive large neighborhood search algorithm and the tabu search algorithm (ALNS_TS) is then proposed. In ALNS_TS, a heuristic solution initialization is developed, five removal operators and five repair operators are designed based on the heuristic characteristics of EDSPO, and a task insert algorithm is proposed for repair operators. Moreover, ALNS_TS introduces the tabu value for each downlink transmission task to avoid repeated searches in the neighborhood of the same solution. Finally, extensive experiments are carried out to verify the effectiveness of the ALNS_TS and the superiority of the satellite-ground optical communication link. Simulation results show that the ALNS_TS performs better in profits and the number of completed tasks compared to the existing downlink scheduling algorithms. Besides, the satellite-ground optical communication link has been proven effective in improving the downlink transmission capacity of the EOS observation system.

关键词： Downlink Earth Observing System Optical fiber communication Microwave antennas Satellites Transmitting antennas scheduling algorithms Adaptive large neighborhood search (ALNS) Earth observation satellites (EOSs) downlink scheduling problem satellite-ground optical communication link tabu search (TS)

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scheduling algorithms Based on Resource Fragmentation for Advance Reservation Tasks

Scheduling Algorithms Based on Resource Fragmentation for Ad...

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4th International Conference on Communication Systems and Network Technologies (CSNT)

作者： Li, Bo Pei, Yijian Wu, Hao Zhao, Xian Shen, Bin Yunnan Univ Sch Informat Sci & Engn Kunming Peoples R China Wuhan Inst Technol Sch Elect & Informat Engn Wuhan Peoples R China

ISBN: (纸本)9781479930708

Advance reservation is an important method to guarantee the quality of service in Grid-like distributed systems. However, reserved jobs will make resource into fragments and decrease utilization. In order to minimize the negative effects of advance reservations, the authors analyzed the generation of resource fragments during reservation and investigated their influence on advance reservation requests in a quantitative way. Based on the quantification, two new scheduling algorithms, Resource Fragment-aware Best Fit (FSB) and Resource Fragment-aware Worst Fit (FSW), were proposed and their performances were investigated via comprehensive simulations. In simulation, mean job size, deadline factor, system load and sever number were chosen as control factors, and the performances of the algorithms were analyzed in terms of job acceptance rate, resource utilization and slowdown. We also compared FSB and FSW with Best Fit, First Fit, Min_LIP and Min_TIP. The simulations show that FSW and FSB can provide higher job acceptance rate, especially under heavy system load.

关键词： grid-computing advance reservation co-allocation scheduling algorithms resource fragments

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Efficient Prefetch and Issue scheduling Approaches for Simultaneous Multithreading Applied to Superscalar RISC-V Processor

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IEEE ACCESS 2025年 13卷 29177-29189页

作者： Ribo, Hananya Greenberg, Shlomo Ben Gurion Univ Negev Sch Elect & Comp Engn IL-8410501 Beer Sheva Israel Sami Shamoon Coll Engn Comp Sci Dept IL-8410802 Beer Sheva Israel

Thread level parallelism (TLP) is a common approach to achieve parallelism where Instruction level parallelism (ILP) is insufficient. Hardware multithreading is a prevalent approach in the micro-architecture layer for tolerating long events such as memory access, mispredictions, and accelerators latencies by utilizing idle cycles and avoiding CPU stalling. Multithreading architectures are commonly used across many processors and various embedded edge devices to improve performance. This work suggests a new 2-way superscalar in-order Multithreading (MT) micro-architecture applied to a superscalar RISC-V processor. This work also proposes efficient prefetch-scheduling and Issue-scheduling algorithms for Simultaneous Multithreading (SMT) processors. The suggested micro-architecture was implemented on an FPGA chip. The results show that the proposed prefetch-scheduling and issue-scheduling algorithms outperform the 2-way superscalar Fine-Grained Multi-Threaded (FGMT) and the Coarse-Grained Multi-Threaded (CGMT) approaches. The proposed architecture is evaluated using the standard MiBench and RISCV benchmarks, demonstrating an average processor core utilization improvement of up to 52% in terms of IPC using four threads.

关键词： Instruction sets Pipelines Multithreading Hardware Prefetching Computer architecture scheduling scheduling algorithms Multicore processing Registers Hardware multithreading prefetch scheduling issue scheduling superscalar RISC-V in-order pipeline

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Deep-Reinforcement-Learning-Based Continuous Workflows scheduling in Heterogeneous Environments

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IEEE INTERNET OF THINGS JOURNAL 2025年第10期12卷 14036-14050页

作者： Wang, Zhi Zhan, Wenhan Duan, Hancong Min, Geyong Huang, Hualong Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China Univ Exeter Coll Engn Math & Phys Sci Dept Comp Sci Exeter EX4 4QF England

Workflow scheduling plays a critical role in optimizing completion time and throughput in distributed cloud environments, leveraging the parallelism of heterogeneous computing resources. However, existing workflow scheduling algorithms often fall short due to heuristic limitations and the challenges in adaptability within heterogeneous settings, leading to suboptimal scheduling solutions. In this article, we present a novel deep reinforcement learning (DRL) framework tailored for continuous workflow scheduling in heterogeneous environments. First, we propose an intelligent scheduler that updates the policy network through interactions with a multitenant environment, triggered by scheduling events. Next, the framework incorporates a graph attention network (GAT) and a self-attention multilayer perceptron (MLP) to preprocess the workflow topology and embed dynamic features of ready tasks and available processors into the state input at each scheduling step. Additionally, a k-dimensional tree-based k-nearest neighbors (kNN) algorithm is employed to map the output action vector to a pair of executed ready task and processor, facilitating the transition from continuous to discrete action spaces and addressing challenges associated with dynamic action spaces. Experimental results demonstrate that our method converges effectively in continuous workflow scheduling scenarios and significantly outperforms the best-known methods in terms of average makespan and load balancing efficiency.

关键词： Processor scheduling Program processors scheduling Heuristic algorithms Costs Clustering algorithms scheduling algorithms Internet of Things Dynamic scheduling Computational efficiency Cloud computing continuous workflows deep reinforcement learning (DRL) graph attention network (GAT) workflow scheduling

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Learning-Augmented scheduling

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IEEE TRANSACTIONS ON COMPUTERS 2024年第11期73卷 2548-2562页

作者： Zhao, Tianming Li, Wei Zomaya, Albert Y. Univ Sydney Sch Comp Sci Sydney NSW 2006 Australia

The recent revival in learning theory has provided us with improved capabilities for accurate predictions. This work contributes to an emerging research agenda of online scheduling with predictions by studying makespan minimization in uniformly related machine non-clairvoyant scheduling with job size predictions. Our task is to design online algorithms that use predictions and have performance guarantees tied to prediction quality. We first propose a simple algorithm-independent prediction error metric to quantify prediction quality. Then we design an offline improved 2-relaxed decision procedure approximating the optimal schedule to effectively use the predictions. With the decision procedure, we propose an online O(min {log eta,log m})-competitive static scheduling algorithm assuming a known prediction error. We use this algorithm to construct a robust O(min {log eta,log m})-competitive static scheduling algorithm that does not assume a known error. Finally, we extend these static scheduling algorithms to address dynamic scheduling where jobs arrive over time. The dynamic scheduling algorithms attain the same competitive ratios as the static ones. The presented algorithms require just moderate predictions to break the Omega(log m) competitive ratio lower bound, showing the potential of predictions in managing uncertainty.

关键词： Prediction algorithms Heuristic algorithms Optimal scheduling Dynamic scheduling Single machine scheduling scheduling algorithms Minimization Learning-augmented algorithms scheduling makespan predictions

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Partitioned scheduling With Shared Resources on Imprecise Mixed-Criticality Multiprocessor Systems

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2025年第1期44卷 65-76页

作者： Zhang, Yi-Wen Ma, Jin-Peng Gu, Zonghua Huaqiao Univ Coll Comp Sci & Technol Xiamen 361021 Peoples R China Hofstra Univ Dept Comp Sci Hempstead NY 11549 USA Umea Univ Dept Appl Phys & Elect S-90187 Umea Sweden

Both resource access protocols and real-time scheduling algorithms have been extensively studied in classic embedded real-time systems. However, there has been relatively little attention given to the resource access protocol and real-time scheduling algorithms in mixed-criticality systems. In this article, we pay attention to the problem of scheduling an imprecise mixed-criticality (IMC) taskset on a multiprocessor platform with shared resources. First, we propose an IMC with MSRP (IMC-MSRP) resource access protocol, which ensures mutually exclusive access to the shared resources for the tasks. Second, we propose the schedulability test based on the IMC-multiprocessor stack resource policy (MSRP) for a given task-to-processor mapping method. Third, we propose a feasible task-to-processor mapping algorithm called resource-aware criticality-unaware worst-fit decreasing (RA-CU-WFD), which first assigns tasks sharing the same resources to the same processor to reduce the global waiting time of the tasks and thus improve the schedulability ratio of the system. And then assigns tasks based on the criticality-unaware worst-fit decreasing (CU-WFD) algorithm. Finally, we conduct experiments using the synthetic tasksets, and the experimental results show that the RA-CU-WFD outperforms the other approaches in terms of the schedulability ratio.

关键词： Task analysis Program processors Job shop scheduling Access protocols scheduling algorithms Quality of service Partitioning algorithms Imprecise mixed-criticality (IMC) multicore partitioned scheduling shared resource

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A survey on the scheduling mechanisms in serverless computing: a taxonomy, challenges, and trends

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第5期27卷 5981-5993页

作者： Ghorbian, Mohsen Ghobaei-Arani, Mostafa Esmaeili, Leila Islamic Azad Univ Dept Comp Engn Qom Branch Qom Iran

In recent years, serverless computing has received significant attention due to its innovative approach to cloud computing. In this novel approach, a new payment model is presented, and a microservice architecture is implemented to convert applications into functions. These characteristics make it an appropriate choice for topics related to the Internet of Things (IoT) devices at the network's edge because they constantly suffer from a lack of resources, and the topic of optimal use of resources is significant for them. scheduling algorithms are used in serverless computing to allocate resources, which is a mechanism for optimizing resource utilization. This process can be challenging due to a number of factors, including dynamic behavior, heterogeneous resources, workloads that vary in volume, and variations in number of requests. Therefore, these factors have caused the presentation of algorithms with different scheduling approaches in the literature. Despite many related serverless computing studies in the literature, to the best of the author's knowledge, no systematic, comprehensive, and detailed survey has been published that focuses on scheduling algorithms in serverless computing. In this paper, we propose a survey on scheduling approaches in serverless computing across different computing environments, including cloud computing, edge computing, and fog computing, that are presented in a classical taxonomy. The proposed taxonomy is classified into six main approaches: Energy-aware, Data-aware, Deadline-aware, Package-aware, Resource-aware, and Hybrid. After that, open issues and inadequately investigated or new research challenges are discussed, and the survey is concluded.

关键词： Serverless computing Function as a service scheduling algorithms Energy-aware scheduling Data-aware scheduling Deadline-aware scheduling Package-aware scheduling Resource-aware scheduling Makespan Serverless edge computing Resource management Performance evaluation

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Tile-Weighted Rate-Distortion Optimized Packet scheduling for 360° Virtual-Reality Video Streaming

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IEEE INTELLIGENT SYSTEMS 2024年第4期39卷 60-72页

作者： Wang, Haopeng Dong, Haiwei El Saddik, Abdulmotaleb Univ Ottawa Ottawa ON K1N 6N5 Canada Huawei Technol Canada Ottawa ON K2K 3J1 Canada Univ Ottawa Ottawa ON K1N 6N5 Canada

A key challenge of 360 degrees virtual-reality (VR) video streaming is ensuring high quality with limited network bandwidth. Currently, most of the studies focus on tile-based adaptive bit-rate streaming to reduce bandwidth consumption, where resources in network nodes are not fully utilized. This article proposes a tile-weighted rate-distortion (TWRD) packet scheduling optimization system to reduce data volume and improve video quality. A multimodal spatial-temporal attention transformer is proposed to predict viewpoint with probability that is used to dynamically weight tiles and their corresponding packets. The packet scheduling problem of determining which packets should be dropped is formulated as an optimization problem solved by a dynamic programming solution. Experiment results demonstrate that the proposed method outperforms the existing methods under various conditions.

关键词： Streaming media scheduling algorithms Distortion Transformers Rate-distortion Bandwidth Visualization Virtual reality

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Privacy-Preserving Link scheduling for Wireless Networks

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IEEE ACCESS 2024年 12卷 109657-109672页

作者： Abbasalizadeh, Maryam Chan, Jeffrey Rayavaram, Pranathi Chen, Yimin Narain, Sashank Univ Massachusetts Lowell Miner Sch Comp & Informat Sci Lowell MA 01854 USA

Wireless communication is now a cornerstone of modern society, propelled by the widespread adoption of IoT devices and sophisticated wireless technologies. As wireless networks grow in complexity, there is an increasing need for efficient scheduling algorithms that can manage resources and adapt to evolving conditions effectively. Link scheduling is critical as it optimizes the use of wireless spectrum and bandwidth, prioritizing high-priority transmissions and minimizing interference. However, traditional link scheduling algorithms have primarily focused on performance, often overlooking the crucial aspect of privacy. This oversight poses significant risks in scenarios where privacy is paramount. To address this issue, we introduce PriLink, a novel link scheduling algorithm that prioritizes privacy without sacrificing performance. PriLink employs a least privilege model, sharing only essential links to protect critical topology details from potential adversaries. Our comprehensive evaluation shows that PriLink not only matches but occasionally surpasses the performance of established benchmarks like Greedy Maximal scheduling and Local Greedy scheduling. Moreover, it offers faster execution times and superior privacy protection. These results highlight PriLink's effectiveness as a robust solution for efficient, real-time, and privacy-preserving link scheduling in dynamic wireless networks.

关键词： Wireless networks Privacy Network topology scheduling scheduling algorithms Wireless communication Interference Link scheduling wireless networks privacy topology concealment

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