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ESBMC v7.7: Efficient Concurrent Software Verification with scheduling, Incremental SMT and Partial Order Reduction: (Competition Contribution) 31th

ESBMC v7.7: Efficient Concurrent Software Verification with ...

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31st International Conference on Tools and algorithms for the Construction and Analysis of Systems, TACAS 2025, which was held as part of the International Joint Conferences on Theory and Practice of Software, ETAPS 2025

作者： Wu, Tong Li, Xianzhiyu Manino, Edoardo Menezes, Rafael Sá Gadelha, Mikhail R. Xiong, Shale Tihanyi, Norbert Petoumenos, Pavlos Cordeiro, Lucas C. The University of Manchester Manchester United Kingdom Federal University of Amazonas Manaus Brazil Igalia A Coruña Spain Eötvös Loránd University Budapest Hungary Arm® Cambridge United Kingdom

ISBN: (纸本)9783031906596

ESBMC v7.7 improves the verification of concurrent C programs by incorporating techniques such as dynamic thread scheduling, incremental SMT solving, and partial order reduction (POR). These improvements enhance the tool’s performance, particularly in exploring complex multi-threaded executions. The new scheduler prioritizes higher-thread identifiers during context switches, which helps explore deeper program states. The use of incremental SMT solving and a refined POR algorithm reduces the exploration of unreachable interleavings and redundant states. These updates enable ESBMC to detect bugs faster, making it a more effective tool for ensuring the safety of multi-threaded applications. © The Author(s) 2025.

关键词： scheduling algorithms

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An Adaptive Fuzzy-Based Two-Layered HRRN CPU Scheduler: FHRRN

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International Journal of Fuzzy System Applications 2022年第1期11卷 1-20页

作者： Raheja, Supriya Amity University Noida India

Fuzzy-based CPU scheduler has become an emerging component of an operating system. It can handle the imprecise nature of parameters used in scheduler. This paper introduces an adaptive fuzzy-based highest response ratio next CPU scheduler which is an extension of conventional CPU scheduler. The proposed scheduler works in two layers. At the first layer, a fuzzy inference system is defined that handles the uncertainties of parameters, and at the second layer, an adaptive scheduling algorithm is used to schedule each task. The proposed scheduler intelligently generates the response ratio for each ready-to-run task which makes the system adaptive at run time. The work is compared with the conventional highest response ratio next scheduling and the existing fuzzy highest ratio next scheduling algorithms. Results validate the better performance of proposed scheduler. The proposed scheduler also provides comparable results with respect to shortest job-first scheduling and shortest remaining task-first scheduling algorithms. Copyright © 2022, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

关键词： scheduling algorithms

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Enhancing investigations in data migration and security using sequence cover cat and cover particle swarm optimization in the fog paradigm

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International Journal of Intelligent Networks 2022年 3卷 204-212页

作者： Saravanan, T. Saravanakumar, S. Department of CSE GITAM University Bengaluru India Department of CSE Jain University Bengaluru India

In recent years, fog and mobile edge computing have grown rapidly due to the large amount of data generated by the Internet of Thing (IoT) devices. It provides a variety of services within the end user IoT environment, but suffers from inefficient scheduling, which results in more significant delays than cloud computing. In this research we propose a data migration procedure that beats the metrics of delay, response time, and load balancing rate in the fog computing paradigm. It is possible to reduce the amount of replicated and integrated data by using Sequence Cover Cat Swarm Optimization (SCCSO) and Sequence Cover Particle Swarm Optimization (SCPSO) by using appropriate Virtual machines (VMs) which lock highly used machines and give service to low used machines in cloud communication storage.;this allows us to obtain resources efficiently in the fog environment. After being tested in the iFogsim climate, The protocols performed better in the iFogsim climate in terms of response time, scheduling time load balancing rate and delay than the other scheduling algorithms. © 2022 The Authors

关键词： scheduling algorithms

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Reproducibility Report for ACM SIGMOD 2023 Paper: "MorphStream: Adaptive scheduling for Scalable Transactional Stream Processing on Multicores"

Reproducibility Report for ACM SIGMOD 2023 Paper: "MorphStre...

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2024 International Conference on Management of Data, SIGMOD ARI Reports 2024

作者： Singh, Ayushi Helmer, Sven Mao, Yancan Zhao, Jianjun Zhang, Shuhao Netflix Los GatosCA United States University of Zurich Zurich Switzerland National University of Singapore Singapore Singapore Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798400711794

We were able to reproduce the findings of the original paper. This includes performance comparisons with other systems, measurements of throughput and latency for different workloads and scheduling strategies, handling of abort decisions, and scalability comparisons. In total, this involves sixteen different figures in the original paper that were reproduced. © 2024 Copyright held by the owner/author(s).

关键词： scheduling algorithms

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Locality-aware Fair scheduling in LLM Serving

arXiv

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arXiv 2025年

作者： Cao, Shiyi Wang, Yichuan Mao, Ziming Hsu, Pin-Lun Yin, Liangsheng Xia, Tian Li, Dacheng Liu, Shu Zhang, Yineng Zhou, Yang Sheng, Ying Gonzalez, Joseph Stoica, Ion UC Berkeley United States LMSYS

Large language model (LLM) inference workload dominates a wide variety of modern AI applications, ranging from multi-turn conversation to document analysis. Balancing fairness and efficiency is critical for managing diverse client workloads with varying prefix patterns. Unfortunately, existing fair scheduling algorithms for LLM serving, such as Virtual Token Counter (VTC), fail to take prefix locality into consideration and thus suffer from poor performance. On the other hand, locality-aware scheduling algorithms in existing LLM serving frameworks tend to maximize the prefix cache hit rate without considering fair sharing among clients. This paper introduces the first locality-aware fair scheduling algorithm, Deficit Longest Prefix Match (DLPM), which can maintain a high degree of prefix locality with a fairness guarantee. We also introduce a novel algorithm, Double Deficit LPM (D2LPM), extending DLPM for distributed setup that can find a balance point among fairness, locality, and load-balancing. Our extensive evaluation demonstrates the superior performance of DLPM and D2LPM in ensuring fairness while maintaining high throughput (up to 2.87× higher than VTC) and low per-client (up to 7.18× lower than state-of-the-art distributed LLM serving system) latency. Copyright © 2025, The Authors. All rights reserved.

关键词： scheduling algorithms

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Multi-objective Flexible flow shop scheduling with limited buffers based on model-driven differential evolution algorithm

Multi-objective Flexible flow shop scheduling with limited b...

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2025 2nd International Conference on algorithms, Software Engineering and Network Security (ASENS)

作者： Lixin Liang Zhifeng Hao School of Mathematics and Computer Science Shantou University Shantou China

ISBN: (数字)9798331520298

ISBN: (纸本)9798331520304

The multi-objective flexible flow shop scheduling problem with limited buffers (MO-FFSS-LB) is widely encountered in modern manufacturing systems. However, the existence of limited buffers greatly increases the complexity of job scheduling and needs to consider job completion time, replacement costs of different recipes, and dynamic buffer balancing simultaneously. To address the challenge of MO-FFSS-LB, this paper proposes a model-driven differential evolution (MDE) algorithm. Firstly, it designs a multi-layer coding mechanism to represent the solution of the problem according to the characteristics of the scheduling problem and then introduces an initialization mechanism to improve the quality of the initial solution and the solving efficiency. Moreover, the proposed MDE utilizes a machine learning-based model trained on historical scheduling data to approximate fitness, enabling rapid population evolution. To further improve the convergence speed and solution quality, a neighborhood search-based local optimization strategy and dynamically adjusted crossover rates and mutation factors strategy are introduced, enhancing the algorithm’s adaptability to complex scheduling problems. Experimental results demonstrate that the MDE algorithm can generate high-quality solutions for the MO-FFSS-LB and outperforms existing traditional scheduling algorithms in several performance metrics. This research not only provides an effective optimization tool for solving complex scheduling problems with limited buffer constraints but also offers new insights and methods for theoretical research and practical applications in related fields.

关键词： Job shop scheduling Machine learning algorithms scheduling algorithms Heuristic algorithms Software algorithms Dynamic scheduling Approximation algorithms Encoding Data models Optimization

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Optimized Multitask scheduling in Cloud Computing Using Advanced Machine Learning Techniques

Optimized Multitask Scheduling in Cloud Computing Using Adva...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Vullam, Nagagopi Raju Geetha, Gorikapudi Rao, Nagamalleswara Vellela, Sai Srinivas Rao, Thalakola Syamsundara Thommandru, Raju Rao, Kolla Naga Sreenivasa Chalapathi Institute of Engineering and Technology Dept. of Cse AP Guntur India Chalapathi Institute of Technology Dept. of CSE-AI AP Guntur India Chalapathi Institute of Technology Dept. of Cse AP Guntur522016 India Chalapathi Institute of Technology Dept. of CSE-DS AP Guntur522016 India Kkr & Ksr Institute of Technology and Sciences Dept. of CSE-DS AP Guntur522017 India Chalapathi Institute of Technology Dept. of Ece AP Guntur522016 India Chalapathi Institute of Technology A.P Guntur522016 India

ISBN: (纸本)9798331527495

The delivery of computing services over the internet is known as cloud computing. One of most important problem in the cloud computing environment task scheduling, which has a direct affect on the platform's overall performance. Tasks are scheduled for specific resources at specific times based on user requests. It mainly focuses maximize resource utilization, minimize make span. Task scheduling in cloud is still a problem even if there are several ways to improve it. One important step in effectively harnessing cloud computing potential is scheduling tasks efficiently. In this work, machine learning technique is presented for improved multitask scheduling in cloud computing. For efficient task scheduling, we propose ML feature-based heuristic task scheduling (MLF-H). Instead of randomly assigning a scheduling algorithm, ML techniques are used to incoming task requests to classify best suitable algorithm for task. Simulations results show that MLF-H task scheduling approach has shortest make span and rapid generalization capabilities when compared to traditional methods. This proves the availability of the approach and the efficiency of the MLF-H scheduling algorithm. © 2025 IEEE.

关键词： scheduling algorithms

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On WVSN Deployment and scheduling for 3D Indoor Monitoring: A Cross-Layer Design Approach

On WVSN Deployment and Scheduling for 3D Indoor Monitoring: ...

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2025 International Conference on Computing, Networking and Communications, ICNC 2025

作者： Wang, Zhonghui Wang, Feng Sabbahein, Khaled Louisiana State University Shreveport ShreveportLA United States University of Mississippi MS United States Mississippi Valley State University Itta BenaMS United States

ISBN: (纸本)9798331520960

Wireless Visual Sensor Networks (WVSNs) are widely used but require effective strategies for coverage, connectivity, and efficient data collection due to high visual data demands. This paper adopts a cross-layer approach, framing sensor deployment and scheduling as an optimization problem. We propose SEED, a scheduling-Aware Enhanced Depth-First search algorithm, capable of achieving optimal solutions given sufficient time. Evaluations show our approach reduces communication latency and minimizes sensor usage effectively. © 2025 IEEE.

关键词： scheduling algorithms

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A simpler QPTAS for scheduling jobs with precedence constraints

arXiv

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arXiv 2025年

作者： Das, Syamantak Wiese, Andreas IIIT-Delhi India Technical University of Munich Germany

We study the classical scheduling problem of minimizing the makespan of a set of unit size jobs with precedence constraints on parallel identical machines. Research on the problem dates back to the landmark paper by Graham from 1966 who showed that the simple List scheduling algorithm is a (2 − m1 )-approximation. Interestingly, it is open whether the problem is NP-hard if m = 3 which is one of the few remaining open problems in the seminal book by Garey and Johnson. Recently, quite some progress has been made for the setting that m is a constant. In a break-through paper, Levey and Rothvoss presented a (1 + ǫ)-approximation with a running time of n(log n)O((m2 /ǫ2) log log n) [STOC 2016, SICOMP 2019] and this running time was improved to quasi-polynomial by Garg [ICALP 2018] and to even nOm,ǫ(log3 log n) by Li [SODA 2021]. These results use techniques like LP-hierarchies, conditioning on certain well-selected jobs, and abstractions like (partial) dyadic systems and virtually valid schedules. In this paper, we present a QPTAS for the problem which is arguably simpler than the previous algorithms. We just guess the positions of certain jobs in the optimal solution, recurse on a set of guessed subintervals, and fill in the remaining jobs with greedy routines. We believe that also our analysis is more accessible, in particular since we do not use (LP-)hierarchies or abstractions of the problem like the ones above, but we guess properties of the optimal solution directly. © 2025, CC BY.

关键词： scheduling algorithms

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FPGA Acceleration and ARM Collaborative Optimization Testing of Multi Task scheduling Algorithm Based on Zynq SoC

FPGA Acceleration and ARM Collaborative Optimization Testing...

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Computer Applications and Information Technology (ISCAIT), International Symposium on

作者： Lanlai Wang Li Xu Wei Liu Xiaohai Hu Yongchang Zhao Shanghai Institute of Aerospace Technonical Foundation Shanghai China

ISBN: (数字)9798331542856

ISBN: (纸本)9798331542863

Existing multi-task scheduling algorithms face challenges in resource utilization and real-time performance. How to efficiently implement task scheduling to optimize system performance has become an urgent problem to be solved. First, a multi-task scheduling framework is designed to divide tasks into different priorities. Then, hardware acceleration of the scheduling algorithm is implemented on the FPGA, and pipeline processing and parallel computing are used to improve throughput. Next, the ARM processor is used for task management and scheduling control, a time-slice-based scheduling strategy is adopted, and dynamic priority adjustment is implemented. Finally, real-time communication between FPGA and ARM is established through an efficient data transfer mechanism (DMA). The experimental result in the paper shows a resource utilization of 74% achieved by the Method in 20 tasks, which far exceeded the 57% attained by the traditional methods. The multi-task scheduling algorithm based on Zynq SoC can boost scheduling efficiency dramatically, and in this way, provide individualized solutions for future embedded system designs.

关键词： Program processors scheduling algorithms System performance Life estimation Multitasking Dynamic scheduling Real-time systems Resource management Field programmable gate arrays Optimization

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