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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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Dynamic Load scheduling Optimization Utilizing the Rao Algorithm for Improved System Performance 1

Dynamic Load Scheduling Optimization Utilizing the Rao Algor...

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1st IEEE International Conference on Smart and Sustainable Developments in Electrical Engineering, SSDEE 2025

作者： Basak, Sourav Bhattacharyya, Biplab Rajbhar, Suraj Kumar Dhanbad Department of Electrical Engineering Dhanbad India

ISBN: (纸本)9798331542108

This paper explores the optimal scheduling of conventional generating units for two dynamic test systems, and enhances the systems performance. The Rao algorithmic approach, a parameter-free population-based optimization method, is utilized to improve the solutions through cooperative interactions on the basis of the best and worst candidate solutions in each iteration. Practical factors, such as ramp-rate limits, valve point effects and transmission loss, were incorporated to reflect real-world operational challenges. The Rao algorithm achieved high-quality solutions in terms of generation cost and reduced pollutant emission. These results highlight the effectiveness of the algorithm in solving complex nonlinear optimization problems and improving the efficiency of dynamic load scheduling. © 2025 IEEE.

关键词： scheduling algorithms

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Slack Time Management for Imprecise Mixed-Criticality Systems With Reliability Constraints

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IEEE TRANSACTIONS ON COMPUTERS 2025年第5期74卷 1577-1588页

作者： Zhang, Yi-Wen Zheng, Hui Huaqiao Univ Coll Comp Sci & Technol Xiamen 361021 Peoples R China

A Mixed-Criticality System (MCS) integrates multiple applications with different criticality levels on the same hardware platform. For power and energy-constrained systems such as Unmanned Aerial Vehicles, it is important to minimize energy consumption of the computing system while meeting reliability constraints. In this paper, we first determine the number of tolerated faults according to the given reliability target. Second, we propose a schedulability test for MCS with semi-clairvoyance and checkpointing. Third, we propose the Energy-Aware scheduling with Reliability Constraint (EASRC) scheduling algorithm for MCS with semi-clairvoyance and checkpointing. It consists of an offline phase and an online phase. In the offline phase, we determine the offline processor speed by reclaiming static slack time. In the online phase, we adjust the processor speed by reclaiming dynamic slack time to further save energy. Finally, we show the performance of our proposed algorithm through experimental evaluations. The results show that the proposed algorithm can save an average of 9.67% of energy consumption compared with existing methods.

关键词： Reliability Checkpointing Fault tolerant systems Fault tolerance Switches Quality of service Dynamic scheduling Heuristic algorithms Energy consumption scheduling algorithms Mixed-criticality systems reliability constraint dynamic slack time checkpointing semi-clairvoyance

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Performance Characteristics and Guidelines of Offloading Middleboxes Onto BlueField-2 DPU

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IEEE TRANSACTIONS ON COMPUTERS 2025年第2期74卷 609-622页

作者： Li, Fuliang Chen, Qin Shen, Jiaxing Wang, Xingwei Cao, Jiannong Northeastern Univ Shenyang 110819 Peoples R China Lingnan Univ Hong Kong 999077 Peoples R China Hong Kong Polytech Univ Hong Kong 999077 Peoples R China

With the rapid growth in data center network bandwidth far outpacing improvements in CPU performance, traditional software middleboxes running on servers have become inefficient. The emerging data processing units aim to address this by offloading network functions from the CPU. However, as DPUs are still a new technology, there lacks comprehensive evaluation of their capabilities for accelerating middleboxes. This paper benchmarks and analyzes the performance of offloading middleboxes onto the NVIDIA BlueField-2 DPU. Three key DPU capabilities are explored: flow tables offloading, ARM subsystem packet processing, and connection tracking hardware offload. By applying these to implement representative middleboxes for firewall, packet scheduling, and load balancing, their performance is characterized and compared to conventional CPU-based versions. Results reveal the high throughput of flow tables offloading for stateless firewalls, but limitations as pipeline depth increases. Packet scheduling using ARM cores is shown to currently reduce performance versus CPU-based scheduling. Finally, while connection tracking hardware offload boosts load balancer bandwidth, it also weakens connection creation abilities. Key lessons on efficient middleboxes offloading strategies with DPUs are provided to guide further research and development. Overall, this paper offers useful benchmarking and analysis of emerging DPUs for accelerating middleboxes in modern data centers.

关键词： Middleboxes Central Processing Unit Firewalls (computing) Throughput Bandwidth Servers scheduling algorithms Computers Program processors Linux Data processing unit performance characteristics offloading middleboxes

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Multipath Bound for DAG Tasks

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2025年第5期44卷 1676-1689页

作者： He, Qingqiang Guan, Nan Zhao, Shuai Lv, Mingsong Great Bay Univ Sch Comp & Informat Technol Dongguan 523000 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510275 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

This article studies the response time bound of a directed acyclic graph (DAG) task. Recently, the idea of using multiple paths to bound the response time of a DAG task, instead of using a single longest path in previous results, was proposed and led to the so-called multipath bound. Multipath bounds can greatly reduce the response time bound and significantly improve the schedulability of DAG tasks. This article derives a new multipath bound and proposes an optimal algorithm to compute this bound. We further present a systematic analysis on the dominance and the sustainability of three existing multipath bounds and the proposed multipath bound. Our bound theoretically dominates and empirically outperforms all existing multipath bounds. What is more, the proposed bound is the only multipath bound that is proved to be self-sustainable.

关键词： Time factors Computational modeling Real-time systems scheduling algorithms Parallel processing Integrated circuits Design automation Sustainable development Job shop scheduling Directed acyclic graph Directed acyclic graph (DAG) task multipath bound real-time scheduling response time bound

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Detecting Interaction Related Bugs in a Multi-Tenant Setting Using Kubernetes

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INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING 2025年第3期35卷 351-374页

作者： Ghimis, Bogdan Univ Bucharest Fac Math & Comp Sci Dept Comp Sci 14 Acad St Bucharest 010014 Romania

Multi-tenant architectures allow multiple users to run similar applications on shared infrastructure, reducing the cost of resources. Developers of such applications must ensure tenant isolation to prevent users from interfering with one another. Concurrency bugs may arise only when a specific set of users is logged in at the same time. This paper introduces a framework for testing user interactions with a server, aimed at detecting timing-related bugs. The framework uses a scheduler to control when users log in and out of the system. If a particular set of users is logged in simultaneously, the server will simulate a potential bug. Users also keep a copy of the server's data, and if the server's data differs from their copy, a bug has occurred, which the user will report. The framework is designed to be flexible, supporting the addition of custom schedulers that can use either static scheduling algorithms or advanced Artificial Intelligence techniques to speed up bug detection more efficiently than traditional fuzz testing methods.

关键词： Fuzz testing testing framework multitenancy architecture Kubernetes baseline scheduling algorithms

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Optimized scheduling Model of Integrated Energy System in Industrial Park Based on Improved MPA 14

Optimized Scheduling Model of Integrated Energy System in In...

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14th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Liang, Shuang Jiang, Qingxuan School of Computer Science Northeast Electric Power University Jilin China

ISBN: (纸本)9798331531935

In the context of the global energy shortage and the increasing emphasis on sustainable development goals, optimizing the scheduling of integrated energy systems in industrial parks has become a critical issue. This paper presents an optimal scheduling model and an improved solution method to enhance energy efficiency in industrial parks. Firstly, we develop an energy efficiency optimization model for the integrated energy system in an industrial park. The model aims to maximize system energy efficiency and minimize operational costs while considering load balance constraints, equipment capacity, operational limits, and renewable energy generation constraints. Secondly, we introduce an enhanced Marine Predators Algorithm (MPA) incorporating an adaptive step-size strategy and a multi-subgroup approach. These improvements enable adaptive adjustment of the iteration step size, accelerating convergence and improving solution accuracy, thus facilitating the efficient solving of the model. Then, The effectiveness of the proposed solution method is validated through standard test functions. Finally, we test the model through simulations. The results show that, when considering both energy efficiency and economic factors, our proposed scheduling model effectively improves energy efficiency. It can theoretically reduce energy costs by 3.13% and save 23.683 tons of standard coal annually, all while maintaining the same output level. © 2025 IEEE.

关键词： scheduling algorithms

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Delay and Load Fairness Optimization With Queuing Model in Multi-AAV Assisted MEC: A Deep Reinforcement Learning Approach

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2025年第2期22卷 1247-1258页

作者： Tang, Qiang Li, Bao Yang, Halvin H. Li, Yan He, Shiming Yang, Kun Changsha Univ Sci & Technol Sch Comp Sci & Commun Engn Changsha 410114 Hunan Peoples R China UCL Dept Elect & Elect Engn London WC1E 7JE England Changchun Inst Technol Sch Comp Technol & Engn Changchun 130012 Peoples R China Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ England

Autonomous aerial vehicles (AAV) can alleviate the computational burden on edge devices through assisted computing. However, with the increase in the number of Internet of Things Devices (IoTDs), it is essential to establish a task queue on the AAV to schedule computing tasks from IoTDs. In addition, the load fairness of AAVs should be optimized to fully utilize the computing resources. Therefore, a multi-AAV-assisted mobile edge computing (MEC) network framework based on the queuing model is proposed, which aims at optimizing the average delay of all user devices and the load fairness of AAVs. Firstly, we prove that the arrangement of tasks with different computing delays on the AAV queue can affect the user's average delay, so a short-job-first (SJF) queuing model is proposed to minimize the average delay of users. On this basis, a joint optimization problem related to the AAV's three-dimensional trajectory and user connection scheduling is formulated. A SJF based low-complexity connection scheduling algorithm is proposed and combined in a deep reinforcement learning (DRL) to solve this NP-hard problem. To evaluate the performance of the proposed algorithm, we compare it with deep deterministic policy gradient (DDPG), particle swarm optimization (PSO), random moving (RM), and local computing (LC). Simulation results show that our algorithm effectively reduces user average delay and enhances AAV load fairness. Finally, SJF is compared with the traditional first-come-first-served (FCFS) queuing model on different algorithms. The results indicate that the average delay of SJF is significantly lower than that of FCFS.

关键词： Autonomous aerial vehicles Delays Computational modeling Trajectory Load modeling Optimization Internet of Things Heuristic algorithms Energy consumption scheduling algorithms Multi-AAV mobile edge computing (MEC) Internet of Things Devices (IoTDs) short-job-first (SJF) deep reinforcement learning (DRL)

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Learning-Based Deadlock-Free Multi-Objective Task Offloading in Satellite Edge Computing With Data-Dependent Constraints and Limited Buffers

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2025年第1期12卷 356-368页

作者： Zhang, Ruipeng Feng, Yanxiang Yang, Yikang Li, Xiaoling Li, Hengnian Xi An Jiao Tong Univ Syst Engn Inst Sch Automat Sci & Engn Xian 710049 Peoples R China Changan Univ Sch Elect & Control Engn Xian 710064 Peoples R China Xian Satellite Control Ctr State Key Lab Astronaut Dynam Xian 710043 Peoples R China

Satellite edge computing (SEC) is important for future network deployments because of its global coverage and low-latency computing services. Nevertheless, due to data dependencies among tasks and limited buffers in satellites, a coupling exists between transmission and computation, and undesired deadlocks may arise. This paper addresses task offloading in SEC and aims to minimize service latency, energy consumption, and time window violations simultaneously. First, a mixed-integer nonlinear programming model is presented. To resolve potential deadlocks, a deadlock amending algorithm (DAA) based on Petri net with polynomial time complexity is proposed. Deadlocks in solutions are amended by finding a transition sequence that corresponding transmission and computation can be performed sequentially. By embedding DAA, we develop a learning-based deadlock-free multi-objective scheduling algorithm (LDMOSA) that combines the exploration of evolutionary algorithms with the perception of reinforcement learning. To enhance the convergence and diversity of solutions, an initialization strategy employing problem-specific constructive heuristics is designed. Then, a learning-based mechanism is used to leverage real-time information to perform adaptive operator selection during the search process. Finally, extensive experiments demonstrate the effectiveness of DAA in resolving deadlocks, and the LDMOSA outperforms state-of-the-art algorithms for task offloading in SEC.

关键词： Satellites System recovery Internet of Things Couplings Petri nets scheduling algorithms Energy resolution Costs Vectors Traffic control Satellite-terrestrial integrated networks satellite edge computing computation offloading multi-task offloading

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Dynamic Time Slice Prediction in Round Robin CPU scheduling using Machine Learning 5

Dynamic Time Slice Prediction in Round Robin CPU Scheduling ...

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Dash, Bismaya Kanta Dev, Prabhu Prasad Pani, Santosh Kumar Kiit Deemed to be University Bhubaneswar India

ISBN: (纸本)9798331522100

Efficient CPU scheduling is crucial for optimizing system performance and resource utilization in multitasking operating systems. The Round Robin (RR) scheduling algorithm, known for its simplicity and fairness, often suffers from inefficiencies due to its use of a constant time slice, leading to increased waiting time (WT) and turnaround time (TAT) under varying workloads. This research addresses these limitations by proposing an optimized RR CPU scheduling algorithm that employs dynamic time slicing (DTS) using Artificial Neural Networks (ANN). DTS is adjusted based on CPU burst time (BT), with the aim of minimizing WT and TAT. Various regression techniques and ANN models are utilized to predict the DTS. These models are trained and evaluated using GWA-T-4 AuverGrid dataset of process attributes, with key performance metrics such as Mean Squared Error (MSE), R-squared (R2), average WT and average TAT used to assess their effectiveness. Experimental results demonstrate that the proposed RR method, especially when optimized with ANN, significantly reduces WT and TAT compared to other RR methods. © 2025 IEEE.

关键词： scheduling algorithms

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