Article
Computer Science, Artificial Intelligence
Yinping Gao, Daofang Chang, Chun-Hsien Chen, Zhenyu Xu
Summary: This paper proposes a digital twin-enabled automated storage yard scheduling framework that utilizes the Internet of Things (IoT), virtual reality, and digital thread technologies. The framework monitors disturbed scenarios during practical operation and visualizes real-time data in the virtual space to adapt to the changing environment. The optimization of storage area, automated stacking cranes (ASCs), and automated guided vehicles (AGVs) is the main focus of this framework. A case study is conducted to demonstrate the effectiveness of the proposed framework in handling uncertainties and making optimization decisions in the port.
ADVANCED ENGINEERING INFORMATICS
(2022)
Article
Computer Science, Theory & Methods
Bo Cui, Yun Hu
Summary: This paper proposes a Blockchain Simulator based on Event-Layered Architecture (BSELA), which improves the efficiency and accuracy of blockchain simulation through the event-driven scheduling mechanism and the time advancement mechanism. The experimental results show that our simulator outperforms existing blockchain simulators in terms of efficiency and accuracy, and the tuning mechanism of the object model improves the node-trust network construction and enhances data transfer efficiency and security.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Sheng Wang, Zhijun Ding, Changjun Jiang
Summary: This article proposes an Elastic Scheduling for Microservices (ESMS) that integrates task scheduling with auto-scaling to minimize the cost of virtual machines while meeting deadline constraints. ESMS uses a statistics-based strategy to determine the configuration of containers under a streaming workload, and an urgency-based workflow scheduling algorithm to assign tasks and determine the type and quantity of instances for scale-up. Via simulation-based experiments and comparison with existing algorithms, ESMS is verified to improve the success ratio of meeting deadlines and reduce costs.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2021)
Review
Computer Science, Artificial Intelligence
Behice Meltem Kayhan, Gokalp Yildiz
Summary: This study conducted a comprehensive literature review on the application of reinforcement learning to machine scheduling problems. By analyzing the related literature, it identified different problem types, objectives, and constraints, and revealed the deficiencies and promising areas in the research.
JOURNAL OF INTELLIGENT MANUFACTURING
(2023)
Article
Computer Science, Information Systems
Keqin Li
Summary: In this paper, we address the challenges of scheduling precedence constrained tasks in a fog computing environment, including precedence constraints, power allocation, and performance-cost tradeoff. The first challenge is handled by the classic list scheduling algorithm and the level-by-level scheduling method in pre-power-allocation algorithms and post-power-allocation algorithms, respectively. The second challenge is dealt with by determining a power allocation strategy before a computation offloading strategy is decided. The third challenge is addressed by defining energy-constrained and time-constrained scheduling problems.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2023)
Article
Automation & Control Systems
Biao Hu, Yinbin Shi, Zhengcai Cao
Summary: This article proposes an adaptive scheduling approach to minimize the energy consumption of vehicular edge computing servers. An auction-bid scheme is developed to decide which roadside unit (RSU) responds to a computing request based on the least energy consumption. The proposed approach effectively decomposes a computing request modeled as a directed acyclic graph (DAG) application and assigns tasks to servers' queues in a specific RSU using a deadline-aware queue jump algorithm.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Theory & Methods
Jonas H. Muller Korndorfer, Ahmed Eleliemy, Ali Mohammed, Florina M. Ciorba
Summary: This work introduces LB4OMP, an open-source dynamic load balancing library that implements successful scheduling algorithms from the literature. Through extensive performance analysis, it is shown that the scheduling techniques in LB4OMP outperform the options in OpenMP for many applications-systems pairs. Node-level load balancing using LB4OMP leads to reduced cross-node load imbalance and improved performance for MPI+OpenMP applications, which is crucial for Exascale computing.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2022)
Review
Computer Science, Theory & Methods
Youyou Kang, Li Pan, Shijun Liu
Summary: This article summarizes the research work on big data analytics job scheduling in cloud environments, categorizes scheduling algorithms based on different research focuses, compares the advantages and disadvantages of existing research, and points out the directions and challenges for future research.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2022)
Article
Construction & Building Technology
Maedeh Taghaddos, Hosein Taghaddos, Ulrich Hermann (Rick), Yasser Mohamed, Simaan AbouRizk
Summary: Scheduling heavy industrial projects is challenging due to their size and complexity, especially when detailed information is lacking. A comprehensive planning framework has been proposed to develop an efficient schedule considering uncertainties in bidding and execution, utilizing historical information and expert opinion to automatically create a near-optimized schedule.
AUTOMATION IN CONSTRUCTION
(2021)
Article
Engineering, Electrical & Electronic
Samuele Zoppi, Jaya Prakash Champati, James Gross, Wolfgang Kellerer
Summary: Interactive applications with automated feedback have a significant impact on the design of future network infrastructures. In this study, we model the feedback loop as a two-hop network to optimize the end-to-end latency and propose semi-static and dynamic scheduling policies to allocate network resources. Results show that our proposed policies achieve close-to-optimal delay violation probability (DVP) and outperform existing algorithms.
IEEE TRANSACTIONS ON COMMUNICATIONS
(2022)
Article
Computer Science, Information Systems
Francesco Pace, Daniele Venzano, Damiano Carra, Pietro Michiardi
Summary: This paper addresses the problem of scheduling user-defined analytic applications, proposes a novel flexible heuristic scheduling algorithm, and presents a full-fledged system design and evaluation. The algorithm outperforms current alternatives in terms of application turnaround times and resource allocation efficiency, as demonstrated by trace-driven simulations and real system traces.
IEEE TRANSACTIONS ON CLOUD COMPUTING
(2023)
Article
Operations Research & Management Science
Victor Abu-Marrul, Rafael Martinelli, Silvio Hamacher, Irina Gribkovskaia
Summary: This paper addresses a parallel machine scheduling problem with non-anticipatory family setup times and batching. It proposes an Iterated Greedy simheuristic with built-in Monte Carlo Simulation to handle the stochastic parameters. Experimental results show that the proposed simheuristic outperforms other algorithms in terms of both objective values and computational times.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Computer Science, Hardware & Architecture
Basharat Mahmood, Naveed Ahmad
Summary: This paper introduces a novel fixed-priority semi-partitioned scheduling algorithm, RM-SPwTS, for addressing the real-time scheduling problem of uniform multicore processors. It is shown that RM-SPwTS outperforms current semi-partitioned scheduling algorithms and achieves a utilization bound of 69%. To the best of our knowledge, RM-SPwTS is the first algorithm in the fixed-priority multicore scheduling category to achieve this bound. Extensive simulations further establish the superior performance of RM-SPwTS compared to its existing counterparts, which can schedule up to 95% more task-sets, improve processor utilization by up to 14%, and reduce the number of required cores by up to 24%.
SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Abdullah Lakhan, Mazin Abed Mohammed, Begonya Garcia-Zapirain, Jan Nedoma, Radek Martinek, Prayag Tiwari, Neeraj Kumar
Summary: This research proposes a cost-efficient and secure vehicular fog cloud computing environment that addresses mobility and offloading costs through a mobility-aware multi-scenario offloading phase and a fully polynomial-time approximation scheme based search task scheduling. It also protects data through a security scheme based on fully-homomorphic encryption. The results show that the proposed work optimizes costs by 40% compared to existing systems.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2022)
Article
Biotechnology & Applied Microbiology
Alexandros Koulouris, Nikiforos Misailidis, Demetri Petrides
Summary: Food processing industries are increasingly adopting digital technologies to ensure product safety and quality, minimize costs, and guarantee timely delivery. The concept of a digital twin provides a digital model of the production system for design, monitoring, and optimization. While facing unique challenges, the implementation of digital modeling approaches has great potential for improving production efficiency in the food processing industry.
FOOD AND BIOPRODUCTS PROCESSING
(2021)
