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5th IEEE International Conference on Communication, Computing and Industry 6.0, C2I6 2024

作者： Aishwarya, Gunda Sekhar, G. Soma Prasad, M Shiva Donthi, Rakesh Kumar Reddy, Chilukala Mahender Medishetti, Santhosh Kumar Mlr Institute of Technology T.S. Hyderabad India Vardhaman College of Engineering T.S. Hyderabad India Geethanjali College of Engineering and Technology T.S. Hyderabad India Gitam Deemed to Be University T.S. Hyderabad India Nalla Narasimha Reddy Education Society's Group of Institutions T.S. Hyderabad India

ISBN: (纸本)9798350379884

Optimizing energy consumption in Cloud-Fog Computing (CFC) environments is essential for enhancing efficiency and sustainability. In this paper we propose a Meta-Heuristic (MH) scheduling algorithm based on the Butterfly Optimization Algorithm (BOA) to effectively manage resource allocation and Task scheduling (TS) between CFC. The proposed approach aims to minimize energy consumption, while improving throughput and optimizing resource utilization. Through simulation results, the BOA-based algorithm demonstrates a reduction in energy consumption by 15.6%, an increase in throughput by 18.4%, and an enhancement in resource utilization by 20.8%, compared to existing scheduling algorithms. These improvements make the BOA a promising solution for energy-efficient and performance-enhancing task scheduling in cloud-fog environments, contributing to more sustainable and responsive systems for processing large-scale, distributed workloads. © 2024 IEEE.

关键词： scheduling algorithms

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Improving Data Locality of Tasks by Executor Allocation in Spark Computing Environment

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IEEE TRANSACTIONS ON CLOUD COMPUTING 2024年第3期12卷 876-888页

作者： Fu, Zhongming He, Mengsi Yi, Yang Tang, Zhuo Univ South China Coll Comp Sci & Technol Hengyang 421001 Peoples R China Hunan Univ Coll Informat Sci & Engn Changsha 410082 Hunan Peoples R China Natl Supercomp Ctr Changsha 410082 Hunan Peoples R China

The concept of data locality is crucial for distributed systems (e.g., Spark and Hadoop) to process Big Data. Most of the existing research optimized the data locality from the aspect of task scheduling. However, as the execution container of Spark's tasks, the executor launched on different nodes can directly affect the data locality achieved by the tasks. This article tries to improve the data locality of tasks by executor allocation in Spark framework. First, because of different communication modes at stages, we separately model the communication cost of tasks for transferring input data to the executors. Then formalize an optimal executor allocation problem to minimize the total communication cost of transferring all input data. This problem is proven to be NP-hard. Finally, we present a greed dropping heuristic algorithm to provide solution to the executor allocation problem. Our proposals are implemented in Spark-3.4.0 and its performance is evaluated through representative micro-benchmarks (i.e., WordCount, Join, Sort) and macro-benchmarks (i.e., PageRank and LDA). Extensive experiments show that the proposed executor allocation strategy can decrease the network traffic and data access time by improving the data locality during the task scheduling. Its performance benefits are particularly significant for iterative applications.

关键词： Task analysis Resource management Sparks scheduling Distributed databases Costs scheduling algorithms Data locality distributed systems executor allocation Spark framework

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A Comparative Analysis of Population-Based Algorithm for Optimizing Cost and Makespan for Task scheduling in Cloud–Fog Environment 3rd

A Comparative Analysis of Population-Based Algorithm for Opt...

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3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Sharma, Shivam Verma, Amandeep Department of Information Technology University Institute of Engineering and Technology Chandigarh India

ISBN: (纸本)9789819726707

As cloud computing makes the use of services and various resources easier for the user, there has been a growing need for efficient and quick resource utilization and services to enhance the performance and that is what fog computing comes in. Fog computing is an extension of cloud computing that takes place at the network's edge. Simply described, it is a hybrid combination of cloud computing and the Internet of things. It is a highly virtualized platform that connects end devices to standard cloud servers and delivers computation, storage, and networking services. Various task scheduling algorithms are used to schedule the tasks at fog nodes that increase the performance of the system. Task scheduling algorithms are classified into stochastic, deterministic, and hybrid algorithms. Our work depicts a performance evaluation of three population-based optimization algorithms which are used for workflow scheduling in a combination of cloud and fog environment. The following algorithms were used to perform the comparison: particle swarm optimization (PSO), genetic algorithm (GA), and a hybrid combination of PSO and GA. The evaluation function consists of three parameters: makespan, cost, and energy. Also, the performance of these algorithms is analyzed by varying the cloud nodes and fog nodes by keeping the number of end devices fixed. The simulator used for the comparative evaluation is the recently proposed FogWorkflowSim. The research proves that hybrid algorithm of the PSO-GA algorithm performs better than the traditional PSO and GA algorithms in terms of cost and makespan. In future, performance will be evaluated by increasing the number of tasks and more workflows would be added to evaluate the performance. The algorithms would be evaluated against the deadline for workflows. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： scheduling algorithms

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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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Multi-Dimensional Conflict Graph-Based scheduling for Varied Deadline Requirements in IWSNs

Multi-Dimensional Conflict Graph-Based Scheduling for Varied...

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2024 China Automation Congress, CAC 2024

作者： Pu, Chenggen Cai, Huawu Wang, Ping Liao, Jinfeng Dong, Changjie China-Korea Belt and Road Joint Laboratory on Industrial Internet of Things Chongqing University of Posts and Telecommunications Chongqng400065 China Institute of Industrial Internet Chongqing University of Posts and Telecommunications Chongqng401122 China

ISBN: (纸本)9798350368604

Satisfying the end-to-end delay requirements of various real-time data flows is a critical challenge in industrial wireless sensor networks (IWSNs). Since each data flow has different deadline constraints, effectively utilizing network resources while ensuring that data flows meet their deadlines becomes an important problem to be addressed. This paper proposes a joint time slot and channel allocation algorithm based on a multi-dimensional conflict graph, aiming to minimize the total end-to-end delay of multiple data flows with different deadline constraints. First, the concept of a minimum integer scheduling unit (MISU) is introduced to represent the minimum transmission period based on the deadlines, allowing for a unified scheduling structure. Next, a flow-link-slot-channel tuple is proposed to describe potential scheduling schemes, and a multidimensional conflict graph (MDCG) is constructed based on the conflicts among tuples. The joint scheduling problem is then transformed into finding the optimal maximum independent set (MIS) in the MDCG. To reduce computational complexity, the original problem is decomposed into a series of maximum weight independent set (MWIS) subproblems using a greedy-based hop-wise scheduling algorithm. Simulation results demonstrate the effectiveness of the proposed algorithm in minimizing end-to-end delay compared to existing algorithms. © 2024 IEEE.

关键词： scheduling algorithms

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Research on Real-Time scheduling Optimization Algorithm of Logistics Big Data Based on Intelligent Algorithm 5

Research on Real-Time Scheduling Optimization Algorithm of L...

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5th International Conference on Computers, Information Processing and Advanced Education, CIPAE 2024

作者： Jierui, Luo Guangdong Business and Technology University Guangdong Zhaoqing China

ISBN: (纸本)9798331527662

This paper proposes a real-time scheduling optimization algorithm based on big data of logistics distribution, aiming to improve the response speed and accuracy of material distribution through the design of intelligent scheduling system. The algorithm model comprehensively considers the dynamic changes of material supply chain, real-time traffic conditions of distribution routes and real-time updates of customer demand. By integrating advanced data analysis technology and machine learning algorithm, the system can monitor the indicators in the logistics distribution process in real time and dynamically adjust the scheduling plan accordingly. In addition, the algorithm also introduces a predictive model to predict potential delivery delays and make scheduling decisions in advance. In order to verify the effectiveness of the algorithm, detailed simulation experiments are carried out in this paper. The experimental results show that compared with the traditional scheduling method, the proposed algorithm shows significant advantages in reducing delivery time, reducing transportation cost and improving customer satisfaction. Especially in peak hours and complex road conditions, the optimization effect of the algorithm is more obvious, which effectively alleviates the pressure of logistics distribution. © 2024 IEEE.

关键词： scheduling algorithms

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VRRC: Empowering Metaverse-Infused Driving Experience for Multiplatoon Vehicles Through IoRT

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IEEE INTERNET OF THINGS JOURNAL 2024年第20期11卷 32416-32428页

作者： Luan, Zeyu Jiang, Yong Lv, Jianhui Yi, Bo Li, Kejun Pengcheng Lab Dept Strateg & Adv Interdisciplinary Res Shenzhen 518000 Peoples R China Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Shenzhen 518071 Peoples R China Pengcheng Lab Shenzhen 518000 Peoples R China Northeastern Univ Coll Comp Sci & Engn Shenyang 110819 Peoples R China

The emergence of metaverse applications and services heralds a new era of immersive driving experiences in future vehicular ad hoc networks (VANETs). This unprecedented metaverse-infused driving experience, however, requires high-throughput transmissions for real-time high-quality 360 degrees video streaming, which challenges today's limited bandwidth resources provisioned by wireless communication infrastructure. To this end, this article introduces virtual road, real connection (VRRC), a novel framework for enhancing the metaverse-infused driving experience in VANET through the Internet of Robotic Things (IoRT). In the face of limited bandwidth resources, VRRC effectively addresses the challenge of high-throughput demand from two key perspectives. First, VRRC reduces redundant transmission by implementing a graph neural network (GNN)-based vehicle clustering method for dynamic multicast group formation, taking into account both the geographical status of vehicles and their communication patterns. Second, VRRC aggregates bandwidth resources across various channels by employing a multiagent reinforcement learning (MARL)-based multipath packet scheduling policy to adapt to heterogeneous channel conditions and dynamic vehicular mobility. Extensive experiments with real-world vehicular traces validate the effectiveness of VRRC and demonstrate its outperformance in reducing redundant traffic by 54% and improving overall throughput by 28%. VRRC represents a substantial leap forward in the integration of the metaverse experience into VANET.

关键词： Vehicular ad hoc networks Vehicle dynamics scheduling algorithms Metaverse Bandwidth Streaming media Internet of Things Graph neural networks (GNNs) metaverse multipath TCP (MPTCP) reinforcement learning vehicular ad hoc network (VANET)

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Hypergraph-Based Active Minimum Delay Data Aggregation scheduling in Wireless-Powered IoT

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IEEE INTERNET OF THINGS JOURNAL 2022年第11期9卷 8786-8799页

作者： Jiao, Xianlong Lou, Wei Guo, Songtao Wang, Ning Chen, Chao Liu, Kai Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

Thanks to the promising wireless power transmission (WPT) technology, wireless-powered Internet of Things (WPIoT) can significantly improve the sustainable service ability of Internet of Things (IoT) with low personnel maintenance costs, and thus, shows remarkable and broad prospects in many applications, especially under the abominable and dangerous environment. Minimum delay data aggregation scheduling (MAS) is a problem of cardinal significance in WPIoT with the objective of timely collecting the data of IoT devices. However, due to the residual energy limitation of IoT devices, WPIoT shows the special feature of adopting the store-charge-and-forward communication mode, which brings new research challenges on designing efficient solutions to the MAS problem. We show that the MAS problem under the physical interference model in WPIoT is NP-hard. To tackle this problem, we propose a delay-efficient data aggregation scheduling algorithm called HADA based on an active data aggregation tree construction method and a novel hypergraph-based link scheduling method. Extensive numerical experiments are conducted to evaluate the performance of our proposed algorithm. The results demonstrate that our HADA algorithm can efficiently improve the performance compared with the existing baseline algorithms.

关键词： Interference Data aggregation Delays Internet of Things Wireless sensor networks scheduling algorithms Wireless communication Active data aggregation tree construction data aggregation scheduling hypergraph link scheduling wireless-powered Internet of Things (WPIoT)

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Approaching Work-Conserving scheduling Algorithm for Mixed Unicast and Multicast in Combined Input and Crosspoint Queued Switch

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IEEE SYSTEMS JOURNAL 2022年第1期16卷 1400-1411页

作者： Yan, Fulong Deng, Xiong Li, Chao Chen, Chen Alibaba Grp Alibaba Cloud Beijing Peoples R China Eindhoven Univ Technol Dept Elect Engn Eindhoven Netherlands Southwest Jiaotong Univ Sch Informat Sci & Technol Ctr Informat Photon & Commun Chengdu 611756 Peoples R China Anhui Univ Inst Phys Sci & Informat Technol Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei Anhui Peoples R China Chongqing Univ Sch Microelect & Commun Engn Chongqing 400044 Peoples R China

A novel scheduling algorithm supporting mixed unicast and multicast traffic and featured by the longest virtual queue first (LVQF) was proposed for combined input and crosspoint queued (CICQ) architecture. Considering the differences between unicast and multicast traffic, LVQF takes the virtual queue length as the weight value for unicast and multicast queues. The weight setting of LVQF coordinates the fairness of unicast and multicast scheduling efficiently. Moreover, the design of LVQF is characterized by approaching the inherent work-conserving of the output queued architecture. By balancing the occupancy of crosspoint buffers, LVQF maintains the switch operating in a work-conserving state to the largest extend. By introducing the traffic regulation module in CICQ, an enhanced approaching work-conserving (AWC) algorithm developed from LVQF (AWC-LVQF) is proposed to further approach the work-conserving state. Simulation results demonstrate that LVQF and AWC-LVQF algorithms can significantly improve the average latency performance with respect to the existing popular algorithms for CICQ switch. In particular, the performance improvement enlarges as the ratio of multicast traffic increases.

关键词： Unicast Switches scheduling Multicast algorithms scheduling algorithms Throughput Regulation Combined input and crosspoint queued (CICQ) packet scheduling unicast and multicast weight

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scheduling Coflows With Dependency Graph

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IEEE-ACM TRANSACTIONS ON NETWORKING 2022年第1期30卷 450-463页

作者： Shafiee, Mehrnoosh Ghaderi, Javad Columbia Univ Dept Elect Engn New York NY 10027 USA

Applications in data-parallel computing typically consist of multiple stages. In each stage, a set of intermediate parallel data flows (Coflow) is produced and transferred between servers to enable starting of next stage. While there has been much research on scheduling isolated coflows, the dependency between coflows in multi-stage jobs has been largely ignored. In this paper, we consider scheduling coflows of multi-stage jobs represented by general DAGs (Directed Acyclic Graphs) in a shared data center network, so as to minimize the total weighted completion time of jobs. This problem is significantly more challenging than the traditional coflow scheduling, as scheduling even a single multi-stage job to minimize its completion time is shown to be NP-hard. In this paper, we propose a polynomial-time algorithm with approximation ratio of O(mu log(m)/log(log(m))), where mu is the maximum number of coflows in a job and m is the number of servers. For the special case that the jobs' underlying dependency graphs are rooted trees, we modify the algorithm and improve its approximation ratio. To verify the performance of our algorithms, we present simulation results using real traffic traces that show up to 53 % improvement over the prior approach. We conclude the paper by providing a result concerning an optimality gap for scheduling coflows with general DAGs.

关键词： Servers Approximation algorithms Schedules Data centers Switches Job shop scheduling Task analysis Multi-stage job coflow scheduling algorithms approximation algorithms data centers

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