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Optimal planning and scheduling of information processes during interaction among mobile objects

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2024年第16期62卷 5905-5924页

作者： Boris, Sokolov Vitaly, Ushakov Valerii, Zakharov Russian Acad Sci St Petersburg Fed Res Ctr 39 14th Line VO St Petersburg 199178 Russia St Petersburg State Univ Aerosp Instrumentat St Petersburg Russia

The purpose of the research was to improve the quality of information process control in the interaction of mobile object grouping. This proposal is based on developing and implementing new combined models with algorithms for optimal planning and scheduling of information processes during interaction among mobile objects. The proposed approach would guarantee the required level of aircraft maintenance at the airport. The novel aspect of the proposed solution is that it divides the spatially distributed task of selecting a plan and a schedule for information processes in mobile objects into two subtasks, describing the spatial and temporal features of the original task. The spatial component of the original problem is described as a static model. Using this model, the optimal distribution of information operations over the resources of mobile objects is carried out without binding these operations to time. The time component is described as a dynamic model, within which the information operations are tied to time and the corresponding resources. Examples of construction and research of synthesized plans and schedules are ***: DM, Dynamic model;FIFO, First-In First-Out;Gr, Grouping;HSC, Hardware and software complex;ITS, Intelligent Transport System;IP, Information Processes;MO, Mobile object;PC, Programmed control;SM, Static model;SMS, Special mathematic support;LDM, Logical-dynamic models

关键词： static planning dynamic scheduling scheduling algorithms optimal program control sequential decomposition intelligent transport systems

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DB-ACO: A Deadline-Budget Constrained Ant Colony Optimization for Workflow scheduling in Clouds

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2024年第2期21卷 1564-1579页

作者： Tao, Siyuan Xia, Yuanqing Ye, Lingjuan Yan, Ce Gao, Runze Beijing Inst Technol Sch Math & Stat Beijing 100081 Peoples R China Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China

With the development of cloud computing, a growing number of workflows are deployed in cloud platform that can dynamically provide cloud resources on demand for users. In clouds, one basic problem is how to schedule workflow under the deadline constraint and minimize the execution cost. As the capability of cloud resources getting higher, the required cost is also rising. Capability of some resources exceeds the need of users, which leads to higher cost, and the budget of users should be considered. In this paper, a novel scheduling algorithm, named DB-ACO, is proposed to minimize the execution cost for the workflow with deadline and budget constraints. DB-ACO is verified on four typical scientific workflows, and the experiments results show it outperforms four state-of-the-art methods, especially for *** to Practitioners-Budget and deadline are important requirements for users in cloud computing, which are used as constraints. Extensive works have been devoted to minimize the cost of workflows execution with different scheduling strategies. However, most of them only consider one single constraint and assume the constraint is simple and loose, which is impractical in actual scenarios due to higher requirement of users. This paper investigates a novel scheduling algorithm DB-ACO to optimize cost under budget and deadline. DB-ACO combines heuristic and meta-heuristic, it uses ant colony optimization to optimize the execution cost under the deadline and budget constraints: each ant sorts tasks on the basis of the combination of the pheromone trail and heuristic information, the deadline and budget are distributed fairly to each task by a novel distribution method, then the service selection rules are introduced to build solution.

关键词： Task analysis Costs Cloud computing scheduling Metaheuristics scheduling algorithms Schedules ant colony optimization deadline budget workflow scheduling

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Two-Sided Delay Constrained scheduling: Managing Fresh and Stale Data

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第4期23卷 3238-3251页

作者： Gursoy, Mustafa Can Mitra, Urbashi Univ Southern Calif Dept Elect & Comp Engn Los Angeles CA 90089 USA

Energy or time-efficient scheduling is of particular interest in wireless communications, with applications in sensor network design, cellular communications, and more. In many cases, wireless packets to be transmitted have deadlines that upper bound the times before their transmissions, to avoid staleness of transmitted data. In this paper, motivated by emerging applications in security-critical communications, age of information, and molecular communications, we expand the wireless packet scheduling framework to scenarios which involve strict limits on the time after transmission, in addition to the conventional pre-transmission delay constraints. As a result, we introduce the scheduling problem under two-sided individual deadlines, which captures systems wherein transmitting too late (stale) and too early (fresh) are both undesired. Subject to said two-sided deadlines, we provably solve the optimal (energy-minimizing) offline packet scheduling problem. Leveraging this result and the inherent duality between rate and energy, we propose and solve the completion-time-optimal offline packet scheduling problem under the introduced two-sided framework. Overall, the developed theoretical framework can be utilized in applications wherein packets have finite lifetimes both before and after their transmission (e.g., security-critical applications), or applications with joint strict constraints on packet delay and information freshness.

关键词： Delays scheduling scheduling algorithms Wireless communication Time factors Minimization Energy efficiency post-transmission delay constraints two-sided delay constraints energy minimization completion time minimization

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Value of Information-Based Packet scheduling Scheme for AUV-Assisted UASNs

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第7期23卷 7172-7185页

作者： Zhuo, Xiaoxiao Wu, Wen Tang, Liang Qu, Fengzhong Shen, Xuemin Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Shanghai 200050 Peoples R China Frontier Res Ctr Peng Cheng Lab Shenzhen 518055 Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Minist Educ Shanghai 316000 Peoples R China Zhejiang Univ Engn Res Ctr Ocean Sensing Technol & Equipment Minist Educ Zhoushan 316000 Peoples R China Univ Waterloo Dept Elect & Comp Engn Waterloo ON Canada

In this paper, we propose a value of information (VoI)-based packet scheduling scheme (VBPS) in autonomous underwater vehicle (AUV)-assisted underwater acoustic sensor networks (UASNs), where AUVs act as mobile sensor nodes to collect data from areas not accessible to static nodes and then relay data via static nodes. VoI is a performance metric to measure the importance of data packets with different levels of urgency. The proposed scheme aims to avoid collision with the ongoing packet transmission of static nodes without their accurate global information. In specific, the static node localization stage and the topology construction stage are carried out to obtain the local information. Furthermore, the transmission scheduling stage is implemented to avoid packet collision and formulates a combinatorial optimization problem maximizing VoI under the constraint of packet collision avoidance. To solve this complicated problem, a low-complexity distributed search algorithm is proposed, which exploits the spatial-temporal reuse to establish data packet collision constraints and then determines the next-hop node and data transmission time for AUVs. In addition, a collaborative search algorithm is proposed to avoid packet collision among different AUVs by enabling collaboration among AUVs. Extensive simulation results under various scenarios demonstrate the superior performance of the proposed scheme.

关键词： scheduling algorithms Wireless communication Heuristic algorithms Collision avoidance Data communication Underwater acoustics Propagation delay Underwater acoustic sensor networks packet scheduling autonomous underwater vehicle value of {information}

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Cloud Computing Resource scheduling Algorithm Based on Unsampled Collaborative Knowledge Graph Network

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IEEE ACCESS 2024年 12卷 186476-186483页

作者： Sun, Haichuan Gu, Liang Dong, Chenni Ma, Xin Liu, Zeyu Li, Zhenxi State Grid Shanxi Elect Power Co Informat & Commun Branch Taiyuan 030021 Peoples R China Beijing CLP Puhua Informat Technol Co Ltd Beijing 100089 Peoples R China

A cloud computing resource scheduling algorithm based on sampled collaborative knowledge graph network is designed to address the issues of lag in the process of cloud computing resource scheduling, high overall load rate, and large transient amplitude and phase errors. Based on graph convolutional neural networks, analyze the target load of cloud platforms, construct multi hop data transmission paths one by one, and perform deep level information load balancing;Establish a multiplexing information transmission model, correct the initial weights of graph convolutional neural networks, combine reverse transmission calculation methods, integrate and balance cloud computing resources, and confirm the optimal resource scheduling plan;Integrating class convolution and human-machine interaction attention mechanism, the value of the previous time series neural unit is transferred to the current neural unit, and the classification output sequence of knowledge graph relational data feature fragments is analyzed. The knowledge graph data fragments are processed based on class convolution and human-machine interaction attention mechanism, and different sizes of linear aggregators are used to capture deep level information, completing the design of cloud computing resource scheduling algorithm. The experimental results show that although the load rate is on the rise, the highest is only 89%, and the scheduling rate is relatively high, ranging from 38.9 to 43.1bps;The energy consumption is relatively low, not exceeding 40.106mW. In terms of transient amplitude and phase, the proposed method can control the error within 2.0. Ensure the efficiency and practical application effectiveness of cloud computing resource scheduling algorithms.

关键词： Cloud computing Knowledge graphs Transient analysis Resource management Convolutional neural networks scheduling algorithms Load modeling Relays Multiplexing Graph convolutional networks Knowledge graph graph convolutional neural network cloud computing resource scheduling

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Heuristic scheduling Algorithm for Workflow Applications in Cloud-Fog Computing Based on Realistic Client Port Communication

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IEEE ACCESS 2024年 12卷 134453-134485页

作者： Chongdarakul, Waralak Aunsri, Nattapol Mae Fah Luang Univ Sch Informat Technol Chiang Rai 57100 Thailand Mae Fah Luang Univ Comp & Commun Engn Capac Bldg Res Ctr Chiang Rai 57100 Thailand

Cloud-fog computing systems require task scheduling to efficiently manage resources and ensure timely task execution in a distributed environment. Fog computing extends computation, storage, and networking to the edge of the network, thereby improving the user experience, and reducing latency, and data transfer. Task scheduling and client-processing resource management in cloud-fog computing affect cloud system performance and client services. Currently, task scheduling methods mainly focus on assigning client tasks to the most suitable cloud resource or virtual machine (VM) to obtain the minimum scheduled time. However, these techniques do not consider a realistic communication model that, allows many parallel tasks to be sent and received simultaneously without delay. This paper introduces a heuristic workflow scheduling algorithm called Collision-Avoid based on Cloud task scheduling (CACS) to assign client workflow represented by a directed acyclic graph (DAG) and execute the most appropriate cloud-fog nodes under the single client I/O port restriction to achieve minimum workflow completion time, least delay, and higher resource performance. FogWorkflowSim was used as the cloud-fog computing system simulation. The experimental results showed that the proposed method achieved much better scheduling results than the other compared algorithms.

关键词： Cloud computing Heuristic algorithms Costs Dynamic scheduling Optimal scheduling scheduling algorithms Resource management Edge computing cloud-fog computing system cloudsim fog computing FogWorkflowSim task scheduling workflow scheduling heuristic task scheduling

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LinFuzz: Program-Sensitive Seed scheduling Greybox Fuzzing Based on LinUCB Algorithm

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IEEE ACCESS 2024年 12卷 74843-74860页

作者： Su, Yinghao Xiong, Dapeng Wan, Ying Shi, Chenghao Zeng, Qingyao Space Engn Univ Inst Grad Beijing 101416 Peoples R China Space Engn Univ Inst Aerosp Informat Beijing 101416 Peoples R China

The mutation-based greybox fuzz testing technique is one of the widely used dynamic vulnerability detection techniques. It generates testcases for testing by mutating input seeds. In the process of fuzz testing, the seed scheduling strategy and energy scheduling strategy impact the test results and efficiency. Existing seed scheduling strategies, however, only consider a few specific seed attributes and ignore contextual information during seed execution. This oversight makes it challenging to prioritize the selection of suitable seeds based on historical fuzz test results. Meanwhile, current methods for calculating coverage lack evaluation of software paths, which makes it easy to waste time on testing high-frequency and low-risk paths. This article proposes a new greybox fuzzing scheme, LinFuzz, which transforms the seed scheduling problem into a contextual multi-armed bandit machine model. It utilizes the LinUCB algorithm to assess the value of seeds for scheduling by considering their historical execution information. At the same time, LinFuzz improves the calculation method for fuzz testing path rewards and the seed energy scheduling algorithm. It allocates more energy for testing low-frequency paths in the testing program, thereby enhancing the efficiency of exploration and the path coverage ability of the testing tool. This article evaluated the proposed LinFuzz on 12 real programs in comparison with other open-source tools such as AFL, AFLFast, FairFuzz, Neuzz, etc. The results show that under the same testing time budget, LinFuzz outperforms other tools in terms of vulnerability discovery quantity and code coverage ability. Compared with complex fuzz testing optimization algorithms, LinFuzz has lower memory consumption and time complexity.

关键词： Fuzzing Testing Resource management Job shop scheduling Context modeling Software algorithms scheduling algorithms Memory management Coverage-guided fuzzing LinUCB algorithm seed scheduling

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When Lyapunov Drift Based Queue scheduling Meets Adversarial Bandit Learning

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IEEE-ACM TRANSACTIONS ON NETWORKING 2024年第4期32卷 3034-3044页

作者： Huang, Jiatai Golubchik, Leana Huang, Longbo Tsinghua Univ Inst Interdisciplinary Informat Sci IIIS Beijing 100084 Peoples R China Univ Southern Calif Dept Comp Sci Los Angeles CA 90089 USA

In this paper, we study scheduling of a queueing system with zero knowledge of instantaneous network conditions. We consider a one-hop single-server queueing system consisting of $K$ queues, each with time-varying and non-stationary arrival and service rates. Our scheduling approach builds on an innovative combination of adversarial bandit learning and Lyapunov drift minimization, without knowledge of the instantaneous network state (the arrival and service rates) of each queue. We then present two novel algorithms SoftMW (SoftMaxWeight) and SSMW (Sliding-window SoftMaxWeight), both capable of stabilizing systems that can be stabilized by some (possibly unknown) sequence of randomized policies whose time-variation satisfies a mild condition. We further generalize our results to the setting where arrivals and departures only have bounded moments instead of being deterministically bounded and propose SoftMW+ and SSMW+ that are capable of stabilizing the system. As a building block of our new algorithms, we also extend the classical EXP3.S algorithm for multi-armed bandits to handle unboundedly large feedback signals, which can be of independent interest.

关键词： Queueing analysis Dynamic scheduling Time-varying systems Knowledge engineering Stability analysis scheduling algorithms Vectors scheduling queueing bandit learning Lyapunov analysis

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Channel-Aware Joint AoI and Diversity Optimization for Client scheduling in Federated Learning With Non-IID Datasets

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第6期23卷 6295-6311页

作者： Ma, Manyou Wong, Vincent W. S. Schober, Robert Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada Friedrich Alexander Univ Erlangen Nuremberg Inst Digital Commun Erlangen Germany

Federated learning (FL) is a distributed learning framework where clients jointly train a global model without sharing their local datasets. In each communication round of FL, a subset of clients are scheduled to participate in training. Recent research has shown that diversity-based FL can improve the convergence performance of FL, especially when the client datasets are not independent and identically distributed (non-IID). In this paper, we show that by considering the channel state information and age of information (AoI) of each client, the convergence of FL can further be improved. We formulate a channel-aware joint AoI and diversity-based client scheduling problem as a constrained Markov decision process (CMDP). By using Lagrangian index and one-step lookahead approaches, we develop a two-stage online algorithm which is scalable and has a low computational complexity. For FL tasks with non-IID client datasets, our results show that the proposed algorithm can speed up the convergence of FL by up to 71%, through reducing the duration of uplink transmission, when compared with three state-of-the-art FL algorithms.

关键词： Convergence Indexes Training Wireless communication Uplink scheduling algorithms scheduling Age of information (AoI) constrained Markov decision process (CMDP) diversity federated learning index policy Lagrangian index

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Performance evaluation of real time scheduling algorithms for multiprocessor systems

Performance evaluation of real time scheduling algorithms fo...

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International Conference on Robotics, Automation, Control and Embedded Systems (RACE)

作者： P. Ramesh Uppu Ramachandraiah Department of Electronics and Instrumentation Engineering Hindustan University Chennai India

In multiple processor systems scheduling, providing real time service guarantee plays an interesting and challenging role. In the recent years of investigation many scheduling algorithms have been proposed, analysed and evaluated. In this study, we intend the performance evaluation of three of these multiprocessor scheduling algorithms namely global Earliest deadline first (EDF), Proportional fair (PF) and a newer optimal algorithm Largest Local Remaining Execution First (LLREF). We empirically investigate the ability and overhead of all three schedulers for randomly generated task sets. Our results verify that LLREF outperforms PF. It also shows that global EDF can't feasibly schedule the task sets as LLREF and PF even though it is outperformer than other two schedulers. Finally our investigation concludes that LLREF scheduler feasibly schedule for any random task sets and also improve the performance compare to P-fair and global EDF.

关键词： Dynamic scheduling scheduling algorithms Real-time systems Schedules Heuristic algorithms

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