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distributed task scheduling for Multiple EOSs via a Game Theory Approach

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2023年第2期59卷 1658-1669页

作者： Feng, Rui Lin, Zhiyun Wu, Peng Han, Zhimin Wang, Bo Hangzhou Dianzi Univ Sch Automat Artificial Intelligence Inst Hangzhou 310018 Peoples R China Southern Univ Sci & Technol Dept Elect & Elect Engn Shenzhen 518055 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China Chinese Acad Sci Ctr space Utilizat Key Lab Space Utilizat Technol & Engn Beijing 100094 Peoples R China

In this study, we investigate the task scheduling problem for multiple earth observation satellites (EOSs) to achieve the maximal task observation ratio with the least repeated tasks. To begin, we build a task graph to describe feasible observation sequences for each EOS while satisfying its kinematic constraints. Next, we construct local utility functions for individual EOSs as metrics, and show that the task scheduling problem is indeed an exact potential game problem. Based on this finding, the collaborative goal for task scheduling is converted to the problem of solving for the Nash equilibrium (NE) of the game. Then, a sequential iterative algorithm and a simultaneous iterative algorithm are developed to solve for the NE in a distributed manner. Also, in the algorithms, a sieving scheme for EOSs is proposed to reduce the searching spaces. It is proved that our algorithms converge to the NE in finite steps and the global utility is shown to be monotonically increasing under certain conditions. Finally, the Bezier curve is used to smoothen the target observation path, based on which a model predictive control scheme is employed to design the control law of EOSs.

关键词： task analysis Games Earth Observing System scheduling Satellite broadcasting Resource management Satellites Cooperative control distributed task scheduling multiagent systems potential game

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distributed task scheduling in Serverless Edge Computing Networks for the Internet of Things: A Learning Approach

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IEEE INTERNET OF THINGS JOURNAL 2022年第20期9卷 19634-19648页

作者： Tang, Qinqin Xie, Renchao Yu, Fei Richard Chen, Tianjiao Zhang, Ran Huang, Tao Liu, Yunjie Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Purple Mt Labs Dept Future Networks Nanjing 211111 Peoples R China Carleton Univ Sch Informat Technol Ottawa ON K1S 5B6 Canada

By delegating the infrastructure management, such as provisioning or scaling to third-party providers, serverless edge computing has recently been widely adopted in several applications, especially Internet of Things (IoT) applications. task scheduling is a critical issue in serverless edge computing as it significantly impacts the quality of user experience. In contrast to the centralized scheduling in the cloud center, serverless edge task scheduling is more challenging due to the heterogeneous and resource-constrained nature of edge resources. This article aims to study the distributed task scheduling for the IoT in serverless edge computing networks, in which heterogeneous serverless edge computing nodes are rational individuals with interests to optimize their own scheduling utility while the nodes only have access to local observations. The task scheduling competition process is formulated as a partially observable stochastic game (POSG) to enable serverless edge computing nodes to noncooperatively schedule tasks and allocate computing resources depending on their locally observed system state, which takes into account the associated task generation state, data queue state, communication channel state, and previous computing resource allocation state. To solve the proposed POSG and deal with the partial observability, a multiagent task scheduling algorithm based on the dueling double deep recurrent Q-network (D3RQN) method is developed to approximate the optimal task scheduling and resource allocation solution. Finally, extensive simulation experiments are conducted to validate the effectiveness and superiority of the proposed scheme.

关键词： Deep reinforcement learning (DRL) distributed task scheduling Internet of Things (IoT) serverless edge computing stochastic game

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distributed task scheduling Algorithm based on Intelligent Computing

Distributed Task Scheduling Algorithm based on Intelligent C...

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International Conference on Intelligent Transportation, Big Data and Smart City (ICITBS)

作者： Zhu Guohua Jianghan Univ Wuhan 430056 Hubei Peoples R China

ISBN: (纸本)9781509004645

Intelligent optimization algorithms are a class of bionic algorithms and are well characterized by its self organizing, self-learning, self-adaptive, implicit parallelism and guided search, etc. These algorithms have the preponderance over solving complex issues and have been widely used in engineering technology, nonlinear optimization, structural design, parallel computing, social science as well as many other fields. Based on improved ant colony algorithm, this paper studies task matching and scheduling for parallel computing. The experiment results show that the performance of proposed task scheduling algorithm is better than traditional ant colony algorithm.

关键词： distributed task scheduling ant colony algorithm parallel computing

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Eco-Friendly Powering and Delay-Aware task scheduling in Geo-distributed Edge-Cloud System: A Two-Timescale Framework

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IEEE ACCESS 2020年 8卷 96468-96486页

作者： Sun, Chunlei Wen, Xiangming Lu, Zhaoming Jing, Wenpeng Zorzi, Michele Beijing Univ Posts & Telecommun Beijing 100876 Peoples R China Beijing Key Lab Network Syst Architecture & Conve Beijing 100876 Peoples R China Beijing Lab Adv Informat Networks BUPT Beijing 100876 Peoples R China Univ Padua Informat Engn Dept I-35131 Padua Italy

Power consumption and task latency are two crucial issues in edge-cloud computing. This paper mainly aims to promote the use of clean power in geo-distributed data centers (DCs) in a deregulated electricity market where customers are allowed to buy power from multiple suppliers, combined with the guarantee of task latency. To alleviate the conflict between frequent switches of servers and the uncertainty of task arrivals in DCs, this paper proposes a two-timescale framework consisting of the long-term capacity planning of geo-distributed DCs and the real-time task dispatching from edge gateways (EGs) to DCs. First, DCs make long-term plans on the number of active servers aiming at the eco-friendly and delay-aware power cost minimization, which is formulated as problem P. Specifically, we introduce a convex pollution indicator function (PIF) to measure the pollution cost of the various types of powers sold by different suppliers, which can encourage the use of cleaner power and improve power savings. Second, in each sub-slot, each EG separately optimizes its individual mixed strategies of task dispatching to DCs with the knowledge of the planned capacities and the real-time queue backlogs of DCs, where a Lyapunov optimization framework is applied. Finally, we give the corresponding distributed algorithm design. Simulation results reveal that our method can realize the trade-off between the power cost and the delay cost of requests, and improve the clean power usage by up to 50%-60% of the total power usage in DCs. Additionally, comparisons with other schemes show that our approach can provide more stable guarantees of task latency in different situations of workload density, which benefits from the diverse-timescale optimizations of capacities of DCs and task routing from EGs to DCs.

关键词： task analysis Servers Optimization Delays Routing scheduling Power demand distributed task scheduling edge-cloud system two-timescale framework pollution indicator function eco-friendly and delay-aware Lyapunov optimization

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Multi-objective Optimization of Real-Time task scheduling Problem for distributed Environments 19

Multi-objective Optimization of Real-Time Task Scheduling Pr...

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6th Conference on the Engineering of Computer Based Systems (ECBS)

作者： Salimi, Maghsood Majd, Amin Loni, Mohammad Seceleanu, Tiberiu Seceleanu, Cristina Sirjani, Marjan Daneshtalab, Masoud Troubitsyna, Elena Univ Tehran Tehran Iran Abo Akad Univ Dept Informat Technol Turku Finland Malardalen Univ Sch Innovat Design & Engn Vasteras Sweden KTH Royal Inst Technol Stockholm Sweden

ISBN: (纸本)9781450376365

Real-world applications are composed of multiple tasks which usually have intricate data dependencies. To exploit distributed processing platforms, task allocation and scheduling, that is assigning tasks to processing units and ordering inter-processing unit data transfers, plays a vital role. However, optimally scheduling tasks on processing units and finding an optimized network topology is an NP-complete problem. The problem becomes more complicated when the tasks have real-time deadlines for termination. Exploring the whole search space in order to find the optimal solution is not feasible in a reasonable amount of time, therefore meta-heuristics are often used to find a near-optimal solution. We propose here a multi-population evolutionary approach for near-optimal scheduling optimization, that guarantees end-to-end deadlines of tasks in distributed processing environments. We analyze two different exploration scenarios including single and multiobjective exploration. The main goal of the single objective exploration algorithm is to achieve the minimal number of processing units for all the tasks, whereas a multi-objective optimization tries to optimize two conflicting objectives simultaneously considering the total number of processing units and end-to-end finishing time for all the jobs. The potential of the proposed approach is demonstrated by experiments based on a use case for mapping a number of jobs covering industrial automation systems, where each of the jobs consists of a number of tasks in a distributed environment.

关键词： distributed task scheduling Real-Time Processing Evolutionary Computing Multi-Objective Optimization

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International Joint Conference on Neural Networks (IJCNN)

作者： Kumar, Lokesh Sadhu, Arup Kumar Dasgupta, Ranjan Tata Consultancy Serv Kolkata India

ISBN: (纸本)9798350359329;9798350359312

scheduling multiple tasks in a real-time, distributed fashion among multiple agents is indeed challenging. scheduling becomes more challenging if task generation is dynamic in nature. This paper proposes an online real-time distributed dynamic task scheduling approach for multi-agent systems. The proposed scheduler is capable of rescheduling while the task generation is dynamic without hampering the current task execution, if any. The scalability of the proposed scheduler is realised by distributing all the agents into a number of groups. distributed grouping makes the scheduler scalable for a large number of agents and tasks. Complexity analysis conveys that the timecomplexity for scheduling is independent of agent count, but depends on the task count along with the agent's payload. While the computational burden for the consensus formation varies linearly with the number of agents and is independent of task count. Simulation results confirm that the proposed scheduler outperforms the relevant reference scheduler in terms of successful consensus attained, average reward attained at consensus, task scheduled, task executed, dynamic scheduling, and scalability. One demonstration is shown in a typical warehouse environment.

关键词： Dynamic task scheduling distributed task scheduling correlated equilibrium Nash equilibrium potential game

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Research on task scheduling and concurrent processing technology for energy internet operation platform

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Global Energy Interconnection 2022年第6期5卷 579-589页

作者： Zhixiang Ji Xiaohui Wang Dan Wu China Electric Power Research Institute Co.Ltd. Beijing 100192P.R.China State Grid Fujian Electric Power Research Institute Fuzhou 350001P.R.China

The energy Internet operation platform provides market entities such as energy users,energy enterprises,suppliers,and governments with the ability to interact,transact,and manage various *** to the large number of platform users,complex businesses,and large amounts of data-mining tasks,it is necessary to solve the problems afflicting platform task scheduling and the provision of simultaneous access to a large number of *** study examines the two core technologies of platform task scheduling and multiuser concurrent processing,proposing a distributed task-scheduling method and a technical implementation scheme based on the particle swarm optimization algorithm,and presents a systematic solution in concurrent processing for massive user *** on the results of this study,the energy internet operation platform can effectively deal with the concurrent access of tens of millions of users and complex task-scheduling problems.

关键词： Energy Internet distributed task scheduling Concurrent processing

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Runtime vs Scheduler: Analyzing Dask's Overheads 15

Runtime vs Scheduler: Analyzing Dask's Overheads

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15th IEEE/ACM Workshop on Workflows in Support of Large-Scale Science (WORKS)

作者： Boehm, Stanislav Beranek, Jakub VSB Tech Univ Ostrava IT4Innovat Ostrava Czech Republic

ISBN: (纸本)9780738110400

Dask is a distributed task framework which is commonly used by data scientists to parallelize Python code on computing clusters with little programming effort. It uses a sophisticated work-stealing scheduler which has been hand-tuned to execute task graphs as efficiently as possible. But is scheduler optimization a worthwhile effort for Dask? Our paper shows on many real world task graphs that even a completely random scheduler is surprisingly competitive with its built-in scheduler and that the main bottleneck of Dask lies in its runtime overhead. We develop a drop-in replacement for the Dask central server written in Rust which is backwards compatible with existing Dask programs. Thanks to its efficient runtime, our server implementation is able to scale up to larger clusters than Dask and consistently outperforms it on a variety of task graphs, despite the fact that it uses a simpler scheduling algorithm.

关键词： distributed task scheduling dask workflow rust

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Study and Analysis of task scheduling Algorithms in Clouds Based on Artificial Bee Colony 2

Study and Analysis of Task Scheduling Algorithms in Clouds B...

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2nd International Congress on Technology, Communication and Knowledge (ICTCK)

作者： Hallaj, Elaheh Tabbakh, Seyyed Reza Kamel Islamic Azad Univ Neyshabur Branch Dept Software Engn Neyshabur Iran Islamic Azad Univ Mashhad Branch Dept Software Engn Mashhad Iran

ISBN: (纸本)9781467397629

task scheduler plays a significant role in cloud computing, since it has a direct effect on cloud performance. As cloud scheduling is known to be an NP-hard problem, swarm intelligence-based techniques are widely used in this paradigm, due to their ability of searching for large scale problem solutions in an efficient manner. This paper, discusses some scheduling algorithms based on artificial bee colony. These algorithms exploit the smartness of bees' behavior to reach an optimal assignment of tasks to resources. An analytical comparison has also been done to briefly show the characteristics and applications of bee colony technique for scheduling in distributed computing area.

关键词： Artificial Bee Colony Cloud Computing distributed task scheduling Swarm Intelligence

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