Multi-task Deep Reinforcement Learning for Scalable Parallel Task Scheduling

Zhang, Lingxin; Qi, Qi; Wang, Jingyu; Sun, Haifeng; Liao, Jianxin

doi:10.1109/bigdata47090.2019.9006027

Cited by 11 publications

(6 citation statements)

References 45 publications

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“…Afterwards it is compared to frequently used algorithms in the field of real time underwater rescue assignments like a neural network based improved self-organizing map (ISOM) and an improved ant colony optimization (IACO). In [65,66] a Multi-DRL approach for scalable parallel Task Scheduling (MDTS) in autonomous driving is introduced. They evaluate the performance of MDTS by comparing it to other popular schedulers (e.g., PSO, least-connection, A3C) through simulation experiments, showing improvement on various parameters.…”

Section: Dispatching and Schedulingmentioning

confidence: 99%

Controlling Fleets of Autonomous Mobile Robots with Reinforcement Learning: A Brief Survey

2022

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Controlling a fleet of autonomous mobile robots (AMR) is a complex problem of optimization. Many approached have been conducted for solving this problem. They range from heuristics, which usually do not find an optimum, to mathematical models, which are limited due to their high computational effort. Machine Learning (ML) methods offer another potential trajectory for solving such complex problems. The focus of this brief survey is on Reinforcement Learning (RL) as a particular type of ML. Due to the reward-based optimization, RL offers a good basis for the control of fleets of AMR. In the context of this survey, different control approaches are investigated and the aspects of fleet control of AMR with respect to RL are evaluated. As a result, six fundamental key problems should be put on the current research agenda to enable a broader application in industry: (1) overcoming the “sim-to-real gap”, (2) increasing the robustness of algorithms, (3) improving data efficiency, (4) integrating different fields of application, (5) enabling heterogeneous fleets with different types of AMR and (6) handling of deadlocks.

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Section: Dispatching and Schedulingmentioning

confidence: 99%

Controlling Fleets of Autonomous Mobile Robots with Reinforcement Learning: A Brief Survey

2022

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“… Ran et al (2019) used the Deep Determining Policy Gradient (DDPG) algorithm to find the optimal task assignment scheme meeting the requirements of the Service Level Agreement (SLA). Zhang et al (2019) proposed a parallel execution multi-task scheduling algorithm based on deep reinforcement learning. And compared with least connection and particle swarm optimization, this algorithm significantly reduces the completion time of the job.…”

Section: Related Workmentioning

confidence: 99%

“…References ( Cui and Xiaoqing, 2018 ; Xiaoqing et al, 2018 ) gave the corresponding weight coefficients of each resource through subjective experience. References ( Zhang et al, 2019 ; Dong et al, 2020 ) did not take into account the transmission cost between resource nodes of the execution results of services in the actual scheduling process of composite services. In the actual environment, the data transmission time between sub-services affects the completion time and operation cost of composite services to some extent.…”

Section: Related Workmentioning

confidence: 99%

A resource scheduling method for reliable and trusted distributed composite services in cloud environment based on deep reinforcement learning

Lei

Duan

et al. 2022

Front. Genet.

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With the vigorous development of Internet technology, applications are increasingly migrating to the cloud. Cloud, a distributed network environment, has been widely extended to many fields such as digital finance, supply chain management, and biomedicine. In order to meet the needs of the rapid development of the modern biomedical industry, the biological cloud platform is an inevitable choice for the integration and analysis of medical information. It improves the work efficiency of the biological information system and also realizes reliable and credible intelligent processing of biological resources. Cloud services in bioinformatics are mainly for the processing of biological data, such as the analysis and processing of genes, the testing and detection of human tissues and organs, and the storage and transportation of vaccines. Biomedical companies form a data chain on the cloud, and they provide services and transfer data to each other to create composite services. Therefore, our motivation is to improve process efficiency of biological cloud services. Users’ business requirements have become complicated and diversified, which puts forward higher requirements for service scheduling strategies in cloud computing platforms. In addition, deep reinforcement learning shows strong perception and continuous decision-making capabilities in automatic control problems, which provides a new idea and method for solving the service scheduling and resource allocation problems in the cloud computing field. Therefore, this paper designs a composite service scheduling model under the containers instance mode which hybrids reservation and on-demand. The containers in the cluster are divided into two instance modes: reservation and on-demand. A composite service is described as a three-level structure: a composite service consists of multiple services, and a service consists of multiple service instances, where the service instance is the minimum scheduling unit. In addition, an improved Deep Q-Network (DQN) algorithm is proposed and applied to the scheduling algorithm of composite services. The experimental results show that applying our improved DQN algorithm to the composite services scheduling problem in the container cloud environment can effectively reduce the completion time of the composite services. Meanwhile, the method improves Quality of Service (QoS) and resource utilization in the container cloud environment.

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“…DRL combines the perception of Deep Learning and the decision making of Reinforcement Learning [1]. Therefore, DRL can implement a variety of tasks requiring both precious perceptions of high dimensional raw inputs and policy control [1,13]. AlphaGO, which defeated the world Go champion, Sedol Lee is a program that is developed by Google DeepMind following the DRL approach.…”

Section: Deep Learning Reinforcement Learning and Deep Reinforcementmentioning

confidence: 99%

“…Uber is also trying to use DRL to teach Grand Theft Auto to handle real cars on real roads. In one of recent research, a Multi-Task Deep reinforcement learning approach for scalable parallel Task Scheduling (MDTS) has been developed by a group of researchers [13]. But, when interacting with complex concurrent computing environments and tasks with distinct features, DLR experiences the curse of high dimensionality for decision making [13].…”

Section: Deep Learning Reinforcement Learning and Deep Reinforcementmentioning

confidence: 99%

Reinforcement Learning in Dynamic Task Scheduling: A Review

2020

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Scheduling is assigning shared resources over time to efficiently complete the tasks over a given period of time. The term is applied separately for tasks and resources correspondingly in task scheduling and resource allocation. Scheduling is a popular topic in operational management and computer science. Effective schedules ensure system efficiency, effective decision making, minimize resource wastage and cost, and enhance overall productivity. It is generally a tedious task to choose the most accurate resources in performing work items and schedules in both computing and business process execution. Especially in real-world dynamic systems where multiple agents involve in scheduling various dynamic tasks is a challenging issue. Reinforcement Learning is an emergent technology which has been able to solve the problem of the optimal task and resource scheduling dynamically. This review paper is about a research study that focused on Reinforcement Learning techniques that have been used for dynamic task scheduling. The paper addresses the results of the study by means of the state-of-theart on Reinforcement learning techniques used in dynamic task scheduling and a comparative review of those techniques.

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Multi-task Deep Reinforcement Learning for Scalable Parallel Task Scheduling

Cited by 11 publications

References 45 publications

Controlling Fleets of Autonomous Mobile Robots with Reinforcement Learning: A Brief Survey

Controlling Fleets of Autonomous Mobile Robots with Reinforcement Learning: A Brief Survey

A resource scheduling method for reliable and trusted distributed composite services in cloud environment based on deep reinforcement learning

Reinforcement Learning in Dynamic Task Scheduling: A Review

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