An effective adaptive adjustment method for service composition exception handling in cloud manufacturing

Wang, Yankai; Wang, Shilong; Yang, Bo; Gao, Bo; Wang, S.B.

doi:10.1007/s10845-020-01652-4

Cited by 26 publications

(8 citation statements)

References 31 publications

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“…However, such heuristic based approaches fail to model stochastic environments with dynamic workloads [3]. This is corroborated by our [9], [26], [27] Heuristics [6], [28], [29], [30] GA [31], [32] MaxWeight [5], [33], [34], [35] Deep RL [3], [36] Policy Gradient This work GOBI/GOBI* results in Section 8. To overcome this, GOBI and GOBI* are able to adapt to dynamic scenarios by constantly learning the mapping of scheduling decisions with objective values.…”

Section: Related Worksupporting

confidence: 57%

“…This makes them both robust to diverse, heterogeneous and dynamic scenarios. Evolutionary models: Other prior works have shown that evolutionary based methods, and generally gradientfree approaches, perform well in dynamic scenarios [6], [28], [29], [30]. Evolutionary approaches like genetic algorithms (GA) lie in the domain of gradient-free optimization methods.…”

Section: Related Workmentioning

confidence: 99%

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COSCO: Container Orchestration Using Co-Simulation and Gradient Based Optimization for Fog Computing Environments

Tuli

Poojara

Srirama

et al. 2022

IEEE Trans. Parallel Distrib. Syst.

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Intelligent task placement and management of tasks in large-scale fog platforms is challenging due to the highly volatile nature of modern workload applications and sensitive user requirements of low energy consumption and response time. Container orchestration platforms have emerged to alleviate this problem with prior art either using heuristics to quickly reach scheduling decisions or AI driven methods like reinforcement learning and evolutionary approaches to adapt to dynamic scenarios. The former often fail to quickly adapt in highly dynamic environments, whereas the latter have run-times that are slow enough to negatively impact response time. Therefore, there is a need for scheduling policies that are both reactive to work efficiently in volatile environments and have low scheduling overheads. To achieve this, we propose a Gradient Based Optimization Strategy using Back-propagation of gradients with respect to Input (GOBI). Further, we leverage the accuracy of predictive digital-twin models and simulation capabilities by developing a Coupled Simulation and Container Orchestration Framework (COSCO). Using this, we create a hybrid simulation driven decision approach, GOBI*, to optimize Quality of Service (QoS) parameters. Co-simulation and the back-propagation approaches allow these methods to adapt quickly in volatile environments. Experiments conducted using real-world data on fog applications using the GOBI and GOBI* methods, show a significant improvement in terms of energy consumption, response time, Service Level Objective and scheduling time by up to 15, 40, 4, and 82 percent respectively when compared to the state-of-the-art algorithms.

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Section: Related Worksupporting

confidence: 57%

Section: Related Workmentioning

confidence: 99%

COSCO: Container Orchestration Using Co-Simulation and Gradient Based Optimization for Fog Computing Environments

Tuli

Poojara

Srirama

et al. 2022

IEEE Trans. Parallel Distrib. Syst.

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“…In the CM model, ICT infrastructure plays a major enabler role by information exchange/sharing, safe communication, coordination, and collaboration services. It plays the hero of an “operating system” or executor, hiding the details of the involved companies (Wang et al 2020 ). It manages distributed services belonging to varied owners and works under Service Oriented Architecture (SOA) using three main entities as, “Service provider”, “Service Manager”, and “Service Requester” (Yi 2020 ; Zhang et al 2019 ).…”

Section: State Of the Artmentioning

confidence: 99%

A blockchain technology based trust system for cloud manufacturing

Barenji

2021

J Intell Manuf

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Cloud manufacturing (CM) is a new networked manufacturing model that delivers various on-demand manufacturing capabilities to the consumers from the providers. In this model, the provider and consumer never meet each other, thus "trust" is the major enabler for starting a collaboration. In another word, a user must be sure that the requested capability will not be provided with malicious results, and the provider should ensure that the payment will be made on time. In this paper, a novel Blockchain Technology (BCT)-based trust system called "Blocktrust" is proposed to address the trust problem of the CM. First, the CM framework that contains the digital firm, capability pool, and digital certificate issuing units is developed, and then, the private blocktrust peer-to-peer network is proposed and implemented based on Hyperledger fabric. Finlay, the feasibility of the blocktrust is examined under different testing scenarios. The reason for using a private network instead of the public is placing restrictions on who is allowed to participate in the network and also enjoying a network with fast transaction speed. Experiments show that the proposed blocktrust embedded CM is credible and practical.

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“…However, no runtime evaluation was conducted. Wang et al presented an approach under Service Composition Exception Handling Adaptive Adjustment (SCEHAA) (Wang et al, 2020). They believed that service composition in Cloud manufacturing has some imperfections in dealing with unexpected, irregular situations such as machinery break down, decrease or increase in service requirement, changes in the quality of provided services, and so, which are interpreted to dynamicity.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This study is dedicated to introducing a real-time service assignment framework, considering the Quality of Services (QoS) as a top priority, also considering logistical issues besides manufacturing services. The impulsive variations in the inputs, such as the quantity of the operations/demands and the Quality of Services, or any unforeseen hindrances due to the manufacturing facilities, are considered dynamic ( Wang et al, 2020 ; Zhou & Yao, 2017 ). The dynamic nature of the problem can lead to complications and imperfections in the Cloud manufacturing service composition.…”

Section: Introductionmentioning

confidence: 99%

Dynamic mutual manufacturing and transportation routing service selection for cloud manufacturing with multi-period service-demand matching

Aghili

Valilai

Haji

et al. 2021

PeerJ Computer Science

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Recently, manufacturing firms and logistics service providers have been encouraged to deploy the most recent features of Information Technology (IT) to prevail in the competitive circumstances of manufacturing industries. Industry 4.0 and Cloud manufacturing (CMfg), accompanied by a service-oriented architecture model, have been regarded as renowned approaches to enable and facilitate the transition of conventional manufacturing business models into more efficient and productive ones. Furthermore, there is an aptness among the manufacturing and logistics businesses as service providers to synergize and cut down the investment and operational costs via sharing logistics fleet and production facilities in the form of outsourcing and consequently increase their profitability. Therefore, due to the Everything as a Service (XaaS) paradigm, efficient service composition is known to be a remarkable issue in the cloud manufacturing paradigm. This issue is challenging due to the service composition problem’s large size and complicated computational characteristics. This paper has focused on the considerable number of continually received service requests, which must be prioritized and handled in the minimum possible time while fulfilling the Quality of Service (QoS) parameters. Considering the NP-hard nature and dynamicity of the allocation problem in the Cloud composition problem, heuristic and metaheuristic solving approaches are strongly preferred to obtain optimal or nearly optimal solutions. This study has presented an innovative, time-efficient approach for mutual manufacturing and logistical service composition with the QoS considerations. The method presented in this paper is highly competent in solving large-scale service composition problems time-efficiently while satisfying the optimality gap. A sample dataset has been synthesized to evaluate the outcomes of the developed model compared to earlier research studies. The results show the proposed algorithm can be applied to fulfill the dynamic behavior of manufacturing and logistics service composition due to its efficiency in solving time. The paper has embedded the relation of task and logistic services for cloud service composition in solving algorithm and enhanced the efficiency of resulted matched services. Moreover, considering the possibility of arrival of new services and demands into cloud, the proposed algorithm adapts the service composition algorithm.

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An effective adaptive adjustment method for service composition exception handling in cloud manufacturing

Cited by 26 publications

References 31 publications

COSCO: Container Orchestration Using Co-Simulation and Gradient Based Optimization for Fog Computing Environments

COSCO: Container Orchestration Using Co-Simulation and Gradient Based Optimization for Fog Computing Environments

A blockchain technology based trust system for cloud manufacturing

Dynamic mutual manufacturing and transportation routing service selection for cloud manufacturing with multi-period service-demand matching

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