In a cloud-native era,the Kubernetes-based workflow engine enables workflow containerized execution through the inherent abilities of Kubernetes.However,when encountering continuous workflow requests and unexpected re...In a cloud-native era,the Kubernetes-based workflow engine enables workflow containerized execution through the inherent abilities of Kubernetes.However,when encountering continuous workflow requests and unexpected resource request spikes,the engine is limited to the current workflow load information for resource allocation,which lacks the agility and predictability of resource allocation,resulting in over and underprovisioning resources.This mechanism seriously hinders workflow execution efficiency and leads to high resource waste.To overcome these drawbacks,we propose an adaptive resource allocation scheme named adaptive resource allocation scheme(ARAS)for the Kubernetes-based workflow engines.Considering potential future workflow task requests within the current task pod’s lifecycle,the ARAS uses a resource scaling strategy to allocate resources in response to high-concurrency workflow scenarios.The ARAS offers resource discovery,resource evaluation,and allocation functionalities and serves as a key component for our tailored workflow engine(KubeAdaptor).By integrating the ARAS into KubeAdaptor for workflow containerized execution,we demonstrate the practical abilities of KubeAdaptor and the advantages of our ARAS.Compared with the baseline algorithm,experimental evaluation under three distinct workflow arrival patterns shows that ARAS gains time-saving of 9.8% to 40.92% in the average total duration of all workflows,time-saving of 26.4% to 79.86% in the average duration of individual workflow,and an increase of 1% to 16% in centrol processing unit(CPU)and memory resource usage rate.展开更多
Unconventional oil and gas resources have become the most important and realistic field for increasing China’s domestic oil and gas reserves and production.At present,the production scale does not match the massive a...Unconventional oil and gas resources have become the most important and realistic field for increasing China’s domestic oil and gas reserves and production.At present,the production scale does not match the massive amount of resources and the rapid growth of proven geological reserves.The challenges of technology,cost,management,and methodology restrict large-scale and economic development.Based on successful practices,a"one engine with six gears"system engineering methodology is put forward,which includes life-cycle management,overall synergy,interdisciplinary cross-service integration,marketoriented operation,socialized support,digitalized management,and low-carbon and green development.The methodology has been proved to be effective in multiple unconventional oil and gas national demonstration areas,including the Jimusar continental shale oil demonstration area.Disruptive views are introduced-namely,that unconventional oil and gas do not necessarily yield a low return,nor do they necessarily have a low recovery factor.A determination to achieve economic benefit must be a pervasive underlying goal for managers and experts.Return and recovery factors,as primary focuses,must be adhered to during China’s development of unconventional oil and gas.The required methodology transformation includes a revolution in management systems to significantly decrease cost and increase production,resulting in technological innovation.展开更多
Traditionally, complex engineering applications （CEAs）, which consist of numerous components （software） and require a large amount of computing resources, usu- ally run in dedicated clusters or high performance co...Traditionally, complex engineering applications （CEAs）, which consist of numerous components （software） and require a large amount of computing resources, usu- ally run in dedicated clusters or high performance computing （HPC） centers. Nowadays, Cloud computing system with the ability of providing massive computing resources and cus- tomizable execution environment is becoming an attractive option for CEAs. As a new type on Cloud applications, CEA also brings the challenges of dealing with Cloud resources. In this paper, we provide a comprehensive survey of Cloud resource management research for CEAs. The survey puts forward two important questions： 1） what are the main chal- lenges for CEAs to run in Clouds？ and 2） what are the prior research topics addressing these challenges？ We summarize and highlight the main challenges and prior research topics. Our work can be probably helpful to those scientists and en- gineers who are interested in running CEAs in Cloud envi- ronment.展开更多
基金supported by the National Natural Science Foundation of China(61873030,62002019).
文摘In a cloud-native era,the Kubernetes-based workflow engine enables workflow containerized execution through the inherent abilities of Kubernetes.However,when encountering continuous workflow requests and unexpected resource request spikes,the engine is limited to the current workflow load information for resource allocation,which lacks the agility and predictability of resource allocation,resulting in over and underprovisioning resources.This mechanism seriously hinders workflow execution efficiency and leads to high resource waste.To overcome these drawbacks,we propose an adaptive resource allocation scheme named adaptive resource allocation scheme(ARAS)for the Kubernetes-based workflow engines.Considering potential future workflow task requests within the current task pod’s lifecycle,the ARAS uses a resource scaling strategy to allocate resources in response to high-concurrency workflow scenarios.The ARAS offers resource discovery,resource evaluation,and allocation functionalities and serves as a key component for our tailored workflow engine(KubeAdaptor).By integrating the ARAS into KubeAdaptor for workflow containerized execution,we demonstrate the practical abilities of KubeAdaptor and the advantages of our ARAS.Compared with the baseline algorithm,experimental evaluation under three distinct workflow arrival patterns shows that ARAS gains time-saving of 9.8% to 40.92% in the average total duration of all workflows,time-saving of 26.4% to 79.86% in the average duration of individual workflow,and an increase of 1% to 16% in centrol processing unit(CPU)and memory resource usage rate.
基金supported by the Project of Basic Science Center for the National Natural Science Foundation of China(72088101)。
文摘Unconventional oil and gas resources have become the most important and realistic field for increasing China’s domestic oil and gas reserves and production.At present,the production scale does not match the massive amount of resources and the rapid growth of proven geological reserves.The challenges of technology,cost,management,and methodology restrict large-scale and economic development.Based on successful practices,a"one engine with six gears"system engineering methodology is put forward,which includes life-cycle management,overall synergy,interdisciplinary cross-service integration,marketoriented operation,socialized support,digitalized management,and low-carbon and green development.The methodology has been proved to be effective in multiple unconventional oil and gas national demonstration areas,including the Jimusar continental shale oil demonstration area.Disruptive views are introduced-namely,that unconventional oil and gas do not necessarily yield a low return,nor do they necessarily have a low recovery factor.A determination to achieve economic benefit must be a pervasive underlying goal for managers and experts.Return and recovery factors,as primary focuses,must be adhered to during China’s development of unconventional oil and gas.The required methodology transformation includes a revolution in management systems to significantly decrease cost and increase production,resulting in technological innovation.
基金We thank the anonymous reviewers for their insight- ful comments and suggestions. This work was supported by the National Science Foundation of China （Grant Nos. 61232008 and 61472151）, Na- tional 863 Hi-Tech Research and Development Program （2015AA01A203 and 2014AA01A302）, the Fundamental Research Funds for the Central Universities （2015TS067）, Anhui Provincial Natural Science Foundation （1408085MF126）.
文摘Traditionally, complex engineering applications （CEAs）, which consist of numerous components （software） and require a large amount of computing resources, usu- ally run in dedicated clusters or high performance computing （HPC） centers. Nowadays, Cloud computing system with the ability of providing massive computing resources and cus- tomizable execution environment is becoming an attractive option for CEAs. As a new type on Cloud applications, CEA also brings the challenges of dealing with Cloud resources. In this paper, we provide a comprehensive survey of Cloud resource management research for CEAs. The survey puts forward two important questions： 1） what are the main chal- lenges for CEAs to run in Clouds？ and 2） what are the prior research topics addressing these challenges？ We summarize and highlight the main challenges and prior research topics. Our work can be probably helpful to those scientists and en- gineers who are interested in running CEAs in Cloud envi- ronment.
