The rise of multi-cloud systems has been spurred.For safety-critical missions,it is important to guarantee their security and reliability.To address trust constraints in a heterogeneous multi-cloud environment,this wo...The rise of multi-cloud systems has been spurred.For safety-critical missions,it is important to guarantee their security and reliability.To address trust constraints in a heterogeneous multi-cloud environment,this work proposes a novel scheduling method called matching and multi-round allocation(MMA)to optimize the makespan and total cost for all submitted tasks subject to security and reliability constraints.The method is divided into two phases for task scheduling.The first phase is to find the best matching candidate resources for the tasks to meet their preferential demands including performance,security,and reliability in a multi-cloud environment;the second one iteratively performs multiple rounds of re-allocating to optimize tasks execution time and cost by minimizing the variance of the estimated completion time.The proposed algorithm,the modified cuckoo search(MCS),hybrid chaotic particle search(HCPS),modified artificial bee colony(MABC),max-min,and min-min algorithms are implemented in CloudSim to create simulations.The simulations and experimental results show that our proposed method achieves shorter makespan,lower cost,higher resource utilization,and better trade-off between time and economic cost.It is more stable and efficient.展开更多
Cloud computing technology is favored by users because of its strong computing power and convenient services.At the same time,scheduling performance has an extremely efficient impact on promoting carbon neutrality.Cur...Cloud computing technology is favored by users because of its strong computing power and convenient services.At the same time,scheduling performance has an extremely efficient impact on promoting carbon neutrality.Currently,scheduling research in the multi-cloud environment aims to address the challenges brought by business demands to cloud data centers during peak hours.Therefore,the scheduling problem has promising application prospects under themulti-cloud environment.This paper points out that the currently studied scheduling problems in the multi-cloud environment mainly include independent task scheduling and workflow task scheduling based on the dependencies between tasks.This paper reviews the concepts,types,objectives,advantages,challenges,and research status of task scheduling in the multi-cloud environment.Task scheduling strategies proposed in the existing related references are analyzed,discussed,and summarized,including research motivation,optimization algorithm,and related objectives.Finally,the research status of the two kinds of task scheduling is compared,and several future important research directions of multi-cloud task scheduling are proposed.展开更多
Cloud computingmakes dynamic resource provisioning more accessible.Monitoring a functioning service is crucial,and changes are made when particular criteria are surpassed.This research explores the decentralized multi...Cloud computingmakes dynamic resource provisioning more accessible.Monitoring a functioning service is crucial,and changes are made when particular criteria are surpassed.This research explores the decentralized multi-cloud environment for allocating resources and ensuring the Quality of Service(QoS),estimating the required resources,and modifying allotted resources depending on workload and parallelism due to resources.Resource allocation is a complex challenge due to the versatile service providers and resource providers.The engagement of different service and resource providers needs a cooperation strategy for a sustainable quality of service.The objective of a coherent and rational resource allocation is to attain the quality of service.It also includes identifying critical parameters to develop a resource allocation mechanism.A framework is proposed based on the specified parameters to formulate a resource allocation process in a decentralized multi-cloud environment.The three main parameters of the proposed framework are data accessibility,optimization,and collaboration.Using an optimization technique,these three segments are further divided into subsets for resource allocation and long-term service quality.The CloudSim simulator has been used to validate the suggested framework.Several experiments have been conducted to find the best configurations suited for enhancing collaboration and resource allocation to achieve sustained QoS.The results support the suggested structure for a decentralized multi-cloud environment and the parameters that have been determined.展开更多
Due to the need for massive device connectivity,low communication latency,and various customizations in 6G architecture,a distributed cloud deployment approach will be more relevant to the space-air-ground-sea integra...Due to the need for massive device connectivity,low communication latency,and various customizations in 6G architecture,a distributed cloud deployment approach will be more relevant to the space-air-ground-sea integrated network scenario.However,the openness and heterogeneity of the 6G network cause the problems of network security.To improve the trustworthiness of 6G networks,we propose a trusted computing-based approach for establishing trust relationships inmulti-cloud scenarios.The proposed method shows the relationship of trust based on dual-level verification.It separates the trustworthy states of multiple complex cloud units in 6G architecture into the state within and between cloud units.Firstly,SM3 algorithm establishes the chain of trust for the system’s trusted boot phase.Then,the remote attestation server(RAS)of distributed cloud units verifies the physical servers.Meanwhile,the physical servers use a ring approach to verify the cloud servers.Eventually,the centralized RAS takes one-time authentication to the critical evidence information of distributed cloud unit servers.Simultaneously,the centralized RAS also verifies the evidence of distributed RAS.We establish our proposed approach in a natural OpenStack-based cloud environment.The simulation results show that the proposed method achieves higher security with less than a 1%system performance loss.展开更多
One of the fundamental problems associated with scheduling workflows on virtual machines in a multi-cloud environment is how to find a near-optimum permutation.The workflow scheduling involves assigning independent co...One of the fundamental problems associated with scheduling workflows on virtual machines in a multi-cloud environment is how to find a near-optimum permutation.The workflow scheduling involves assigning independent computational jobs with conflicting objectives to a set of virtual machines.Most optimization methods for solving non-deterministic polynomial-time hardness(NP-hard)problems deploy multi-objective algorithms.As such,Pareto dominance is one of the most efficient criteria for determining the best solutions within the Pareto front.However,the main drawback of this method is that it requires a reasonably long time to provide an optimum solution.In this paper,a new multi-objective minimum weight algorithm is used to derive the Pareto front.The conflicting objectives considered are reliability,cost,resource utilization,risk probability and makespan.Because multi-objective algorithms select a number of permutations with an optimal trade-off between conflicting objectives,we propose a new decisionmaking approach named the minimum weight optimization(MWO).MWO produces alternative weight to determine the inertia weight by using an adaptive strategy to provide an appropriate alternative for all optimal solutions.This way,consumers’needs and service providers’interests are taken into account.Using standard scientific workflows with conflicting objectives,we compare our proposed multi-objective scheduling algorithm using minimum weigh optimization(MOS-MWO)with multi-objective scheduling algorithm(MOS).Results show that MOS-MWO outperforms MOS in term of QoS satisfaction rate.展开更多
The benefits of cloud storage come along with challenges and open issues about availability of services, vendor lock-in and data security, etc. One solution to mitigate the problems is the multi-cloud storage, where t...The benefits of cloud storage come along with challenges and open issues about availability of services, vendor lock-in and data security, etc. One solution to mitigate the problems is the multi-cloud storage, where the selection of service providers is a key point. In this paper, an algorithm that can select optimal provider subset for data placement among a set of providers in multicloud storage architecture based on IDA is proposed, designed to achieve good tradeoff among storage cost, algorithm cost, vendor lock-in, transmission performance and data availability. Experiments demonstrate that it is efficient and accurate to find optimal solutions in reasonable amount of time, using parameters taken from real cloud providers.展开更多
With the rapid popularity of cloud computing paradigm, disaster recovery using cloud resources becomes an attractive approach. This paper presents a practical multi-cloud based disaster recovery service model： DR- Cl...With the rapid popularity of cloud computing paradigm, disaster recovery using cloud resources becomes an attractive approach. This paper presents a practical multi-cloud based disaster recovery service model： DR- Cloud. With DR-Cloud, resources of multiple cloud service providers can be utilized cooperatively by the disaster recovery service provider. A simple and unified interface is exposed to the customers of DR-Cloud to adapt the heterogeneity of cloud service providers involved in the disaster recovery service, and the internal processes between clouds are invisible to the customers. DR-Cloud proposes multiple optimization scheduling strategies to balance the disaster recovery objectives, such as high data reliability, low backup cost, and short recovery time, which are also transparent to the customers. Different data scheduling strategies based on DR-Cloud are suitable for different kinds of data disaster recovery scenarios. Experimental results show that the DR-Cloud model can cooperate with cloud service providers with various parameters effectively, while its data scheduling strategies can achieve their optimization objectives efficiently and are widely applicable.展开更多
Humankind is facing unprecedented global environmental and social challenges in terms of food,water and energy security,resilience to natural hazards,etc.To address these challenges,international organizations have de...Humankind is facing unprecedented global environmental and social challenges in terms of food,water and energy security,resilience to natural hazards,etc.To address these challenges,international organizations have defined a list of policy actions to be achieved in a relatively short and medium-term timespan.The development and use of knowledge platforms is key in helping the decision-making process to take significant decisions(providing the best available knowledge)and avoid potentially negative impacts on society and the environment.Such knowledge platforms must build on the recent and next coming digital technologies that have transformed society–including the science and engineering sectors.Big Earth Data(BED)science aims to provide the methodologies and instruments to generate knowledge from numerous,complex,and diverse data sources.BED science requires the development of Geoscience Digital Ecosystems(GEDs),which bank on the combined use of fundamental technology units(i.e.big data,learning-driven artificial intelligence,and network-based computing platform)to enable the development of more detailed knowledge to observe and test planet Earth as a whole.This manuscript contributes to the BED science research domain,by presenting the Virtual Earth Cloud:a multi-cloud framework to support GDE implementation and generate knowledge on environmental and social sustainability.展开更多
基金supported in part by the National Natural Science Foundation of China(61673123,61603100)in part by the Natural Science Foundation of Guangdong Province,China(2020A151501482)。
文摘The rise of multi-cloud systems has been spurred.For safety-critical missions,it is important to guarantee their security and reliability.To address trust constraints in a heterogeneous multi-cloud environment,this work proposes a novel scheduling method called matching and multi-round allocation(MMA)to optimize the makespan and total cost for all submitted tasks subject to security and reliability constraints.The method is divided into two phases for task scheduling.The first phase is to find the best matching candidate resources for the tasks to meet their preferential demands including performance,security,and reliability in a multi-cloud environment;the second one iteratively performs multiple rounds of re-allocating to optimize tasks execution time and cost by minimizing the variance of the estimated completion time.The proposed algorithm,the modified cuckoo search(MCS),hybrid chaotic particle search(HCPS),modified artificial bee colony(MABC),max-min,and min-min algorithms are implemented in CloudSim to create simulations.The simulations and experimental results show that our proposed method achieves shorter makespan,lower cost,higher resource utilization,and better trade-off between time and economic cost.It is more stable and efficient.
基金supported by Science and Technology Development Foundation of the Central Guiding Local under Grant No.YDZJSX2021A038the National Natural Science Foundation of China under Grant No.61806138China University Industry-University-Research Collaborative Innovation Fund (Future Network Innovation Research and Application Project)under Grant No.2021FNA04014.
文摘Cloud computing technology is favored by users because of its strong computing power and convenient services.At the same time,scheduling performance has an extremely efficient impact on promoting carbon neutrality.Currently,scheduling research in the multi-cloud environment aims to address the challenges brought by business demands to cloud data centers during peak hours.Therefore,the scheduling problem has promising application prospects under themulti-cloud environment.This paper points out that the currently studied scheduling problems in the multi-cloud environment mainly include independent task scheduling and workflow task scheduling based on the dependencies between tasks.This paper reviews the concepts,types,objectives,advantages,challenges,and research status of task scheduling in the multi-cloud environment.Task scheduling strategies proposed in the existing related references are analyzed,discussed,and summarized,including research motivation,optimization algorithm,and related objectives.Finally,the research status of the two kinds of task scheduling is compared,and several future important research directions of multi-cloud task scheduling are proposed.
文摘Cloud computingmakes dynamic resource provisioning more accessible.Monitoring a functioning service is crucial,and changes are made when particular criteria are surpassed.This research explores the decentralized multi-cloud environment for allocating resources and ensuring the Quality of Service(QoS),estimating the required resources,and modifying allotted resources depending on workload and parallelism due to resources.Resource allocation is a complex challenge due to the versatile service providers and resource providers.The engagement of different service and resource providers needs a cooperation strategy for a sustainable quality of service.The objective of a coherent and rational resource allocation is to attain the quality of service.It also includes identifying critical parameters to develop a resource allocation mechanism.A framework is proposed based on the specified parameters to formulate a resource allocation process in a decentralized multi-cloud environment.The three main parameters of the proposed framework are data accessibility,optimization,and collaboration.Using an optimization technique,these three segments are further divided into subsets for resource allocation and long-term service quality.The CloudSim simulator has been used to validate the suggested framework.Several experiments have been conducted to find the best configurations suited for enhancing collaboration and resource allocation to achieve sustained QoS.The results support the suggested structure for a decentralized multi-cloud environment and the parameters that have been determined.
基金This work was supported by the Ministry of Education and China Mobile Research Fund Project(MCM20200102)the 173 Project(No.2019-JCJQ-ZD-342-00)+2 种基金the National Natural Science Foundation of China(No.U19A2081)the Fundamental Research Funds for the Central Universities(No.2023SCU12129)the Science and Engineering Connotation Development Project of Sichuan University(No.2020SCUNG129).
文摘Due to the need for massive device connectivity,low communication latency,and various customizations in 6G architecture,a distributed cloud deployment approach will be more relevant to the space-air-ground-sea integrated network scenario.However,the openness and heterogeneity of the 6G network cause the problems of network security.To improve the trustworthiness of 6G networks,we propose a trusted computing-based approach for establishing trust relationships inmulti-cloud scenarios.The proposed method shows the relationship of trust based on dual-level verification.It separates the trustworthy states of multiple complex cloud units in 6G architecture into the state within and between cloud units.Firstly,SM3 algorithm establishes the chain of trust for the system’s trusted boot phase.Then,the remote attestation server(RAS)of distributed cloud units verifies the physical servers.Meanwhile,the physical servers use a ring approach to verify the cloud servers.Eventually,the centralized RAS takes one-time authentication to the critical evidence information of distributed cloud unit servers.Simultaneously,the centralized RAS also verifies the evidence of distributed RAS.We establish our proposed approach in a natural OpenStack-based cloud environment.The simulation results show that the proposed method achieves higher security with less than a 1%system performance loss.
基金supported by Putra Grant,University PutraMalaysia,under Grant 95960000 and in part by the Ministry of Education(MOE)Malaysia.
文摘One of the fundamental problems associated with scheduling workflows on virtual machines in a multi-cloud environment is how to find a near-optimum permutation.The workflow scheduling involves assigning independent computational jobs with conflicting objectives to a set of virtual machines.Most optimization methods for solving non-deterministic polynomial-time hardness(NP-hard)problems deploy multi-objective algorithms.As such,Pareto dominance is one of the most efficient criteria for determining the best solutions within the Pareto front.However,the main drawback of this method is that it requires a reasonably long time to provide an optimum solution.In this paper,a new multi-objective minimum weight algorithm is used to derive the Pareto front.The conflicting objectives considered are reliability,cost,resource utilization,risk probability and makespan.Because multi-objective algorithms select a number of permutations with an optimal trade-off between conflicting objectives,we propose a new decisionmaking approach named the minimum weight optimization(MWO).MWO produces alternative weight to determine the inertia weight by using an adaptive strategy to provide an appropriate alternative for all optimal solutions.This way,consumers’needs and service providers’interests are taken into account.Using standard scientific workflows with conflicting objectives,we compare our proposed multi-objective scheduling algorithm using minimum weigh optimization(MOS-MWO)with multi-objective scheduling algorithm(MOS).Results show that MOS-MWO outperforms MOS in term of QoS satisfaction rate.
基金This study is supported by the National Natural Science Foundation of China(61370069), the National High Technology Research and Development Program("863"Program) of China (2012AA012600), the Cosponsored Project of Beijing Committee of Education,the Fundamental Research Funds for the Central Universities (BUPT2011RCZJ16) and China Information Security Special Fund (NDRC).
文摘The benefits of cloud storage come along with challenges and open issues about availability of services, vendor lock-in and data security, etc. One solution to mitigate the problems is the multi-cloud storage, where the selection of service providers is a key point. In this paper, an algorithm that can select optimal provider subset for data placement among a set of providers in multicloud storage architecture based on IDA is proposed, designed to achieve good tradeoff among storage cost, algorithm cost, vendor lock-in, transmission performance and data availability. Experiments demonstrate that it is efficient and accurate to find optimal solutions in reasonable amount of time, using parameters taken from real cloud providers.
基金supported by the National High-Tech Research and Development(863)Program of China(No.2012AA012609)
文摘With the rapid popularity of cloud computing paradigm, disaster recovery using cloud resources becomes an attractive approach. This paper presents a practical multi-cloud based disaster recovery service model： DR- Cloud. With DR-Cloud, resources of multiple cloud service providers can be utilized cooperatively by the disaster recovery service provider. A simple and unified interface is exposed to the customers of DR-Cloud to adapt the heterogeneity of cloud service providers involved in the disaster recovery service, and the internal processes between clouds are invisible to the customers. DR-Cloud proposes multiple optimization scheduling strategies to balance the disaster recovery objectives, such as high data reliability, low backup cost, and short recovery time, which are also transparent to the customers. Different data scheduling strategies based on DR-Cloud are suitable for different kinds of data disaster recovery scenarios. Experimental results show that the DR-Cloud model can cooperate with cloud service providers with various parameters effectively, while its data scheduling strategies can achieve their optimization objectives efficiently and are widely applicable.
基金The research leading to these results benefited from funding by the European Union's Horizon 2020 Framework Programme research and innovation programme[under grant agreements:n.689443(ERA-PLANET),n.777536(EOSC-hub),n.776136(EDGE),n.34538(EO Value),n.101039118(GPP)]by the European Space Agency[under ESA Contracts:n.4000123005/18/IT/CGD(DAB4EDGE)and n.4000138128/22/I/AG(DAB4GPP)]European Commission CNECT(grant n.35713).
文摘Humankind is facing unprecedented global environmental and social challenges in terms of food,water and energy security,resilience to natural hazards,etc.To address these challenges,international organizations have defined a list of policy actions to be achieved in a relatively short and medium-term timespan.The development and use of knowledge platforms is key in helping the decision-making process to take significant decisions(providing the best available knowledge)and avoid potentially negative impacts on society and the environment.Such knowledge platforms must build on the recent and next coming digital technologies that have transformed society–including the science and engineering sectors.Big Earth Data(BED)science aims to provide the methodologies and instruments to generate knowledge from numerous,complex,and diverse data sources.BED science requires the development of Geoscience Digital Ecosystems(GEDs),which bank on the combined use of fundamental technology units(i.e.big data,learning-driven artificial intelligence,and network-based computing platform)to enable the development of more detailed knowledge to observe and test planet Earth as a whole.This manuscript contributes to the BED science research domain,by presenting the Virtual Earth Cloud:a multi-cloud framework to support GDE implementation and generate knowledge on environmental and social sustainability.
