Web of Things （WoT） makes it possible to connect tremendous embedded devices to web in Representational State Transfer （REST） style. Some lightweight RESTful protocols have been proposed for the WoT to replace the...Web of Things （WoT） makes it possible to connect tremendous embedded devices to web in Representational State Transfer （REST） style. Some lightweight RESTful protocols have been proposed for the WoT to replace the HTTP protocol running on embedded devices. However, they keep the principal characteristic of the REST style. In particular, they support one-to-one requests in the client-server mode by four standard RESTful methods （GET, PUT, POST, and DELETE）. This characteristic is however inconsistent with the practical networks of embedded devices, which typically perform a group operation. In order to meet the requirement of group communication in the WoT, we propose a resource-oriented protocol called SeaHttp to extend the REST style by introducing two new methods, namely BRANCH and COMBINE respectively. SeaHttp supports parallel processing of group requests by means of splitting and merging them. In addition SeaHttp adds spatiotemporal attributes to the standard URI for naming a dynamic request group of physical resource. Experimental results show that SeaHttp can reduce average energy consumption of group communication in the WoT by 18.5%, compared with the Constrained Application Protocol （CoAP）.展开更多
Many Task Computing(MTC)is a new class of computing paradigm in which the aggregate number of tasks,quantity of computing,and volumes of data may be extremely large.With the advent of Cloud computing and big data era,...Many Task Computing(MTC)is a new class of computing paradigm in which the aggregate number of tasks,quantity of computing,and volumes of data may be extremely large.With the advent of Cloud computing and big data era,scheduling and executing large-scale computing tasks efficiently and allocating resources to tasks reasonably are becoming a quite challenging problem.To improve both task execution and resource utilization efficiency,we present a task scheduling algorithm with resource attribute selection,which can select the optimal node to execute a task according to its resource requirements and the fitness between the resource node and the task.Experiment results show that there is significant improvement in execution throughput and resource utilization compared with the other three algorithms and four scheduling frameworks.In the scheduling algorithm comparison,the throughput is 77%higher than Min-Min algorithm and the resource utilization can reach 91%.In the scheduling framework comparison,the throughput(with work-stealing)is at least 30%higher than the other frameworks and the resource utilization reaches 94%.The scheduling algorithm can make a good model for practical MTC applications.展开更多
To address the challenges posed by resource shortage or surplus to enterprises productivity,Internet platforms have been widely used,which can balance shortage and surplus in broader environments. However,the existing...To address the challenges posed by resource shortage or surplus to enterprises productivity,Internet platforms have been widely used,which can balance shortage and surplus in broader environments. However,the existing resource management models lack openness,sharing ability and scalability,which make it difficult for many heterogeneous resources to co-exist in the same system. It is also difficult to resolve the conflicts between distributed self-management and centralized scheduling in the system. This paper analyzes the characteristics of resources in the distributed environment and proposes a new resource management architecture by considering the resource aggregation capacity of cloud computing. The architecture includes a universal resource scheduling optimization model which has been applied successfully in double-district multi-ship-scheduling multi-container-yard empty containers transporting of international shipping logistics. Applications in all these domains prove that this new resource management architecture is feasible and can achieve the expected effect.展开更多
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences under Grant No.XDA06010403the International Science and Technology Cooperation Program of China under Grant No.2013DFA10690+1 种基金the National Natural Science Foundation of China under Grant No.61003293the Beijing Natural Science Foundation under Grant No.4112054
文摘Web of Things （WoT） makes it possible to connect tremendous embedded devices to web in Representational State Transfer （REST） style. Some lightweight RESTful protocols have been proposed for the WoT to replace the HTTP protocol running on embedded devices. However, they keep the principal characteristic of the REST style. In particular, they support one-to-one requests in the client-server mode by four standard RESTful methods （GET, PUT, POST, and DELETE）. This characteristic is however inconsistent with the practical networks of embedded devices, which typically perform a group operation. In order to meet the requirement of group communication in the WoT, we propose a resource-oriented protocol called SeaHttp to extend the REST style by introducing two new methods, namely BRANCH and COMBINE respectively. SeaHttp supports parallel processing of group requests by means of splitting and merging them. In addition SeaHttp adds spatiotemporal attributes to the standard URI for naming a dynamic request group of physical resource. Experimental results show that SeaHttp can reduce average energy consumption of group communication in the WoT by 18.5%, compared with the Constrained Application Protocol （CoAP）.
基金ACKNOWLEDGEMENTS The authors would like to thank the reviewers for their detailed reviews and constructive comments, which have helped improve the quality of this paper. The research has been partly supported by National Natural Science Foundation of China No. 61272528 and No. 61034005, and the Central University Fund （ID-ZYGX2013J073）.
文摘Many Task Computing(MTC)is a new class of computing paradigm in which the aggregate number of tasks,quantity of computing,and volumes of data may be extremely large.With the advent of Cloud computing and big data era,scheduling and executing large-scale computing tasks efficiently and allocating resources to tasks reasonably are becoming a quite challenging problem.To improve both task execution and resource utilization efficiency,we present a task scheduling algorithm with resource attribute selection,which can select the optimal node to execute a task according to its resource requirements and the fitness between the resource node and the task.Experiment results show that there is significant improvement in execution throughput and resource utilization compared with the other three algorithms and four scheduling frameworks.In the scheduling algorithm comparison,the throughput is 77%higher than Min-Min algorithm and the resource utilization can reach 91%.In the scheduling framework comparison,the throughput(with work-stealing)is at least 30%higher than the other frameworks and the resource utilization reaches 94%.The scheduling algorithm can make a good model for practical MTC applications.
基金Support by the National Key Technology Research and Development Program of China(No.2012BAA13B01,2014BAF07B02)the National Natural Science Foundation of China(No.61273038)+1 种基金Natural Science Foundation of Shandong Province(No.ZR2015FM006)Science and Technology Major Project of the Ministry of Science and Technology of Shandong Province(No.2015ZDXX0201B02)
文摘To address the challenges posed by resource shortage or surplus to enterprises productivity,Internet platforms have been widely used,which can balance shortage and surplus in broader environments. However,the existing resource management models lack openness,sharing ability and scalability,which make it difficult for many heterogeneous resources to co-exist in the same system. It is also difficult to resolve the conflicts between distributed self-management and centralized scheduling in the system. This paper analyzes the characteristics of resources in the distributed environment and proposes a new resource management architecture by considering the resource aggregation capacity of cloud computing. The architecture includes a universal resource scheduling optimization model which has been applied successfully in double-district multi-ship-scheduling multi-container-yard empty containers transporting of international shipping logistics. Applications in all these domains prove that this new resource management architecture is feasible and can achieve the expected effect.
