Recently, several novel computing paradigms are proposed, e.g., fog computing and edge computing. In such more decentralized computing paradigms, the location and resource for code execution and data storage of end ap...Recently, several novel computing paradigms are proposed, e.g., fog computing and edge computing. In such more decentralized computing paradigms, the location and resource for code execution and data storage of end applications could also be optionally distributed among different places or machines. In this paper, we position that this situation requires a new transparent and usercentric approach to unify the resource management and code scheduling from the perspective of end users. We elaborate our vision and propose a software-defined code scheduling framework. The proposed framework allows the code execution or data storage of end applications to be adaptively done at appropriate machines under the help of a performance and capacity monitoring facility, intelligently improving application performance for end users. A pilot system and preliminary results show the advantage of the framework and thus the advocated vision for end users.展开更多
基金supported in part by Initiative Scientific Research Program in Tsinghua University under Grant No.20161080066in part by International Science&Technology Cooperation Program of China under Grant No.2013DFB10070
文摘Recently, several novel computing paradigms are proposed, e.g., fog computing and edge computing. In such more decentralized computing paradigms, the location and resource for code execution and data storage of end applications could also be optionally distributed among different places or machines. In this paper, we position that this situation requires a new transparent and usercentric approach to unify the resource management and code scheduling from the perspective of end users. We elaborate our vision and propose a software-defined code scheduling framework. The proposed framework allows the code execution or data storage of end applications to be adaptively done at appropriate machines under the help of a performance and capacity monitoring facility, intelligently improving application performance for end users. A pilot system and preliminary results show the advantage of the framework and thus the advocated vision for end users.
