Recently,analyzing big data on the move is booming.It requires that the hardware resource should be low volume,low power,light in weight,high-performance,and highly scalable whereas the management software should be f...Recently,analyzing big data on the move is booming.It requires that the hardware resource should be low volume,low power,light in weight,high-performance,and highly scalable whereas the management software should be flexible and consume little hardware resource.To meet these requirements,we present a system named SOCA-DOM that encompasses a mobile system-on-chip array architecture and a two-tier“software-defined”resource manager named Chameleon.First,we design an Ethernet communication board to support an array of mobile system-on-chips.Second,we propose a two-tier software architecture for Chameleon to make it flexible.Third,we devise data,configuration,and control planes for Chameleon to make it“software-defined”and in turn consume hardware resources on demand.Fourth,we design an accurate synthetic metric that represents the computational power of a computing node.We employ 12 Apache Spark benchmarks to evaluate SOCA-DOM.Surprisingly,SOCA-DOM consumes up to 9.4x less CPU resources and 13.5x less memory than Mesos which is an existing resource manager.In addition,we show that a 16-node SOCA-DOM consumes up to 4x less energy than two standard Xeon servers.Based on the results,we conclude that an array architecture with fine-grained hardware resources and a software-defined resource manager works well for analyzing big data on the move.展开更多
基金the Key Research and Development Program of Guangdong Province of China under Grant No.2019B010155003the National Natural Science Foundation of China under Grant Nos.61672511,61702495,and 61802384the Shenzhen Institute of Artificial Intelligence and Robotics for Society,The Chinese University of Hong Kong,Shenzhen,and the Alibaba Innovative Research Project for Large-Scale Graph Pattern Discovery,Analysis,and Query Techniques.
文摘Recently,analyzing big data on the move is booming.It requires that the hardware resource should be low volume,low power,light in weight,high-performance,and highly scalable whereas the management software should be flexible and consume little hardware resource.To meet these requirements,we present a system named SOCA-DOM that encompasses a mobile system-on-chip array architecture and a two-tier“software-defined”resource manager named Chameleon.First,we design an Ethernet communication board to support an array of mobile system-on-chips.Second,we propose a two-tier software architecture for Chameleon to make it flexible.Third,we devise data,configuration,and control planes for Chameleon to make it“software-defined”and in turn consume hardware resources on demand.Fourth,we design an accurate synthetic metric that represents the computational power of a computing node.We employ 12 Apache Spark benchmarks to evaluate SOCA-DOM.Surprisingly,SOCA-DOM consumes up to 9.4x less CPU resources and 13.5x less memory than Mesos which is an existing resource manager.In addition,we show that a 16-node SOCA-DOM consumes up to 4x less energy than two standard Xeon servers.Based on the results,we conclude that an array architecture with fine-grained hardware resources and a software-defined resource manager works well for analyzing big data on the move.
