摘要
In current cluster computing, several distributed frameworks are designed to support elasticity for business services adapting to environment fluctuation. However, most existing works support elasticity mainly at the resource level, leaving application level elasticity support problem to domain-specific frameworks and applications. This paper proposes an actor-based general approach to support application-level elasticity for multiple cluster computing frameworks. The actor model offers scalability and decouples language-level concurrency from the runtime environment. By extending actors, a new middle layer called Unisupervisor is designed to ＂sit＂ between the resource management layer and application framework layer. Actors in Unisupervisor can automatically distribute and execute tasks over clusters and dynamically scale in/out. Based on Unisupervisor, high-level profiles （MasterSlave, MapReduce, Streaming, Graph, and Pipeline） for diverse cluster computing requirements can be supported. The entire approach is implemented in a prototype system called UniAS. In the evaluation, both benchmarks and real applications are tested and analyzed in a small scale cluster. Results show that UniAS is expressive and efficiently elastic.
In current cluster computing, several distributed frameworks are designed to support elasticity for business services adapting to environment fluctuation. However, most existing works support elasticity mainly at the resource level, leaving application level elasticity support problem to domain-specific frameworks and applications. This paper proposes an actor-based general approach to support application-level elasticity for multiple cluster computing frameworks. The actor model offers scalability and decouples language-level concurrency from the runtime environment. By extending actors, a new middle layer called Unisupervisor is designed to ＂sit＂ between the resource management layer and application framework layer. Actors in Unisupervisor can automatically distribute and execute tasks over clusters and dynamically scale in/out. Based on Unisupervisor, high-level profiles （MasterSlave, MapReduce, Streaming, Graph, and Pipeline） for diverse cluster computing requirements can be supported. The entire approach is implemented in a prototype system called UniAS. In the evaluation, both benchmarks and real applications are tested and analyzed in a small scale cluster. Results show that UniAS is expressive and efficiently elastic.
基金
Acknowledgements This work was supported by the National High-Tech Research and Development Plan of China （2015AA01A202）, National Basic Research Program of China （973） （2011CB302604）, and the National Natural Science Foundation of China （Grant Nos. 61272154 and 61421091）.
