Scale-Out vs. Scale-Up Techniques for Cloud Performance and Productivity

Kai Hwang, Yue Shi, Xiaoying Bai
2014 2014 IEEE 6th International Conference on Cloud Computing Technology and Science  
An elastic cloud provisions machine instances upon user demand. Auto-scaling, scale-out, scale-up, or any mixture techniques are used to reconfigure the user cluster as workload changes. We evaluate three scaling strategies to upgrade the performance, efficiency and productivity of elastic clouds like EC2, Rackspace, etc. We developed new performance models and run the HiBench benchmark to test Hadoop performance on various EC2 configurations. The strengths and shortcomings of three scaling
more » ... tegies are revealed in our HiBench experiments: (1). Scale-out overhead is shown lower than that experienced in scale-up or mixed scaling clouds. Scale-out to a larger cluster of small nodes demonstrated high scalability. (2). Scaling up and mixed scaling have high performance in using smaller clusters with a few powerful machine instances. (3). With a mixed scaling mode, the cloud productivity is shown upgradable with higher flexibility in applications with performance/cost tradeoffs.
doi:10.1109/cloudcom.2014.66 dblp:conf/cloudcom/HwangSB14 fatcat:fnmnjmen2ngw5c5eushwyrs2jy