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

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, Rack space, etc. We developed new performance models and run the Hi Bench benchmark to test Hadoop performance on various EC2 configurations. The strengths and shortcomings of three scaling strategies are revealed in our Hi Bench 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.