Empirical Evaluation of Cloud Workload Forecasting Techniques (Cloud 2016)


Many predictive resource scaling approaches have been proposed to overcome the limitations of the conventional reactive approaches most often used in clouds today. In general, due to the complexity of clouds, these reactive approaches were often forced to make significant limiting assumptions in either the operating conditions/requirements or expected workload patterns. As such, it is extremely difficult for cloud users to know which – if any – existing workload predictor will work best for their particular cloud activity, especially when considering highly variable workload patterns, non-trivial billing models, variety of resources to add/subtract, etc. To solve this problem, we conduct comprehensive evaluations for a variety of workload predictors in real-world cloud configurations. The workload predictors cover four classes of 21 predictors: naive, regression, temporal, and non-temporal methods. We simulate a cloud application under four realistic workload patterns, two different cloud billing models, and three different styles of predictive scaling. Our evaluation confirms that no workload predictor is universally best for all workload patterns, and shows that Predictive Scaling-out + Predictive Scaling-in has the best cost efficiency and the lowest jobdeadline miss rate in cloud resource management, on average providing 30% better cost efficiency and 80% less job deadline miss rate compared to other styles of predictive scaling.

In 8th IEEE International Conference on Cloud Computing (Cloud 2016), June 27 - July 2, 2016, San Francisco, USA