Log2Sim: Automating What-If Modeling and Prediction for Bandwidth Management of Cloud Hosted Web Services

For resource management purpose, administrators usually need to perform what-if analyses to predict the impact of any workload growths or planned changes on the performance of web services. A what-if analysis requires not only the design of system models, but also the workload models that represent the real-world user behavior. Existing methods of workload characterization based on probabilistic graphical models are quite complex if there are many web services provided by a system. Meanwhile, bandwidth resource is usually not taken into account in many related works, though it is a relatively expensive resource in cloud markets. In fact, it's very challenging to predict the network throughput of modern web services due to the factors of client-side caching, miscellaneous service responses and complex network transportation. In this paper we propose a methodology of what-if analysis named Log2Sim for the bandwidth management of web systems. We use a lightweight workload model to describe user behavior, an automated mining approach to obtain characteristics of workloads and responses from massive web logs, and traffic-aware simulations to predict the impact on the network throughput and the response time within changing contexts of user behavior. We also choose a real-life web system as use case to evaluate the effectiveness, accuracy and stability of this methodology.