Dynamic scalability and contention prediction in public infrastructure using Internet application profiling

Recent advances in cloud computing have been attracting more customers to host their applications in public and private clouds, where resources are shared among several users by means of virtualization and exposed remotely as services. Infrastructure-as-a-Service (IaaS) comes on top of these services, where it gives more control over the provisioned resources – typically – based on online monitoring of specific metrics (e.g., CPU, Memory, and Network). One of the key challenges in such shared environments is identifying bottlenecks in customers’ applications and predicting potential resource contentions. Identifying real bottlenecks in enterprise applications entails understanding the behavior of these applications in the first place. Predicting potential resource contentions requires additional information rather than traditional metrics that are used to guide resource provisioning. Another factor that exacerbates the challenge of resource prediction in enterprise applications is the use of multi-tiers architecture, because the impact of contention in one tier might propagate to other tiers.