LAUSR

In today's IT platforms, the capability to accurately account overall resource usage among applications is crucial for variety of management actions (e.g., capacity planning, dynamic resource reallocation and/or load balancing). However, in the environments where small number of shared services cater to a large number of distinct entities' requests, resource accounting becomes significantly challenging. First, the overall resource consumption at the shared service is the aggregate of the resource consumption for multiple remote entities whose identities are not visible to the shared service. Second, even if such information becomes available, common monitoring tools (e.g., top, iostat) are unable to deliver accurate break-down of resource consumption since sharing occurs at sub-instance level (i.e., service instances are not exclusive). We study inherent challenges of performing resource accounting of shared resource. We compare two nonintrusive approaches having different balance between local monitoring and collective inference - (i) LR that uses easily-available tools which provide aggregate measurement and applying well-known linear regression as inference, and (ii) Rameter that puts more emphasis on gathering fine-grained per-thread information from within the hypervisor and applying light inference on the data. Evaluation shows that Rameter offers less than 1% error in accounting whereas LR's error fluctuates between 5-150%.