MS Thesis Defense: Muthuveer Somanathan
From CS Colloquium
On Friday August 8, 2008, at 10:30 am, Muthuveer Somanathan will be defending his Master Thesis
"TOWARDS AN ADAPTATION HEURISTIC FOR THE LINUX 2.6 VIRTUAL MEMORY MANAGER (VMM) PARAMETER SWAP CLUSTER MAX (SCM) WITH RESPECT TO PROGRAM BEHAVIOR: A FIRST STEP"
The goal of an Operating System (OS) is to efficiently manage system resources so that they are available to applications when they are needed. The operating system's resource management policies and the parameters that influence the behavior of these policies are set at OS compilation time and are fixed irrespective of the type of workload being executed. Since different workloads have different resource usage patterns, such a generalized management policy suits the needs of some workloads more than others. To help alleviate this problem, the Linux operating system includes customization features that allow system administrators to select the resource management policies and parameters that best serve their workloads. One drawback of this solution is that the administrators need to be knowledgeable about both the workloads' resource-usage patterns as well as the inner workings of the operating system. An alternate solution would be to let the OS dynamically detect resource usage patterns at run time and adapt its own policies and parameters accordingly, thereby eliminating the need for the user to participate in OS adaptation.
This thesis is a first step towards a dynamic adaptation heuristic for one such parameter of the Linux Virtual Memory Manager (VMM), namely, Swap Cluster Max (SCM). Kandiraju and Portillo have shown that SCM is a good candidate for dynamic adaptation. This thesis extends Portillo's work and makes two main contributions to the dynamic adaptation of SCM. First, this thesis develops a workload generator that can simulate phases of a class of applications. A phase of an application is characterized by its Page Fault Rate (PFR). Second, using the workload generator, this thesis experiments with SCM at different phases of a class of applications and makes a first step towards a dynamic adaptation heuristic for SCM.
