Position summary: hinting for goodness´ sake

Modern operating systems and adaptive applications offer an overwhelming number of parameters affecting application latency, throughput, image resolution, audio quality, and so on. We are designing a system to automatically tune resource allocation and application parameters at runtime, with the aim of maximizing user happiness or goodness. Consider a 3D graphics application that operates at variable resolution, trading output fidelity for processor time. Simultaneously, a data mining application adapts to network and processor load by migrating computation between the client and storage node. We must allocate resources between these applications and select their adaptive parameters to meet the user´s overall goals. Since the user lacks the time and expertise to translate his preferences into parameter values, we would like the system to do this. Existing systems lack the right abstractions for applications to expose information for automated parameter tuning. Goodness hints are the solution to this problem. Applications use these hints to tell the operating system how resource allocations will affect their goodness (utility). Goodness hints are used by the operating system to make resource allocation decisions and by applications to tune their adaptive parameters. Our contribution is a decomposition of goodness hints into manageable and independent pieces and a methodology to automatically generate them.