Energy Management in Virtualized Environments Gaurav Dhiman, Giacomo Marchetti, Raid Ayoub, Tajana Simunic Rosing (CSE-UCSD) Inside Xen Hypervisor Online Learning Algorithm Virtualization DPMDPM Performs dynamic evaluation of a set of DPM and DVFS policies Performs dynamic evaluation of a set of DPM and DVFS policies at run time and selects the best suited for the current workload at run time and selects the best suited for the current workload Guarantees convergence and performance close to that of the best Guarantees convergence and performance close to that of the best available policy in the set available policy in the set OS implementation and Results Summary Summary Hypervisor VM scheduler implementation Hypervisor VM scheduler implementation Power Management: DPM/DVFS Workload characterization aware Adaptive Behavior Motivations and Goals Lower datacenter energy consumption Lower datacenter energy consumption Handle non-stationary workloads Handle non-stationary workloads Service - VM - Customization Service - VM - Customization Energy Oriented Scheduler Energy Oriented Scheduler -Implements a scheduler capable of adapting to workload (guest) characteristics -Migration: Guest balancing and clustering -Co-locate guests to free up resources -Online Learning Algorithm Supported by NSF-GreenLight project, CNS, Sun Microsystems, UC Micro, Cisco, GSRC/DARPA Supported by NSF-GreenLight project, CNS, Sun Microsystems, UC Micro, Cisco, GSRC/DARPA CPU0CPU0CPU1CPU1N/WN/WCPU2CPU2CPUnCPUnHDDHDD Hypervisor Hypervisor Guest n I/OCPUs Hardware I/O Intensive? CPU Intensive? Guest 1 Guest 2 AppsApps OSOS AppsApps OSOS AppsApps OSOS Credit Scheduler Workload Characterization Online Learning Algorithm VM Scheduling Virtual Machine Power Oriented Scheduling Virtual Machine Power Oriented Scheduling Workload migration across physical machine Workload migration across physical machine Minimize impact on performance Minimize impact on performance Workload characterization Workload characterization - I/O Intensiveness: Maintain metrics - I/O Intensiveness: Maintain metrics for I/O accesses per guest for I/O accesses per guest - CPU Intensiveness: Use CPU - CPU Intensiveness: Use CPU performance counters performance counters CPU intensive (µ ->1) vs Memory intensive (µ -> 0) CPU intensive (µ ->1) vs Memory intensive (µ -> 0) µ = measure of CPU intensiveness µ = measure of CPU intensiveness Leakage impact (ρ) Leakage impact (ρ) DVFSDVFS For qsort Higher energy savings Lower Perf Delay Identifies both CPU-intensive and memory intensive phases correctly Avg. μ time % 75% CPU intensive mem intensive Energy Saving/Performance Delay Results for CPU Experimental Setup Workloads: qsort, djpeg, blowfish, dgzip Workloads: qsort, djpeg, blowfish, dgzip CPU Xscale CPU Xscale Controller Working Set Device :Dormant Experts Expert selection :Operational Expert Manages Power Expert 1 Expert 2 Expert N Expert 3 DPM & DVFS Experimental Setup AMD quad core CPU AMD quad core CPU SPEC benchmarks SPEC benchmarksBenchmarkFreq%delay %Energy savingsPM-i PM-1PM-2PM-3 mcf bzip art sixtrack Device Trace Name t RI σ t RI HDD HDDHP-1Trace HP-2 Trace HP-3 Trace t RI : Average Request Inter-arrival Time (in sec) t RI : Average Request Inter-arrival Time (in sec) ExpertCharacteristics Fixed Timeout Timeout = 7*T be Adaptive Timeout (Douglis, USENIX’95) Initial timeout = 7*T be ; Adjustment = +1T be /-1T be Exponential Predictive (Hwang, ICCAD’97) I n+l = a i n + (1 – a).I n with a = 0.5 TISMDP (Simunic, TCAD’01) Optimized for delay constraint of 3.5% on HP-1 trace PolicyDescriptionPM-1 switch CPU to ACPI state C1 (remove clock supply) and move to lowest voltage setting PM-2 switch CPU to ACPI state C6 (remove power) PM-3 switch CPU to ACPI state C6 and switch the memory to self- refresh mode Recent CPUs might perform better with a “run to sleep” policy due to: Improved CPU efficiency Idle power management support Idle power management support Power/Performance Results for HDD HP-1 trace Comparison with fixed timeout experts