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Inside Xen Hypervisor

25%. CPU intensive. Avg. μ. Lower Perf Delay. Higher energy savings. mem intensive. 0.75. 75%. 0.4. time. Apps. Apps. Apps. Working Set. OS. OS. OS. Expert 1. Expert 2. Expert 3. Expert N. Guest 1. Guest 2. Guest n. Expert selection. Manages Power. Controller. Device.

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Inside Xen Hypervisor

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  1. 25% CPU intensive Avg. μ Lower Perf Delay Higher energy savings mem intensive 0.75 75% 0.4 time Apps Apps Apps Working Set OS OS OS Expert 1 Expert 2 Expert 3 Expert N Guest 1 Guest 2 Guest n Expert selection Manages Power Controller Device Hypervisor Credit Scheduler :Operational Expert :Dormant Experts Workload Characterization Online Learning Algorithm I/O Intensive? CPU Intensive? Hardware I/O CPUs N/W HDD CPU0 CPU1 CPU2 CPUn VM Scheduling Energy Saving/Performance Delay Results for CPU DPM: With Individual Experts Policy HP1 Trace HP2 Trace HP3 Trace %delay %energy %delay %energy %delay %energy Oracle 0 68.17 0 65.9 0 71.2 Timeout 4.2 49.9 4.4 46.9 3.3 55 Ad Timeout 7.7 66.3 8.7 64.7 6 67.7 Summary Hypervisor VM scheduler implementation • Power Management: DPM/DVFS • Workload characterization aware • Adaptive Behavior TISMDP 3.4 44.8 2.26 36.7 1.8 42.3 Predictive 8 66.6 9.2 65.2 6.5 68 DPM: With Online Learning Preference HP-1 Trace HP-2 Trace HP-3 Trace %delay %energy %delay %energy %delay %energy Low delay I V High energy savings 3.5 45 2.61 37.41 2.55 49.5 Power/Performance Results for HDD HP-1 trace Comparison with fixed timeout experts 6.13 60.64 5.86 54.2 4.36 61.02 7.68 65.5 8.59 64.1 5.69 66.28 Energy Management in Virtualized Environments Gaurav Dhiman, Giacomo Marchetti, Raid Ayoub, Tajana Simunic Rosing (CSE-UCSD) Inside Xen Hypervisor Motivations and Goals • Energy Oriented Scheduler • Implements a schedulercapable of adapting to workload (guest) characteristics • Migration: Guest balancing and clustering • Co-locate guests to free up resources • Online Learning Algorithm • Lower datacenter energy consumption • Handle non-stationary workloads • Service - VM - Customization • Workload characterization - I/O Intensiveness: Maintain metrics for I/O accesses per guest - CPU Intensiveness:Use CPU performance counters • Virtual Machine Power Oriented Scheduling • Workload migration across physical machine • Minimize impact on performance Online Learning Algorithm • Performs dynamic evaluation of a set of DPM and DVFS policies at run time and selects the best suited for the current workload • Guarantees convergence and performance close to that of the best available policy in the set Virtualization DVFS For qsort • CPU intensive (µ ->1) vs Memory intensive (µ -> 0) • µ = measure of CPU intensiveness • Leakage impact (ρ) Experimental Setup • Workloads: qsort, djpeg, blowfish, dgzip • CPU Xscale DPM & DVFS Experimental Setup • AMD quad core CPU • SPEC benchmarks Identifies both CPU-intensive and memory intensive phases correctly DPM OS implementation and Results Recent CPUs might perform better with a “run to sleep” policydue to: • Improved CPU efficiency • Idle power management support Supported by NSF-GreenLight project, CNS, Sun Microsystems, UC Micro, Cisco, GSRC/DARPA

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