Data Center Network Minerals Tutorial

Transcription

1 7/1/9 vmanage: Loosely Coupled Platform and Virtualization Management in Data Centers Sanjay Kumar (Intel), Vanish Talwar (HP Labs), Vibhore Kumar (IBM Research), Partha Ranganathan (HP Labs), Karsten Schwan (Georgia Tech) Problem Silos of management domains App Mgmt (eg, SLA mgmt, patch updates) Virtualization/OS Mgmt (eg, VM prov, runtime monitoring) Platforms Mgmt (eg, server config, power mgmt) Subsystem-level solutions across layers Inefficiency and redundancy Instability in data center High costs for customers 1

2 7/1/9 An illustrative problem with silos Avg Power Resp Time ResponseTime (msecs) Significant violations & instability due to oscillatory behavior 3 1 July 9 An illustrative problem with silos 4 Oscillations among SLA and power violations 35 4 Avg Power Resp Time ResponseTime(msecs) Significant violations & instability due to oscillatory behavior 4 1 July 9

3 7/1/9 vmanage: New enablements for coordinated mgmt Management Node Cluster Virtualization Manager Utilization Power Usage Local Virtualization Access Point Virtualization & App Sensors & Actuators Platform Manager Platform Sensors & Actuators Node (s) 5 1 July 9 vmanage: New enablements for coordinated mgmt Management Node Cluster Virtualization Manager Power Usage Coordinator Utilization Local Virtualization Access Point Registry & Proxy Service Virtualization & App Sensors & Actuators Key building blocks Coordinator 1 Platform Manager Platform Sensors & Actuators Node (s) 6 1 July 9 3

4 7/1/9 vmanage: New enablements for coordinated mgmt Platform-aware Virtualization Management Management Node Cluster Virtualization Manager Power Usage Coordinator Utilization Local Virtualization Access Point Registry & Proxy Service Virtualization & App Sensors & Actuators Key building Blocks Virtualizationaware Platform Management Coordinator 1 Platform Manager Platform Sensors & Actuators Node (s) 7 1 July 9 vmanage: New enablements for coordinated mgmt Platform-aware Virtualization Management ManagementNode Cluster Virtualization Manager Power Usage Coordinator Utilization Local Virtualization Access Point Registry & Proxy Service Virtualization & App Sensors & Actuators Key building blocks Virtualizationaware Platform Management Benefits Coordinator Platform Manager PlatformSensors & Actuators Node (s) Challenges Structured & automated Loosely-coupled & extensible Works with legacy controllers Discovery & meta-data registration Coordinated policies Data center stability 8 1 July 9 4

5 7/1/9 Coordinated policies Current State of Art Node 1 Requests Virtualization Mgr VM Resource Requirements Node Node 3 Node n Coordinated policies Platform-aware Virtualization Manager Node 1 Requests Virtualization Mgr Coordinator VM Res Rqt + Power Budget VM migration VM migration Node Node 3 Node n 5

6 7/1/9 Coordinated policies Platform-aware Virtualization Manager Node 1 Requests Virtualization Mgr eg, P( t, t T) Coordinator VM Res Rqt + Power Budget + Stability t rj R t T F (a (t),t) M v j T j Node Node 3 Node n Coordinated policies Current State of Art Power Violations Power Mgr Perf counters P State actuators 1 1 July 9 6

7 7/1/9 Coordinated policies Virtualization-aware Power Manager To Platform-aware Virtualization Manager SLA notifications Power Violations Power Mgr Coordinator SLA + power violations P State actuators VM migration actuators 13 1 July 9 Stability Platform-aware Virtualization Manager Node 1 Requests Virtualization Mgr Coordinator VM Res Rqt + Power Budget + Stability Stability criterion Pick node having highest probability for the placement decision to remain valid over certain duration in the future Node Node 3 Node n 7

8 7/1/9 Stability (contd) Assume we can do offline profiling of applications or behavior traces available ie mean, standard deviation, PDF are known apriori Average probability with which the host can provide sufficient resources of a particular type to a set M of VMs over a given time interval T is p rj ( t, t T) t T t F M v j (a (t), t) If we assume the PDF to have a normal distribution T j F v M j (x, t) 1 (1 erf x μ σ v M j v M j (t) (t) ) Prototype implementation Per-node view Guest VM (s) Dom-M Dom Application SLA Sensors OS Power Manager Coordinator Registry & Proxy Services Virt Sensors, Actuators; Power Actuators Power Sensors Xen Hypervisor ilo Firmware x86 Hardware ilo 8

9 7/1/9 Experimental Setup Hardware Dual-core dual-socket machines with Intel 515 processors 4 GB memory each machine Applications Rubis multi-tier app (4 VMs) Nutch Web app (1 VM) Webserver app (1VM) Batch model app - CPU-intensive custom script Sensors and actuators Custom SLA sensors ilo power sensors and offline model-based calibration XenMon for utilization CPU freq driver for p-state actuations VM migration actuator ( using Xen api ) Evaluation Results 8 VMs run over hours on a 13-node testbed 1 Nutch instances, 3 RUBiS instances, 6 static webserver instances 1 5 SLA Violations (normalized to Base) Base (no Coordinated coordination) Solution (vmanage) Average Power (Watts) Base (no Coordinated coordination) Solution (vmanage) Significantly better QoS (71%) Improved power savings (1%) Better stability (54%) Stability (# VM migrations) Base (no Coordinated cordination) Solution (vmanage) 18 1 July 9 9

10 7/1/9 Snapshot of prototype operation Response Application response time Powerusage July 9 Response Snapshot of prototype operation s at low load Application response time Powerusage July 9 1

11 7/1/9 Response Snapshot of prototype operation at high load Application response time Violations Powerusage Violations July 9 Response Snapshot of prototype operation Coordinated manager at low load Application response time Powerusage Lower Power July 9 11

12 7/1/9 Response Snapshot of prototype operation Coordinated manager at high load Application response time Powerusage VM migration VM migration July 9 Response Snapshot of prototype operation Coordinated manager at high load July 9 Application response time Powerusage VM migration VM migration Richness of information & actuation 1

13 7/1/9 Benefits of our approach Additional experimentation Effects of stabilizer in coordinated solution SLA Violations (normalized to Base) Base C1 C C Power (normalized to Base) Base C1 C C Stability (# migrations) Base C1 C C3 C1: First fit coordinated placement C: Best fit coordinated placement C3: Stability-aware coordinated placement Related Work Several individual mgmt solutions for virtualization mgmt, platform mgmt, application mgmt exist in isolated silos and represent partial subsystem-level solutions Few recent studies towards coordinated and unified mgmt [Raghavendra8], [Nathuji7], [Verma8], [Kephart7, Das8], [Chen8], [Adve] lack a systematic systems/architecture approach to the coordination problem across hw-sw some focused on ad-hoc solutions dealing with limited actuators only Overall, vmanage takes a loosely-coupled and practical approach works with most existing mgmt infrastructures easy plug-and-play of coordination solutions extensible with multiple actuators Real prototype solution with enterprise applications running on a large number of virtual machines 13

14 7/1/9 Summary Management silos a critical and relevant problem Our contributions architecture for cross-layer coordination in mgmt systems mechanisms for unified discovery, coordination policies, and stability Xen-based prototypes; experimentation on real testbeds Future Work applying coordination mechanisms to more use cases extending the architecture for large scale (targeting millions of managed objects) 7 1 July 9 14