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JustRunIt: Experiment-Based
      Management with Xen

Wei Zheng1    Ricardo Bianchini1        Yoshio Turner2
      J. Renato Santos2         G. Janakiraman3

                1Rutgers University
                     2HP Labs
                      3Skytap




                    Xen Summit 2009
Data Center Management
• Challenging
   – Variety of tasks: Resource allocation, software/hardware
     upgrades, application and OS configuration…
   – Complex: Affect performance, availability, energy consumption
   – Getting worse: larger scale data centers hosting more diverse
     and dynamic workloads
• Automation might save us
   – Using analytical models: insight into system behavior,
     fast exploration of large parameter spaces
• Modeling has some important drawbacks
   – Expensive to develop, often relies on simplifying assumptions,
     may require re-calibration and re-validation as systems evolve
Our Approach:
     Experiment-Based Management
• Experiments may be a better approach than modeling
  for many management tasks
   – Low cost: time and energy consumed by a few machines
   – No simplifying assumptions
   – No need for calibration/validation
• Use of VMs in production facilitates experimentation

  JustRunIt – Infrastructure for experiment-based
      management of virtualized data centers hosting
      multiple services
JustRunIt Architecture
                                                    Coordinates
  Experimenter                                                                     Checker
                                                 Experiment results
                            Experiment results
              Coordinates




                                                     X         X


                                                     X   X     X


                                                                                  Interpolator
          Driver

 Heuristics

 Coordinate
 Range
                                                                      X   I   I   X              T   T
 Time Limit
                                                                      I   I   I    I
                                                                      X   X   I   X                  T


Management Entity
Experimenter
           • Step 1: Clone subset of
VM           production system to a
             sandbox
Experimenter
           • Step 1: Clone subset of
VM           production system to a
             sandbox
              – VM cloning: Modify Xen live
                migration to resume original
VM              VM instead of destroying it
              – Storage cloning: LVM copy-on-
                write snapshot for sandbox VM
Experimenter
           • Step 1: Clone subset of
VM           production system to a
             sandbox
              – VM cloning: Modify Xen live
                migration to resume original
VM              VM instead of destroying it
              – Storage cloning: LVM copy-on-
                write snapshot for sandbox VM
              – L2/L3 network address
                translation: implemented in
                driver domain netback driver
                to prevent network address
                conflict
Experimenter
           • Step 1: Clone subset of
             production system to a sandbox
VM
              – VM cloning: Modify Xen live
                migration to resume original VM
                instead of destroying it
              – Storage cloning: LVM copy-on-
VM
                write snapshot for sandbox VM
              – L2/L3 network address
                translation: implemented in
                driver domain netback driver to
                prevent network address conflict
              – Apply configuration changes :
                e.g., resource allocation
              – Resume execution
A1
                                A2
                         W1            D1
                         W2            D2
                                A1
                                 A2
                                S_A2
                         W1            D1
                         W2            D3
                                A2
                                A3
                         S_W2
                          W2           D2
                         W3            D3
                                A2
                                A3
                         W1            D1
                                       S_D2
                         W2             D2
                                A1
                         W3            D3
                                A2
                                              Replies
                                A3
             Requests

Assess performance and
energy for different
                         S_W2   S_A2   S_D2
configurations
Experimenter
    Step 2: Workload replay using network proxies
•

              In-Proxy         Tier-N VM          Out-Proxy

                              Sandbox VM

• Proxies filter requests/replies from the sandbox VM
• Emulates the timing and functional behavior of
  preceding and following service tiers
     – Application protocol level requests/replies (e.g. HTTP)
• In-proxy replays with fixed delay
     – Delay needed if sandbox is faster than production system
Proxies
                  In-Proxy            Tier-N VM     Out-Proxy

                                     Sandbox VM

    Req0     t0     Resp0    t0’   Resp0   ts0’   Req0   t00   Resp0   t00’
    Req1     t1     Resp1    t1’                  Req1   t01   Resp1   t01’
                                   Resp1   ts1’
                                   Resp2   ts2’
    Req2     t2     Resp2    t2’                  Req2   t02   Resp2   t02’
                                   Resp3   ts3’
    Req3     t3     Resp3    t3’                  Req3   t03   Resp3   t03’


                    Respn tn’ Respn tsn’
    Reqn tn                                       Reqn t0n Respn t0n’


                       Online &     Sandbox
Throughput
Mean response time Online &         Sandbox
Experiment Driver
                                     CPU
• Fill in results matrix             Freq X       X        X

  within a time limit
                                           X      X        X


                                                           X
                                                  X
                                           X
    Corners
•                                    (min,min)        CPU allocation
    Midpoints (recursive)
•
    Heuristics
•
    − Cancel experiments if gain for a resource addition falls below
      a threshold
    − Cancel experiments for tiers that do not produce the largest
      gains from a resource addition
Evaluation Overview
• Implementation
  – Xen (< 50 lines python, <250 lines of C in netback)
  – Proxies for HTTP, mod_jk, MySQL (800-1500 LoC each)
     • Based on Tinyproxy codebase
     • Two services (auction and bookstore)
• Case studies
  – Resource management: CPU
  – Evaluation of hardware upgrade
• Proxy and Replay Overhead
  – Throughput: No impact
  – Response time: < 5 ms impact
Case Study 1: Resource Management
• Goal: satisfy < 50ms response time using
  minimum resource
• JustRunIt determines performance as function
  of resource allocation
• Management entity assigns resources using a
  bin packing algorithm (simulated annealing)
  – Minimize resource usage and VM migrations
• Compare experiment-based with a highly
  accurate model
Case Study 1 Results
                                    60

                                    50




               Response Time (ms)
                                    40                                                    Service 0 RT
JustRunIt                                                                                 Service 1 RT
                                    30
                                                                                          Service 2 RT
                                                                                          Service 3 RT
                                    20

                                    10

                                     0
                                         1   2   3   4         5          6   7   8   9
                                                         Time (minutes)
                                    60

                                    50
             Response Time (ms)




Highly                              40                                                    Service 0 RT
accurate                                                                                  Service 1 RT
                                    30
                                                                                          Service 2 RT
modeling                                                                                  Service 3 RT
                                    20

                                    10

                                    0
                                         1   2   3   4         5          6   7   8   9
                                                         Time (minutes)
Case Study 2: Hardware Upgrades
• Sandbox on upgraded hardware
• Bin packing finds the number of new
  machines to accommodate the workload
• Example scenario: two services, each app
  server uses 90% of one CPU core on old
  hardware
  – New machine requires 72%
  – Example too small to show any consolidation
    benefits of upgrading
Conclusions
• JustRunIt infrastructure can support multiple
  (automated) management tasks
   – Answers “what-if” questions realistically and transparently
     using actual experiments
• Possible directions include:
   –   Tier interactions
   –   Hypothetical workload mix
   –   Validate administrator actions
   –   Tradeoff between accuracy and experiment cost (resource
       usage and experiment time required)
Related Work
• Modeling, simulation of data centers
   – Development and validation cost, simulation slow-down
• Scaled down emulation
   – DieCast [NSDI08] uses VMs and time dilation for emulation (JustRunIt
     targets subset of mgmt tasks, native execution, and minimal additional
     hardware)
• Sandboxing for managing data centers
   – Prior work from Rutgers (validating operator actions OSDI04
     USENIX06, entire data center experiments EUROSYS07) and file server
     verification (Tan et al USENIX 05)
   – Very different infrastructure for JustRunIt (extrapolation, verification,
     application-transparency using VM cloning – practicality)

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XS Oracle 2009 Just Run It

  • 1. JustRunIt: Experiment-Based Management with Xen Wei Zheng1 Ricardo Bianchini1 Yoshio Turner2 J. Renato Santos2 G. Janakiraman3 1Rutgers University 2HP Labs 3Skytap Xen Summit 2009
  • 2. Data Center Management • Challenging – Variety of tasks: Resource allocation, software/hardware upgrades, application and OS configuration… – Complex: Affect performance, availability, energy consumption – Getting worse: larger scale data centers hosting more diverse and dynamic workloads • Automation might save us – Using analytical models: insight into system behavior, fast exploration of large parameter spaces • Modeling has some important drawbacks – Expensive to develop, often relies on simplifying assumptions, may require re-calibration and re-validation as systems evolve
  • 3. Our Approach: Experiment-Based Management • Experiments may be a better approach than modeling for many management tasks – Low cost: time and energy consumed by a few machines – No simplifying assumptions – No need for calibration/validation • Use of VMs in production facilitates experimentation JustRunIt – Infrastructure for experiment-based management of virtualized data centers hosting multiple services
  • 4. JustRunIt Architecture Coordinates Experimenter Checker Experiment results Experiment results Coordinates X X X X X Interpolator Driver Heuristics Coordinate Range X I I X T T Time Limit I I I I X X I X T Management Entity
  • 5. Experimenter • Step 1: Clone subset of VM production system to a sandbox
  • 6. Experimenter • Step 1: Clone subset of VM production system to a sandbox – VM cloning: Modify Xen live migration to resume original VM VM instead of destroying it – Storage cloning: LVM copy-on- write snapshot for sandbox VM
  • 7. Experimenter • Step 1: Clone subset of VM production system to a sandbox – VM cloning: Modify Xen live migration to resume original VM VM instead of destroying it – Storage cloning: LVM copy-on- write snapshot for sandbox VM – L2/L3 network address translation: implemented in driver domain netback driver to prevent network address conflict
  • 8. Experimenter • Step 1: Clone subset of production system to a sandbox VM – VM cloning: Modify Xen live migration to resume original VM instead of destroying it – Storage cloning: LVM copy-on- VM write snapshot for sandbox VM – L2/L3 network address translation: implemented in driver domain netback driver to prevent network address conflict – Apply configuration changes : e.g., resource allocation – Resume execution
  • 9. A1 A2 W1 D1 W2 D2 A1 A2 S_A2 W1 D1 W2 D3 A2 A3 S_W2 W2 D2 W3 D3 A2 A3 W1 D1 S_D2 W2 D2 A1 W3 D3 A2 Replies A3 Requests Assess performance and energy for different S_W2 S_A2 S_D2 configurations
  • 10. Experimenter Step 2: Workload replay using network proxies • In-Proxy Tier-N VM Out-Proxy Sandbox VM • Proxies filter requests/replies from the sandbox VM • Emulates the timing and functional behavior of preceding and following service tiers – Application protocol level requests/replies (e.g. HTTP) • In-proxy replays with fixed delay – Delay needed if sandbox is faster than production system
  • 11. Proxies In-Proxy Tier-N VM Out-Proxy Sandbox VM Req0 t0 Resp0 t0’ Resp0 ts0’ Req0 t00 Resp0 t00’ Req1 t1 Resp1 t1’ Req1 t01 Resp1 t01’ Resp1 ts1’ Resp2 ts2’ Req2 t2 Resp2 t2’ Req2 t02 Resp2 t02’ Resp3 ts3’ Req3 t3 Resp3 t3’ Req3 t03 Resp3 t03’ Respn tn’ Respn tsn’ Reqn tn Reqn t0n Respn t0n’ Online & Sandbox Throughput Mean response time Online & Sandbox
  • 12. Experiment Driver CPU • Fill in results matrix Freq X X X within a time limit X X X X X X Corners • (min,min) CPU allocation Midpoints (recursive) • Heuristics • − Cancel experiments if gain for a resource addition falls below a threshold − Cancel experiments for tiers that do not produce the largest gains from a resource addition
  • 13. Evaluation Overview • Implementation – Xen (< 50 lines python, <250 lines of C in netback) – Proxies for HTTP, mod_jk, MySQL (800-1500 LoC each) • Based on Tinyproxy codebase • Two services (auction and bookstore) • Case studies – Resource management: CPU – Evaluation of hardware upgrade • Proxy and Replay Overhead – Throughput: No impact – Response time: < 5 ms impact
  • 14. Case Study 1: Resource Management • Goal: satisfy < 50ms response time using minimum resource • JustRunIt determines performance as function of resource allocation • Management entity assigns resources using a bin packing algorithm (simulated annealing) – Minimize resource usage and VM migrations • Compare experiment-based with a highly accurate model
  • 15. Case Study 1 Results 60 50 Response Time (ms) 40 Service 0 RT JustRunIt Service 1 RT 30 Service 2 RT Service 3 RT 20 10 0 1 2 3 4 5 6 7 8 9 Time (minutes) 60 50 Response Time (ms) Highly 40 Service 0 RT accurate Service 1 RT 30 Service 2 RT modeling Service 3 RT 20 10 0 1 2 3 4 5 6 7 8 9 Time (minutes)
  • 16. Case Study 2: Hardware Upgrades • Sandbox on upgraded hardware • Bin packing finds the number of new machines to accommodate the workload • Example scenario: two services, each app server uses 90% of one CPU core on old hardware – New machine requires 72% – Example too small to show any consolidation benefits of upgrading
  • 17. Conclusions • JustRunIt infrastructure can support multiple (automated) management tasks – Answers “what-if” questions realistically and transparently using actual experiments • Possible directions include: – Tier interactions – Hypothetical workload mix – Validate administrator actions – Tradeoff between accuracy and experiment cost (resource usage and experiment time required)
  • 18. Related Work • Modeling, simulation of data centers – Development and validation cost, simulation slow-down • Scaled down emulation – DieCast [NSDI08] uses VMs and time dilation for emulation (JustRunIt targets subset of mgmt tasks, native execution, and minimal additional hardware) • Sandboxing for managing data centers – Prior work from Rutgers (validating operator actions OSDI04 USENIX06, entire data center experiments EUROSYS07) and file server verification (Tan et al USENIX 05) – Very different infrastructure for JustRunIt (extrapolation, verification, application-transparency using VM cloning – practicality)