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Energy-efficient Dynamic Capacity Provisioning in Server Farms

Energy-efficient Dynamic Capacity Provisioning in Server Farms. VARUN GUPTA Carnegie Mellon University. Partly based on joint work with : Anshu l Gandhi Mor Harchol-Balter Mike Kozuch (CMU) (CMU) (Intel Research ).

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Energy-efficient Dynamic Capacity Provisioning in Server Farms

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  1. Energy-efficient Dynamic Capacity Provisioning in Server Farms VARUN GUPTA Carnegie Mellon University Partly based on joint work with: Anshul Gandhi MorHarchol-Balter Mike Kozuch (CMU) (CMU) (Intel Research)

  2. The “provisioning for peak” problem Data Center Capacity ? Demand (t) Time (t) PROBLEM: Want to turn servers OFF/ON to match (t)… … and also minimize setup penalties!

  3. First Attempt: ON/OFF policy (t) # Busy servers # Jobs Mean Job Size = 1s Setup delay = 200s Busy power = 240W

  4. First Attempt: ON/OFF policy (t) # Busy servers # Jobs Mean Job Size = 1s Setup delay = 200s Busy power = 240W

  5. First Attempt: ON/OFF policy (t) # Busy servers # Jobs Mean Job Size = 1s Setup delay = 200s Busy power = 240W

  6. First Attempt: ON/OFF policy (t) # Busy servers # Jobs Mean Job Size = 1s Setup delay = 200s Busy power = 240W WITH SETUP: E[Power] = 51.9 kW E[Response time] = 13s NO SETUP: E[Power] = 14.4 kW E[Response time] = 1s

  7. First Attempt: ON/OFF policy (t) # Busy servers # Jobs Mean Job Size = 1s Setup delay = 200s Busy power = 240W Can we do better than ON/OFF? . Add inertia while turning servers OFF

  8. Our Prescription: DELAYEDOFF Turn OFF a server after idle for twait twait independent of (t)! new arrival routed to the most recently busy (MRB) server arriving job turns a server ON if all servers busy

  9. twait timers

  10. twait timers

  11. twait timers

  12. Our Prescription: DELAYEDOFF THEOREM: For Poisson arrivals, as the load , the number of ON servers is concentrated around --- Proof Intuition --- Step 1: An equivalent system view k jobs run on the “first” k servers Step 2: Analysis of idle periods of M/G/∞ Time from a k(k-1) to the next (k-1)k transition

  13. Intuition for idle periods 1. # jobs  Normal with mean and variance  2. 3. Events happen at rate  4. Mean idle period of server 3 2 MRB  servers for any constant twait! In practice, we choose twait to amortize setup cost 1

  14. ON/OFF (t) # Busy+idle servers # Jobs DELAYEDOFF Mean Job Size = 1s Setup delay = 200s Busy power = 240W

  15. ON/OFF (t) # Busy+idle servers # Jobs DELAYEDOFF Mean Job Size = 1s Setup delay = 200s Busy power = 240W

  16. ON/OFF (t) # Busy+idle servers # Jobs DELAYEDOFF Mean Job Size = 1s Setup delay = 200s Busy power = 240W DELAYEDOFF: E[Power] = 18.9 kW E[Response time] = 1.002s NO SETUP: E[Power] = 14.4 kW E[Response time] = 1s .

  17. DELAYEDOFF (t) # Busy+idle servers # Jobs SQ. ROOT W/ LOOKAHEAD (“optimal offline”) Mean Job Size = 1s Setup delay = 200s Busy power = 240W

  18. DELAYEDOFF (t) # Busy+idle servers # Jobs SQ. ROOT W/ LOOKAHEAD (“optimal offline”) Mean Job Size = 1s Setup delay = 200s Busy power = 240W

  19. DELAYEDOFF (t) # Busy+idle servers # Jobs SQ. ROOT W/ LOOKAHEAD (“optimal offline”) Mean Job Size = 1s Setup delay = 200s Busy power = 240W DELAYEDOFF: E[Power] = 18.9 kW E[Response time] = 1.002s  w/LOOKAHEAD: E[Power] = 15.8 kW E[Response time] = 1.036s

  20. DELAYEDOFF mods Speed scaling algorithms A simple proxy for MRB routing Heterogeneous servers Managing Virtual Machines in the Cloud Wear-leveling/Performance tradeoffs

  21. Effect of DELAYEDOFF on speed-scaling Power P(s) Speed (s) Q: Optimal speed to balance energy-performance? A: your favorite metric =

  22. A simple proxy for MRB routing • MRB requires a lot of state updates • Proxy policy • Assign static ranks to servers • Route a new arrival to highest ranked idle server • Almost the same performance as MRB • + easier to implement than MRB • + easy to extend 5 4 3 (MRB) Rank-based 2 1 Rank

  23. DELAYEDOFF for heterogeneous servers less efficient 5 Assign ranks to servers based on efficiency (1 = most efficient) Route a new arrival to highest ranked idle server 4 3 2 1 more efficient

  24. Managing Virtual Machines in the Cloud 2GB RAM 1GB 1GB 2GB RAM 1GB 2GB RAM

  25. Managing Virtual Machines in the Cloud 6 Split physical servers into “virtual” servers Assign static ranks to virtual servers Route a new VM request to highest ranked idle virtual server Turn the physical server OFF after each virtual server has idled for twait 5 4 3 2 1

  26. Conclusions Max Capacity Demand ? Time Problem: unpredictable demand and non-trivial setup costs DELAYEDOFF : A new traffic-oblivious capacity scaling scheme Extensions to real-world scenarios

  27. The Importance of Being MRB THEOREM [MRB]: As the load , the number of ON servers is concentrated around THEOREM [Round-Robin]: As the load , for constant job sizes, the number of ON servers is Arrival rate = λ N servers

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