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Energy-Price-Driven Query Processing 
in 
Multi-center Web 
Search Engines 
Enver Kayaaslan (Bliken University) 
B. Barla Cambazoglu (Yahoo! Research) 
Roi Blanco (Yahoo! Research) 
Flavio Junqueira (Yahoo! Research) 
Cevdet Aykanat (Bilken University)
Overview 
• Large scale web search engines 
• Is it possible to decrease the energy financial costs? 
– Reducing the electric bill (>>35M$ annual) 
– Shifting workload between datacenters 
- 2 - 
• Agenda 
– Motivation 
– Problem Definition 
– Algorithm 
– Experiments
Centralized Web Search Engines 
- 3 -
- 4 - 
Query 
Central 
Broker 
Cluster 
Cluster 
Cluster 
Cluster 
Query Execution
In-side cluster processing 
- 5 - 
Query 
Cluster 
Result
- 6 - 
Multi-center WSE
- 7 - 
Metrics 
• Query Response Time 
– Under a satisfactory amount (400ms) 
• User to data center latency 
• Query processing operations 
– Query degradation 
• Peak Sustainable Throughput 
– Query overflows 
– Number of clusters 
• Total Energy Consumption 
– Query processing operations 
• Total Energy Cost (electric bill) 
– Energy price
Space/temporal variation in query workload 
- 8 -
Space/Temporal variation in energy price 
Spatial Temporal 
- 9 -
• To decrease the total energy cost, exploiting 
- Spatio-temporal variation in energy prices 
- Spatio-temporal variation in query workloads 
- 10 - 
• Constraints: 
– Limited hardware 
– Bounded response times 
Goal
- 11 - 
Query forwarding
- 12 - 
Result 
Query forwarding
- 13 - 
Result 
Query forwarding
0 Timeline 
- 14 - 
Problem definition
- 15 - 
Problem definition 
Minimize Financial Cost 
Response Time Constraint 
Workload Constraint
Query degradation rate 
Response Time Constraint 
- 16 - 
Query Degradation Rate
- 17 - 
Query overflow rate 
Workload Constraint 
Query Overflow Rate 
Realized workload 
=
Online Workload shifting algorithm 
- 18 - 
Remote Centers 
? 
Local Center 
User query
Workload estimation (no forwarding) 
0 Timeline 
Current time 
0 Timeline 
Current time 
0 Timeline 
Current time 
- 19 -
- 20 - 
 Estimates future 
workloads 
 Current energy prices 
 Capacities 
 Shifts workload 
evenly assuming 
every expensive data-center 
will forward 
queries evenly 
 Probability is the ratio 
of the forwarding rate 
to the estimated 
workload 
 Sort of conservative 
Generating probabilities
- 21 - 
Generating probabilities 
 Estimates future 
workloads 
 Current energy prices 
 Capacities 
 Shifts workload 
evenly assuming 
every expensive data-center 
will forward 
queries evenly 
 Probability is the ratio 
of the forwarding rate 
to the estimated 
workload 
 Sort of conservative
- 22 - 
Generating probabilities 
 Estimates future 
workloads 
 Current energy prices 
 Capacities 
 Shifts workload 
evenly assuming 
every expensive data-center 
will forward 
queries evenly 
 Probability is the ratio 
of the forwarding rate 
to the estimated 
workload 
 Sort of conservative
- 23 - 
A 
B 
C 
D 
Remote Centers 
Picking a data-center 
Local Center 
User query 
w.p. pA(A)
Query Workload Price Configuration 
- 24 - 
U T 
S ST 
Energy price configuration
- 25 - 
Set-up 
• 5 Datacenters 
• 38M queries, over 4 days 
• Turn caching on (lowers PST of the back-end) 
• Tuned capacities to a low query overflow rate (<0.005)
- 26 - 
Results (I) 
• 63% of the queries served by the cache 
– Reduces the PST of the backend 
• Average query response time increases (from 66ms to 
around 100ms) 
• Query degradation rate increases but is kept <5% for a 
budget of 400ms (none if budget > 800ms) 
• Overflow rate is the same as in the non-forwarding scenario 
• Forwarding rate is proportional to price variation 
• Despite network latencies forwarding is possible
Aggregate hourly query forwarding rate 
- 27 - 
 Global Time 
 Forwarding depends on ordinal 
ranking of current prices 
 In spatial it dominates price 
ordinal ranking more than intra-day 
variations (S correlates 
with ST) 
 Traffic and forwarding inverse 
relation 
 Takes into account energy 
prices
- 28 - 
 Relative to U setup 
 Increasing saving with larger 
response time limit 
 ST > S > T (r = ∞) 
 About 35% saving at ST 
Savings in electric cost
Savings vs electric price vs forwarding rate 
- 29 - 
 Local Time (where the query 
is processed) 
 T setup, correlates with 
temporal effects 
 r = 800 (~ ∞) 
 Query forwarding rate: 
positive correlation 
 Electric price: 
negative correlation 
 What happens at 17:00?
- 30 - 
Conclusions 
• Presented the reduction of the electric bill in distributed 
search engines as an online optimization framework 
• A practical algorithm, based on shifting query workloads 
• Evaluation of potential savings via realistic simulations 
• Depending on electric price distribution, energy costs can be 
significantly reduced by shifting query workloads to energy-cheap 
data centers. 
– By maintaining the overflow rate equal to that of a centralized 
engine 
– By keeping the query degradation rate < 5% 
• The higher the variability in the configuration the higher the 
savings
- 31 - 
Future work 
• Price-aware crawling 
• Price-aware indexer 
• Energy-aware caches 
• Green search engines!
Reducing the financial cost per query 
- 32 -
- 33 - 
 No bound on response time, 
r = ∞ 
 Almost all can be answered 
under 800ms, 5% under 
400ms 
 U does not forward 
 T forwards more 
Query forwarding vs budget
- 34 - 
PST 
(for each datacenter) 
Window Size 
(for each price configuration) 
PST and window size
Query processing time estimation 
- 35 - 
Query q = {t1, t2, t3} 
LIST 1 
LIST2 
LIST3 
t1 
t2 
t3 
results = {r11,r15, r22, r26 r31} 
#operations ~ c + w(|LIST1| + |LIST2| + |LIST3|)

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  • 1. Energy-Price-Driven Query Processing in Multi-center Web Search Engines Enver Kayaaslan (Bliken University) B. Barla Cambazoglu (Yahoo! Research) Roi Blanco (Yahoo! Research) Flavio Junqueira (Yahoo! Research) Cevdet Aykanat (Bilken University)
  • 2. Overview • Large scale web search engines • Is it possible to decrease the energy financial costs? – Reducing the electric bill (>>35M$ annual) – Shifting workload between datacenters - 2 - • Agenda – Motivation – Problem Definition – Algorithm – Experiments
  • 3. Centralized Web Search Engines - 3 -
  • 4. - 4 - Query Central Broker Cluster Cluster Cluster Cluster Query Execution
  • 5. In-side cluster processing - 5 - Query Cluster Result
  • 6. - 6 - Multi-center WSE
  • 7. - 7 - Metrics • Query Response Time – Under a satisfactory amount (400ms) • User to data center latency • Query processing operations – Query degradation • Peak Sustainable Throughput – Query overflows – Number of clusters • Total Energy Consumption – Query processing operations • Total Energy Cost (electric bill) – Energy price
  • 8. Space/temporal variation in query workload - 8 -
  • 9. Space/Temporal variation in energy price Spatial Temporal - 9 -
  • 10. • To decrease the total energy cost, exploiting - Spatio-temporal variation in energy prices - Spatio-temporal variation in query workloads - 10 - • Constraints: – Limited hardware – Bounded response times Goal
  • 11. - 11 - Query forwarding
  • 12. - 12 - Result Query forwarding
  • 13. - 13 - Result Query forwarding
  • 14. 0 Timeline - 14 - Problem definition
  • 15. - 15 - Problem definition Minimize Financial Cost Response Time Constraint Workload Constraint
  • 16. Query degradation rate Response Time Constraint - 16 - Query Degradation Rate
  • 17. - 17 - Query overflow rate Workload Constraint Query Overflow Rate Realized workload =
  • 18. Online Workload shifting algorithm - 18 - Remote Centers ? Local Center User query
  • 19. Workload estimation (no forwarding) 0 Timeline Current time 0 Timeline Current time 0 Timeline Current time - 19 -
  • 20. - 20 -  Estimates future workloads  Current energy prices  Capacities  Shifts workload evenly assuming every expensive data-center will forward queries evenly  Probability is the ratio of the forwarding rate to the estimated workload  Sort of conservative Generating probabilities
  • 21. - 21 - Generating probabilities  Estimates future workloads  Current energy prices  Capacities  Shifts workload evenly assuming every expensive data-center will forward queries evenly  Probability is the ratio of the forwarding rate to the estimated workload  Sort of conservative
  • 22. - 22 - Generating probabilities  Estimates future workloads  Current energy prices  Capacities  Shifts workload evenly assuming every expensive data-center will forward queries evenly  Probability is the ratio of the forwarding rate to the estimated workload  Sort of conservative
  • 23. - 23 - A B C D Remote Centers Picking a data-center Local Center User query w.p. pA(A)
  • 24. Query Workload Price Configuration - 24 - U T S ST Energy price configuration
  • 25. - 25 - Set-up • 5 Datacenters • 38M queries, over 4 days • Turn caching on (lowers PST of the back-end) • Tuned capacities to a low query overflow rate (<0.005)
  • 26. - 26 - Results (I) • 63% of the queries served by the cache – Reduces the PST of the backend • Average query response time increases (from 66ms to around 100ms) • Query degradation rate increases but is kept <5% for a budget of 400ms (none if budget > 800ms) • Overflow rate is the same as in the non-forwarding scenario • Forwarding rate is proportional to price variation • Despite network latencies forwarding is possible
  • 27. Aggregate hourly query forwarding rate - 27 -  Global Time  Forwarding depends on ordinal ranking of current prices  In spatial it dominates price ordinal ranking more than intra-day variations (S correlates with ST)  Traffic and forwarding inverse relation  Takes into account energy prices
  • 28. - 28 -  Relative to U setup  Increasing saving with larger response time limit  ST > S > T (r = ∞)  About 35% saving at ST Savings in electric cost
  • 29. Savings vs electric price vs forwarding rate - 29 -  Local Time (where the query is processed)  T setup, correlates with temporal effects  r = 800 (~ ∞)  Query forwarding rate: positive correlation  Electric price: negative correlation  What happens at 17:00?
  • 30. - 30 - Conclusions • Presented the reduction of the electric bill in distributed search engines as an online optimization framework • A practical algorithm, based on shifting query workloads • Evaluation of potential savings via realistic simulations • Depending on electric price distribution, energy costs can be significantly reduced by shifting query workloads to energy-cheap data centers. – By maintaining the overflow rate equal to that of a centralized engine – By keeping the query degradation rate < 5% • The higher the variability in the configuration the higher the savings
  • 31. - 31 - Future work • Price-aware crawling • Price-aware indexer • Energy-aware caches • Green search engines!
  • 32. Reducing the financial cost per query - 32 -
  • 33. - 33 -  No bound on response time, r = ∞  Almost all can be answered under 800ms, 5% under 400ms  U does not forward  T forwards more Query forwarding vs budget
  • 34. - 34 - PST (for each datacenter) Window Size (for each price configuration) PST and window size
  • 35. Query processing time estimation - 35 - Query q = {t1, t2, t3} LIST 1 LIST2 LIST3 t1 t2 t3 results = {r11,r15, r22, r26 r31} #operations ~ c + w(|LIST1| + |LIST2| + |LIST3|)

Editor's Notes

  1. Each center keeps a replica of its index