Energy-Price-Driven Query Processing in Multi-center WebSearch Engines

Nov 21, 2014Download as PPTX, PDF1 like768 views

Concurrently processing thousands of web queries, each with a response time under a fraction of a second, necessitates maintaining and operating massive data centers. For large-scale web search engines, this translates into high energy consumption and a huge electric bill. This work takes the challenge to reduce the electric bill of commercial web search engines operating on data centers that are geographically far apart. Based on the observation that energy prices and query workloads show high spatio-temporal variation, we propose a technique that dynamically shifts the query workload of a search engine between its data centers to reduce the electric bill. Experiments on real-life query workloads obtained from a commercial search engine show that significant financial savings can be achieved by this technique.

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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Entity-linking is a natural-language--processing task that consists in identifying strings of text that refer to a particular item in some reference knowledge base. One instance of entity-linking can be formalized as an optimization problem on the underlying concept graph, where the quantity to be optimized is the average distance between chosen items. Inspired by this application, we define a new graph problem which is a natural variant of the Maximum Capacity Representative Set. We prove that our problem is NP-hard for general graphs; nonetheless, it turns out to be solvable in linear time under some more restrictive assumptions. For the general case, we propose several heuristics: one of these tries to enforce the above assumptions while the others try to optimize similar easier objective functions; we show experimentally how these approaches perform with respect to some baselines on a real-world dataset.

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Nowadays, successful applications are those which contain features that captivate and engage users. Using an interactive news retrieval system as a use case, in this paper we study the effect of timeline and named-entity components on user engagement. This is in contrast with previous studies where the importance of these components were studied from a retrieval effectiveness point of view. Our experimental results show significant improvements in user engagement when named-entity and timeline components were installed. Further, we investigate if we can predict user-centred metrics through user's interaction with the system. Results show that we can successfully learn a model that predicts all dimensions of user engagement and whether users will like the system or not. These findings might steer systems that apply a more personalised user experience, tailored to the user's preferences.

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Large knowledge bases consisting of entities and relationships between them have become vital sources of information for many applications. Most of these knowledge bases adopt the Semantic-Web data model RDF as a representation model. Querying these knowledge bases is typically done using structured queries utilizing graph-pattern languages such as SPARQL. However, such structured queries require some expertise from users which limits the accessibility to such data sources. To overcome this, keyword search must be supported. In this paper, we propose a retrieval model for keyword queries over RDF graphs. Our model retrieves a set of subgraphs that match the query keywords, and ranks them based on statistical language models. We show that our retrieval model outperforms the-state-of-the-art IR and DB models for keyword search over structured data using experiments over two real-world datasets.

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Traditional probabilistic relevance frameworks for informational retrieval refrain from taking positional information into account, due to the hurdles of developing a sound model while avoiding an explosion in the number of parameters. Nonetheless, the well-known BM25F extension of the successful Okapi ranking function can be seen as an embryonic attempt in that direction. In this paper, we proceed along the same line, defining the notion of virtual region: a virtual region is a part of the document that, like a BM25F-field, can provide a (larger or smaller, depending on a tunable weighting parameter) evidence of relevance of the document; differently from BM25F fields, though, virtual regions are generated implicitly by applying suitable (usually, but not necessarily, positional-aware) operators to the query. This technique fits nicely in the eliteness model behind BM25 and provides a principled explanation to BM25F; it specializes to BM25(F) for some trivial operators, but has a much more general appeal. Our experiments (both on standard collections, such as TREC, and on Web-like repertoires) show that the use of virtual regions is beneficial for retrieval effectiveness.

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A Web search engine must update its index periodically to incorporate changes to the Web. We argue in this paper that index updates fundamentally impact the design of search engine result caches, a performance-critical component of modern search engines. Index updates lead to the problem of cache invalidation: invalidating cached entries of queries whose results have changed. Naive approaches, such as flushing the entire cache upon every index update, lead to poor performance and in fact, render caching futile when the frequency of updates is high. Solving the invalidation problem efficiently corresponds to predicting accurately which queries will produce different results if re-evaluated, given the actual changes to the index. To obtain this property, we propose a framework for developing invalidation predictors and define metrics to evaluate invalidation schemes. We describe concrete predictors using this framework and compare them against a baseline that uses a cache invalidation scheme based on time-to-live (TTL). Evaluation over Wikipedia documents using a query log from the Yahoo! search engine shows that selective invalidation of cached search results can lower the number of unnecessary query evaluations by as much as 30% compared to a baseline scheme, while returning results of similar freshness. In general, our predictors enable fewer unnecessary invalidations and fewer stale results compared to a TTL-only scheme for similar freshness of results.

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We study the problem of finding sentences that explain the relationship between a named entity and an ad-hoc query, which we refer to as entity support sentences. This is an important sub-problem of entity ranking which, to the best of our knowledge, has not been addressed before. In this paper we give the first formalization of the problem, how it can be evaluated, and present a full evaluation dataset. We propose several methods to rank these sentences, namely retrieval-based, entity-ranking based and position-based. We found that traditional bag-of-words models perform relatively well when there is a match between an entity and a query in a given sentence, but they fail to find a support sentence for a substantial portion of entities. This can be improved by incorporating small windows of context sentences and ranking them appropriately.

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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

#7: Each center keeps a replica of its index