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Elastic Cloud Caches for Accelerating Service-Oriented Computations Gagan Agrawal Ohio State University Columbus, OH David Chiu Washington State University Vancouver, WA
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2 Cloud Computing Pay-As-You-Go Computing ‣ Running 1 machine for 10 hours = running 10 machines for 1 hour Elasticity ‣ Cloud applications can stretch and contract their resource requirements “Infinite resources”
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3 Outline ‣ Accelerating Data Intensive Services Using the Cloud Motivating Application Design of an Elastic Cache ‣ Performance Evaluation Up-Scaling (cache expansion) Down-Scaling (cache contraction) ‣ Future Work & Conclusion
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4 Motivating Application Data Sources
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5 Computing & Storage Resources Geoinformatics Cyber Infrastructure: Lake Erie
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6 Shared/Proprietary Web Services = Web Service Geoinformatics Cyber Infrastructure: Lake Erie
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7... Service Interaction with Cyber Infrastructure Service Infrastructure
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8 Service Interaction with Cyber Infrastructure... invoke results Service Infrastructure
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9 Problem: Query Intensive Circumstances... Service Infrastructure
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10 Outline ‣ Accelerating Data Intensive Services Using the Cloud Motivating Application Design of an Elastic Cache ‣ Performance Evaluation Up-Scaling (cache expansion) Down-Scaling (cache contraction) ‣ Future Work & Conclusion
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11 Designing an Elastic Cache Compute Cloud... Service Infrastructure A B
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12 Designing an Elastic Cache... Service Infrastructure A B Cache Requests Inserts Misses node = (k mod 2)
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13 Eventual Overloading... Service Infrastructure A B Cache Requests Inserts Misses node = (k mod 2)
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14 Scaling up to Meet Demand... Service Infrastructure A B Cache Requests Compute Cloud C node = (k mod 2)
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15 Issues with Naive Hashing... Service Infrastructure A B Cache Requests node = (k mod 3) C How to incorporate node C with least amount of “disruption?”
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16... A B 75 25 8 Hash Intervals (buckets) Distributed Hashtables (DHT)
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17... A B 75 25 8 invoke: service(35) (35 mod 100) = 35 Which proxy has the page? h(k) = (k mod 100) h(35) Distributed Hashtables (DHT)
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A B 75 25 8 50 C Only records hashing into (25,50] need to be moved from A to C! DHT to Minimize Hash Disruption when Scaling
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19 That’s Not Completely Elastic ‣ What about relaxing the amount of nodes to help save Cloud save costs? ‣ First, we need an eviction scheme
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20 Exponential Decay Eviction ‣ At eviction time: A value,, is calculated for each data record in the evicted slice is higher: if was accessed more recently if was accessed frequently If is lower than some threshold, evict
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21 Outline ‣ Accelerating Data Intensive Services Using the Cloud Motivating Application Design of an Elastic Cache ‣ Performance Evaluation Up-Scaling (cache expansion) Down-Scaling (cache contraction) ‣ Future Work & Conclusion
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22 Experimental Configuration Application ‣ Shoreline Extraction ‣ Takes 23 seconds to complete without benefits of cache ‣ Executed on a miss ‣ Amazon EC2 Cloud Each Cloud node: Small Instances (Single core 1.2Ghz, 1.7GB, 32bits) Ubuntu Linux Caches start out cold Data stored in memory only
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23 Experimental Configuration ‣ Our approach exploits an elastic Cloud environment: ‣ We compare GBA against statically allocated Cloud environments: 2 fixed nodes (static-2) 4 fixed nodes (static-4) 8 fixed nodes (static-8) Cache overflow --> LRU eviction
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24 Relative Speedup Querying Rate: 255 invocations/sec
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25 Cache Expansion/Migration Times Querying Rate: 255 invocations/sec
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26 Experimental Configuration ‣ Amazon EC2 Cloud Each Cloud node: Small Instance (Single core 1.2Ghz, 1.7GB, 32bits) Caches start out cold Data stored in memory When 2 nodes become < 30% capacity, merge ‣ Sliding Window Configuration: Time Slice: 1 sec Size: 100 Time Slices
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27 Data Eviction: 50/255/50 queries per sec Sliding Window Size = 100 sec 50 q/sec255 q/sec50 q/sec
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28 Cache Contraction: 50/255/50 queries per sec
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29 Cache Contraction: 50/255/50 queries per sec
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30 Experimental Summary ‣ Caching Web service results reduces mean execution times significantly for our application ‣ Cloud node allocation is a huge overhead, but the cost is amortized over average execution times ‣ On average, our approach uses less nodes (and thus, less cost) than statically allocated schemes
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31 Outline ‣ Accelerating Data Intensive Services Using the Cloud Motivating Application Design of an Elastic Cache ‣ Performance Evaluation Up-Scaling (cache expansion) Down-Scaling (cache contraction) ‣ Future Work & Conclusion
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32 Conclusion ‣ We introduced to some challenges in the Cloud: Controlling Cost Real-time system management (downscaling, upscaling) ‣ We saw how the Cloud’s elasticity could be harnessed to accelerate service-oriented processes
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33 Future/Current Work
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34 Thank you ‣ Questions and Comments? David Chiu - david.chiu@wsu.edu Gagan Agrawal - agrawal@cse.ohio-state.eduagrawal@cse.ohio-state.edu In memory of Prof. Yuri Breitbart (1940 -- 2010)
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