Cloud Computing. What s the Big Deal? Michael J. Carey Information Systems Group CS Department UC Irvine

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1 Cloud Computing and Big Data: What s the Big Deal? Michael J. Carey Information Systems Group CS Department UC Irvine

2 What Is Cloud Computing? Cloud computing is a model for enabling ubiquitous, convenient, on demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. NIST, Sept

3 Clouds (According to NIST) Essential characteristics On demand self service Broad network access Resource pooling Rapid elasticity Measured service Cloud types Private cloud Community cloud Public cloud Hybrid cloud Service Models Software as a Service (Saas) ex: Salesforce.com Platform as a Service (PaaS) ex: Google Apps Infrastructure as a Service (Iaas) ex: Amazon s EC2 2

4 Your Life and the Cloud Examples E mail File storage (music, videos, ) Google Docs Social networks Pros Access your stuff from anywhere (on any device) On demand storage and computing for your stuff Usually free (funded by advertising or data sales) Enterprise class services for individuals and small businesses Cons Your stuff is now in someone else s hands Your stuff is visible to the cloud service provider So: Availability, trust, privacy, (Many issues!) 3

5 It s Cloudy and It s Raining Data! Mushrooming Internet use and data generation Tweets, blogs, social networks (statuses, check ins, shared content), click streams, logs, Facebook: > 845M active users, > 8B messages/day Twitter: > 140M active users, > 340M tweets/day Everyone is getting interested Trade press and popular press: Big Data! Enterprises, Web companies, online businesses, governments, public health researchers, social scientists, Untapped value and countless new opportunities to understand, optimize, and/or compete 4

6 Actually, It s Un Raining Data Sometimes called our digital exhaust ( ) 5

7 Big Data Opportunities What can we learn from Big Data? What should I buy or listen to? Who should my new best friends be? How do my customers feel about my products? Who s going to win the Presidential election? How can my candidate win the Presidential election? Are we on the verge of a flu epidemic? Is there an impending uprising in Switzerland? Is there new terrorist activity afoot? How could we have handled [insert situation] better? Some believe that it s time to start simply keeping everything for 5+ years before throwing it away! Ex: Barga (Microsoft keynote), NSA (Utah data center), 6

8 Big Data Technology Challenges include: Data is Big : How to scale? Data is Fast : How to keep up? Data is semi/unstructured: How to model? Many sources: How to ingest and integrate? Spatial, text, and graph data: How to analyze? Current commercial answer: Open source version of Google MapReduce (Hadoop) Open source version of Google File System (HDFS) Various higher level languages: Pig, Jaql, Hive, 7

9 ASTERIX Project (UCI) Data loads and feeds from external sources AQL queries/results Data publishing Hi Speed Network Asterix Client Interface AQL Compiler Metadata Manager Hyracks Dataflow Engine Dataset / Feed Storage LSM Tree Manager... Asterix Client Interface AQL Compiler Metadata Manager Hyracks Dataflow Engine Dataset / Feed Storage LSM Tree Manager Shared Nothing Cluster 8

10 Semistructured Data Management ASTERIX Cloud Layers Parallel Database Systems Data Intensive Computing Our approach: Ask not what cloud software can do for us, but what we can do for cloud software! We re asking this about Big Data in our current UCI work : ASTERIX: Scalable big data management platform Algebricks: Query algebra and optimizer for parallel data analysis Hyracks: Dataflow engine for data intensive computing 9

11 Semistructured Data Management Parallel Database Systems Data Intensive Computing ASTERIX Status Third year of a three year ($2.7M) NSF project Project spans UCI and UCR, also UCSD Hyracks dataflow engine Tested on 180 node Yahoo! cluster (720 disks, 1440 cores) Significantly beat the Big Data big gorilla (Hadoop) Shared w/others (UCSC, SDSC, Rice, NTUA, MITRE, Yahoo!) UCI projects: AQL, Hivesterix (Facebook s HiveQL) Outside projects: ScalOps (Yahoo!), VXQuery (Apache) ASTERIX big data management system (BDMS) Parallel queries, similarity queries, spatial data, Native storage/indexing, internal/external/feed datasets, Targeting a 2012 open source release (Google code) 10

12 That s It! 11

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