Like us on Facebook! Big Data in the Cloud: Computational Challenges and The Way Forward

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1 Big Data in the Cloud: Computational Challenges and The Way Forward Marcos Vaz Salles Assistant Professor, University of Copenhagen (DIKU) Like us on Facebook!

2 About the Speaker Marcos Vaz Salles Tenure-track Assistant Professor, University of Copenhagen (DIKU) Postdoc: Cornell University PhD: ETH Zurich Mission: Find creative ways to expand the reach of the 30+ years of top-level R&D invested in database technology, broadly defined Examples: Database techniques for scientific simulations, games, search and integration, geospatial data 2

3 Where does your most important data live? 3

4 Where does your most important data live? DATABASES! 4

5 Historical Justification for Databases 5

6 Historical Justification for Databases Common applications Record maintenance, banking, government Complex implementation Concurrency, integrity, durability, storage, representation, Enough abstraction Operating systems virtualize low-level hardware Competing platforms No virtualization of platform: IBM, DEC, Data-Driven Applications Data Sharing (DBMS) Virtualization (Operating Systems) Platforms (Hardware) 6

7 Historical Justification for Databases Common applications Record maintenance, banking, government Complex implementation Concurrency, integrity, durability, storage, representation, Enough abstraction Operating systems virtualize low-level hardware Competing platforms No virtualization of platform: IBM, DEC, Data-Driven Applications But the Cloud today is completely different?! Data Sharing (DBMS) Virtualization (Operating Systems) Platforms (Hardware) 7

8 The Cloud Today Common applications Big Data, Data warehousing, SOA Complex implementation Data consistency and management, distribution, scalability, fault tolerance Enough abstraction Cloud IaaS virtualizes enormous clusters of machines Competing platforms No virtualization of platform: Amazon, Microsoft, Data-Driven Applications Data Sharing (????) Virtualization (Cloud IaaS) Platforms (Cloud Datacenter) 8

9 The Cloud Today Common applications Big Data, Data warehousing, SOA mplex implementation Data consistency and management, distribution, scalability, fault tolerance Enough abstraction Cloud IaaS virtualizes enormous clusters of machines Competing platforms No virtualization of platform: Amazon, Microsoft, Data-Driven Applications Challenge: What Data Sharing (????) should be the new Data Sharing Virtualization (Cloud IaaS) Abstraction in the Cloud? Platforms (Cloud Datacenter) 9

10 From Databases to Dataclouds While there were databases in the past, we will have dataclouds in the future Databases Database Management System (DBMS) Dataclouds Datacloud Management System (DCMS) Emerging application systems already being built! 10 But at high cost, constrained by skills gap With less features than desired Roadblock in the way of Software as a Service (SaaS) and Big Data revolutions

11 Challenges in Dataclouds and DCMS Programming, programming, programming Resources, resources, resources Scale, scale, scale 11

12 Challenges in Dataclouds and DCMS Programming, programming, programming Re-use or create new programming abstractions? How to incorporate data into software engineering? Resources, resources, resources How to deal with virtualized environments and abstract cost? Scale, scale, scale How to scale applications to petabytes automatically? 12

13 ClouDiA: A Cloud Deployment Advisor Initial work on deployment of latency-sensitive data services in public clouds Simulation analytics, e.g., multiagent simulations Search engines Key-value stores Acknowledgment Joint work with Tao Zou, Ronan LeBras, Alan Demers, and Johannes Gehrke at Cornell University 13 Best paper nominee at VLDB 2013 Talk includes slides by Tao Zou

14 Instance Allocation in Public Clouds Cloud Cloud Provider s Tenant s View 14

15 Instance Allocation in Public Clouds Core Cloud Provider s View Sub-Aggregation Aggregation TOR TOR TOR 15

16 Network Latencies in Public Clouds Mean Latency Heterogeneity in EC2 Challenge: How to avoid long links? Mean Latency Stability in EC2 Challenge: Can this be done out-of-the-box? (i.e. no changes to the cloud or the application) 16

17 Examples of Latency Sensitive Applications Scientific Simulation (time-to-solution) Key-value Store (response time) Opportunity: Longest Link Communication graphs are not complete. A careful logical to physical mapping can help! Search Aggregation Service Pipelines (response time) (response time) Opportunity: Gamble with over-allocation. Longest Path 17

18 Architecture of ClouDiA ClouDiA Allocate Instances (+ Extra Instances) Public Cloud Tenant Communication Graph Get Measurements Search Mapping Objectives Deployment Plan Start Application Terminate Extra Instances 18

19 Measuring Network Distance Goal: obtain a reliable estimate of link costs Approximations that are easy to obtain Hop counts Length of Common IP Prefix do not work Accurate distance: pair-wise network latencies Large number of measurements for each pair To observe enough latency jitters Interferences at end points Heavily application and network dependent Can t model exactly measure without interference 19

20 Measuring Network Latencies Without interference Most efficient method: staged Stage i Stage i+1 No concurrent send/recv at end points Parallelism with minimal coordination 20

21 Architecture of ClouDiA ClouDiA Allocate Instances (+ Extra Instances) Public Cloud Tenant Communication Graph Get Measurements Search Mapping Objectives Deployment Plan Start Application Terminate Extra Instances 21

22 Search Mapping (Longest Link) NP-Hard To find a solution of cost = OPT To find a solution of cost <= OPT + α To find a solution of cost <= (1+ε)OPT Hard to approximate Goal: Find a good solution within timeout Two formulations Mixed-Integer Programming O(N 2 ) boolean variables Constraint Programming (*) O(N) integer variables An objective cost has to be give a priori 22

23 Experimental Settings 100 to 150 m1.large instances in EC2 Aggregation workload only uses up to 50 IBM ILOG CPLEX Optimizer/CP Optimizer Multi-core, a single machine Three workloads: Behavioral simulation Synthetic aggregation query Key-value store 23

24 Effect of Over-Allocation 100 instances + 10%-50% over-allocation Get Measurements + Search Deployment < 10 minutes 24

25 Overall Improvement 10% over-allocation 15%-55% reduction in time-to-solution or response time 25

26 Wrap-up Big Data in Dataclouds Programming, programming, programming Resources, resources, resources Scale, scale, scale ClouDiA An initial step in resource optimization in public clouds Next steps: Build a datacloud! Tons of research challenges open Ongoing collaborations with Danish Geodata Agency (GST) and the HIPERFIT center Collaborate with us too! Thank you! HIPERFIT 26

27 Backup Slides 27

28 Searching using Constraint Programming Give Give an an objective c: c: 1. Remove 1. all all links links with with cost cost > c > c 2. Find 2. Find a subgraph a isomorphism k=4 0 Timeout Timeout cost A lot of distinct latency values Bi-section search? finding no solution takes time k-means clustering? works well with proper k

29 Summary of Node Deployment Objectives Minimize cost of worst link Minimize cost of longest path Optimization Methods Akin to graph embedding problem, but with minimization goals Mixed-integer programming (MIP) formulation for both objectives Constraint programming (CP) formulation also for worst link Greedy easy to beat Network measurements Staged message exchange to measure costs More details on the paper! 29

30 Experiments with ClouDiA on Amazon EC2 Workloads & Setup Behavioral simulation 30 Fish simulation by Couzin et al., Nature 2D mesh 100 Amazon EC2 large instances Minimize Worst Link objective Synthetic aggregation workload Models search engines, distributed text databases Multi-level aggregation tree 50 Amazon EC2 large instances Minimize Longest Path objective Key-value store workload Bipartite graph of front-end servers and storage servers 100 Amazon EC2 large instances Minimize Worst Link objective used, but not perfect fit