The data streaming processing paradigm and its use in modern fog architectures

1. The data streaming processing paradigm and its use in modern fog architectures Vincenzo Gulisano, Ph.D.

2. Agenda • Who we are (my group) • Motivation • The data streaming processing paradigm • Challenges and research questions • Conclusions • Bibliography 2

3. Agenda • Who we are (my group) • Motivation • The data streaming processing paradigm • Challenges and research questions • Conclusions • Bibliography 3

4. Who we are Chalmers university Computer Science and Engineering department Distributed Computing and Systems research group 4

5. Who we are • ~15 PhD degrees awarded • ~12 PhD students, 5 Postdocs and 6 faculty • Acknowledged internationally as leading group in practical multicore synchronization algorithms and programming (results adopted by Java JDK, C++, Intel, NVIDIA) • Extensive network of academic and industrial collaborations and has been continuously supported by national and international projects 5

6. Distributed Computing and Systems Research Group Department of Computer Science and engineering Chalmers University of Technology At our research team: Research expertise & projects Cyber Security Efficient parallel & stream computing Distributed systems IoT & Sensor Networks 6

7. Agenda • Who we are • Motivation • The data streaming processing paradigm • Challenges and research questions • Conclusions • Bibliography 7

8. • IoT • Edge • Fog • Cloud • Cyber-Physical System • Big Data ... BUZZ WORDS 8

10. 10

11. IoT enables for increased awareness, security, power-efficiency, ... large IoT systems are complex traditional data analysis techniques alone are not adequate! 11

12. AMIs [1,2,3,4] VNs [5,6] • demand-response • scheduling [7] • micro-grids • detection of medium size blackouts [8] • detection of non technical losses • ... • autonomous driving • platooning • accident detection [9] • variable tolls [9] • congestion monitoring [10] • ... IoT enables for increased awareness, security, power-efficiency, ... 12

13. AMIs VNs large IoT systems are complex Characteristics [15]: 1. edge location 2. location awareness 3. low latency 4. geographical distribution 5. large-scale 6. support for mobility 7. real-time interactions 8. predominance of wireless 9. heterogeneous 10. interoperability / federation 11. interaction with the cloud 13

14. traditional data analysis techniques alone are not adequate! [13,14] 1. does the infrastructure allow for billions of readings per day to be transferred continuously? 2. the latency incurred while transferring data, does that undermine the utility of the analysis? 3. is it secure to concentrate all the data in a single place? [11] 4. is it smart to give away fine-grained data? [12] 14

16. Main Memory Motivation DBMS vs. DSMS Disk 1 Data Query Processing 3 Query results 2 Query Main Memory Query Processing Continuous Query Data Query results 16

17. Before we start... about data streaming and Stream Processing Engines (SPEs) An incomplete list of SPEs (cf. related work in [18]): time Borealis The Aurora Project STanfordstREamdatAManager NiagaraCQ COUGAR StreamCloud Covering all of them / discussing which use cases are best for each one out of scope... the following show connection between what is being presented and a certain SPE 17

18. data stream: unbounded sequence of tuples sharing the same schema Example: vehicles’ speed reports time Field Field vehicle id text time (secs) text speed (Km/h) double X coordinate double Y coordinate double A 8:00 55.5 X1 Y1 Let’s assume each source (e.g., vehicle) produces and delivers a timestamp sorted stream A 8:07 34.3 X3 Y3 A 8:03 70.3 X2 Y2 18

19. continuous query (or simply query): Directed Acyclic Graph (DAG) of streams and operators OP OP OP OP OP OP OP source op (1+ out streams) sink op (1+ in streams) stream op (1+ in, 1+ out streams) 19

20. data streaming operators Two main types: • Stateless operators • do not maintain any state • one-by-one processing • if they maintain some state, such state does not evolve depending on the tuples being processed • Stateful operators • maintain a state that evolves depending on the tuples being processed • produce output tuples that depend on multiple input tuples OP OP 20

21. stateless operators Filter ... Map Union ... Filter / route tuples based on one (or more) conditions Transform each tuple Merge multiple streams (with the same schema) into one 21

22. stateless operators Filter ... Map Union ... source: http://storm.apache.org/releases/2.0.0-SNAPSHOT/Trident-tutorial.html 22

23. stateless operators Filter ... Map Union ... source: https://ci.apache.org/projects/flink/flink-docs-release-1.0/apis/streaming/index.html 23

24. stateless operators Filter ... Map Union ... source: http://spark.apache.org/docs/latest/streaming-programming-guide.html#transformations-on-dstreams 24

25. stateful operators Aggregate information from multiple tuples (e.g., max, min, sum, ...) Join tuples coming from 2 streams given a certain predicate Aggregate Join 25

26. stateful operators source: http://storm.apache.org/releases/2.0.0-SNAPSHOT/Trident-tutorial.html source: http://spark.apache.org/docs/latest/streaming-programming- guide.html#transformations-on-dstreams source: http://spark.apache.org/docs/latest/streaming-programming- guide.html#transformations-on-dstreams 26

27. Wait a moment! if streams are unbounded, how can we aggregate or join? 27

28. windows and stateful analysis [18] Stateful operations are done over windows: • Time-based (e.g., tuples in the last 10 minutes) • Tuple-based (e.g., given the last 50 tuples) time [8:00,9:00) [8:20,9:20) [8:40,9:40) Usually applications rely on time-based sliding windows 28

29. time-based sliding window aggregation (count) Counter: 4 time [8:00,9:00) 8:05 8:15 8:22 8:45 9:05 Output: 4 Counter: 1 Counter: 2 Counter: 3 Counter: 3 time 8:05 8:15 8:22 8:45 9:05 [8:20,9:20) we assumed each source produces and delivers a timestamp sorted stream! What happens if this is not the case? 29

30. windows and stateful analysis 30

31. basic operators and user-defined operators Besides a set of basic operators, SPEs usually allow the user to define ad-hoc operators (e.g., when existing aggregation are not enough) 31

32. sample query For each vehicle, raise an alert if the speed of the latest report is more than 2 times higher than its average speed in the last 30 days. time A 8:00 55.5 X1 Y1 A 8:07 34.3 X3 Y3 A 8:03 70.3 X2 Y2 32

33. Remove unused fields Map Field vehicle id time (secs) speed (Km/h) X coordinate Y coordinate Field vehicle id time (secs) speed (Km/h) Compute average speed for each vehicle during the last 30 days Aggregate Field vehicle id time (secs) avg speed (Km/h) Join Check condition Filter Field vehicle id time (secs) speed (Km/h) Join on vehicle id Field vehicle id time (secs) avg speed (Km/h) speed (Km/h) sample query 33

34. M A J F sample query Notice: • the same semantics can be defined in several ways (using different operators and composing them in different ways) • Using many basic building blocks can ease the task of distributing and parallelizing the analysis (more in the following...) 34

35. Why data streaming, then? Expressive Online Parallel & Distributed 35

36. M A J F At the edge, in parallel at each vehicle M A J F 36

37. M A J FAt the cloud 37

38. Distributed, edge + fog + cloud A J M F 38

40. 1. Distributed deployment 2. Parallel deployment 3. Ordering and determinism 4. Shared-nothing vs shared-memory parallelism 5. Load balancing 6. Elasticity 7. Fault tolerance 8. Data sharing Challenges and research questions 40

41. Picture source: Modern OS, by A. Tanenbaum Data analysts Stream Processing Engine OS / Hardware How can research support these layers? Why is it challenging? 41

42. A minimal example (... the data analyst) tell me the average speed (per week) of each car Road Side Unit (RSU) 42

43. Parallelization challenges compute average speed (per week) of each car compute average speed (per week) of each car Does this work? What if a car travels close to different RSUs (and sends data to both)? 43

44. 1 3 8 9 13 6.8 1 3 2 10 1 3 2 10 6 44

45. Parallelization challenges compute average speed (per week) of each car Odd plate number Even plate numberSend to other RSU compute average speed (per week) of each car Even plate number Odd plate numberSend to other RSU They are now communicating! 45

46. Balancing challenges compute average speed (per week) of each car Odd plate number Even plate number Send to other RSU compute average speed (per week) of each car Even plate number Odd plate number Send to other RSU Too much pollution, we will introduce the alternate license plates policy! Thanks! Now I am using only 50% of my resources... 46

47. Balancing challenges compute average speed (per week) of each car Dynamic condition TRUE Dynamic condition FALSE Send to other RSU compute average speed (per week) of each car Dynamic condition FALSE Dynamic condition TRUE Send to other RSU They are now communicating! There must be monitoring! We need state transfer protocols! 47

48. Balancing challenges Time responsible for responsible for dynamic condition change (in between the week!) What about the data already sent to ? tell me the average speed (per week) of each car 48

49. Adapting challenges compute average speed (per week) of each car Dynamic condition TRUE Dynamic condition FALSE Send to other RSU compute average speed (per week) of each car Dynamic condition FALSE Dynamic condition TRUE Send to other RSU What about the data already sent to ? 49

50. Balancing challenges compute average speed (per week) of each car Dynamic condition TRUE Dynamic condition FALSE Send to other RSU compute average speed (per week) of each car Dynamic condition FALSE Dynamic condition TRUE Send to other RSU They are now communicating! There must be monitoring! We need state transfer protocols! We need backups! What about the data sent between and ? Cars need to buffer data too! Cars need to buffer data too! 50

51. Faulttolerance Elasticity Loadbalancing Determinism Parallel execution of streaming operators 51 Parallel execution of streaming applications DDoSdetection andmitigation Intrusiondetection Datavalidation Differentially privateaggregation Vehicularnetworks analysis SmartGrids/AMI analysis Security and privacy IoT Cyber-Physical Systems Provenance and custom scheduling

52. Synchronization / Data structures Faulttolerance Elasticity Loadbalancing Determinism Parallel execution of streaming operators 52 Parallel execution of streaming applications Many-core systems / FPGAs DDoSdetection andmitigation Intrusiondetection Datavalidation Differentially privateaggregation Vehicularnetworks analysis SmartGrids/AMI analysis Parallel joins Parallel aggregates Joins modeling Security and privacy IoT Cyber-Physical Systems Provenance and custom scheduling

53. • Palyvos-Giannas, D., Gulisano, V., & Papatriantafilou, M. (2019). GeneaLog: Fine- grained data streaming provenance in cyber-physical systems. Parallel Computing • Najdataei, H., Nikolakopoulos, Y., Papatriantafilou, M., Tsigas, P., & Gulisano, V. (2019). STRETCH: Scalable and Elastic Deterministic Streaming Analysis with Virtual Shared-Nothing Parallelism. Proceedings of the 13th ACM International Conference on Distributed and Event-based Systems, DEBS 2019 • Palyvos-Giannas, D., Gulisano, V., & Papatriantafilou, M. (2019). Haren: A Framework for Ad-Hoc Thread Scheduling Policies for Data Streaming Applications. Proceedings of the 13th ACM International Conference on Distributed and Event-based Systems, DEBS 2019 • Havers, B., Duvignau, R., Najdataei, H., Gulisano, V., Koppisetty, A. C., & Papatriantafilou, M. (2019). DRIVEN: a Framework for Efficient Data Retrieval and Clustering in Vehicular Networks. 35th IEEE International Conference on Data Engineering, ICDE 2019 • Duvignau, R., Gulisano, V., Papatriantafilou, M., & Savic, V. (2019). Streaming piecewise linear approximation for efficient data management in edge computing. Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, SAC 2019 • Walulya, I., Palyvos-Giannas, D., Nikolakopoulos, Y., Gulisano, V., Papatriantafilou, M., & Tsigas, P. (2018). Viper: A module for communication-layer determinism and scaling in low-latency stream processing. Future Generation Comp. Syst., 88 • Rooij, J. van, Gulisano, V., & Papatriantafilou, M. (2018). LoCoVolt: Distributed Detection of Broken Meters in Smart Grids through Stream Processing. Proceedings of the 12th ACM International Conference on Distributed and Event-based Systems, DEBS 2018 Provenance Determinism Vehicular networks AMI Parallelism Determinism Elasticity 53 Custom scheduling Vehicular networks Vehicular networks DeterminismParallelism Smart Grids / AMIs

54. • Najdataei, H., Nikolakopoulos, Y., Gulisano, V., & Papatriantafilou, M. (2018). Continuous and Parallel LiDAR Point-Cloud Clustering. 38th IEEE International Conference on Distributed Computing Systems, ICDCS 2018 • Gulisano, V., Nikolakopoulos, Y., Cederman, D., Papatriantafilou, M., & Tsigas, P. (2017). Efficient Data Streaming Multiway Aggregation through Concurrent Algorithmic Designs and New Abstract Data Types. TOPC • Zacheilas, N., Kalogeraki, V., Nikolakopoulos, Y., Gulisano, V., Papatriantafilou, M., & Tsigas, P. (2017). Maximizing Determinism in Stream Processing Under Latency Constraints. BT - Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems, DEBS 2017 • Gulisano, V., Papadopoulos, A. V., Nikolakopoulos, Y., Papatriantafilou, M., & Tsigas, P. (2017). Performance Modeling of Stream Joins. Proceedings of the 11th ACM International Conference on Distributed and Event- based Systems, DEBS 2017 • Geethakumari, P. R., Gulisano, V., Svensson, B. J., Trancoso, P., & Sourdis, I. (2017). Single window stream aggregation using reconfigurable hardware. International Conference on Field Programmable Technology, ICFPT 2017 • Gulisano, V., Tudor, V., Almgren, M., & Papatriantafilou, M. (2016). BES: Differentially Private and Distributed Event Aggregation in Advanced Metering Infrastructures. Proceedings of the 2nd ACM International Workshop on Cyber-Physical System Security, CPSS@AsiaCCS • Gulisano, V., Callau-Zori, M., Fu, Z., Jiménez-Peris, R., Papatriantafilou, M., & Patiño-Martínez, M. (2015). STONE: A streaming DDoS defense framework. Expert Syst. Appl., 42 54 Parallelism Clustering Parallelism DeterminismStream aggregates Parallelism Determinism Synchronization / Data structures ModelingStream joins FPGAs DeterminismStream aggregates Differentially private aggregation DDoS detection and mitigation

55. • Gulisano, V., Nikolakopoulos, Y., Papatriantafilou, M., & Tsigas, P. (2015). Scalejoin: A deterministic, disjoint-parallel and skew-resilient stream join. 2015 IEEE International Conference on Big Data, Big Data 2015 • Gulisano, V., Nikolakopoulos, Y., Walulya, I., Papatriantafilou, M., & Tsigas, P. (2015). Deterministic real-time analytics of geospatial data streams through ScaleGate objects. Proceedings of the 9th ACM International Conference on Distributed Event-Based Systems, DEBS ’15 • Gulisano, V., Almgren, M., & Papatriantafilou, M. (2014). METIS: A Two-Tier Intrusion Detection System for Advanced Metering Infrastructures. International Conference on Security and Privacy in Communication Networks - 10th International ICST Conference, SecureComm 2014 • Gulisano, V., Jiménez-Peris, R., Patiño-Martínez, M., Soriente, C., & Valduriez, P. (2012). StreamCloud: An Elastic and Scalable Data Streaming System. IEEE Trans. Parallel Distrib. Syst., 23 • Gulisano, V., Jiménez-Peris, R., Patiño-Martínez, M., & Valduriez, P. (2010). StreamCloud: A Large Scale Data Streaming System. BT - 2010 International Conference on Distributed Computing Systems, ICDCS 2010 55 Parallelism DeterminismStream joins Parallelism Determinism Synchronization / Data structures Intrusion detection Parallelism Determinism Parallelism Determinism

57. Millions of years of evolution Millions of sensors • Store information • Iterate multiple times over data • Think, do not rush through decisions • ”Hard-wired” routines • Real-time decisions • High-throughput / low-latency Should I (really) have an extra piece of cake? Danger!!! Run!!! Humans 57

58. Years / Decades of evolution Millions of sensors What traffic congestion patterns can I observe frequently? Don’t take over, car in opposite lane! • Store information • Iterate multiple times over data • Think, do not rush through decisions Databases, data mining techniques... Data streaming, distributed and parallel analysis • Continuous analysis • Real-time decisions • High-throughput / low-latency Computers (cyber-physical / IoT systems) 58

59. Agenda • Motivation • The data streaming processing paradigm • Challenges and research questions • Conclusions • Bibliography 59

60. Bibliography 1. Zhou, Jiazhen, Rose Qingyang Hu, and Yi Qian. "Scalable distributed communication architectures to support advanced metering infrastructure in smart grid." IEEE Transactions on Parallel and Distributed Systems 23.9 (2012): 1632-1642. 2. Gulisano, Vincenzo, et al. "BES: Differentially Private and Distributed Event Aggregation in Advanced Metering Infrastructures." Proceedings of the 2nd ACM International Workshop on Cyber-Physical System Security. ACM, 2016. 3. Gulisano, Vincenzo, Magnus Almgren, and Marina Papatriantafilou. "Online and scalable data validation in advanced metering infrastructures." IEEE PES Innovative Smart Grid Technologies, Europe. IEEE, 2014. 4. Gulisano, Vincenzo, Magnus Almgren, and Marina Papatriantafilou. "METIS: a two-tier intrusion detection system for advanced metering infrastructures." International Conference on Security and Privacy in Communication Systems. Springer International Publishing, 2014. 5. Yousefi, Saleh, Mahmoud Siadat Mousavi, and Mahmood Fathy. "Vehicular ad hoc networks (VANETs): challenges and perspectives." 2006 6th International Conference on ITS Telecommunications. IEEE, 2006. 6. El Zarki, Magda, et al. "Security issues in a future vehicular network." European Wireless. Vol. 2. 2002. 7. Georgiadis, Giorgos, and Marina Papatriantafilou. "Dealing with storage without forecasts in smart grids: Problem transformation and online scheduling algorithm." Proceedings of the 29th Annual ACM Symposium on Applied Computing. ACM, 2014. 8. Fu, Zhang, et al. "Online temporal-spatial analysis for detection of critical events in Cyber-Physical Systems." Big Data (Big Data), 2014 IEEE International Conference on. IEEE, 2014. 60

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62. Bibliography 17. Geiger, Andreas, et al. "Vision meets robotics: The KITTI dataset." The International Journal of Robotics Research (2013): 0278364913491297. 18. Gulisano, Vincenzo Massimiliano. StreamCloud: An Elastic Parallel-Distributed Stream Processing Engine. Diss. Informatica, 2012. 19. Cardellini, Valeria, et al. "Optimal operator placement for distributed stream processing applications." Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems. ACM, 2016. 20. Costache, Stefania, et al. "Understanding the Data-Processing Challenges in Intelligent Vehicular Systems." Proceedings of the 2016 IEEE Intelligent Vehicles Symposium (IV16). 21. Cormode, Graham. "The continuous distributed monitoring model." ACM SIGMOD Record 42.1 (2013): 5-14. 22. Giatrakos, Nikos, Antonios Deligiannakis, and Minos Garofalakis. "Scalable Approximate Query Tracking over Highly Distributed Data Streams." Proceedings of the 2016 International Conference on Management of Data. ACM, 2016. 23. Gulisano, Vincenzo, et al. "Streamcloud: An elastic and scalable data streaming system." IEEE Transactions on Parallel and Distributed Systems 23.12 (2012): 2351-2365. 24. Shah, Mehul A., et al. "Flux: An adaptive partitioning operator for continuous query systems." Data Engineering, 2003. Proceedings. 19th International Conference on. IEEE, 2003. 62

63. Bibliography 25. Cederman, Daniel, et al. "Brief announcement: concurrent data structures for efficient streaming aggregation." Proceedings of the 26th ACM symposium on Parallelism in algorithms and architectures. ACM, 2014. 26. Ji, Yuanzhen, et al. "Quality-driven processing of sliding window aggregates over out-of-order data streams." Proceedings of the 9th ACM International Conference on Distributed Event-Based Systems. ACM, 2015. 27. Ji, Yuanzhen, et al. "Quality-driven disorder handling for concurrent windowed stream queries with shared operators." Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems. ACM, 2016. 28. Gulisano, Vincenzo, et al. "Scalejoin: A deterministic, disjoint-parallel and skew-resilient stream join." Big Data (Big Data), 2015 IEEE International Conference on. IEEE, 2015. 29. Ottenwälder, Beate, et al. "MigCEP: operator migration for mobility driven distributed complex event processing." Proceedings of the 7th ACM international conference on Distributed event-based systems. ACM, 2013. 30. De Matteis, Tiziano, and Gabriele Mencagli. "Keep calm and react with foresight: strategies for low-latency and energy-efficient elastic data stream processing." Proceedings of the 21st ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. ACM, 2016. 31. Balazinska, Magdalena, et al. "Fault-tolerance in the Borealis distributed stream processing system." ACM Transactions on Database Systems (TODS) 33.1 (2008): 3. 32. Castro Fernandez, Raul, et al. "Integrating scale out and fault tolerance in stream processing using operator state management." Proceedings of the 2013 ACM SIGMOD international conference on Management of data. ACM, 2013. 63

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