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Flexible Network Analytics in the Cloud

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Flexible Network Analytics in the Cloud Jon Dugan & Peter Murphy ESnet Software Engineering Group October 18, 2017 TechEx 2017, San Francisco Introduction ● Harsh realities of network analytics ● netbeam ● Demo ● Technology Stack ● Alternative Approaches ● Lessons Learned 2 Architecture 3 The Harsh Realities of Network Analytics 1. It’s a mess 3. There’s always more ● Your data isn’t neat and tidy ● More devices & more telemetry 2. Things change 4. It’s never really done ● What you need today may not ● Time and money are limited be what you need tomorrow. 4 Coping strategies 1. It’s a mess 3. There’s always more ● Design knowing things won’t ● Rely on the cloud for scaling be tidy 2. Things change 4. It’s never really done ● Keep raw data to keep your ● “What” not “How” options open 5 netbeam Network Analytics in Google Cloud Three Pillars 1. Real time analytics ○ Low latency, incomplete 2. Offline analytics ○ High latency, complete 3. Flexible data model ○ Changing needs? Recompute from raw data! Secret sauce: Apache Beam 6 What is Apache Beam? 1. The Beam Programming Model 2. SDKs for writing Beam pipelines 3. Runners for existing distributed processing backends ○ Apache Apex ○ Apache Flink ○ Apache Spark ○ Google Cloud Dataflow ○ Local runner for testing Slide courtesy of the Apache Beam Project 7 The Evolution of Apache Beam Colossus BigTable PubSub Dremel Google Cloud Dataflow Spanner Megastore Millwheel Flume Apache Beam MapReduce Slide courtesy of the Apache Beam Project 8 SNMP collection Old SNMP system Architecture Diagram system Apache Beam (Stream Processing) avro Bigtable Apache Beam BigQuery BigQuery (realtime) (Batch Processing) (immutable) (historical) ... API Client 9 SNMP collection Old SNMP system Architecture Diagram system Apache Beam (Stream) avro ● Google Pubsub ● Uses Python outside Align/rates of Google Cloud to poll devices and write Bigtable Rollups (realtime) BigQuery BigQuery to Pubsub topic 5m, 1h, 1d avg (immutable) (historical) ● Code within Google Cloud subscribes to Percentiles topic to process data ... API Client 10 SNMP collection Old SNMP system Architecture Diagram system Apache Beam (Stream) avro ● Apache Beam / Google Dataflow Align/rates ● Stream processing ● Subscribes to Bigtable Rollups (realtime) BigQuery BigQuery Pubsub topic 5m, 1h, 1d avg (immutable) (historical) Percentiles ... API Client 11 SNMP collection Old SNMP system Architecture Diagram system Apache Beam (Stream) avro ● Apache Beam / Google Dataflow Align/rates ● Stream processing ● Subscribes to Bigtable Rollups (realtime) BigQuery BigQuery Pubsub topic 5m, 1h, 1d avg (immutable) (historical) ● Raw data is written to BigQuery Percentiles ... API Client 12 SNMP collection Old SNMP system Architecture Diagram system Apache Beam (Stream) avro ● Apache Beam / Google Dataflow Align/rates ● Stream processing ● Subscribes to Bigtable Rollups (realtime) BigQuery BigQuery Pubsub topic 5m, 1h, 1d avg (immutable) (historical) ● Raw data is written to BigQuery Percentiles ● Real time transformed data ... (e.g. aligned data rates) written to Bigtable API ● Writes and makes use of meta data in BigTable (not shown) Client 13 SNMP collection Old SNMP system Architecture Diagram system Apache Beam (Stream) avro ● Cloud Bigtable ● Like HBase Align/rates ● Write to cells in rows, indexed by keys Bigtable Rollups (realtime) BigQuery BigQuery ● We write 1 day of 5m, 1h, 1d avg (immutable) (historical) data to a single row (columns are the time Percentiles of day, key is metric and day) ... ● Fast access to row by key, can serve data from here API ● Store one year Client 14 SNMP collection Old SNMP system Architecture Diagram system Apache Beam (Stream) avro ● BigQuery ● Data warehousing Align/rates solution ● Cheap storage, SQL Bigtable Rollups (realtime) BigQuery BigQuery access, but not 5m, 1h, 1d avg (immutable) (historical) suitable for real-time access Percentiles ● Allows SQL queries for ad hoc ... investigation ● We store our source of truth here API Client 15 SNMP collection Old SNMP system Architecture Diagram system Apache Beam (Stream) avro ● BigQuery ● Data warehousing Align/rates solution ● Cheap storage, SQL Bigtable Rollups (realtime) BigQuery BigQuery access, but not 5m, 1h, 1d avg (immutable) (historical) suitable for real-time access Percentiles ● Allows SQL queries for ad hoc ... investigation ● We store our source of truth here API ● Also store historical data (7 years), imported via avro files Client 16 SNMP collection Old SNMP system Architecture Diagram system Apache Beam (Stream) avro ● Apache Beam / Google Dataflow Align/rates ● Batch processing ● Run with cron job Bigtable Rollups (realtime) BigQuery BigQuery 5m, 1h, 1d avg (immutable) (historical) Percentiles ... API Client 17 SNMP collection Old SNMP system Architecture Diagram system Apache Beam (Stream) avro ● Apache Beam / Google Dataflow Align/rates ● Batch processing ● Run with cron job Bigtable Rollups (realtime) BigQuery BigQuery ● Recalculate Bigtable 5m, 1h, 1d avg (immutable) (historical) data each night from source of truth in Percentiles BigQuery ... API Client 18 SNMP collection Old SNMP system Architecture Diagram system Apache Beam (Stream) avro ● Apache Beam / Google Dataflow Align/rates ● Batch processing ● Run with cron job Bigtable Rollups (realtime) BigQuery BigQuery ● Recalculate Bigtable 5m, 1h, 1d avg (immutable) (historical) data each night from source of truth in Percentiles BigQuery ● Process Bigtable ... rows into new rows of 5min, 1 hr and 1 day aggregations API Client 19 SNMP collection Old SNMP system Architecture Diagram system Apache Beam (Stream) avro ● Apache Beam / Google Dataflow Align/rates ● Batch processing ● Run with cron job Bigtable Rollups (realtime) BigQuery BigQuery ● Recalculate Bigtable 5m, 1h, 1d avg (immutable) (historical) data each night from source of truth in Percentiles BigQuery ● Process Bigtable ... rows into new rows of 5min, 1 hr and 1 day aggregations API ● Additional pre-computed views e.g. percentiles for traffic distribution over a month Client 20 SNMP collection Old SNMP system Architecture Diagram system Apache Beam (Stream) avro ● API ● Currently runs on Align/rates App Engine ● Node.js Bigtable Rollups (realtime) BigQuery BigQuery ● Serves data out of 5m, 1h, 1d avg (immutable) (historical) Bigtable ● Timeseries data is Percentiles served as ‘tiles’, each tile is one row ... ● Would like to use Cloud Endpoints and provide a gRPC Dataserver API service (node.js) ● Looking forward to grpc-web solution Client 21 Use case example: Historical Trends 22 Use case example: Historical Trends Per-day SNMP collection Interface totals Stream to BQ BigQuery system Bigtable Old SNMP system avro BigQuery (historical) Per-month totals Bigtable rows Dataserver API (node.js) Jan 1 Jan 2 ... Dec31 snmp-daily::2017-08::$interface 1.8 Pb 1.9 Pb ... 3.1 Pb Client Jan 1991 Feb 1991 ... Sep 2017 snmp-monthly-totals 28 Gb 29 Gb ... 56 Pb 23 Use case: real time anomaly detection Generates avg for each interface over the past 3 months for that hour/day SNMP collection Baseline Stream to BQ BigQuery system generation Bigtable Anomaly detection Compares baseline to real time values to generate current deviation from normal Dataserver API Mon Mon Mon Sun (node.js) 12am 1am 2am ... 11pm baseline::5m::avg::$interface 2.1 1.9 0.3 ... 0.5 Client iface-1 iface-2 ... iface-n anomaly::5m::avg +0.1 +2.0 ... -1.5 24 Use case example: Percentiles 25 Use case example: Percentiles Daily rollups 5m avg SNMP collection Stream to Bigtable system Bigtable Percentiles Bigtable rows Dataserver API 1 2 ... 8640 (node.js) rollup-month-5m::2017-08::$interface::in 6Gbps 5Gbps ... 2Gbps Client 1 pct 2 pct ... 99 pct percentiles::2017-08::$interface::in 0.1 Gbps 0.3 Gbps ... 22.1Gbps 26 Demo 27 Example: Computing Total Traffic # Python Beam SDK pipeline = beam.Pipeline('DirectRunner') (pipeline | 'read' >> ReadFromText('./example.csv') | 'csv' >> beam.ParDo(FormatCSVDoFn()) | 'ifName key' >> beam.Map(group_by_device_interface) | 'group by iface' >> beam.GroupByKey() | 'compute rate' >> beam.FlatMap(compute_rate) | 'timestamp key' >> beam.Map(lambda row: (row['timestamp'], row['rateIn'])) | 'group by timestamp' >> beam.GroupByKey() | 'sum by timestamp' >> beam.Map(lambda rates: (rates[0], sum(rates[1]))) | 'format' >> beam.Map(lambda row: '{},{}'.format(row[0], row[1])) | 'save' >> beam.io.WriteToText('./total_by_timestamp')) pipeline.run() Full code available at: http://x1024.net/blog/2017/05/chinog-flexible-network-analytics-in-the-cloud/ 28 Our Stack ● Apache Beam using Scio Cloud Cloud ● Google Cloud Platform Dataflow Pub/Sub ○ Dataflow ○ Bigtable ○ BigQuery Cloud BigQuery Bigtable ○ Pub/Sub ○ App Engine ● Languages App Cloud ○ Scala Engine Endpoints ○ Javascript / Typescript ○ Python 29 Current Status & Future Plans Current Future Alpha version for SNMP data: More types of data: ● Ingest to BigQuery is working ● Flow data ● Migration of historical data is ● perfSONAR implemented. Awaiting final details before full conversion Machine Learning ● Streaming ingest to Bigtable still in Anomaly Detection process ● Early version of utilization visualization “Mash up” various data sources ● Simple data server can provide data to clients, but gRPC API coming ● Interface timeseries charts functional 30 Why not InfluxDB, Elastic or ${FAVORITE_DB} ● We have a data processing problem, not a data storage problem per se. ○ Beam and the ecosystem around it give a huge amount of flexibility -- can try new ideas as they occur to us ○ Ability to move to different
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  • Researching Algorithmic Institutions Essay

    Researching Algorithmic Institutions Essay

    Researching Algorithmic Institutions RESEARCHING ALGORITHMIC governance produces an essentially harnessed to the pursuit of procedures experimental condition of institutionality. That and realization of rules. Experiments lend INSTITUTIONS which becomes available for ‘disruption’ or themselves to the goal-oriented world of Liam Magee and Ned Rossiter ‘innovation’ — both institutional encodings — algorithms. As such, the invention of new Like any research centre that today is equally prescribed within silicon test-beds institutional forms and practices would seem 3 investigates the media conditions of social that propose limits to political possibility. antithetical to experiments in algorithmic organization, the Institute for Culture and Experiments privilege the repeatability and governance. Yet what if we consider Society modulates functional institutional reproducibility of action. This is characteristic experience itself as conditioned and governance with what might be said to be, of algorithmic routines that accommodate made possible by experiments in operatively and with a certain conscientious variation only through the a priori of known algorithmic governance? attention to method, algorithmic experiments. statistical parameters. Innovation, in other Surely enough, the past few decades have In this essay we convolve these terms. words, is merely a variation of the known seen a steady transformation of many As governance moves beyond Weberian within the horizon of fault tolerance. institutional settings. There are many studies proceduralism toward its algorithmic Experiments in algorithmic governance that account for such change as coinciding automation, research life itself becomes are radically dissimilar from the experience with and often directly resulting from the ways subject to institutional experimentation. of politics and culture, which can be in which neoliberal agendas have variously Parametric adjustment generates sine wave- understood as the constitutive outside of impacted organizational values and practices.