Performance of the Relational Grid Monitoring Architecture (R-GMA) CMS data challenges. The nature of the problem. What is GMA ? And what is R-GMA ? Performance test description Performance test results Conclusions

The Nature of the problem As part of the preparations for data taking CMS is performing DATA CHALLENGES. Large number of simulated events to optimise detectors and prepare software Enormous processing requirements BUT each event is independent of all the others each event can be generated on a machine without any interaction with any other

The local solution Work split between farms. How to handle the book-keeping ? a data-base automatically updated Implemented via a job wrapper BOSS Output to and is intercepted and the information is recorded in a mySQL production database. Event generation and job accounting decoupled

The local solution (schematic) Database Machine Submission Machine UI Worker Node (WN) WN

The grid solution (schematic) Database Machine Submission Machine UI

Grid Monitoring Architecture (GMA) of the GGF Producer Consumer Registry (Directory services) register producer locate producer address of producer data Ask for data

R-GMA (Relational GMA) Developed for E(uropean) D(ata) G(rid) Extends the GMA in two important ways 1.Introduces a time stamp on the data. 2.A relational implementation 3.Hides the registry behind the API Can be used for information and monitoring Each V irtual O rganisation appears to have one RDBMS

The syntax of R-GMA The user interface to R-GMA is via SQL statements (not all SQL statements and structures are supported) Information is advertised via a table create Information is published via insert Information is read via select … from table The first read request registers the consumer as interested in this data. Relational queries are supported NOTE : sql is the interface – it should not be supposed an actual database lies behind it.

Fit between R-GMA and BOSS R-GMA can be dropped into the framework with very little disruption 1.Set up calls for mySQL are replaced by those for R-GMA producers 2.An archiver (joint consumer/producer) runs on a single machine which collects the data from all the running jobs and writes it to a local database (and possible republishes it). The data can then be queried either by direct mySQL calls or via R-GMA consumer (a distributed database has been created)

Database BOSS LAN Connection R-GMA WAN Connection Fit between R-GMA and BOSS (i)

R-GMA Measurements The architecture of GMA clearly provides a putative solution to the wide area monitoring problem. BUT Does a specific implementation provide a practical solution Before entrusting CMS production to R-GMA, we must be confident that it will perform. What load will it fail at and why ?

Message time distribution from 44 jobs 35 chars.

Simulation of a CMS job Multi-threaded job each thread produces messages. Length 35 chars, suitable distribution. Threads starting time distribution can be altered. One machine delivers the R-GMA load of a farm. R-GMA servlet R-GMA consumer

Simulation of the CMS Grid One machine per grid cluster providing loads of greater than the cluster R-GMA consumer R-GMA servlet R-GMA servlet R-GMA servlet R-GMA servlet

Current status R-GMA can survive loads of around 20% of the current CMS requirements and does provides a grid method for monitoring. An overload of a factor 2 jobs causes problems after about five minutes running. We believe these instabilities are soluble. When production starts in earnest we will compare reality with our model.

GridICE Server Installation 16

Brief Introduction GridICE: –is a distributed monitoring tool for grid systems –integrates with local monitoring systems –offers a web interface for publishing monitoring data at the Grid level –fully integrated in the LCG-2 Middleware gridice-clients data collector installation and configuration for each site ralized by the Yaim scripts. 17

System Requirements Suggested Operating system is Scientific Linux with a minimal installation The GridICE server should be installed on a performant machine –PostgreSQL service - RAM intensive demand –Apache web server - RAM-CPU intensive demand 18

Core Packages & Dependencies The GridICE server software is composed by three core packages: 1.gridice-core (setup and maintenance scripts / discovery components) 2.gridice-www (web interface scripts and components) 3.gridice-plugins (monitoring scripts) Plus several dependencies: –Apache http web server –PostgreSQL database server –Nagios monitoring tool –... 19

The Four Main Phases of Monitoring 20 Generation Distributing Presenting Processing Sensors inquiring entities and encoding the measurements according to a schema Transmission of the events from the source to any interested parties (data delivery model: push vs. pull; periodic vs. aperiodic) Processing and abstract the number of received events in order to enable the consumer to draw conclusions about the operation of the monitored system e.g., filtering according to some predefined criteria, or summarising a group of events

The GridICE Approach 21

Generating Events Generation of events: –Sensors: typically perl scripts or c programs. –Schema: GLUE Schema v GridICE extension. –System related (e.g., CPU load, CPU Type, Memory size). –Grid service related (e.g., CE ID, queued jobs). –Network related (e.g., Packet loss). –Job usage (e.g., CPU Time, Wall Time). –All sensors are executed in a periodic fashion. 22

Distributing Events Distribution of events: – Hierarchical model. Intra-site: by means of the local monitoring service – default choice, LEMON ( Inter-site: by offering data through the Grid Information Service. Final Consumer: depending on the client application. – Mixed data delivery model. Intra-site: depending on the local monitoring service (push for lemon). Inter-site: depending on the GIS (current choice, MDS 2.x, pull). Final consumer: pull (browser/application), push (publish/subscribe notification service coming on the next release). 23

Presenting Events Data stored in a RDBMS used to build aggregated statistics. Data retrieved from the RDBMS are encoded in XML files. XSL to XHTML transformations to publish aggregated data in a Web context. 24

Monitoring a Grid 25

Challenges for Data Collection The distribution of monitoring data is strongly characterised by significant requirements (e.g., Scalability, Heterogeneity, Security, System Health) None of the existing tools satisfy all of these requirements Grid data collection should be customized depending on what are the needs of your Grid users selected 26

Challenges for Data Presentation Different Grid users are interested in different subset of Grid data and different aggregation levels Usability principles should be taken into account to help users finding relevant Grid monitoring information A sintetic data aggregation is crucial to permit a drill- down navigation (from the general to te detailed) of the Grid data 27

Grid Monitoring Architecture (GMA) of the GGF Producer Consumer Registry (Directory services) register producer locate producer address of producer data Ask for data

R-GMA (Relational GMA) Developed for E(uropean) D(ata) G(rid) Extends the GMA in two important ways 1.Introduces a time stamp on the data. 2.A relational implementation 3.Hides the registry behind the API Can be used for information and monitoring Each V irtual O rganisation appears to have one RDBMS

The user interface to R-GMA is via SQL statements (not all SQL statements and structures are supported) Information is advertised via a table create Information is published via insert Information is read via select … from table The first read request registers the consumer as interested in this data. Relational queries are supported NOTE : sql is the interface – it should not be supposed an actual database lies behind it. The syntax of R-GMA

R-GMA can be dropped into the framework with very little disruption 1.Set up calls for mySQL are replaced by those for R-GMA producers 2.An archiver (joint consumer/producer) runs on a single machine which collects the data from all the running jobs and writes it to a local database (and possible republishes it). The data can then be queried either by direct mySQL calls or via R-GMA consumer (a distributed database has been created) Fit between R-GMA and BOSS

Database BOSS LAN Connection R-GMA WAN Connection Fit between R-GMA and BOSS (i)

How is Ganglia different from Nagios Ganglia is architecturally designed to perform efficiently in very large monitoring environments: each Ganglia gmond performs its service checks locally, reporting in at a regular interval to the gmetad. Nagios performs its service checks by polling each device across a network connection and waiting for a response (known as "active checks"), which can be more resource and bandwidth intensive. Nagios uses the results of its active checks to determine state by comparing the metrics it polls to thresholds. These state changes can in turn be used to generate notifications and customizable corrective actions. Ganglia, by contrast, has no built-in thresholds, and so does not generate events or notifications. The general rule of thumb has been: if you need to monitor a limited number of aspects of a large number of identical devices, use Ganglia; if you want to monitor lots of aspects of a smaller number of different devices, use Nagios. But those distinctions are blurring as Ganglia supports more and more devices, and as Nagios' scalability improves. 10/7/2015T.R.LEKHAA/AP/IT/SNSCE33

How is Ganglia different from Nagios The problem with ganglia and all the other external web pages we have been looking at is that you have to look at them! If all is well with your system you don’t want to have to look. This is where Nagios comes in. It can be setup to alert you when something goes wrong, or a value passes a threshold. 10/7/2015T.R.LEKHAA/AP/IT/SNSCE34

Monitoring: What?

Monitoring: How(1)? Monitor Node Grid middleware Monitoring Architecture IperfER PingER UDPmon MiperfER bbcp/ftp Tools installed on dedicated & similar node at each centre MESH Publication service 30 mins

Monitoring: How(2)?

Network Weather Service

Introduction “NWS provides accurate forecasts of dynamically changing performance characteristics from a distributed set of metacomputing resources” What will be the future load (not current load) when a program is executed? Producing short-term performance forecasts based on historical performance measurements The forecasts can be used by dynamic scheduling agents

Introduction Resource allocation and scheduling decisions must be based on predictions of resource performance during a timeframe NWS takes periodic measurements of performance and using numerical models, forecasts resource performance

NWS Goals Components –Persistent state –Name server –Sensors Passive (CPU availability) Active (Network measurements) –Forecaster

Architecture

Performance measurements Using sensors CPU sensors –Measures CPU availability –Uses uptime vmstat Active probes Network sensors –Measures latency and bandwidth Each host maintains –Current data –One-step ahead predictions –Time series of data

Network Measurements

Issues with Network Sensors Appropriate transfer size for measuring throughput Collision of network probes Solutions –Tokens and hierarchical trees with cliques

Available CPU measurement

The formulae shown does not take into account job priorities Hence periodically an active probe is run to adjust the estimates

Predictions To generate a forecast, forecaster requests persistent state data When a forecast is requested, forecaster makes predictions for existing measurements using different forecast models Dynamic choice of forecast models based on the best Mean Absolute Error, Mean Square Prediction Error, Mean Percentage Prediction Error Forecasts requested by: –InitForecaster() –RequestForecasts() Forecasting methods –Mean-based –Median based –Autoregressive

Forecasting Methods Notations: Prediction Accuracy: Mean Absolute Error (MAE) is the average of the above Prediction Method:

Forecasting Methods – Mean-based

Forecasting Methods – Mean-based 4. 5.

Forecasting Methods – Median-based

Autoregression 1. a i found such that it minimizes the overall error. r i,j is the autocorellation function for the series of N measurements.

Forecasting Methodology

Forecast Results

Forecasting Complexity vs Accuracy Semi Non-parametric Time Series Analysis (SNP) – an accurate but complicated model Model fit using iterative search Calculation of conditional expected value using conditional probability density

Sensor Control Each sensor connects to other sensors and perform measurements O(N 2 ) To reduce the time complexity, sensors organized in hierarchy called cliques To avoid collisions, tokens are used Adaptive control using adaptive token timeouts Adaptive time-out discovery and distributed leader election protocol