WP3 Werner Nutt (Heriot-Watt University) R-GMA – DataGrid’s Monitoring System 1/7/2003.

WP3

Werner Nutt (Heriot-Watt University)

<[email protected]>

R-GMA – DataGrid’s Monitoring System

1/7/2003

Werner Nutt - 1/7/2003R-GMA -DataGrid's Monitoring System 2

WP3

RGMA = Relational Grid Monitoring Architecture

• Grid Monitoring and Information System developed within DataGrid (Work Package 3)

• Based on the “Grid Monitoring Architecture” of the Global Grid Forum

• Code is open source and freely available

Homepage: type “wp3” into Google


WP3Contributors

• Heriot-Watt, Edinburgh – Andrew Cooke, Alasdair Gray, Lisha Ma, Werner Nutt

• IBM-UK – James Magowan, Manfred Oevers, Paul Taylor

• Queen Mary, University of London– Roney Cordenonsi

• CCLRC/PPARC – Rob Byrom, Laurence Field, Steve Hicks, Manish Soni,

Antony Wilson, Jason Leake– Linda Cornwall, Abdeslem Djaoui, Steve Fisher, Robin Middleton

• SZTAKI, Hungary– Peter Kacsuk, Norbert Podhorszki

• Trinity College Dublin– Brian Coghlan, Stuart Kenny, David O’Callaghan


WP3Overview

• Grid monitoring: Requirements

• The R-GMA approach: A virtual monitoring database

• Components of R-GMA:– Schema– Producers and Consumers– Registry– Republishers

• Query Planning


WP3

Major Components of DataGrid

StorageElement

User Interface Resource Broker

Logging and Bookkeeping

ReplicaCatalogue

Computer

Computer

Computer

Computer

Computer Computer

ComputingElement

MonitoringSystem

StatusInformation

Data Transfer

Job Submission


WP3

WP7: R-GMA Collects Network Monitoring Data


WP3The Grid Monitoring Problem

In a Grid we have– Computers– Storage elements– Network nodes and connections– Application programmes, …

Monitoring:– What is the current state of the system?– How did the system behave in the past ?


WP3

Monitoring Data Come in two Kinds

A Grid monitoring system makes available two kinds of data

• static data “pools”, e.g., databases on – network topology, nodes connected – applications available (versions, licences, ...)

• “streams” of data, e.g.,– sensor data (cpu load, network traffic, ...)

Data streams may give rise to data pools if they are archived

Today: R-GMA is tailored towards streams, but not pools


WP3

Examples of Monitoring Queries

• “Show me the (average) cpu-load of computers at Heriot-Watt!”

• “Between which nodes was yesterday the average transportation time for 1 MB packets higher than than 0.… seconds?”

• For every computing element CE, how many computers of CE have currently a cpu-load of no “ more than 30%?”


WP3Grid Monitoring Requirements

• Support for publishing data “pools” and “streams”

• Support for locating data sources (automatic, if possible)

• Queries with different temporal interpretations

(continuous, latest state, history)

• Scalability (there may be thousands of data sources)

• Resilience to failure (data sources may become unavailable)

• Flexibility (we don’t know which queries will be posed)


WP3

Architecture Approach 1: A Monitoring Data Warehouse

Idea:– store all data about the Grid status into a huge

database– and query it

Not realistic:• Loading takes time• Data occupy space• Connections to the warehouse may fail• Often monitoring data flow as data streams, and

queries ask for data streams as output


WP3

Approach 2: Monitoring with a “Multi-agent System”

The Grid Monitoring Architecture (GMA) of the Global Grid Forumdistinguishes between:

Consumer

Producer

Monitoring-ApplicationData BaseSensor

DirectoryService

find/register

• Consumers of information

• Producers of information

• Directory Service– Producers register their

supply– Consumers register their

demand

Directory Service mediates between producers and consumers


WP3Questions about GMA:

• Which kinds of producers and consumers are there?

• In which language do producers register their supply and consumers their demand ?

• What is the meaning of a registration?

• How does a consumer find suitable producers? And how does a producer find suitable consumers?

• Producers have different capabilities to answer queries (e.g. selections, joins, …).

Which of them should they register?


WP3R-GMA: A Virtual Monitoring

Data Warehouse• Language of producers and consumers:

relational queries (SQL)• Vocabulary: Relations in a global schemaConsumer

DB-Producer

Global Schema S

DB

Stream Producer

Sensor

V1V2...

Vn

VViews on S

Registry

Query • Consumer: poses queries over global schema

• Producer: – has a type (stream p., database p.)

– publishes relations R1, … ,Rk

– for every R, registers a simple view V on the global schema


WP3Schema & Contributions

CPULoad (Global Schema)

Country Site Facility Load Timestamp

UK RAL CDF 0.3 19055711022002

UK RAL ATLAS 1.6 19055611022002

UK GLA CDF 0.4 19055811022002

UK GLA ALICE 0.5 19055611022002

CH CERN ALICE 0.9 19055611022002

CH CERN CDF 0.6 19055511022002

CPULoad (Stream Producer 3)

CH CERN ATLAS 1.6 19055611022002

CH CERN CDF 0.6 19055511022002


UK RAL CDF 0.3 19055711022002

UK RAL ATLAS 1.6 19055611022002


UK GLA CDF 0.4 19055811022002

UK GLA ALICE 0.5 19055611022002


WP3Contributions are Views

CPULoad (Producer 1)

UK RAL CDF 0.3 19055711022002

UK RAL ATLAS 1.6 19055611022002

CPULoad (Producer 2)

UK GLA CDF 0.4 19055811022002

UK GLA ALICE 0.5 19055611022002

SELECT * FROM cpuLoad

WHERE country = ’UK’ AND site = ’RAL’

SELECT * FROM cpuLoad

WHERE country = ’UK’ AND site = ’GLA’


WP3Keys in the Global Schema

Network throughput:

tp(src, dest, method, pcktSize, timestamp, time)

Intuitively, tp has the primary key

(src, dest, method, pcktSize, timestamp).

We need to know the primary keys• to understand the global schema• to answer latest snapshot queries

Primary keys are declared, but not enforced!

Although, sometimes they hold globally if they hold locally !


WP3Metaphor: Roles and Agents

R-GMA Clients: Grid components or Grid applications

• Clients can play the roles of producers or consumers

A client would need special capabilities for a role:

• Clients are supported in their roles by agents

Implementation:• APIs for client roles: “new StreamProducer(…)”• Agents are objects on a Web server


WP3Primary Producers

Database producer• supports queries over fixed set of tuples (static queries)

• can be used to publish a database

Stream producer• supports queries over changing set of tuples

(continuous queries)

• supports “latest snapshot queries”– offers up-to-date values for each primary key in a db

Today: DatabaseProducer’s and StreamProducer’s in R-GMA are different from the above!


WP3

Communication Modes of Stream Producers

Stream Producers may offer two communication modes for continuous queries:

– lossless (… but tuples could become stale)– lossy (… but tuples are fresh)

ProducerServlet

IIIIIIII...

Producer ConsumerConsumer

Servlet

IIIIIIII...

Queue Queue

Today: R-GMA’s StreamProducer’s are resilient and support lossless communication


WP3

Republishers Publish Query Answers

Archiver: shows the history of a stream.

Stream Republisher: enables – merging, – thinning, – summarising of streams …

into database into stream

Static Query Materialised View --

ContinuousQuery Archiver

StreamRepublisher


WP3Republishers in R-GMA Today

Republishers are called “archivers” (although some of them don't archive anything)

An archiver (= republisher)

• is defined by a query • consumes only from “stream producers”• publishes the query result according to its type, using

– a “stream producer”, or– a “latest snapshot producer”, or– a “database producer” (which keeps an archive)

Republishers are used to answer complex queries!


WP3

The Next Step: Hierarchies of Stream Republishers

country= ‘uk’

NationalRepublisher

site= ‘hw’

site=‘ral’

Local/siteRepublisher

Stream Producers

ral hw


WP3

Republisher Hierarchies:The Issues

• Republishers are defined by queries:hierarchies have to be maintained automatically

• new stream producers must only be added to republishers at “lowest level”

• hierarchy has to be replanned if a republisher fails• difficult: transition from one plan to the other

without loss of tuples

• How well can we describe the content of a stream?Possibly need for descriptions that join

• stream relations CPULoad(machineID, load, timestamp)• static relations locatedAt(machineID, site)


WP3

What is the Meaning of a Query in R-GMA?

Assumption: the views of (primary) producers are selections on a single relation, i.e., queries of the form

SELECT * FROM cpu_load WHERE machine_id = ‘AB123’ AND loc = ‘hw’

(each producer contributes its parts of a relation)

• The virtual database contains the union of the data of all the primary producers

• Conceptually, a query is evaluated over the entire virtual db


WP3

Stream Queries can have Various Temporal Interpretations

Consider a query over the relation “Transport Time”

tt(src, dest, pcktSize, method, timestamp, time)

SELECT * FROM ttWHERE src = ral AND dest = bologna

What is meant? Measurements– from now ? (Continuous Query)

– up until now ? (History Query)

– right now ? (Latest Snapshot Query)

Today: Queries can be “flagged” with their type


WP3

Advanced Queries:

Mixing Temporal Query Types

• “Which connections have currently a transportation time that is higher than last week's average?”

(latest snapshot and history)

• “Show me the cpu load of those machines where it is lower than yesterday's load average!”

(continuous and history)

We do not intend to support such queries by R-GMA!


WP3

In R-GMA Query Answering Needs MediationSuppose P1, P2 publish for tp (throughput)

P1: … WHERE src = hw P2: … WHERE src = ral AND pcktSize > 20

A global consumer poses its query over global relations

SELECT * FROM tp WHERE pcktSize > 10

A mediator translates this into queries over local relations

SELECT * FROM P1.tp WHERE pcktSize > 10UNIONSELECT * FROM P2.tp

Today: R-GMA’s mediator handles simple queries like the one above


WP3

• Global consumers pose queries over global relations

SELECT * FROM tp WHERE pcktSize > 10 ,

which are translated into queries over local relations

SELECT * FROM P1.tp WHERE pcktSize > 10UNIONSELECT * FROM P2.tp

• Local consumers pose queries over local relations directly

SELECT * FROM P1.tp WHERE method = ping

Today: a consumer can be global or local, but local relations cannot be referred to explicitly

Global and Local Consumers


WP3

How does the Mediator Find Suitable Publishers?

P1, P2, P3 publish for tt (Transport Time)

P1: … src = hwP2: … src = ral AND pcktSize > 20P3: … src = ral AND method = ping

Q: SELECT * FROM tt WHERE src = ral AND method = ping

We see: P1 is not suitable for Q, but P2 and P3 are. Why?

src = hw AND src = ral AND method = ping is never true

src = ral AND pcktSize > 20 AND … is sometimes true

Satisfiability Test! Today: implemented


WP3

… So Which Publishers Should the Mediator Ask?

P2: … src = ral AND pcktSize > 20P3: … src = ral AND method = ping

Q: SELECT * FROM tt WHERE src = ral AND method = ping

All answers to Q returned by P2 are also returned by P3 :

whenever src = ral AND pcktSize > 20 AND src = ral AND method = ping

is true, then src = ral AND method = ping AND src = ral AND method = ping

is true.

Hence, R-GMA only needs to ask P3 Entailment Test!

Needed for Republisher Hierarchies! (not yet implemented)


WP3

… But What Did the Producers Promise?

P registers view V

Does P promise– some of V ? (sound description)

– all of V? (sound and complete description)

• The Entailment Test only makes sense when the registered views are sound and complete descriptions

• Producers should register completeness flags


WP3

… Why May a Producer not be Complete?

• The language of views is more restricted than the language of queries

Hence: republishers may be unable to say exactly what they publish

• Archivers may archive in lossy mode

• Producers may lose tuples

• A producer may not know everything about the real world

Open to debate


WP3Summary (1)

Monitoring data come in Pools and Streams

Global Schema• primary keys

Types of Stream Queries• continuous vs. history vs. latest snapshot

Producers

• DB producers: publish database

• stream producers: lossless vs. lossy communication modes

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WP3 Werner Nutt (Heriot-Watt University) R-GMA – DataGrid’s Monitoring System 1/7/2003.

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