Management and usage of large scale infrastructures.

Transcription

1 Management and usage of large scale infrastructures

2 Grid Computing and clouds Ian Foster on Grids : Resource sharing & coordinated problem solving in dynamic, multi-institutional virtual organizations. Clouds: sharing of resources to achieve coherence and economies of scale, similar to a utility (like the electricity grid) over a network Should be easy to use and could be easy to manage 2

3 User point of view 3

4 Several goals Low latency High Throughput How many jobs can I finish in a timeframe Low cost How long do I have to wait my job completion How much does it cost to me Low complexity How long do I have to manage my jobs 4

5 Reduce the complexity Standardization bodies Use of open protocols High level QoS Abstraction levels Grid Everything is a resource Cloud PaaS, IaaS, SaaS Services!!! 5

6 Frontends First contact Web Site Dedicated Eclipse Plugin Developper only Command line Expert only 6

7 Why doing it simple? Framework for building submission websites 7

8 Cloud version : same complexity 8

9 Simple Job Submission Submit job to a GRAM service default factory EPR generate job RSL to default localhost Command example: % globusrun-ws -submit -c /bin/touch touched_it Submitting job...done. Job ID: uuid:002a6ab d9-bae6-0002a5ad41e5 Termination time: 01/07/ :55 GMT Current job state: Active Current job state: CleanUp Current job state: Done Destroying job...done. 9

10 Simple Job Submission : Cloud version 10

11 Security is complex In clouds Isolation and no sharing Delegated to other layers In grids Virtual organization Cooperation between sites Trust mechanisms 11

12 Grid Security Infrastructure (GSI) Based on certificates Several CA (Certificate Authorities) Trust relations are inherited from CA Communications are based on SSL Coarse grained Not adapted for reading few bytes in a file 12

13 Grid Security Infrastructure (GSI) 13

14 Timing and methodology Clouds Everything by hand, you have what you pay PaaS / SaaS / IaaS Deployment/Development depends on what you buy Grids Standardized (everything is a resource) Can do everything so everything is a pain 14

15 Example of Grid data communication Globus WSRF : Web Service Resource Framework Data access is a service 15

16 Provider point of view 16

17 Job flow in grids : Question? How many decisions 17

18 Basic useful services VO Management Service: resources allocation to each Virtual Organization. Resource Discovery and Management Service Job Management Service And much more: security (authentication, authorisation, data management) All all services interact: example Job Management Service needs Resource Discovery Need Standardization for interfaces to services Example: JobSubmissionService has a submitjob() method 18

19 Base infrastructure to implement the architecture OGSA? OGSA: Open Grid Services Architecture The method invocation should also be standardized. Corba? RMI? RPC? No : Web Services!! But need Stateful Web Services! WSRF: Web Services Resource Framework 19

20 The Web services WSDL/SOAP/HTTP pancake In theory extensible and generic. In reality complex and monolitic 20

21 Going more inside Web services invocations You don t have to program the stubs/nor the SOAP requests/responses Just like Corba and RMI 21

22 From stateless to stateful WS Using the concept of resources 22

23 WS-Resources Web Service + Resource = WS-Resources To address these, we need a endpoint reference to specify the resource Think how simple are DNS, RmiRegistry... Nope 23

24 Specification, WSRF and more WS-ResourceProperties: defined in the WSDL interface WS-ResourceLifetime: manage lifecycle of the WS-Resources WS-ServiceGroup: group services or WS-Resources together allow to find in the group services meeting a particular property allow also to address all services of the group by one entry point WS-BaseFaults: for fault reporting WS-Notification: producer/consumer mode WS-Addressing: to address the WS-Resources 24

25 Grid middleware Provides WS-R Grid middleware IS WS-R 25

26 Writing a WSRF Web/Grid Service Five Steps, only! 1. Define the service s interface. This is done with WSDL 2. Implement the service. This is done with Java. 3. Define the deployment parameters. This is done with WSDD and JNDI 4. Compile everything and generate a GAR file. This is done with Ant 5. Deploy service. This is also done with a GT4 tool 26

27 A example service interface In Java or IDL, the description is simple public interface Math { public void add(int a); public void subtract(int a); public int getvaluerp(); } 27

28 WSDLservice description <?xml version="1.0" encoding="utf-8"?> <definitions name="mathservice targetnamespace=" MathService_instance" xmlns=" xmlns:tns=" vice_instance" xmlns:wsdl=" xmlns:wsrp=" xmlns:wsrpw=" xmlns:wsdlpp=" xmlns:xsd=" <wsdl:import namespace=" 28 location="../../wsrf/properties/ws-resourceproperties.wsdl" />

29 <! ==== P O R T T Y P E ========== > <porttype name="mathporttype" wsdlpp:extends="wsrpw:getresourceproperty" wsrp:resourceproperties="tns:mathresourceproperties"> <operation name="add"> <input message="tns:addinputmessage"/> <output message="tns:addoutputmessage"/> </operation> <operation name="subtract"> <input message="tns:subtractinputmessage"/> <output message="tns:subtractoutputmessage"/> </operation> <operation name="getvaluerp"> <input message="tns:getvaluerpinputmessage"/> <output message="tns:getvaluerpoutputmessage"/> </operation> </porttype> </definitions> 29

30 <! ====== M E S S A G E S ====== > <message name="addinputmessage"> <part name="parameters" element="tns:add"/> </message> <message name="addoutputmessage"> <part name="parameters" element="tns:addresponse"/> </message> <message name="subtractinputmessage"> <part name="parameters" element="tns:subtract"/> </message> <message name="subtractoutputmessage"> <part name="parameters" element="tns:subtractresponse"/> </message> <message name="getvaluerpinputmessage"> <part name="parameters" element="tns:getvaluerp"/> </message> <message name="getvaluerpoutputmessage"> <part name="parameters" element="tns:getvaluerpresponse"/> </message> 30

31 <! === T Y P E S ======== > <types> <xsd:schema targetnamespace=" thservice_instance" xmlns:tns=" ice_instance" xmlns:xsd=" <! REQUESTS AND RESPONSES > <xsd:element name="add" type="xsd:int"/> <xsd:element name="addresponse"> <xsd:complextype/> </xsd:element> <xsd:element name="subtract" type="xsd:int"/> <xsd:element name="subtractresponse"> <xsd:complextype/> </xsd:element> <xsd:element name="getvaluerp"> <xsd:complextype/> </xsd:element> <xsd:element name="getvaluerpresponse" type="xsd:int"/> 31

32 <! RESOURCE PROPERTIES > <xsd:element name="value" type="xsd:int"/> <xsd:element name="lastop" type="xsd:string"/> <xsd:element name="mathresourceproperties"> <xsd:complextype> <xsd:sequence> <xsd:element ref="tns:value" minoccurs="1" maxoccurs="1"/> <xsd:element ref="tns:lastop" minoccurs="1" maxoccurs="1"/> </xsd:sequence> </xsd:complextype> </xsd:element> </xsd:schema> </types> 32

33 From stateless to stateful WS Using the concept of resources 33

34 If you are still alive, you still have to Actually write the code Configure the deployment With WSDD and JNDI Compile everithing with the right libraries Generate a GAR file: Grid Archive Deploy into a container And it was a simple stateless service! Most people just run code and forget about services 34

35 Behind the scene how does it work? 35

36 Grids : Globus GRAM 4, everything is specified compute element and service host(s) g at e Delegation de l e g a te sudo d el e GRAM services xfer request client bm i t job su compute element local job contr ol GRAM adapter GridFTP RFT local sched. user job FTP control FTP data GridFTP remote storage element(s) 36

37 Clouds : OpenStack : somewhat specified OpenStack : Communication and meta-data 37

38 Structure Monitoring Analyze Decision Implementation MAPE-K loop Concept view: actually several cooperative decisions 38

39 Monitoring Grid : Integrated monitoring Ganglia NWS, Network Weather Service (adds prediction) Nagios Cloud Provider : integrated User : no access to provider data If you want something, deploy it 39

40 Monitoring example : Ganglia Goal: High performance Small messages to reduce network impact Hierarchical structure with aggregation nodes Scalability (few thousand nodes) Several components XDR for portable non-intrusive communication RRDtool for data storage and manipulation XML for data format Open Source 40

41 Analyze Metrics Computed using raw data from monitoring ex: Energy consumption Grid: usually performance How many jobs are running How many are waiting How far are the deadlines Everything is at 100% Energy does (not) matter 41

42 Analyze Metrics Cloud Abstract «performance» do not exist : only users (QoS) Provider has an infrastructure point of view Unused resources Cost (electricity & management) Some classical metrics (Question : for who?) Performance Energy Reliability Dynamism 42

43 Decision Grids : already said Most important : where and when to run tasks Clouds User: Optimize QoS Start new instances Modify resource allocation of current instances Provider: save money (and electricity) Consolidation Switching on/off servers 43

44 Grid exemple : backfilling Question : If 5 is longer, can we move 4? What could be the negative impact? 44

45 Cloud exemple : steps for consolidation 45

46 Consolidation Limits Real servers don't switch off Service interruption (even if few ms) Isolation Scheduling in general Fairness QoS evaluation Multi-metrics for antagonist objectives «Performance», Energy, Resilience, Dynamism Question: How to manage reliability? 46

47 Execute User: Depends on the application Reconfiguration Data migration (web server, database) Scalability of the application Provider Latency problems: Switching on/off a nodes: ~ 1 min Scale problem Switching on/off 1000 nodes: power peaks 47

48 What about Peer to Peer? 48

49 Control? Several type of Peer to Peer systems Corporate Distributed File system Work Stealing Cooperative Protein folding BitCoins 49

50 Distributed Hash Table Main point of contact : DHT Manages meta-data Manages all data File systems Work sharing Several libraries Kademlia Chord 50

51 Comparison with Grids and Clouds More specific Toward simple data management Distributed file sharing Toward computation on simple data Protein folding BitCoins Work stealing Some good properties Low possibilities but simple to implement Decentralized Question : Decisions? 51

52 Hype Cycle for Emerging Technologies, Gartner

53 Bibliography The Grid 2: Blueprint for a New Computing Architecture. Ian Foster, Carl Kesselman The Globus Toolkit 4 Programmer s Tutorial, Borja Sotomayor A view of cloud computing Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A.,... & Zaharia, M. OpenStack: toward an open-source solution for cloud computing Sefraoui, Omar, Mohammed Aissaoui, and Mohsine Eleuldj Peer-to-peer computing Milojicic, Dejan S., Vana Kalogeraki, Rajan Lukose, Kiran Nagaraja, Jim Pruyne, Bruno Richard, Sami Rollins, and Zhichen Xu 53