Cloud Compu)ng and Virtualisa)on
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1 Cloud Compu)ng and Virtualisa)on John Brooke ITS and IMG group in CS March 31 st 2014 Thanks to Ralf Ratering for material on slides 9-18
2 Structure and aims The lecture seeks to place the phenomenon of Cloud Compu)ng in its context. Cloud Compu)ng is a par)cular form of Distributed Compu)ng which is crucially dependent on the concept of Virtualisa)on. We first discuss previous forms of virtualised distributed compu)ng, in par)cular the Grid Programming Environment from Intel. Then we consider what dis)nguishes Cloud Compu)ng and why it arouses so much current interest.
3 What is Computational Power? Power in physics is defined as energy/ time. In computing we consider dataprocessing/time. The processing needs of an organisation may vary, with spikes in consumption. This leads to the concept of elastic computing, extra resource available as needed. Ultimately this is scheduling problem, with a sufficiently large pool of resources, the illusion of elasticity can be provided. CS6022 3
4 The power of scale Organisa)ons such as Amazon, Google, MicrosoV own a scale of resources that can be used to support elas)c compu)ng. However we must introduce uniformity in the compu)ng interfaces, we cannot go looking for libraries, storage spaces, etc.. as we move the compu)ng around the datacenter. It is necessary to run virtualised images or services which package all the necessary sovware dependencies.
5 Costing computational resource Scalar cost in tokens C o s t Job vector 1 Cost vector User Job CS6022 5
6 Computational Resource Computational jobs ask questions about the internal structure of the provider of computational power in a manner that an electrically powered device does not. For example, do we require specific compilers, libraries, disk resource, visualization servers? What if it goes wrong, do we get support? If we transfer data and methods of analysis over the Internet is it secure? CS6022 6
7 RR space request RR and RP Spaces RP space A request B sync RP space RR space RP space C Request referral D Figure 1: Request from RR space at A mapped into resource providers at B and C, with C forwarding a request formulated in RR space to RP space at D. B and C synchronize at end of workflow before results returned to the initiator A. CS6022 7
8 Grid Compu)ng Applica)ons GPE Grid SDK Grid Beans Client Framework Web Resource Framework (WSRF) WSRF- enabled Servers Grid Programming Library Open Grid s Architecture (OGSA) WSRF- enabled Storage WSRF- enabled Network High-level Grid API Descriptions n Resources (CIM) n Jobs (JSDL) n Workflows (BPEL) Operations n Job management n File transfers n Brokering n Steering, etc. CS6022 8
9 Standards JSDL (Job Submission Description Language) High level job description that can be submitted to all target systems offering a JSDL interface CIM (Common Information Model) Used to describe resources Usage of CIM management interfaces for Grid administration BPEL (Business Process Execution Language) Integration of Grid Bean services into larger business process workflows WS* (WS-Addressing, WSRF, WSN, etc.) Interoperation with other Grid Middleware OGSA (Open Grid s Architecture) Share components with other architectures CS6022 9
10 GPE Concept Grid SDK Expert Client Deploy GridBeans Web Client Portlets Plugins Plugins GridBeans Application Client GridBean Application Portal Plugins Plugins GridBeans Load GridBeans Query target system info Submit workflows GridBean Registry Invoke Workflow Engine Register Invoke WSRF s UNICORE, Globus, OGSA-DAI, etc. GPE Client Framework Grid s CS
11 Atomic s GPE Client Framework Common set of WSRF service interfaces UNICORE/GS Implementa)on UNICORE Atomic Interfaces GTK4 Implementa)on GTK4 GPE Implementa)on Java TSI Atomic service interfaces define basic set of operations and properties that have to be available on a Grid Different impementations of interfaces for different infrastructures CS
12 GPE Atomic s Client Manage target system Manage jobs on target system Manage files on storage Invoke Manage imports to storage Manage exports from storage Target System (TSS) Job Management (JMS) File Management (FMS) File Import (FIS) File Export (FES) Web Server Invoke Target System Interface GPE Java TSI Target System CS
13 GPE Atomic Interfaces Operations Resource Type Properties Extends WS- RP/LT Job-EPR submit(jsdl, InitialTT) Target System (TSS) Target System Memory=1Gb Running Jobs=23 etc. Extends WS- RP/LT start() abort() hold() resume() Job Management (JMS) create Job1 Job2 Job3 Status=Running OriginalJSDL=<JSDL..> ExecutionJSDL=<JSDL..> etc. Extends WS- RP/LT listdirectory(path) createdirectory(path) changepermissions(path, perm) copy(path, path) rename(path, path) delete(path) import(path, protocol, ispipe) export(path, protocol, ispipe) Extends WS- RP/LT putchunk(offset, size) File Management (FMS) File Import (FIS) TS-Property create Storage1 File Import 1 Storage2 File Import 2 AvailableFileSpace=127kb SupportedProtocols= {GridFTP, UPL, scp} etc. Sourcefile=c:/tmp/test.txt DestFile=/tmp/test.txt Transferred=300kb etc. Extends WS- RP/LT getchunk(offset, size) File Export (FES) CS File Export 1 File Export 2 Sourcefile=/tmp/output.txt DestFile=c:/tmp/output.txt Transferred=12kb etc.
14 Discovering services: the role of a registry Register/ Update Site A Target System (TSS) Client Registry Target System (TSS) Query Registry Broker Site B Registry CS Registry collects resource properties from target systems Target systems notify Registry about property changes WS-Notification Registry may be installed per site across different organizations
15 GPE Registry Query Registry DataBase Register/ Update TSS Dynamic Properties TSS TSS TSS IDB TSS Static Properties TSS installed Applications and application interfaces IDB: Static Properties IDB: Incarnations Registry may be installed on one site/system with the actual TSS, but also can be used across different organizations TSS contacts Registry on startup to register and update the information using notifications CS
16 GPE Workflows Client EPR submit(bpel) getstatus() Workflow Engine Web Server create Workflow Instance orchestrate Target System Job Management File Management File Import File Export Target System Plain Web Web Server Use BPEL to orchestrate WSRF services in complex workflows Allows integration into larger business processes CS
17 Virtualization in Grid Computing Virtual Target System Virtual Target System Virtual Machines are dynamically created and configured according to user request VMM (Virtual Machine Manager) Physical Hardware Resources Protection of sensitive user data Other partitions on the same machine will remain unaffected if one partition crashes Virtual machines may even migrate during run-time for instance when the hosting hardware is needed from a different user with high priority, when the system needs to shut down, etc. CS
18 Virtualization Architecture in GPE C L I E N T query, delete OS images 1. send resource request 7. reference to deployed VM 2. request matching OS image Virtual Target System Factory 5. request missing software for OS image request missing software from job request OS Image Repository 4. create VM with OS image Application Deployment store OS image 3. deploy OS image 6. Install software Management Interface Real Machine Virtual Machine OS TSI Virtual Machine OS 8. query, submit, store, shutdown, pause, migrate,... Virtual Target System manage VM 9. submit job TSI CS TSI = Target System Interface
19 Characteristics of Cloud Computing As defined by the US NIST On-demand self service. Broad network access. Resource Pooling. Rapid elasticity. Measured service. CS
20 oriented computing NIST defines the following service models Software as a (SaaS) example Office 365 Platform as a (PaaS) example Google Apps. Infrastructure as a (IaaS) example Amazon Web s. CS
21 Private and public clouds Clouds can be classified by accessibility Private Cloud, entirely owned by one organisation very like a private Grid Community Cloud pooled between organisations, again like a Grid. Public cloud widely available over the Internet, payment models. Hybrid cloud, private but allows Cloudbursting CS
22 A model for portal access to Clouds Cloud 2: access VMs Virtual Node Virtual Node Virtual Node Portal 1: request VMs by pafern AutoPilot Gateway Virtual Node Virtual Node Pladorm Image Virtual Node Virtual Node Pladorm Image Virtual Node Virtual Disks Interface Pladorm Descriptors describe Accoun)ng Database AutoPilot Gateway Auto Pilot Engine Cloud Client Library control, mount, exec
23 Taverna in the cloud Taverna is a widely used system for running workflows that process scien)fic data mainly from the life sciences. Taverna currently runs mainly on desktop machines, however they do not have the power to process the quan))es of data coming from modern genomics sequencing (called Next Genera)on Sequencing) A possible solu)on is to run Taverna in the Cloud via a server side component, Taverna Server
24 Standard View of Taverna Server TavServ Back End Portal Taverna Server Front End TavServ Back End TavServ Back End Server Host
25 Cloud Back- End s TavServ Back End Portal Taverna Server Front End TavServ Back End TavServ Back End Server Host
26 Simple Taverna Cloud TavServ Back End Portal Taverna Server Front End TavServ Back End TavServ Back End Cloud VM Instance
27 Discre)zed Taverna Cloud Cloud VMs TavServ Back End Portal Taverna Server Front End TavServ Back End TavServ Back End Cloud Controller/Messaging e.g. Amazon
28 Cloud Compu)ng and Mobile Compu)ng Cloud compu)ng and mobile compu)ng can be very complementary, Cloud is the server side and mobile is the the agile interface. Cloud allows data and applica)ons to be accessed from mul)ple devices and loca)ons without )me consuming and error prone data copying e.g Apple s IoS links to the Apple Cloud. Cloud provides extra power to mobile devices, e.g. Siri.
29 Case study: supporting water engineers Wireless Sensor Network Real-time Sensor Observations Computa(onal Model On-demand Resource Allocation & Usage Simulations Results Real-World System Predic(ons of Future Events/Condi(ons Cloud Computing Infrastructure Sensor- Grid Compu)ng = WSN + Cloud Compu)ng Allows construc)ng real- )me models Applica)ons includes the monitoring and control of natural hazards, built environment and target/human surveillance 29
30 Integration of a distributed workforce with mobile devices Wireless Sensor Network Real-time Sensor Observations Computa(onal Model On-demand Resource Allocation & Usage Current State Predictions of Future Events and Conditions Real-World System Proactive and Reactive Operational Decisions A Distributed System of Human Workforce Cloud Computing Infrastructure 30
31 Managing physical infrastructure Focus of research work is on existing distributed Infrastructures: cover longer distances comprise of hundreds of thousands of elements having complex topologies e.g. all-main models operate for long period up to a century Examples of Distributed Infrastructure: Water Distribution System Electricity Grids Oil and Gas Network Network of about 4000 nodes UK water distribution and sewer pipes Water pipes - 397,401 km (Earth - Moon: 384,403 km) Sewer pipes - 354,066 km 31
32 Junc)ons and pipes overlay
33 Satellite view
34 Design of the functionality of the system We build on current Grid middleware for computation (heavyweight Grid/ Cloud) and link it to mobile and roaming devices with a role-based lightweight architecture based on messaging protocols. In this way we include the engineering process. 34
35 Dynamic and Predic(ve DST Dynamic and Predic(ve DST Web based Clients Queries, Planned Future Ac)ons, Alert Subscrip)ons Results/Alerts Alerts REST based API Queries/Results GIS Data Alerts Extended Simula(on Toolkit Concurrency Control Mechanism Asynchronous Messaging Queues Conven(onal Computa(onal Model Grid/Cloud Compu(ng Resources Sensors Update Scheduling of Future Decisions Data warehouse Filtered/Processed Data Wireless Sensor Network Real- )me raw data Data Acquisi(on & Processing Agent SPRING Framework 35
36 Interac)on of DST with RESTfull Client- RESTful Simulation Data Warehouse GET-GIS_DATA() GetGISData() XML-GIS_Data createexperiment() PlanWhatIfDecisions() POST-runExperiment() runsimulation() extractwhat-ifdecisions() GetCurrentStatus() GetScheduledDecisions() runsimulation() simulationresult() addsimts() XML-Simulation_Result PlanFutureDecisions() POST-Register_Decision() AcquireResourceLock() If SimTS(Si)>WTS(Ri) then write PFAix if SimTS(Si)>WTS(Ri) then SUCCESS else ERROR 36
37 Implementa)on on PDAs and SmartPhones 37
38 Reasons for current success of Cloud Model Easy to understand business model. Payment by credit card or by subscrip)on. Organisa)ons with data centres that are on a scale where elas)c compu)ng and economies of scale become possible. A user is now represented by mul)ple devices, several of them mobile. A central virtual repository is needed to keep the user s data consistent. Dropbox is the classic example of this.
39 Poten)al problems for Cloud Model Compared to Grid Compu)ng standards in the Cloud world are very undeveloped. Strong risk of vendor lock- in. With Cloud not only your sovware but also your data and even your iden)ty become locked to vendors. Controversies over Facebook show the the very high stakes here. For very high performance applica)ons, virtualisa)on is a performance hit. Network access is expensive and slow rela)ve to all other features of Cloud Compu)ng.
40 More informa)on Amazon AWS documenta)on gives a good descrip)on of how IaaS works. a good source for Grid compu)ng informa)on. hfp://en.wikipedia.org/wiki/cloud_compu)ng. There are lots of books, I have used Cloud Compu)ng Bible by Barry Sosinsky for general informa)on and Programming Amazon EC2 by van Vliet and Paganelli, O Reilly books for hands- on.
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