Grids and Peer-to-Peer Networks for e-Science

Grids and Peer-to-Peer Networks for e-Science
SPECTS San Diego July PTLIU Laboratory for Community Grids Geoffrey Fox and Community Grid Staff and Students Computer Science, Informatics, Physics Indiana University, Bloomington IN 9/21/2018 uri="

uri="http://grids.ucs.indiana.edu/ptliupages" email="gcf@indiana.edu"
Summary Grid: Global Computing Infrastructure with a myriad of heterogeneous devices connected by diverse networks Measure and study their performance Related to but different from classical parallel computing performance studies Web services: New object models providing universality in a service model of electronic capability Simulate, data access/storage etc. Nodes of application level systems one can model Systems involve multiple devices connected together – synchronization of these is performance driver Communities or virtual organizations are e-Science collective systems 9/21/2018 uri="

Trends of Importance Resources of increasing performance or functionality Computers (ASCI, Earth Simulator to TeraGrid), storage, sensors, networks, PDA’s More and more data distributed around the world Applications of increasing sophistication Size, multi-scales, multi-disciplines Compose simulations from different disciplines New algorithms and mathematical techniques Traditional Computer science Compilers, Parallelism, Objects, Components Grid and Internet Concepts and Technologies Enabling new applications XML, Web Services, Portals, Collaboration 9/21/2018 uri="

Projected Top 500 Until Year 2009
First, Tenth, 100th, 500th, SUM of all 500 Projected in Time Earth Simulator from Japan 9/21/2018 uri="

PACI 13.6 TF Linux TeraGrid 574p IA-32 Chiba City 256p HP X-Class 32 32 Caltech 32 Nodes 0.5 TF 0.4 TB Memory 86 TB disk Argonne 64 Nodes 1 TF 0.25 TB Memory 25 TB disk 32 32 128p Origin 24 32 128p HP V2500 32 HR Display & VR Facilities 24 8 8 5 92p IA-32 5 HPSS HPSS 24 4 Extreme Black Diamond OC-12 Chicago & LA DTF Core Switch/Routers Cisco 65xx Catalyst Switch (256 Gb/s Crossbar) ESnet HSCC MREN/Abilene Starlight OC-48 Calren OC-48 OC-12 NTON OC-12 ATM Juniper M160 GbE SDSC 256 Nodes 4.1 TF, 2 TB Memory 225 TB disk NCSA 500 Nodes 8 TF, 4 TB Memory 240 TB disk Juniper M40 Juniper M40 vBNS Abilene Calren ESnet OC-12 OC-12 OC-3 vBNS Abilene MREN OC-12 2 2 OC-12 OC-3 Myrinet Clos Spine 8 4 HPSS 8 UniTree 2 Sun Starcat 4 Myrinet Clos Spine = 32x 1GbE 1024p IA-32 320p IA-64 1176p IBM SP Blue Horizon 16 = 64x Myrinet 14 4 = 32x Myrinet 1500p Origin A Grid of a 1000 distributed systems e-Science links to all sensors and all desktops, all university systems, and PDA’s of all researchers Sun E10K = 32x FibreChannel = 8x FibreChannel 10 GbE 32 quad-processor McKinley Servers 4GF, 8GB memory/server) 32 quad-processor McKinley Servers 4GF, 12GB memory/server) Fibre Channel Switch 16 quad-processor McKinley Servers 4GF, 8GB memory/server) Cisco 6509 Catalyst Switch/Router IA-32 nodes 9/21/2018 uri="

Small Devices Increasing in Importance
Integration of PDA’s and supercomputers (etc.) implies very heterogeneous systems spanning traditional performance fields There is growing interest in wireless portable displays in the confluence of cell phone and personal digital assistant markets By 2005, 60 million internet ready cell phones sold each year 65% of all Broadband Internet accesses via non desktop appliances CM5 9/21/2018 uri="

The HPCC Thrust has run its course?
The 1990 HPCC 10 year initiative was largely aimed at parallel computing enabling large scale simulations for a broad range of computational science and engineering problems It was in many ways a success and we have methods and machines that can (begin to) tackle most 3D simulations ASCI simulations particularly impressive DoE still putting substantial resources into basic software and algorithms from adaptive meshes to PDE solver libraries Machines are still increasing in performance exponentially and should achieve petaflops in next 7-10 years Not obvious that there will be major changes in parallel computer architecture and methodology 9/21/2018 uri="

e-Science implies integration of data and researchers around the model and builds on Parallel Computers for Simulation Sensors (satellites or ground based) for data Databases for knowledge Networks to link people, computers and data e-Science Data Assimilation Information Simulation Information Technology Model Datamining Ideas Reasoning Computational Science 9/21/2018 uri="

Classic Grid Architecture
Resources Database Database Content Access Composition Middle Tier Brokers Service Providers Netsolve Security Collaboration Computing Middle Tier becomes Web Services Clients Users and Devices 9/21/2018 uri="

Astronomy is Facing a Major Data Avalanche
One e-Science Example Astronomy is Facing a Major Data Avalanche: Multi-Terabyte Sky Surveys and Archives (Soon: Multi-Petabyte), Billions of Detected Sources, Hundreds of Measured Attributes per Source … Astronomy is Facing a Major Data Avalanche Total area of 3m+ telescopes in the world in m2, total number of CCD pixels in Megapix, as a function of time. Growth over 25 years is a factor of 30 in glass, 3000 in pixels. 9/21/2018 uri="

The Changing Style of Observational Astronomy
The Old Way: Now: Future: Pointed, heterogeneous observations (~ MB - GB) Large, homogeneous sky surveys (multi-TB, ~ sources) Multiple, federated sky surveys and archives (~ PB) Small samples of objects (~ ) Archives of pointed observations (~ TB) Virtual Observatory The Changing Style of Observational Astronomy Astronomy at the desktop not at the telescope 9/21/2018 uri="

What is the NVO? - Content
Specialized Data: Spectroscopy, Time Series, Polarization Source Catalogs, Image Data Information Archives: Derived & legacy data: NED,Simbad,ADS, etc Analysis/Discovery Tools: Visualization, Statistics Query Tools Standards 9/21/2018 uri="

What is the NVO? - Components
Data Providers Surveys, observatories, archives, SW repositories Service Providers Query engines, Compute engines Information Providers e.g. ADS, NED, ... 9/21/2018 uri="

Grid/P2P Use of Internet I
Cohen’s Rival Estimate Mainly Digital Video ROBERT B. COHEN, PH.D. COHEN COMMUNICATIONS GROUP Global Grid Forum Toronto Feb 9/21/2018 uri="

Grid/P2P Use of Internet II
S2S Server to Server Digital Video “on demand” } P2P Grid 9/21/2018 uri="

Use of Object Technologies I
The claimed commercial success in using Object and component technology has not yet been a clear success in HPCC and indeed in modeling & simulation Object technologies do not naturally support either high performance or parallelism C++ can be high performance but Java (as a language) is not uniformly so (it is improving) We suggest that Web Services could change this Fortran (including Fortran90) will continue to decline in importance and interest – the community should prefer not to use it It’s use will not attract the best students Not essential to write modules in object oriented language It is essential to package modules in object framework 9/21/2018 uri="

Use of Object Technologies II
There is emerging HPCC component architecture allowing production of more modern libraries (integration Infrastructure) DoE has very large CCA – Common Component Architecture – effort Package software (“system and applications”) as distributed objects – not as traditional libraries CORBA HLA Java and Web Services are not naturally high performance as component models High performance often not essential for coarse grain objects Web Services support multiple implementations allowing performance functionality trade-off 9/21/2018 uri="

Object Size & Distributed/Parallel Simulations
All interesting systems consist of linked entities Particles, grid points, people or groups thereof Linkage translates into message passing Cars on a freeway Phone calls Forces between particles Amount of communication tends to be proportional to surface area of entity whereas simulation time proportional to volume So communication/computation is surface/volume and decreases in importance as entity size increases In parallel computing, communication synchronized; in distributed computing “self contained objects” (whole programs) which can be scheduled asynchronously 9/21/2018 uri="

Some Problem Classes Classic HPCC: synchronized objects with regular time structure (communication overhead decreases as problem size increases) Includes PDE and interacting particle based applications Give scaling parallelism on large MPP’s Grid: Internet Technology and Commercial Application Integration: Large objects with modest communications and without difficult time synchronization Compose as independent (pipelined) services Includes some approaches to multi-disciplinary simulation linkage Hardest: smallish objects with irregular time synchronization Event driven simulations (HLA-RTI) used here Sets of Grid Points Sets of Services (programs) Sets of macroscopic objects 9/21/2018 uri="

What is a Web Service I A web service is a computer program running on either the local or remote machine with a set of well defined interfaces (ports) specified in XML (WSDL) In principle, computer program can be in any language (Fortran .. Java .. Perl .. Python) and the interfaces can be implemented in any way what so ever Interfaces can be method calls, Java RMI Messages, CGI Web invocations, totally compiled away (inlining) but The simplest implementations involve XML messages (SOAP) and programs written in net friendly languages like Java and Python Web Services separate the meaning of a port (message) interface from its implementation Enhances/Enables Re-usable component model of ANY electronic resource 9/21/2018 uri="

What is a Web Service II Web Services have important implication that ALL interfaces are XML messages based. In contrast Most Windows programs have interfaces defined as interrupts due to user inputs Most software have interfaces defined as methods which might be implemented as a message but this is often NOT explicit Security Catalog Payment Credit Card Warehouse shipping WSDL interfaces 9/21/2018 uri="

Raw Resources Clients Raw Data Raw Data Render to XML Display Format
(Virtual) XML Data Interface Web Service (WS) WS WS WS etc. XML WS to WS Interfaces WS (Virtual) XML Knowledge (User) Interface Render to XML Display Format (Virtual) XML Rendering Interface Clients 9/21/2018 uri="

Details of WSDL Protocol Stack
UDDI finds where programs are remote( (distributed) programs are just Web Services WSFL links programs together (under revision?) WSDL defines interface (methods, parameters, data formats) SOAP defines structure of message including serialization of information HTTP is negotiation/transport protocol TCP/IP is layers 3-4 of OSI Physical Network is layer 1 of OSI UDDI or WSIL WSFL WSDL SOAP or RMI HTTP or SMTP or IIOP or RMTP TCP/IP Physical Network 9/21/2018 uri="

XML Skin XML Skin Message Or Event Based Inter Connection Soft ware Resource Soft ware Resource Data base e-Science/Grid/P2P Networks are XML Specified Resources connected by XML specified messages Implementation of resource and connection may or may not be XML 9/21/2018 uri="

What is a Grid Web Service?
There are generic Grid system services: security, collaboration, persistent storage, universal access OGSA (Open Grid Service Architecture) is implementing these as extended Web Services An Application Web Service is a capability used either by another service or by a user It has input and output ports – data is from sensors or other services Consider Satellite-based Sensor Operations as a Web Service Satellite management (with a web front end) Each tracking station is a service Image Processing is a pipeline of filters – which can be grouped into different services Data storage is an important system service Big services built hierarchically from “basic” services Portals are the user (web browser) interfaces to Web services 9/21/2018 uri="

Distributed Sensor Web Service
Output Web Service ports Universal sensor access for people/computers Input Web Service ports Different format Sensor Data 9/21/2018 uri="

Application Web Services
Filter1 WS Filter2 WS Filter3 WS Build as multiple Filter Web Services Prog1 WS Prog2 WS Build as multiple interdisciplinary Programs Data Analysis WS Simulation WS Visualization WS Note Service model integrates sensors, sensor analysis, simulations and people An Application Web Service is a capability used either by another service or by a user It has input and output ports – data is from users, sensors or other services Big services built hierarchically from “basic” services Sensor Data as a Web service (WS) Data Analysis WS Sensor Management WS Visualization WS Simulation WS SLE (space Link Extension) as a WS 9/21/2018 uri="

The Application Service Model
As bandwidth of communication (between) services increases one can support smaller services A service “is a component” and is a replacement for a library in case where performance allows Services (components) are a sustainable model of software development – each service has documented capability with standards compliant interfaces XML defines interfaces at several levels WSDL at Service interface level and XSIL or equivalent for scientific data format A service can be written as Perl, Python, Java Servlet, Enterprise Javabean, CORBA (C++ or Fortran) Object … Communication protocol can be RMI (Java), IIOP (CORBA) or SOAP (HTTP, XML) …… 9/21/2018 uri="

Some General Grid or Web Services
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Some Science Web Services
These build on general (community) web services 9/21/2018 uri="

Education as a Web Service
Can link to Science as a Web Service and substitute educational modules “Learning Object” XML standards already exist from IMS/ADL – need to update architecture Web Services for virtual university include: Registration Performance (grading) Authoring of Curriculum Online laboratories for real and virtual instruments Homework submission Quizzes of various types (multiple choice, random parameters) Assessment data access and analysis Synchronous Delivery of Curricula Scheduling of courses and mentoring sessions Asynchronous access, data-mining and knowledge discovery Learning Plan agents to guide students and teachers 9/21/2018 uri="

Different Web Service Organizations
Everything is a resource implemented as a Web Service, whether it be: back end supercomputers and a petabyte data Microsoft PowerPoint and this file All Resources communicate via messages Grids and Peer to Peer (P2P) networks can be integrated by building both in terms of Web Services with different (or in fact sometimes the same) implementations of core services such as registration, discovery, life-cycle, collaboration and event or message transport ….. Gives a Peer-to-Peer Grid 9/21/2018 uri="

Peer to Peer Grid JXTA Integrate P2P and Grid/WS JXTA
Database Database JXTA Web Service Interfaces Event/ Message Brokers Integrate P2P and Grid/WS Peer to Peer Grid Web Service Interfaces JXTA A democratic organization Peer to Peer Grid 9/21/2018 uri="

Role of Event/Message Brokers
We will use events and messages interchangeably An event is a time stamped message Our systems are built from clients, servers and “event brokers” These are logical functions – a given computer can have one or more of these functions In P2P networks, computers typically multifunction; in Grids one tends to have separate function computers Event Brokers “just” provide message/event services; servers provide traditional distributed object services as Web services There are functionalities that only depend on event itself and perhaps the data format; they do not depend on details of application and can be shared among several applications NaradaBrokering is designed to provide these functionalities MPI provided such functionalities for all parallel computing 9/21/2018 uri="

NaradaBrokering implements an Event Web Service
(Virtual) Queue Web Service 2 Destination Source Matching Filter Routing workflow WSDL Ports Broker Filter is mapping to PDA or slow communication channel (universal access) – see our PDA adaptor Workflow implements message process Routing illustrated by JXTA Destination-Source matching illustrated by JMS using Publish-Subscribe mechanism 9/21/2018 uri="

Features of Event Service I
MPI nowadays aims at a microsecond latency The Event Web Service aims at a millisecond latency Typical distributed system travel times are many milliseconds (to seconds for Geosynchronous satellites) Different performance/functionality trade-off Messages are not sent directly from P to S but rather from P to Broker B and from Broker B to subscriber S Synchronous systems: B acts as a real-time router/filterer Messages can be archived and software multicast Asynchronous systems: B acts as an XML database and workflow engine Subscription is in each case, roughly equivalent to a database query 9/21/2018 uri="

Features of Event Web Service II
In principle Message brokering can be virtual and compiled away in the same way that WSDL ports can be bound in real time to optimal transport mechanism All Web Services are specified in XML but can be implemented quite differently Audio Video Conferencing sessions could be negotiated using SOAP (raw XML) messages and agree to use certain video codecs transmitted by UDP/RTP There is a collection of XML Schema – call it GXOS – specifying event service and requirements of message streams and their endpoints One can sometimes compile message streams specified in GXOS to MPI or to local method call Event Service must support dynamic heterogeneous protocols 9/21/2018 uri="

Features of Event Web Service III
The event web service is naturally implemented as a dynamic distributed network Required for fault tolerance and performance A new classroom joins my online lecture A broker is created to handle students – multicast locally my messages to classroom; handle with high performance local messages between students Company X sets up a firewall The event service sets up brokers either side of firewall to optimize transport through the firewall Note all message based applications use same message service Web services imply ALL applications are (possibly virtual) message based 9/21/2018 uri="

Broker Network (P2P) Community For message/events service Broker Broker (P2P) Community Resource Broker Broker Broker Data base (P2P) Community Software multicast Broker (P2P) Community 9/21/2018 uri="

System Structure I Systems are a dynamic mix of structured and unstructured entities P2P systems like JXTA support unstructured systems realized by opportunistic messaging “broadcast locally” over a certain “network distance” Java Message Service JMS supports structured systems where clients (message endpoints) link to one of a known set of “central servers” Event system must support Advertise capability – Publish Advertise need – Subscribe both for type and form of messages Transport designated messages/events 9/21/2018 uri="

Single Server P2P Illusion
Traditional Collaboration Architecture e.g. commercial WebEx (JMS Style) Data base Collaboration Server 9/21/2018 uri="

System Structure II One could think that the world is a well defined structure of unstructured systems Unstructured dynamic systems are P2P (JXTA) Peer Groups Peer Groups could be cluster of students in a class for distance learning or cluster of Grid (OGSA) Web services generated to support running a job But maybe it is a set of structured communities with unstructured connection NaradaBrokering needs to support both models and those in between Currently has JMS mode, JXTA mode and Native (most powerful) mode P2P usually thought of as a set of “unruly dangerous clients” but can equally well be used securely as a middleware interaction mode between web services 9/21/2018 uri="

Database Database MP Group Grid Middleware Grid Middleware Grid Middleware MP G r o u p MP G r o u p MP Group MP=Middleware Peer 9/21/2018 uri="

Community Grids Laboratory Activities I
Core NaradaBrokering Event Service Operation in JMS or JXTA mode to demonstrate integration of central and peer-to-peer mode Focus is Performance and Capabilities (see later) Garnet synchronous collaboration environment used for distance education and seminars Built first on commercial JMS but ported to Narada – shows that one can afford to use message service in synchronous application sharing Interface of Garnet to PDA with message size filtering and optimized HHMS message service This filtering also needed for slow clients – mix of dial-ups and Internet2 clients in a collaboration Event system supports (XML) client profiles 9/21/2018 uri="

NaradaBrokering Performance Results
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NaradaBrokering and JMS
Low Rate; Small Messages 9/21/2018 uri="

NaradaBrokering and JXTA
Comparing Pure JXTA, Narada-JXTA and Direct P2P There is a bug in JXTA and this was only just fixed Narada-JXTA provides JXTA guaranteed long distance delivery Small Payload Larger Payload 9/21/2018 uri="

JXTA is getting slower Client  JXTA  JXTA  Client Client  JXTA  Narada  JXTA  Client Client  JXTA  JXTA  Client multicast Narada Client Pure Narada 2 hops Client Narada 9/21/2018 uri="

Collaborative SVG Viewer PC to PDA
Batik Viewer on PC PC Collaboration system PowerPoint can be converted to SVG via Illustrator or Web export Collaborative SVG Viewer PC to PDA 9/21/2018 uri="

PDA Collaboration Event Filter
GMSME : iPaq H3650, WinCE 3.0, Personal-Java Wireless 11 Mbit/s IEEE b GMS = JMS or Narada Doing This now 9/21/2018 uri="

Community Grids Laboratory Activities II
Use of JMS (Narada) to support asynchronous collaboration including early GXOS Schema XML based News Groups and Web Site management Integrated with Apache Slide and Jetspeed portals Audio-Video Conferencing as a Web service H323 and SIP as Web services using XML Session Schema NaradaBrokering support of UDP Computing Portals as Web services; NaradaBrokering could support events (status, performance, job flow) linking operational job to control servers and researchers 9/21/2018 uri="

NaradaBrokering Futures
Higher Performance – reduce minimum transit time to around one millisecond Substantial operational testing Security – allow Grid (Kerberos/PKI) security mechanisms Support of more protocols with dynamic switching as in JXTA – SOAP, RMI, RTP/UDP Integration of simple XML database model using JXTA Search to manage distributed archives More formal specification of “native mode” and dynamic instantiation of brokers General Collaborative Web services 9/21/2018 uri="

Collaborative Web Service Access
Intercept and multicast messages produced by Web Service Web Service Interceptor Providing General Services Collaboration as a Web Service Collaborative Web Service Set Collaboration and Message Mode Master Client Web Service has a port on which collaborative modes set Web Service can be “front-end” (in middle tier) to complex back-end object Event (Message) Service Client Client 9/21/2018 uri="

Collaborative Replicated Web Services
Intercept and multicast messages SENT to Web Service Web Service Interceptor Providing General Services Set Collaboration Mode Web Service Master Object Display Object Viewer Object Web Service Object Viewer Object Display Event (Message) Service Web Service Object Viewer Object Display 9/21/2018 uri="

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