Grid and Cloud Computing in Indonesia : challenges and prospects - PowerPoint Presentation

Slide1

Grid and Cloud Computing in Indonesia : challenges and prospects

1

Heru

Suhartanto

Faculty of Computer Science,

Universitas

Indonesia

E-mail:

heru@cs.ui.ac.id

Presented at University of YARSI

– General Course – on 27-th April 2011

A revised version of presentation at ICACSIS2010,

http://icacsis2010.cs.ui.ac.id/

Soon the presentation will be available at

http://hsuhartanto.wordpress.com

Slide2

OutlinesHungry problems that need super computing resources. (examples and types)

Why Grid and Cloud computing (definition, structure, ….)Some past and current works

The development of the first Indonesia Grid infrastructure

parallel Molecular dynamics process in drug design based on typical Indonesian plants on Cluster environment;

and IndoEdu-grid design for Indonesian e-learning resources based on Grid computing. Prospects in the future and some proposals to overcome the challenges will be covered and this includes cloud computing.Next coming works

2

Slide3

3

3

Resource Hungry Applications

[Ref

Hai Jin and Raj

Buyya

]

Solving grand challenge applications using computer

modeling

, simulation and analysis

Life Sciences

CAD/CAM

Aerospace

Military Applications

Digital Biology

Military Applications

Military Applications

Internet &

Ecommerce

Slide4

Types of hungry application [ref: Coddington]

4

Information simulation - Compute dominate

Information repository - Storage dominate

Information access - Communication dominateInformation integration - System of systemsThese applications are impossible to be solved using ordinary computing resources

Slide5

We need to run faster, but How?There are 3 ways to improve performance:

Work HarderWork Smarter

Get Help

Computer Analogy

Using faster hardwareOptimized algorithms and techniques used to solve computational tasksMultiple computers to solve a particular task5

Slide6

In Summary – need more computing powerImprove the operating speed of processors & other components

constrained by the speed of light, thermodynamic laws,

& the high financial costs for processor fabrication

Connect multiple processors together & coordinate their computational efforts

parallel computersallow the sharing of a computational task among multiple processors6

Ref: Buyya

Slide7

What will be our choices?7

Supercomputer ?

Cluster

Computing

? Grid Computing ? Cloud Computing?

Slide8

But these may be difficult to others, so?8

We need to ‘collect’ these resources and share them among the needed people.

This lead to Grid Computing concept.

Slide9

Examples of Grid Computing9

http://www.pragma-grid.net/

The Pacific Rim Application and Grid Middleware Assembly (PRAGMA) was formed in 2002 to establish sustained collaborations and advance the use of grid technologies in applications among a community of investigators working with leading institutions around the Pacific Rim.

Four working groups focus our activities in the areas of:

* Resources and Data * Biosciences * Telescience * Global Earth Observatory (GEO)

Slide10

More on PRAGMA10

members have been doing a combination of the following:

- join their resources with PRAGMA grid

http://goc.pragma-grid.net/pragma-doc/userguide/join.html

http://goc.pragma-grid.net/pragma-doc/computegrid.html- running grid applications in PRAGMA gridhttp://goc.pragma-grid.net/pragma-doc/userguide/pragma_user_guide.htmlhttp://goc.pragma-grid.net/wiki/index.php/Applications- develop, integrate, enhance, implement and share software in PRAGMA gridhttp://goc.pragma-grid.net/wiki/index.php/Main_Page#Middleware

Our recent focus is virtualization. Some sites have been actively working together on VM technology.

http://goc.pragma-grid.net/wiki/index.php/Virtualization

Slide11

More examples on Grid computing applications/researches

11

Deteksi

kerusakan pipa, Inspeksi 100 km pipa dgn garis tengah 50 inci, data yang terkumpul 280 Terabytes (2.8 x 10^{14} bytes), kecepatan transfer 2.8 Gb.

Hanya

bisa

diproses oleh SDK Grid computing, [ ref: inspektionmolch : http://www.hpe.fzk.de/projekt/molch/, akses 27 Sep 08]Analisis data aktifitas

otak yang dikumpulkan

dari instrument MEG (Magnmetoencephatolgraphy) adalah topik riset yg sangat

penting

karena mendorong

para dokter

untuk identifikasi

simptom penyakit. Kerja sama Grid Lab – Univ Melbourne, Nimrod-G Project Monash Univ, dan MEG project – Osaka Univ [ref:

http://www.gridbus.org/neurogrid/, akses 27 sep 08]Novartis Institute for Biomedical Research perlu 6 tahun waktu proses dgn komputer super,

namun dengan PC Grid berjumlah 3700 desktop Pc, hanay perlu waktu proses 12 jam. Hemat dana sekitar 200 juta dollar untuk tiga tahun, kekuatan komputasi tercapai lebih dari 5 Tera-flops [Ian Foster, www.globus.org]

Slide12

Grid computing definition12

the combination of computer resources from multiple administrative domains to reach a common goal. The

Grid

can be thought of as a

distributed system with non-interactive workloads that involve a large number of files. Infrastruktur komputasi yang menyediakan akses berskala besar terhadap

sumber

daya

komputasi yang tersebar secara geografis namun saling terhubung menjadi satu

kesatuan fasilitas.

Sumber daya ini termasuk antara lain supercomputer, system storage, sumber sumber data,

dan instrument instrument

.

Slide13

13

Grid computing physical structure [Ian Foster]

Slide14

14

Grid Architecture [GridBus]

Slide15

Grid computing initiative from neighbor countriesThailand – ThaiGrid Started at 2002

Funding : $ 6M (3 years)10 univ., Weather Forecast Services, NECTEC

158 CPUs

Singapore – NGP (National Grid Project)

Started September 20023 univ., 5 ministries (MOE, MOH, MITA, MINDEF, MTI)MalaysiaProposal “National Technology Roadmap for Grid Computing” submitted to MOSTI (initiator: MIMOS Berhad, th. 2005)Regional forums:SEA Grid Forum (3 countries)ApGrid (14 countries)

15

Slide16

Grid is not easy to developed and maintained16

Ask others to provide them, and users use them as a

Services

then Grid computing will be function as Cloud computing;

Slide17

17Services in the Cloud

S

oftware as a Service (

SaaS

)Platform as a Service (PaaS)Infrastructure as a Service (IaaS)

Slide18

18

SaaS – bisa dalam

bentuk

Aplikasi seperti CRM – customer relationship management, Email,PaaS – Platform, antara lain Programming Language, APIs, Development Environment,IaaSVirtualization : Provisioning, Virtualization, billing,

Hardware : Memory, computation, Storage

Colocation

: the data center owner rents out floor space and provides power and cooling as well as a network connection

Slide19

19Some cloud vendors: amazon

Aws.amazon.com,

amazon

web services (AWS) offers a large number of cloud services. Focuses on Elastic Compute Cloud (EC2) and its supplementary storage services

EC2 offers the user a choice of virtual machine templates that can be instantiated in a shared and virtualized environment,Each virtual machine is called Amazon Machine Image. The customer can use pre-packaged AMIs from Amazon and 3rd parties or they can build their own.

Slide20

20 Appian- www.appian.com

Offers management

softwares

to design an deploy business processes. The tool is available as a web portal for both business process designers and users,the design is faciliated with a graphic user interface that maps processes to web forms,End users are then able to access the functionality through a dash board of forms,Executives and managers can access the same web site for bottleneck analysis, real time visibility and aggregated high level analysis

Slide21

21Google: apps.google.com , appengine.google.com

Google App Engine is a platform service. It provides basic run time environment, it eliminates many of the system administration and development challenges involved in building applications scale to million users,

Another infrastructural services, used primarily by Google applications themselves is Google Big Table. It is a fast and extremely large-scale DBMS designed to scale into

petabyte

range across “hundreds or thousands of machines”On the SaaS, google offers some free and competitively priced services including Gmail, Google Calendar, Talk, Docs, and sites.

Slide22

22Cloud computing services by Indonesians?

Gratis:

Esfindo

(

SaaS), InGrid (IaaS), …… Bayar : telkomcloud, webhosting, collocation, ….

Slide23

Defining Clouds: There are many views for what is cloud computing?Over 20 definitions:http://cloudcomputing.sys-con.com/read/612375_p.htm

Buyya’s definition:"A Cloud is a type of parallel and distributed system consisting of a collection of inter-connected and

virtualised

computers that are dynamically provisioned and presented as one or more unified computing resources based on service-level agreements established through negotiation between the service provider and consumers.”Keywords: Virtualisation (VMs), Dynamic Provisioning (negotiation and SLAs), and Web 2.0 access interface23

Segala

kebutuhan

pengelolaan data di Internet dengan sumber daya yang disiapkan oleh suatu provider. [. H Suhartanto

, 2011]

Slide24

Clouds based on Ownership and Exposure [ref: Buyya]24

Private/Enterprise Clouds

Cloud computing

model run

within a company’s

own Data Center /

infrastructure for

internal and/or

partners use.

Public/Internet Clouds

3rd party,

multi-tenant Cloud

infrastructure

& services:

* available on

subscription basis

(pay as you go)

Hybrid/Mixed Clouds

Mixed usage of

private and public

Clouds:

Leasing public

cloud services

when private cloud

capacity is

insufficient

Slide25

(Promised) Benefits of (Public) Clouds [ref: Buyya]No upfront infrastructure investment

No procuring hardware, setup, hosting, power, etc..On demand access

Lease what you need and when you need..

Efficient Resource Allocation

Globally shared infrastructure, can always be kept busy by serving users from different time zones/regions...Nice PricingBased on Usage, QoS, Supply and Demand, Loyalty, …Application AccelerationParallelism for large-scale data analysis, what-if scenarios studies…Highly Availability, Scalable, and Energy EfficientSupports Creation of 3rd Party Services & Seamless offeringBuilds on infrastructure and follows similar Business model as Cloud

25

Slide26

Prospects in Indonesia26

some previous research works are available

The development of internet infrastructures among universities;

Some related courses are offered in universitities

Slide27

Indonesia - ICT readiness

National network infrastructure provided by

telecommunication industries

‏

Combining terrestrial and satellite connectionsTerrestrial: optical fiber

, copper, digital micro wave; (wireless and on-wire)

‏

Pengguna

Internet :

40 juta

Pelanggan

telp seluler: 105 juta

Nizam

,

presentasi

Aptikom 2011

Slide28

Konfigurasi Zona Perguruan Tinggi

Topologi “INHERENT” tahun 2010

Nizam

, 2011 at APTIKOM meeting

Slide29

Status -2010

Jumlah koneksi

82 PTN (32 sebagai Local Nodes)

224

PTS12 KopertisSEAMEO-SeamolecKapasitas bandwidth

Advance:

155

Mbps

Medium: 8 Mbps

Basic: 2 MbpsSelf-funding: (leased line 512 – 1 M; wireless 11-55 M)Network configuration:

scale-free network

Cita-cita ke depan: Higher Education super corridor dengan dark fiber sehingga koneksi antar perguruan tinggi minimal 1 GBps dan backbone nasional 10 GBps (Thailand antar PT sudah 1-10 GBPs)

Nizam

, 2011 at APTIKOM meeting

Slide30

The InGRID Architecture (now in problem )

30

inGRID

PORTAL

Globus

Head Node

INHERENT

User

User

Linux/Sparc

Cluster

Globus

Head Node

Linux/x86

Cluster

Windows/x86

Cluster

Solaris/x86

Cluster

Globus

Head Node

UI

I*

U*

Custom

PORTAL

Slide31

H/W specsinGRID PortalSUN Fire X2100, AMD Opteron Processor (2.4 GHz, dual core), 2 GB Memory, 80 GB Disk, 2 10/100/1000 Mbps NICs, DVD-ROM Drive

Globus Head NodeSUN Fire X2100, AMD Opteron Processor (2.2 GHz, dual core), 1 GB Memory, 80 GB Disk, 2 10/100/1000 Mbps NICs, DVD-ROM Drive

Linux Cluster (

16

nodes)SUN Fire X2100, AMD Opteron Processor (2.2 GHz, dual core), 1 GB Memory, 80 GB Disk, 2 10/100/1000 Mbps NICsStorage ServerDual Xeon Processor (3.0GHz), 2 GB Memory, 1 TB Disk31

Slide32

S/W specsUser Interface:UCLA Grid Portal

MiddlewareGlobus ToolkitJob Scheduler:

Sun Grid Engine (SGE)

Programming:

C, JavaParalel: MPICHApplications:Chemistry:GromachBiology:BlastComputer Graphic:PovrayUtilities:

Matrics multiplication, Sort, Octave (

Matlab-like

)

32

Slide33

inGRID: Portalhttp://grid.ui.ac.id/portal

33

Slide34

Molecular dynamics simulation and docking34

Ari Wibisono, Heru Suhartanto, Arry Yanuar, Performance Analysis of Curcumin Molecular Dynamics Simulation using GROMACS on Cluster Computing Environment, this conference.

Muhammad Hilman, Heru Suhartanto, Arry Yanuar, Performance Analysis of Embarrassingly Parallel Application on Cluster Computer Environment : A Case Study of Virtual Screening with Autodock Vina 1.1 on Hastinapura Cluster, this conference.

Slide35

Molecular dynamic simulationused to study the solvation of proteins, the interaction of DNA-protein complexes and lipid systems, and study the ligand binding and folding of proteins.

to produce a trajectory of molecules in a finite time period, where each the molecules in these simulations have positional parameters and momentum.be used to assist drug discovery. The usage of computers offer a method of in-silico as a complement to the method in-vitro and in-vivo that are commonly used in the process of drug discovery. Terminology in-silico, analog with in-vitro and in-vivo, refers to the use of computer in drug discovery studies

GROMACS is used in the simulation.

35

Slide36

Molecular Docking and Virtual ScreeningMolecular docking is a computational procedure that attempts to predict non covalent binding of macromolecules. The goal is to predict the bound conformations and the binding affinity.

The prediction process is based on information that embedded inside the chemical bond of substance.Autodock Vina is used in the simulation.

36

Slide37

Gromacs speed up on Cluster

No

Time Step

Amount of Processor

2

3

4

5

1

200ps

1.85

2.64

3.07

3.74

2

400ps

1.84

2.46

3.13

3.73

3

600ps

1.83

2.42

3.04

3.69

4

800ps

2.03

2.47

3.09

3.76

5

1000ps

1.87

2.51

3.14

3.82

37

Slide38

The Autodock running time

38

Slide39

Design and Simulation of Indonesian Education Grid Topology using Gridsim Toolkit

discusses the design and simulation of an e-learning computer network topology, based on Grid computing technology, for Indonesian schools called the Indonesian Education Grid (abbreviated as IndoEdu-Grid).The establishment of such network without Grid computing capabilities will lead to redundancies of the idle resources.

We proposed scenarios that have different network topologies based on their routers and links configuration. Each scenario will be run in the simulator using two packet scheduling algorithms, one will be FIFO (First In First Out) Scheduler and the other SCFQ (Self-Clocked Fair Queuing) Scheduler.

The processing time of the job’s packets will be evaluated to determine the most effective network topology for IndoEdu-Grid

39

Slide40

The entitiesThe entities of our design are resources, users, and jobs or GridletsResource entities are responsible to perform computation on job entities in form of Gridlets sent by one or more users and send it back to the user. Our work uses one resource for each province; each resource consists of one Machine and each Machine consists of 4 PEs (processing elements).

Users are entities responsible to submit jobs in form of Gridlet objects to the resources. The users are programmed to send jobs to a particular resource at the same time, thus we are able to gain more knowledge on the performance of Grid system in its peak load, when all the users are accessing the resource at the same time.

Jobs in GridSim are represented as the objects of the class Gridlet provided by GridSim. In our work, each user will create three Gridlets having different lengths–5000 MI (millions instructions), 3000 MI, and 1000 MI. This was aimed to simulate the real situation where a user does not just send one job, but it can also send more than one job with different sizes and needs of computation powers.

40

Slide41

The first scenario is a representation of our thought that divides the whole territory of Indonesia into three main sections–the western, central, and eastern part of Indonesia. Each of these three sections will be subdivided into parts or units that are smaller–the islands and/or archipelagos.

41

Slide42

42

The second scenario is a representation of our thought that divides the whole territory of Indonesia directly into islands and/or archipelagos units. These islands and/or archipelagos will be divided again into province units.

Slide43

The simulation environmentHardware

Intel® Core™ 2 Duo T5800 processor with 2.0 GHz clock speed, 800 MHz FSB (Front Side Bus), and 2 MB L2 cache.

2048 MB RAM (

Random Access Memory

) with shared dynamically with Mobile Intel® Graphics Media Accelerator 4500MHD.320 GB Fujitsu MHZ2320BH G2 SATA harddisk with 5400 rpm rotation speed.Software32-bit Microsoft Windows Vista™ Business operating system.JDK (Java Development Kit) version 1.6.0_05 with Java™ Runtime Environment 1.6.0_05-b13.

GridSim version 5.0 beta.

The simulation was run 10 times in each scenario to increase the validity of simulation results, and then the results were averaged.

SCFQ scheduling algorithm, even-numbered users are set to have a weight 1, indicating that they have a higher priority, while odd-numbered users are set to have a weight 0, indicating that they have normal priority. This weighting is useful to determine the type of service (ToS) which is owned by the packets sent by the users.

FIFO scheduling algorithm, all users by default are set to have a weight 0, so all sent packets will have the same ToS.

43

Slide44

The simulation results

44

Average Simulation Results Data for the Entire Provinces per Gridlet Using FIFO and SCFQ Scheduling Algorithm

Job = Gridlet, which simulates the job packets that contain information about the length of jobs in units of MI (millions instruction), the length of input and output files in units of bytes, starting and finishing execution time, and the owner of the jobs. three Gridlets #0, #1, #2 has different lengths–5000 MI (millions instructions), 3000 MI, and 1000 MI, respectively.

Slide45

More ProspectsMore people are becoming interested in shared computing facilities,

Many free of charge grid development tools are available,Develop a strong unit that capable building the Grid infrastructure, but it needs commitment and dedication from at least university level and government,

or

INHERENT can be improved, it will open more collaboration among universities,

Nusantara Super Highway Rampung di 2015, "Nusantara Super Highway berbasis optical network merupakan

kelanjutan

dari

cita-cita Telkom untuk menyatukan Indonesia

melalui

visi Nusantara 21 yang sudah dimulai sejak

2001 dengan

teknologi

berbasis

satelit,"http://www.detikinet.com/read/2011/04/19/143116/1620709/328/nusantara-super-highway-rampung-di-2015?i99110110545

Slide46

ChallengesUnreliable electricity supplies

No coordination at national level to have ICT research and development programs involving across government and private organizations Relies on grant fund which leads to other negatives effects such as,

Most Indonesian funding resources do not allow hardware (computers) investment (only spare parts are allowed

 )

Permanent human resources that manage the Grid,Maintenance of the grid to adapt with current technology development.Many organization are “very protective” to their computing resources, only a few are willing to share them.46

Slide47

47Only few (may one or two) faculties teach cluster, cloud and grid Computing. So only few master and understand them.

Perhaps Cloud computing is the alternative solution in one way, however ……….the cloud itself has some challenges

Challenges - cont

Slide48

Cloud Computing Challenges: Dealing with too many issues [ref Buyya]

48

Uhm, I am not quite

clear…Yet another

complex IT paradigm?

Virtualization

QoS

Service Level

Agreements

Resource Metering

Billing

Pricing

Provisioning

on Demand

Utility & Risk Management

Scalability

Reliability

Energy Efficiency

Security

Privacy

Trust

Legal &

Regulatory

Software Eng. Complexity

Programming Env.

& Application Dev.

Slide49

Well, no need to wait, “ibadah” – the show must go on ….future works with positive impacts are waiting

49

More bioinformatics, medical informatics, image analysis, finance with GPU

computing environment,

Indonesian Egov Grid servicesIndonesian Archeology and Culture-Grid servicesIndonesian Health-Grid services

Slide50

50

ABCGrid, http://abcgrid.cbi.pku.edu.cn (akses 3 Oktober

2008), also by Ying Sun,

Shuqi

Zhao, Huashan Yu, Ge Gao and Jingchu Luo. (2007) ABCGrid: Application for Bioinformatics Computing Grid. Bioinformatics Rajkumar Buyya, www.gridbus.org/megha;

www.buyya.com

; www.manjrasoft.com

GCIC, http://www.gridcomputing.com/, akses 25 Sep 2008.

Globus, http://www.globus.org, akses 25 Sep 2008Gridbus Application, http://www.gridbus.org/applications.html, akses 25 Sep 2008Gridbus

Middleware, http://www.gridbus.org/middleware/, akses 25 Sep 2008

GridGain, http://www.gridgain.com, akses 15 Sep 2008Ivo Bahar, Heru Suhartanto, Design and Simulation of Indonesian Education Grid Topology using

Gridsim Toolkit, to appear at Asian Journal of Information Technology, 2010

H. Suhartanto,

Kajian Perangkatbantu

Komputasi tersebar

berbasis Message Passing, Makara Teknologi, Vol 10, No 2, 2006, page 72 – 81.H. Suhartanto, Peluang dan

tantangan Aplikasi Grid Computing di Indonesia, pidato pengukungan guru besar, 2008.InGrid, https://grid.ui.ac.id/gridsphere/gridsphere, akses 28 Sep 2008

Jardiknas, http://jardiknas.diknas.go.id/, akses 28 Sep 2008John Rhoton, cloud computing explained, 2nd ed, recursice press, 2010References

Slide51

51

Molecular Docking, http://grid.apac.edu.au/OurUsers/MolecularDocking, akses 27 Sep 2008 Molecular Docking Definition,

http://en.wikipedia.org/wiki/Docking_(molecular)

, akses 3 Oktober 2008

MultimediaGrid, http://www.gridbus.org/papers/MultimediaGrid-MJCS2007.pdf, akses 27 Sep 2008NeuroGrid, http://www.gridbus.org/neurogrid/, akses 27 Sep 2008Paul Coddington, Distribute and High Performance Computing course, University of Adelaide, 2002 UK national HPC service, http://www.csar.cfs.ac.uk/user_information/grid/grid-middleware.shtmlPeluang dan tantangan Aplikasi Grid Computing di Indonesia Page 12 of 12Pipeline – Inspektionmolch: http://www.hpe.fzk.de/projekt/molch/, akses 27 Sep 2008

Top500, http://www.top500.org, di akses 14 September 2008.

Wahid Chrabakh, Computational Grid Computing: Application Viewpoint, Computer Science, Major Exams, UCSB, ppt file,

Zlatev, Z. and Berkowicz, R. (1988), Numerical treatment of large-scale air pollutant models, Comput. Math. Applic., 16, 93 -- 109

Slide52

Thank you !52