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

Outlines Hungry 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

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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 ApplicationsDigital Biology Military ApplicationsMilitary Applications

Internet & Ecommerce

Types of hungry application [ref: Coddington]

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• Information simulation - Compute dominate• Information repository - Storage dominate• Information access - Communication dominate• Information integration - System of systems

• These applications are impossible to be solved using ordinary computing resources

We need to run faster, but How?

There are 3 ways to improve performance:Work HarderWork SmarterGet Help

Computer AnalogyUsing faster hardwareOptimized algorithms and techniques used to solve computational tasks

Multiple computers to solve a particular task

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In Summary – need more computing power

Improve 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 computers allow the sharing of a computational task among

multiple processors

6Ref: Buyya

What will be our choices?

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Supercomputer ?Cluster Computing ?

Grid Computing ? Cloud Computing?

But these may be difficult to others, so?

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

This lead to Grid Computing concept.

Examples of Grid Computing

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• 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)

More on PRAGMA

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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 grid• http://goc.pragma-grid.net/pragma-doc/userguide/

pragma_user_guide.html• http://goc.pragma-grid.net/wiki/index.php/Applications

• - develop, integrate, enhance, implement and share software in PRAGMA grid• http://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

More examples on Grid computing applications/researches

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• 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]

Grid computing definition

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• 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.

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Grid computing physical structure [Ian Foster]

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Grid Architecture [GridBus]

Grid computing initiative from neighbor countries

Thailand – ThaiGrid Started at 2002 Funding : $ 6M (3 years) 10 univ., Weather Forecast Services, NECTEC 158 CPUs

Singapore – NGP (National Grid Project) Started September 2002 3 univ., 5 ministries (MOE, MOH, MITA, MINDEF, MTI)

Malaysia Proposal “National Technology Roadmap for Grid

Computing” submitted to MOSTI (initiator: MIMOS Berhad, th. 2005)

Regional forums: SEA Grid Forum (3 countries) ApGrid (14 countries)

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Grid is not easy to developed and maintained

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Ask others to provide them, and users use them as a Services then Grid

computing will be function as Cloud computing;

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Services in the Cloud

• Software as a Service (SaaS)• Platform as a Service (PaaS)• Infrastructure as a Service (IaaS)

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• SaaS – bisa dalam bentuk Aplikasi seperti CRM – customer relationship management, Email,

• PaaS – Platform, antara lain Programming Language, APIs, Development Environment,

• IaaS• Virtualization : 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

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Some 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.

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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

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Google: 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.

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Cloud computing services by Indonesians?

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

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 interface

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Segala kebutuhan pengelolaan data di Internet dengan sumber daya yang disiapkan oleh suatu provider. [. H Suhartanto, 2011]

Clouds based on Ownership and Exposure [ref: Buyya]

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Private/Enterprise Clouds

Cloud computingmodel 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 publiccloud services

when private cloud capacity is insufficient

(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 Pricing

Based on Usage, QoS, Supply and Demand, Loyalty, … Application Acceleration

Parallelism for large-scale data analysis, what-if scenarios studies…

Highly Availability, Scalable, and Energy Efficient Supports Creation of 3rd Party Services & Seamless offering

Builds on infrastructure and follows similar Business model as Cloud

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Prospects in Indonesia

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• some previous research works are available

•The development of internet infrastructures among universities;

•Some related courses are offered in universitities

Indonesia - ICT readiness

National network infrastructure provided by telecommunication industries Combining terrestrial and satellite connections Terrestrial: optical fiber, copper, digital micro wave;

(wireless and on-wire) Pengguna Internet : 40 juta Pelanggan telp seluler: 105 juta

Nizam, presentasi Aptikom 2011

Konfigurasi Zona Perguruan Tinggi

Medan

Padang Panjang

Padang

Pekanbaru

Jambi

Padang STSI

Palembang

Bandar lampung

Bengkulu

Serang Jkt UI

Bogor

Jkt UT

Bandung

Semarang

Denpasar

PotianakSamarinda

Manado

Manado

Gorontalo

Palu

Makasar

Manukwari

Ambon

Kupang

SoloMataram

Purwokerto

Malang

Jogya Jember

Bangkalan

Ternate

`

Kendari

Singaraja

Tual

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34

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2811

12

10

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58

1

30

9

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18

13

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16

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Jayapura

3

4

Jkt DIKTI

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2

LhokseumawePoltek

Banjarmasin

Banda AcehUnsyiah

LhokseumaweUnimal

Surabaya

155 Mbps

16 Mbps

2 Mbps

1 Mbps

2 Mbps

Catatan:Total Link teresterial: 41Link VSAT:12Total link : 53

Palangkaraya

8 Mbps

4 Mbps

Batam56

Pol Smr55

54Pangkep

JarDikNas

Topologi “INHERENT” tahun 2010

Nizam, 2011 at APTIKOM meeting

Status -2010 Jumlah koneksi

82 PTN (32 sebagai Local Nodes) 224 PTS 12 Kopertis SEAMEO-Seamolec

Kapasitas bandwidth Advance: 155Mbps Medium: 8 Mbps Basic: 2 Mbps Self-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

The InGRID Architecture (now in problem )

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inGRIDPORTAL

GlobusHead Node

INHERENT

INHERENT

User

User

Linux/SparcClusterGlobus

Head Node

Linux/x86Cluster

Windows/x86Cluster

Solaris/x86Cluster

GlobusHead Node

UI I*

U*

CustomPORTAL

H/W specs

inGRID Portal SUN 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 Node SUN 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 NICs

Storage Server Dual Xeon Processor (3.0GHz), 2 GB Memory, 1 TB Disk

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S/W specs

User Interface: UCLA Grid Portal

Middleware Globus Toolkit

Job Scheduler: Sun Grid Engine

(SGE) Programming:

C, Java Paralel: MPICH

Applications: Chemistry:

Gromach Biology:

Blast Computer Graphic:

Povray Utilities:

Matrics multiplication, Sort, Octave (Matlab-like)

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

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Molecular dynamics simulation and docking

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• 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.

Molecular dynamic simulation

used 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

Molecular Docking and Virtual Screening

Molecular 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.

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Gromacs speed up on Cluster

No Time StepAmount 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

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The Autodock running time

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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

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The entities The entities of our design are resources, users, and jobs or

Gridlets Resource 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.

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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.

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Sumatera Leaf Router

NAD Server

NAD Users

10 Mbps

10 Mbps

Lampung Server

10 Mbps

Sumatera Edge Router

100 Mbps

Java Edge Router

Java Leaf Router

BantenServer

JatimServer

10 Mbps10 Mbps

100 Mbps

Jatim Users

10 Mbps

10 Mbps

BantenUsers

Kalimantan Edge Router

Kalimantan Leaf Router

KalbarServer

KaltimServer

10 Mbps

10 Mbps

100 Mbps

Kaltim Users

10 Mbps

10 Mbps

KalbarUsers

Sulawesi Edge Router

Sulawesi Leaf Router

SulbarServer

SultraServer

10 Mbps

10 Mbps

100 MbpsSultraUsers

10 Mbps

10 Mbps

SulbarUsers

Bali-NTB-NTT Edge Router

BaliServer

NTTServer

10 MbpsNTT Users

10 Mbps

Bali UsersBali-NTB-NTTLeaf Router

100 Mbps

NTBServer

NTB Users

10 Mbps10 Mbps

Maluku Edge Router

MalukuServer

Maluku Users

10 Mbps10 Mbps

PapuaEdge Router

PapuaServer

Papua Users

10 Mbps 10 Mbps

1 Gbps

1 Gbps

1 Gbps

Maluku Leaf Router

100 Mbps

PapuaLeaf Router

100 Mbps

LampungUsers

10 Mbps

1 Gbps

10 Mbps10 Mbps

1 Gbps

WestIndCore Router

CentraltIndCore Router

1 Gbps

1 Gbps

EastIndCore Router

500 Mbps

500 Mbps500 Mbps

Backbone Link

Sumatera Leaf Router

NAD Server

NAD Users

10 Mbps

10 Mbps

Lampung Server

10 Mbps

Sumatera Edge Router

100 Mbps

Java Edge Router

JavaLeaf Router

BantenServer

JatimServer

10 Mbps10 Mbps

100 Mbps

Jatim Users

10 Mbps

10 Mbps

BantenUsers

Kalimantan Edge Router

Kalimantan Leaf Router

KalbarServer

KaltimServer

10 Mbps

10 Mbps

100 Mbps

Kaltim Users

10 Mbps

10 Mbps

KalbarUsers

Sulawesi Edge Router

Sulawesi Leaf Router

SulbarServer

SultraServer

10 Mbps

10 Mbps

100 MbpsSultraUsers

10 Mbps

10 Mbps

SulbarUsers

Bali-NTB-NTT Edge Router

BaliServer

NTTServer

10 MbpsNTT Users

10 Mbps

Bali UsersBali-NTB-NTTLeaf Router

100 Mbps

NTBServer

NTB Users

10 Mbps10 Mbps

Maluku Edge Router

MalukuServer

Maluku Users

10 Mbps10 Mbps

PapuaEdge Router

PapuaServer

Papua Users

10 Mbps

10 Mbps

1 Gbps

1 Gbps

1 Gbps

1 Gbps

1 Gbps

Maluku Leaf Router

100 Mbps

PapuaLeaf Router

100 Mbps

LampungUsers

10 Mbps

1 Gbps

10 Mbps10 Mbps

1 Gbps

Backbone Link

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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.

The simulation environment

Hardware 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. Software

32-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.

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The simulation results

Processing Time (in Simulation Seconds) Scheduling

Algorithm Scenario

Gridlet#0 Gridlet#1 Gridlet#2 Scenario 1 239.76471 184.89620 124.45739

FIFO Scenario 2 240.23045 185.26774 124.11812 Scenario 1 235.50311 180.73233 124.67395

SCFQ Scenario 2 235.78695 181.59782 124.05540

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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.

More Prospects More 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?i991101105

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Challenges Unreliable 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.

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Only 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

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

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Uhm, I am not quite clear…Yet another

complex IT paradigm?

Virtualization

QoS

Service Level

Agreements

Resource Metering

Billing

Pricing

Provisioning on DemandUtility & Risk Management

Scalability

Reliability

Energy Efficiency

Security

Privacy

Trust

Legal &

Regulatory

Software Eng. Complexity

Programming Env. & Application Dev.

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

impacts are waiting

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• More bioinformatics, medical informatics, image analysis, finance with GPU computing environment,

• Indonesian Egov Grid services• Indonesian Archeology and Culture-Grid

services• Indonesian Health-Grid services

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• 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 2008• Gridbus Application, http://www.gridbus.org/applications.html, akses 25 Sep 2008• Gridbus Middleware, http://www.gridbus.org/middleware/, akses 25 Sep 2008 GridGain, http://www.gridgain.com, akses 15 Sep 2008• Ivo 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 2008• John Rhoton, cloud computing explained, 2nd ed, recursice press, 2010

References

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• 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 2008

• NeuroGrid, http://www.gridbus.org/neurogrid/, akses 27 Sep 2008• Paul 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.shtml

• Peluang dan tantangan Aplikasi Grid Computing di Indonesia Page 12 of 12• Pipeline – 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

Thank you !

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