Grid and Cloud Computing in Indonesia : challenges and prospects

1. Grid and Cloud Computing in Indonesia : challenges and prospects HeruSuhartanto 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

2. 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

3. Resource Hungry Applications [Ref Hai Jin and Raj Buyya] • Solving grand challenge applications using computer modeling, simulation and analysis Aerospace Internet & Ecommerce Life Sciences 3 Digital Biology CAD/CAM Military Applications Military Applications Military Applications

4. Types of hungry application [ref: Coddington] • 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

5. We need to run faster, but How? • There are 3 ways to improve performance: • Work Harder • Work Smarter • Get Help • Computer Analogy • Using faster hardware • Optimized algorithms and techniques used to solve computational tasks • Multiple computers to solve a particular task

6. 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 Ref: Buyya

7. What will be our choices? Supercomputer ? Cluster Computing ? Grid Computing ? Cloud Computing?

8. But these may be difficult to others, so? We need to ‘collect’ these resources and share them among the needed people. This lead to Grid Computing concept.

9. Examples of Grid Computing • 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)

10. More on PRAGMA • 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

11. More examples on Grid computing applications/researches • Deteksikerusakanpipa, Inspeksi 100 km pipadgngaristengah 50 inci, data yang terkumpul 280 Terabytes (2.8 x 10^{14} bytes), kecepatan transfer 2.8 Gb. Hanyabisadiprosesoleh SDK Grid computing, [ ref: inspektionmolch : http://www.hpe.fzk.de/projekt/molch/, akses 27 Sep 08] • Analisis data aktifitasotak yang dikumpulkandari instrument MEG (Magnmetoencephatolgraphy) adalahtopikrisetygsangatpentingkarenamendorongparadokteruntukidentifikasisimptompenyakit. Kerjasama Grid Lab – Univ Melbourne, Nimrod-G Project MonashUniv, dan MEG project – Osaka Univ [ref: http://www.gridbus.org/neurogrid/, akses 27 sep 08] • Novartis Institute for Biomedical Research perlu 6 tahunwaktuprosesdgnkomputer super, namundengan PC Grid berjumlah 3700 desktop Pc, hanayperluwaktuproses 12 jam. Hematdanasekitar 200 juta dollar untuktigatahun, kekuatankomputasitercapailebihdari 5 Tera-flops [Ian Foster, www.globus.org]

12. Grid computing definition • 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. • Infrastrukturkomputasi yang menyediakanaksesberskalabesarterhadapsumberdayakomputasi yang tersebarsecarageografisnamunsalingterhubungmenjadisatukesatuanfasilitas. Sumberdayainitermasukantara lain supercomputer, system storage, sumbersumber data, dan instrument instrument.

13. Grid computing physical structure [Ian Foster]

14. Grid Architecture [GridBus]

15. 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)

16. Grid is not easy to developed and maintained Ask others to provide them, and users use them as a Servicesthen Grid computing will be function as Cloud computing;

17. Services in the Cloud • Software as a Service (SaaS) • Platform as a Service (PaaS) • Infrastructure as a Service (IaaS)

18. SaaS – bisadalambentukAplikasiseperti 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

19. 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.

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

21. 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.

22. Cloud computing services by Indonesians? Gratis: Esfindo (SaaS), InGrid (IaaS), …… Bayar : telkomcloud, webhosting, collocation, ….

23. 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 virtualisedcomputers that are dynamically provisioned and presented as one or more unified computing resources based on service-level agreements established through negotiationbetween the service provider and consumers.” • Keywords: Virtualisation (VMs), Dynamic Provisioning (negotiation and SLAs), and Web 2.0 access interface Segalakebutuhanpengelolaan data di Internet dengansumberdaya yang disiapkanolehsuatu provider. [. H Suhartanto, 2011]

24. Private/Enterprise Clouds Public/Internet Clouds Hybrid/Mixed Clouds 3rd party, multi-tenant Cloud infrastructure & services: * available on subscription basis (pay as you go) Cloud computingmodel run within a company’s own Data Center / infrastructure forinternal and/or partners use. Mixed usage of private and public Clouds: Leasing publiccloud serviceswhen private cloud capacity is insufficient Clouds based on Ownership and Exposure [ref: Buyya]

25. (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

26. Prospects in Indonesia • some previous research works are available • The development of internet infrastructures among universities; • Some related courses are offered in universitities

27. 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 : 40juta • Pelanggantelpseluler: 105 juta Nizam, presentasiAptikom 2011

28. Topologi “INHERENT” tahun 2010 Konfigurasi Zona Perguruan Tinggi Nizam, 2011 at APTIKOM meeting

29. 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

30. inGRIDPORTAL GlobusHead Node Windows/x86Cluster User GlobusHead Node Linux/x86Cluster Linux/SparcCluster GlobusHead Node Solaris/x86Cluster User CustomPORTAL The InGRIDArchitecture (now in problem ) U* INHERENT UI I*

31. 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

32. 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)

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

34. Molecular dynamics simulation and docking • 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.

35. 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.

36. 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.

37. Gromacs speed up on Cluster

38. The Autodock running time

39. 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

40. 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.

41. 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.

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.

43. 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 GBFujitsu 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.

44. The simulation results 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.

45. 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 Rampungdi 2015, "Nusantara Super Highway berbasis optical network merupakankelanjutandaricita-cita Telkom untukmenyatukan Indonesia melaluivisi Nusantara 21 yang sudahdimulaisejak 2001 denganteknologiberbasissatelit,"http://www.detikinet.com/read/2011/04/19/143116/1620709/328/nusantara-super-highway-rampung-di-2015?i991101105

46. 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.

47. Challenges - cont 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

48. Pricing Scalability Resource Metering QoS Reliability Service Level Agreements Billing Provisioning on Demand Utility & Risk Management Legal &Regulatory Security Uhm, I am not quite clear…Yet another complex IT paradigm? Programming Env. & Application Dev. Privacy Trust Software Eng. Complexity Cloud Computing Challenges: Dealing with too many issues [ref Buyya] Virtualization Energy Efficiency

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

50. References • ABCGrid, http://abcgrid.cbi.pku.edu.cn (akses 3 Oktober 2008), also by Ying Sun, Shuqi Zhao, Huashan Yu, GeGao and JingchuLuo. (2007) ABCGrid: Application for Bioinformatics Computing Grid. Bioinformatics • RajkumarBuyya, 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 • IvoBahar, HeruSuhartanto, Design and Simulation of Indonesian Education Grid Topology using Gridsim Toolkit, to appear at Asian Journal of Information Technology, 2010 • H. Suhartanto, KajianPerangkatbantuKomputasitersebarberbasis Message Passing, MakaraTeknologi, Vol 10, No 2, 2006, page 72 – 81. • H. Suhartanto, PeluangdantantanganAplikasi Grid Computing di Indonesia, pidatopengukungan 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