Yes, it is cloudy and raining this week, and the media has been reporting a glut of stories about cloud computing already.  But the IBM Center has released a new report “Moving to the Cloud:  An Introduction to Cloud Computing in Government” that is targeted to executives, not technologists. 

The buzz in the technology world around cloud computing is almost deafening, but for good reason.  The report lays out the many reasons for its use, including economies of scale, better collaboration, and usage-based pricing (you pay only for what you use, like electricity).  For example, a school district in Tennessee established a private cloud and was able to cut its IT support costs by 60 percent.  These kind of savings catch people’s eyes.

The author, Dr. David Wyld of Southeastern Louisiana University, notes that “Cloud computing is an emerging concept.  It has many names, including:  grid computing, utility computing, and on-demand computing.  Indeed, one of the hindrances . . . is the lack of understanding of what it is—and isn’t. . . “  He relies on the definition developed by the National Institute of Standards and Technology, which lays out the essential characteristics of cloud computing:  on-demand self-service, ubiquitous network access, locaction-independent resource pooling, rapid elasticity, and measured service.

While still a geeky definition, more importantly, Wyld points to the fact that we’ve all used cloud computing already – Gmail, YouTube, GoogleDocs, Flickr, and Ning are all common examples in the commercial world.  And Wyld says over 4,000 universities have converted their email systems to cloud computing as well.

But government has been a bit more cautious in its adoption.  Wyld gives examples of the use of cloud in government, lays out ten challenges to public managers in the use of cloud computing (like security and interoperability), and offers some predictions on the road ahead.  He also offers links to some useful video primers, like the one from the UK’s Computing Magazine as well as the one from GSA’s Apps.gov.

A key takeaway for government managers:  cloud computing has potential to transform government IT but it won’t be a silver bullet.  It will most likely be more useful for mission support services such as e-mail, web servers, data storage, and for common cross-government services such as accounting, procurement, and collaboration tools.  But agencies will likely be more cautious in using it for core mission business applications such as the Social Security payroll reporting system or the air traffic control routing system.

No, this post is not about how the enhancements to the power grid will run your data center better. That’s not the “grid” I’m writing about. Grid computing is the combination of computer resources from multiple domains applied to a common task, that requires a great number of computer processing cycles or the need to process large amounts of data.

One example of Grid computing is the SETI (Search for Extraterrestrial Intelligence) @Home project, where PC users worldwide donate unused processor cycles to help the search for signs of extraterrestrial life by analyzing signals coming from outer space.

Another project at Oxford University’s Centre for Computational Drug Discovery uses more than a million PCs looking for a cure for cancer. People around the world give a few CPU cycles from their PCs through “screen saver time.” The project eventually will analyze 3.5 billion molecules for cancer-fighting potential. They have already used more than 50,000 years of CPU power.

It’s like having your very own supercomputer. Have you come across grid computing at your workplace?

Cluster Computing didefiniskan sebagai: 

“A cluster is a type of parallel and distributed system, which consists of a collection of inter-connected
stand-alone computers working together as a single integrated computing resource.”

dengan kata lain, cluster computing diartikan sebagai sistem yang terdiri dari lebih dari satu PC yang saling terhubung yang berfungsi sebagai satu kesatuan sumber daya komputasi yang terintegrasi. dimana sifat dari sistem ini adalah sistem yang paralel dan terdistribusi.

Grid Computing didefinisikan sebagai:

“A Grid is a type of parallel and distributed system that enables the sharing, selection, and aggregation of
geographically distributed ‘autonomous’ resources dynamically at runtime depending on their availability,
capability, performance, cost, and users’ quality-of-service requirements.”

Dengan kata lain, Grid Computing dapat diartikan sebagai sistem yang paralel dan terdistribusi yang memungkinkan saling berbagi, pemilihan, dan pengumpulan terhadap sumber daya komputasi secara dinamis pada saat runtime, tergantung pada ketersediaan, kemampuan, kinerja, biaya dan requirement terhadap quality of service yang diminta oleh pengguna, dimana sistem ini terdistribusi secara geografis dan bekerja secara otonom. 

Cloud Computing is defined as:

“A Cloud is a type of parallel and distributed system consisting of a collection of inter-connected and
virtualized computers that are dynamically provisioned and presented as one or more unified computing
resource(s) based on service-level agreements established through negotiation between the service provider
and consumers.”

Hal yang perlu diperhatikan pada cloud computing adalah virtualized. Konsep ini pada dikembangkan dari konsep dasar yang diimplementasikan pada teknologi virtual machine. Dalam cloud computing, mesin-mesin tersebut dapat dikelola (provisioning) sesuai dengan permintaan user untuk dapat memenuhi Service Level Agreement (SLA) yang telah disepakati, yang diatur dengan menggunakan web service.

source : ”Cloud Computing and Emerging IT Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility” by Rajkumar Buyya, Chee Shin Yeo, Srikumar Venugopal, James Broberg, and Ivona Brandic

Our client in Redwood City, CA is in need of a seasoned system administrator and operations engineer to manage, maintain and support multiple environments supporting several different architectures and environments. The successful candidate will have strong exposure to large web site deployments with multiple tiers include web front-ends, middleware components, backend services, data storage systems and 2nd/3rd tier components like caching and search layers.

Requirements:

• 7 years Linux admin experience
• 5 years RHEL admin experience
• 5 years RPM management and admin experience
• 3 years working with MySQL
• Sound understanding of web services and related technologies (HTTP, REST, XML, JSON, etc)
• Experience with Linux web servers (Apache2, nginx, lighttpd, etc)
• Experience with monitoring systems and components (nagios, cacti, etc)
• Exposure to scripting languages such as Perl, Python, etc.

Really nice-to-haves but not required:

• Experience with Erlang
• Experience with Ruby on Rails
• Exposure to Jabber/XMPP
• Exposure to distributed / grid computing
• ejabberd administration and configuration
• Experience with second and third tier services and components like memcached (or like systems), search applications (lucene, xapian, sphinx, etc), etc.

Realistically, these are also nice to haves but not required:

• Exposure to functional programming languages.
• Exposure to the Facebook Platform, Open Social and or other web services and systems
• Comfortable working in an Agile work environment.
• Plays games and occasionally keeps tabs on the gaming world/industry.

If interested, please send us your resume along with the rate per hour, contact number, and availability for a phone interview to raj@mindsource.com.

(Note: Data Grids in computing can also refer to a UI Component. We will not be dealing with these types of Data Grids in this Blog.)

Data Grids are highly concurrent distributed data structures. They typically allow you to address a large amount of memory and store data in a way that is quick to access. They also tend to feature low latency retrieval and maintain adequate copies across a network to provide resilience to server failure. It is in essence a data structure where data can be evenly distributed across the network. As you add servers (or nodes) to the network you are adding storage space. Load Balancing policies are not needed.

Data Grids and Grid Computing are concepts that are distinct from Cloud Computing even though the terms frequently go hand in hand. Grid Computing in turn is also distinct from a Data Grid. Cloud Computing is effectively an evolution of Grid Computing wherehardware and software resources and services are provided in the Cloud (Internet). Grid Computing divides pieces of a program into several thousand computers.

A Data Grid, on the other hand, is essentially Grid Computing that is concerned with data. Unlike other types of Grid Computing where, if one piece of a program fails pieces on other resources will also fail, a good Data Grid will have backup of data across the network.  This distributed data will be shared and managed within the Cloud.

A number of Data Grid Platforms have been created for storing and managing data across computer networks. JBoss, for example, have recently launched the Infinispan (http://www.jboss.org/infinispan) open source Data Grid platform which is written in Java.  Infinispan gives a good overview of not just its own platform but also of Data Grids generally. As Infinispan point out the grid creates new copies of lost data if a server fails and puts this copy on other servers. Data Lookups are no longer directed to a single database server which greatly alleviates a major bottleneck for most Enterprise Applications. Gigaspaces (http://www.gigaspaces.com/xap) also offer an In-Memory Data Grid in their eXtreme Application Platform (XAP) that partitions data and distributes the data across large numbers of servers.

In the context of Green IT what role can Data Grids play? With respect to Wireless Sensor Networks (WSNs) , it is predicted there will be millions of sensors deployed in mesh networks in years to come in diverse applications including security, agriculture and energy management. All of these devices emit data readings that need to be stored and translated into information that can be used for decision making which can result in millions of data sets being stored every minute. Given the potential dispersed deployment of sensors thoughout a wide area there is an issue regarding the capturing and storing of this data. Data Grids can provide a solution here whereby sensor data readers or the sensors themselves can relay data to the nearest server on a Data Grid. A Data Grid would also avoid bottlenecks in analysing this data and retrieving it for Business Intelligence purposes.

Smart Grids and the meters therein could also benefit from the provision of a Data Grid. In current field trials of Smart Meters, data is relayed back to a central server every half an hour. Smart Meters will ultimately be deployed in every household and business premises in the vast majority of industrialised countries so the issues relating to data volumes and system bottlenecks applicable to WSNs are also relevant here. The issue of data management for smart grids is further exacerbated when we consider that smart meters will also be deployed for water and natural gas consumption as well as electricity. The other element we need to consider is the securing of smart meters and the data they emit. The encryption or signing of data will be an additional load that could be dealt with within a Data Grid rather than on a centralised server.

In the case of Wind Turbines the case for Data Grids initially seems less clear. Wind Farms can certainly benefit from Cloud Computing – for example, the optimisation of a Wind Farm using simulation services in the Cloud was recently demonstrated (See http://www.computerweekly.com/Articles/2009/07/21/236976/cloud-based-simulation-cuts-engineers-design-costs.htm).  However, when we consider that different Wind Turbine manufacturers have different ways of transmitting data and often have different raw data for what is essentially the same metric it is clear that this data will need to be captured and processed before it becomes meaningful information. Given the processing load, one possible solution could be to distribute data emitted from Wind Farms on a per farm or per manufacturer basis throughout a Data Grid. Furthermore, given that the energy and availability metrics determine a Wind Farm’s revenue this data will need to be retrieved in a timely fashion. This will be easier to do with a Data Grid than a centralised server.

Vertoda is examining Data Grids and their use in our framework. We are researching the development of a Cloud Edition of our Framework and given the volume of data the networked devices we manage can emit a Data Grid will certainly play a major role.

——————————————————————————
CLOUDS Lab to Release GridSim Toolkit 5.0

http://www.cloudbus.org/gridsim/

——————————————————————————

The Cloud Computing and Distributed Systems (CLOUDS) Laboratory at
the University of Melbourne, Australia has released the next-version of
Grid simulation software, the GridSim Toolkit 5.0.

This new version of GridSim brings a new package (i.e. gridsim.parallel) that
provides:

* A graphical user interface for debugging resource allocation policies.
* Several allocation policies for scheduling parallel jobs on clusters and
and supercomputers. The policies include aggressive backfilling, conservative
backfilling, advance reservation with conservative backfilling, selective
backfilling and agressive backfilling with multiple resource partitions.

* An implementation of the workload model by Lublin and Feitelson for modelling
the workload of clusters and supercomputers.

* A data structure to facilitate the scheduling of jobs and advance reservations.
This data structure is described as an appendix on Marcos Assuncao’s PhD thesis.

* Logging features using Java logging API.

In addition, other improvements have made GridSim more compatible with
Java 5 and 6. Some bugs have been fixed and some changes in SimJava were
required to enable a simulation to be paused and resumed.

All components developed as part of the GridSim Toolkit are released as
“open source” under the GPL license to encourage innovation and pass full
freedom to our users.

The early version of our GridSim toolkit has been used/downloaded by several
academic and commercial organizations around the world including:
University of Southern California (USA), California Institute of Technology (USA),
Argonne National Labs (USA), University of Manchester (UK), CERN,
Universidad de Santiago de Compostela (Spain), Indian Institute of Technology,
Tsinghua University (China), Sun Microsystems, IBM Research, Unisys, HP,
Northrop Grumman Information Technology, British Telecom and EMC Corp.

The contributors to the GridSim software (from early to new versions) are:
* Rajkumar Buyya, CLOUDS Lab @ The University of Melbourne.
* Manzur Murshed, GSCIT @ Monash University, Australia.
* Anthony Sulistio, CLOUDS Lab @ The University of Melbourne.
* Gokul Poduval and Chen-Khong Tham,
Dept. of Electrical & Computer Engineering @ National University of Singapore.
* Marcos Dias de Assuncao, INRIA Lyon @ Ecole Normale Superieure de Lyon
* Uros Cibej and Borut Robic, Faculty of Computer and Information Service,
The University of Ljubljana, Slovenia.
* Agustin Caminero, Department of Computing Systems,
Universidad de Castilla La Mancha (UCLM), Spain.
* James Broberg, CLOUDS Lab @ The University of Melbourne.
* Saurabh Garg, CLOUDS Lab @ The University of Melbourne.

To download the GridSim software, please visit the CLOUDS Lab web site at

http://www.cloudbus.org/gridsim/

Join the GridSim mailing lists at

http://sourceforge.net/projects/gridsim

——————————————————————————

I would like to share some recent updates in Cloud and Grid computing area coming out of my group:

1. New name for our research group – CLOUDS Lab.

Since 2007, GRIDS Lab has been working on Cloud Computing along with Grid Computing. During the last 2 years, Cloud Computing related R&D activities have peaked in GRIDS Lab and we launched a new flagship
project, called Cloudbus, for Cloud Computing. In recognition of this
new thrust in Cloud Computing, we renamed GRIDS Lab as CLOUDS Lab!

2. Recently CLOUDS Lab team members have published/wrote a number of
papers related to Grid and Cloud Computing. To share a few:

- J. Broberg, R. Buyya, and Z. Tari, MetaCDN: Harnessing ‘Storage
Clouds’ for high performance content delivery, Journal of Network and
Computer Applications, Volume 32, Issue 5, September 2009, Pages 1012-1022.

Available at: http://dx.doi.org/10.1016/j.jnca.2009.03.004

- Alexandre di Costanzo, Marcos Dias de Assuncao, and Rajkumar Buyya,
Building a Virtualized Distributed Computing Infrastructure by
Harnessing Grid and Cloud Technologies, IEEE Internet Computing, Volume
13, Number 5, Pages: 24-33, ISSN: 1089-7801, IEEE Computer Society
Press, Los Alamitos, CA, USA, September/October 2009.

Available at: http://www.buyya.com/papers/InternetComp-Cloud-2009.pdf

- Christian Vecchiola, Suraj Pandey, and Rajkumar Buyya,
High-Performance Cloud Computing: A View of Scientific Applications,
Proceedings of the 10th International Symposium on Pervasive Systems,
Algorithms and Networks (I-SPAN 2009, IEEE CS Press, USA), Kaohsiung,
Taiwan, December 14-16, 2009.

Available at: http://www.buyya.com/papers/HPCC-ISPAN2009-Keynote.pdf
-

Oct 1st update: Here is a clarification from GridRepublic’s Jon, ”GridRepublic is not a volunteer computing project rather it is a gateway for discovery, management, and joining of the various volunteer computing projects …” Thanks Jon.

*******

I recently looked into a bit of the grid computing stuff. So it is rather interesting for me to hear about GridRepublic (a Volunteer Distributed Grid Computing project). The idea seems really cool. It will take some studying and research (which I don’t have time to do yet) before I can recommend or suggest using it because one needs to be careful of the potential power it may release for good and for ill.

[via BMD]

Historias como las de Steve Jobs y Steve Wozniak, son todavía posibles en la actualidad. En la informática queda todavía muchísimo por inventar y la clave es encontrar una idea que cale en la comunidad o sea lo suficientemente innovadora para crear una nueva tendencia o interesar a una empresa como complemento o incluso motor de sus productos.

No hay nada más que ver, por ejemplo, el cambio de enfoque que en Internet produjo la web 2.0 y las distintas soluciones técnicas que han salido desde entonces (y que no paran de salir ya que soluciones como Twitter, Facebook, Flicker, Picasa, etc… son relativamente recientes). En el mundo 2.0 todavía no está todo explotado, la clave es seguir innovando.

La administración electrónica es otro campo donde todavía queda mucho por hacer, porque incluso aunque sea un concepto un tanto antiguo (en realidad muy desgastado por haberse hablado tanto, aunque todavía quede mucho por hacer) el grado de penetración de la misma tanto en España como en el resto del mundo deja muchísimo margen de maniobra.

La Green IT, la cloud computing e incluso la grid computing (aunque este más antiguo) son otros campos relativamente nuevos en los que la capacidad de innovación es prácticamente ilimitada, ya que pese a que hay mucha teoría sobre la mesa, el número de implementaciones realizadas en estas tecnologías todavía es muy reducido (en relación a todo lo que se habla y se escribe de ellos) y el número de implantaciones también lo es (para el mercado tan grande que existe).

En el mundo del desarrollo puro y duro de software tambień hay mucho que inventar y potenciar, como por ejemplo los generadores de código.

Estos son simplemente algunos ejemplos de conceptos o tendencias en los cuales se puede seguir innovando, pero las posibilidades son prácticamente ilimitadas.

Por tanto, todavía (y durante muchísimo años) habrá lugar para que aquellas personas creativas y con talento sigan poniendo sobre la mesa soluciones innovadoras que puedan cambiar sus vidas y hacer el mundo un poquito mejor.

“A computational grid is a hardware and software infrastructure that provides dependable, consistent, pervasive, and inexpensive access to high-end computational capabilities”

Foster, I., & Kesselman, C.
Computational Grids

Algunos enlaces :

1. Ian Foster
2. Carl Kesselman
3. Globus Toolkit
4. The Anatomy of the Grid

Wikipedia – “DRMAA or Distributed Resource Management Application API is a high-level Open Grid Forum API specification for the submission and control of jobs to one or more Distributed Resource Management Systems (DRMS) within a Grid architecture. The scope of the API covers all the high level functionality required for Grid applications to submit, control, and monitor jobs to local Grid DRM systems.”

Implementations:

• http://drmaa.org/implementations.php
• http://gridengine.sunsource.net/
• http://wiki.fedstage.com/FedStage%20DRMAA%20for%20LSF
• http://sourceforge.net/projects/lsf-drmaa/
• http://sourceforge.net/projects/xgrid-drmaa/

]]> http://ninadpachpute.wordpress.com/2009/09/15/open-grid-forum/ Mon, 14 Sep 2009 20:01:07 +0000 ninadp http://ninadpachpute.wordpress.com/2009/09/15/open-grid-forum/

Wikipedia – “The Open Grid Forum (OGF) is the community of users, developers, and vendors leading the global standardization effort for grid computing. It was formed in 2006 in a merger of the Global Grid Forum and the Enterprise Grid Alliance. The OGSA, OGSI, and JSDL standards were created by the OGF. The OGF models its creations of open standards on the IETF.”

Wikipedia – “Load Sharing Facility (or simply LSF) is a commercial computer software job scheduler sold by Platform Computing. It can be used to execute batch jobs on networked Unix and Windows systems on many different architectures.”

Also See:

• Platform Computing Adopting DRMAA Standards
• DRMAA or Distributed Resource Management Application API

Source : ZDNet

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The Domain Name System (DNS) is a system which allows ‘Name” servers to map a textual or descriptive name (eg www.google.com) to a singular or multiple IP addresses. DNS is arranged in a hierarchical model to implement a number of domain “levels”. The main reason behind DNS is that people are able to memorize descriptive names rather than ranges of numbers. In contrast to this Direct Host Control Protocol allows the lease of an allocated IP configuration to a computer or device, this configuration contains information such as:

• IP Address

• Gateway

• DNS Server address

• Subnet

• Proxy

When comparing DNS and DHCP the obvious difference is in the type of data which is allocated, where DNS allocates a descriptive name to an IP address and DHCP allocates an IP configuration to a device.

When implementing a online business site/server it is important to use a static IP address rather than a dynamically assigned address, the main reason behind this is the DNS system; when an domain name is assigned to a static IP address that address will remain constant and will not change, although when a domain name is assigned to a dynamic IP address it may constantly change with each new DHCP renewal and will take time to reassign the name, which may cause the named site/server to be inaccessible from the assigned name.

Virtual Private Networks can be used for a number of roles within a business to business system, the main role would act as a secure channel to access private sections of the site, it could also be used for businesses to access each others servers on the network and send/receive things such as customer details, stock availability, pricing, orders etc. This not only speeds up business processes but also aid to improve data accuracy, another method which aids to increase the speed of business processing is Grid computing , this involves clustering a number of lower powered computers and sharing CPU resources across the clustered network which then virtually acts as one large computer, this enables the computing power to increase with each computer added to the cluster enabling applications to run faster.

Alongside VPN’s and Grid computing services wireless mesh network s can also play a vital role in the evolution of business to business systems, a wireless mesh network involves a number of overlapping wireless nodes which are organized in a mesh architecture, the coverage of the different nodes within this make up a single network or ‘mesh cloud’ allowing users to roam freely within the network without losing a connection and still retaining a quality of service.

Whilst VPN’s and Grid computing services play major roles in the business to business applications of today Cloud applications/systems are rapidly changing to be the future of e-commerce, Cloud applications are virtual services which are available over the internet , these services often attempt to replace the need for the installation of an application on a computer. This means that businesses are able to access these services online and are then able to perform functions such as data sharing and peer to peer networking.

References

Sebesta, R. (2008) “Programming The World Wide Web”

Wikipedia “Wireless Mesh Networks” http://en.wikipedia.org/wiki/Wireless_mesh

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