(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. A Data Grid 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.

An area of computing that's gaining traction in recent years in Cloud Computing. Essentially, Cloud Computing refers to the provision of services over the Internet. Users of these services will need no detailed technical knowledge to avail of them. There are three recognised types of Cloud Computing - Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). The term 'Cloud' is an analogy for the Internet as the Internet is usually depicted as a Cloud in network diagrams.

IaaS is where an organisation outsources equipment such as Computer Servers, Storage Devices and Network Components and users access this equipment over the Internet. Common examples of IaaS include Web Server Hosting and Platform Virtualization. Platform Virtualization is where a computing system that has been partitioned at every level (applications, operating system, processors etc.)  runs on a single platform. This means that different users can access what are effectively distinct subsystems over the Cloud. In summary, IaaS offers computer infrastructure that can be accessed remotely over the Cloud.

PaaS is the delivery of a platform over the Internet. A platform here can be defined as a computing workspace to enable users to develop and build web applications without the user installing any development tools on their own computer. Examples of PaaS include LongJump (http://www.longjump.com)  which enables you to build Web Applications by remotely accessing development tools via your web browser and WaveMaker Cloud Edition for developing AJAX applications (http://www.wavemaker.com).

Originally Cloud Computing and SaaS were effectively interchangeable terms. Essentially, SaaS is a form of software deployment whereby, instead of installing software, users would access it over the Internet. The most well known company in the SaaS space is probably Salesforce.com (http://www.salesforce.com) where subscribers can use Customer Relationship Management (CRM) software over the Internet.

There are two aspects in any discussion of Cloud Computing and Green IT. The first perspective is that of energy savings which there is some debate about (See http://greenmonk.net/how-green-is-cloud-computing/ and http://blogs.sun.com/marchamilton/entry/how_green_is_cloud_computing). the perspective that we will examine is how Cloud Computing can enable and enhance the operations of 'Green' Energy networked devices such as smart meters and wind turbines. 

Smart Meters have a number of issues that could be alleviated by Cloud Computing. The two major problems utility companies are encountering with Smart Meters are a lack of standardisation from vendors and security. We have discussed the issue of security and smart meters in a previous blog but the lack of standardisation means that organisations who source their smart meters from multiple suppliers need to have software processes in place to format and translate the data from each vendor. Such processes can be expensive in terms of processing power and might be better outsourced to an IaaS organisation which will provide the infrastructure requirements to manage and store smart meter data and transform it into information for business decision making.

The proliferation of smart meter vendors and the lack of standardisation leads to extra work for IT departments in developing software to capture smart meter data. Vertoda is currently working on a development kit to enable software engineers to write programs to capture data from any smart meter and integrate the program with the Vertoda Framework. This in turn provides a pool of information for the rest of the organisation's software and systems. In addition to installing this kit on a PC, users will also be able to access the kit online as a PaaS.

Smart Meters provide what is for many organisations a new pool of data. Water, Gas and Electricity Meters are being installed by organisations of all sizes and many of these organisations will not want to install software to manage this data. Cloud Computing then can provide solutions to manage smart grid infastructure, develop software solutions using a remote platform or offer SaaS to manage and capture data from smart meters.

Wind Turbines are another category of equipment that could derive benefits from Cloud Computing. The data generated by Wind Turbines is comprehensive but two areas we will deal with here are energy and maintenance data. The energy being generated by a Wind Farm is of interest to both the Wind Farm and their Utility customer. This data should be available in a timely fashion to both parties. Currently, as both organisations have separate IT infrastructures we are dealing with what are effectively unintegrated Information Systems, sometimes resulting in inconsistent data between the organisations - a problem as this is effectively how Wind Farms measure revenue. One solution Cloud Computing can offer a Wind Farm and its utility customer is in IaaS where energy data is stored on a central shared server on the Internet for both organisations.

Maintenance data is also key to the operation of a Wind Farm. Availability is a key metric for organisations and turbine manufacturers are subject to contractual penalties if availability exceeds a certain threshold. For this reason, a shared system using IaaS might be appropriate here too. Of course, there are political and confidentiality reasons why a Wind Farm might not want to interact with its suppliers and customers in this way but you can partition your system on IaaS so that suppliers and customers see only the data that is relevant to them. Platform Virtualization would be an appropriate solution here.

The transformation of Wind Turbine data into information that the company can use for assessing revenue or equipment performance is a complex task. Again, different manufacturers have different formats for their data and for making their data available. A centralised SaaS for capturing and managing this data would be useful here.

Vertoda is examining the role Cloud Computing can play in its solution offerings. We plan to release a Cloud Edition of our development kit and are planning to offer the system as a subscription service. Vertoda can also enable an individual organisation's IaaS solutions by securing data transferred from Smart Meters and Wind Turbines and by offering a central middleware system that will capture and translate data into meaningful and timely information for organisations.