Cloud computing versus Grid computing

[dhilipkumar]

Cloud computing versus Grid computing

Learn how you can use Infrastructure as a Service to get a full computer infrastructure using Amazon's Elastic Compute Cloud (EC2). This article helps you plan ahead for working with cloud by knowing how cloud computing compares to grid computing,, how you can resolve issues in cloud and grid computing, and what security issues exist with data recovery and managing private keys in a pay-on-demand environment.


Cloud computing versus grid computing


Introduction

You may have wondered about cloud computing as compared to grid computing. In this article, I talk about cloud computing service types and the similarities and differences between cloud and grid computing. I look at why cloud computing may be advantageous over grid computing, what issues to consider in both, and some security concerns. I use Amazon Web Services as an example.

To get cloud computing to work, you need three things: thin clients (or clients with a thick-thin switch), grid computing, and utility computing. Grid computing links disparate computers to form one large infrastructure, harnessing unused resources. Utility computing is paying for what you use on shared servers like you pay for a public utility (such as electricity, gas, and so on).

With grid computing, you can provision computing resources as a utility that can be turned on or off. Cloud computing goes one step further with on-demand resource provisioning. This eliminates over-provisioning when used with utility pricing. It also removes the need to over-provision in order to meet the demands of millions of users.

Infrastructure as a Service and more
A consumer can get service from a full computer infrastructure through the Internet. This type of service is called Infrastructure as a Service (IaaS). Internet-based services such as storage and databases are part of the IaaS. Other types of services on the Internet are Platform as a Service (PaaS) and Software as a Service (SaaS). PaaS offers full or partial application development that users can access, while SaaS provides a complete turnkey application, such as Enterprise Resource Management through the Internet.

To get an idea of how Infrastructure as a Service (IaaS) is used in real life, consider The New York Times that processed terabytes of archival data using hundreds of Amazon's EC2 instances within 36 hours. If The New York Times had not used EC2, it would have taken it days or months to process the data.

The IaaS divides into two types of usage: public and private. Amazon EC2 uses public server pools in the infrastructure cloud. A more private cloud service uses groups of public or private server pools from an internal corporate data center. You can use both types to develop software within the environment of the corporate data center, and, with EC2, temporarily extend resources at low cost—say for testing purposes. The mix may provide a faster way of developing applications and services with shorter development and testing cycles.

Amazon Web services

With EC2, customers create their own Amazon Machine Images (AMIs) containing an operating system, applications, and data, and they control how many instances of each AMI run at any given time. Customers pay for the instance-hours (and bandwidth) they use, adding computing resources at peak times and removing them when they are no longer needed. The EC2, Simple Storage Service (S3), and other Amazon offerings scale up to deliver services over the Internet in massive capacities to millions of users.

Amazon provides five different types of servers ranging from simple-core x86 servers to eight-core x86_64 servers. You do not have to know which servers are in use to deliver service instances. You can place the instances in different geographical locations or availability zones. Amazon allows elastic IP addresses that can be dynamically allocated to instances.

Cloud computing

With cloud computing, companies can scale up to massive capacities in an instant without having to invest in new infrastructure, train new personnel, or license new software. Cloud computing is of particular benefit to small and medium-sized businesses who wish to completely outsource their data-center infrastructure, or large companies who wish to get peak load capacity without incurring the higher cost of building larger data centers internally. In both instances, service consumers use what they need on the Internet and pay only for what they use.

The service consumer no longer has to be at a PC, use an application from the PC, or purchase a specific version that's configured for smartphones, PDAs, and other devices. The consumer does not own the infrastructure, software, or platform in the cloud. He has lower upfront costs, capital expenses, and operating expenses. He does not care about how servers and networks are maintained in the cloud. The consumer can access multiple servers anywhere on the globe without knowing which ones and where they are located.

Grid computing

Cloud computing evolves from grid computing and provides on-demand resource provisioning. Grid computing may or may not be in the cloud depending on what type of users are using it. If the users are systems administrators and integrators, they care how things are maintained in the cloud. They upgrade, install, and virtualize servers and applications. If the users are consumers, they do not care how things are run in the system.

Grid computing requires the use of software that can divide and farm out pieces of a program as one large system image to several thousand computers. One concern about grid is that if one piece of the software on a node fails, other pieces of the software on other nodes may fail. This is alleviated if that component has a failover component on another node, but problems can still arise if components rely on other pieces of software to accomplish one or more grid computing tasks. Large system images and associated hardware to operate and maintain them can contribute to large capital and operating expenses.

Similarities and differences

Cloud computing and grid computing are scalable. Scalability is accomplished through load balancing of application instances running separately on a variety of operating systems and connected through Web services. CPU and network bandwidth is allocated and de-allocated on demand. The system's storage capacity goes up and down depending on the number of users, instances, and the amount of data transferred at a given time.

Both computing types involve multitenancy and multitask, meaning that many customers can perform different tasks, accessing a single or multiple application instances. Sharing resources among a large pool of users assists in reducing infrastructure costs and peak load capacity. Cloud and grid computing provide service-level agreements (SLAs) for guaranteed uptime availability of, say, 99 percent. If the service slides below the level of the guaranteed uptime service, the consumer will get service credit for receiving data late.

The Amazon S3 provides a Web services interface for the storage and retrieval of data in the cloud. Setting a maximum limits the number of objects you can store in S3. You can store an object as small as 1 byte and as large as 5 GB or even several terabytes. S3 uses the concept of buckets as containers for each storage location of your objects. The data is stored securely using the same data storage infrastructure that Amazon uses for its e-commerce Web sites.

While the storage computing in the grid is well suited for data-intensive storage, it is not economically suited for storing objects as small as 1 byte. In a data grid, the amounts of distributed data must be large for maximum benefit.A computational grid focuses on computationally intensive operations. Amazon Web Services in cloud computing offers two types of instances: standard and high-CPU.

Issues to consider

Four issues stand out with cloud and grid computing: threshold policy, interoperability issues, hidden costs, and unexpected behavior.

Threshold policy

Let's suppose I had a program that did credit card validation in the cloud, and we hit the crunch for the December buying season. Higher demand would be detected and more instances would be created to fill that demand. As we moved out of the buying crunch, the need would be diminished and the instances of that resource would be de-allocated and put to other use.

To test if the program works, develop, or improve and implement, a threshold policy in a pilot study before moving the program to the production environment. Check how the policy detects sudden increases in the demand and results in the creation of additional instances to fill in the demand. Also check to determine how unused resources are to be de-allocated and turned over to other work.