Collegenote
- Defining the Cloud
- Cloud Computing
- Emergence of Cloud Computing
- Cloud Based Service Models
- Grid Computing or Cloud Computing
- Components of Cloud Computing
- Cloud Computing Deployment Models
- Benefits of using cloud models
- Legal Issues in Using Cloud Models
- Ethical issues in cloud computing
- Characteristics of Cloud Computing
- Evolution of cloud computing
- Challenges for cloud computing
- Grid Computing
- Distributed Computing in Grid and Cloud
- Cloud Communication
- Communication-as-a-Service (CaaS)
- Advantages of CaaS
- Infrastructure-as-a-Service (IaaS)
- Popular IaaS solutions
- Modern On-Demand Computing
- Amazon’s Elastic Cloud
- Amazon EC2 Service Characteristics
- Elastic IP Addressing
- Monitoring-as-a-Service (MaaS)
- The Traditional On-Premises Model
- Platform-as-a-Service (PaaS)
- Key Characteristics of PaaS
- Software-as-a-Service
- SaaS Implementation Issues
- The key characteristics of SaaS
- Benefits of SaaS Model
- Jericho Cube Model
- XaaS
- Evolution from Managed service providers (MSP) to Cloud Computing
- Single Purpose architectures to multi-purpose architectures
- Service Oriented Architectures (SOA)
- Combining Cloud and SOA
- Characterizing SOA
- Open Source Software in Data Centers
- Data center virtualization
- Cloud data center
Grid Computing or Cloud Computing #
Grid computing is often confused with cloud computing. Grid computing is a form of distributed computing that implements a virtual supercomputer made up of a cluster of networked or Internetworked computers acting in unison to perform very large tasks. Many cloud computing deployments today are powered by grid computing implementations and are billed like utilities, but cloud computing can and should be seen as an evolved next step away from the grid utility model. There is an ever-growing list of providers that have successfully used cloud architectures with little or no centralized infrastructure or billing systems, such as the peer-to-peer network BitTorrent and the volunteer computing initiative SETI@home.
Service commerce platforms are yet another variation of SaaS and MSPs. This type of cloud computing service provides a centralized service hub that users interact with. Currently, the most often used application of this platform is found in financial trading environments or systems that allow users to order things such as travel or personal services from a common platform (e.g., Expedia.com or Hotels.com), which then coordinates pricing and service delivery within the specifications set by the user.
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.
The difference between grid computing and cloud computing is hard to grasp because they are not always mutually exclusive. In fact, they are both used to economize computing by maximizing existing resources. However, the difference between the two lies in the way the tasks are computed in each respective environment. In a computational grid, one large job is divided into many small portions and executed on multiple machines. This characteristic is fundamental to a grid; not so in a cloud. The computing in cloud is intended to allow the user to avail of various services without investing in the underlying architecture. Cloud services include the delivery of software, infrastructure, and storage over the Internet (either as separate components or a complete platform) based on user demand.
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.
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