Grid computing, Cloud Computing, and Big-data Computing are used as distributed computing mechanisms. What are common and overlapping concepts? Why are research efforts that are spent on one not easily applicable to others? Which research issues can be handled in common? Are research efforts of various researchers used effectively?
In myview, virtualization, networking, and parallel computing are the common. How ever, traffic shaping and resource utilization are also quite prominent.
As you say, both grid anc cloud are distributed computation paradigms. Also
cloud computing and grid computing are both scalable. Scalability is acheived through load balancing of application instances running on a multitude of operating. CPU and network bandwidth is allocated and de-allocated on demand.
However, there are several differences, for example:
1) grid efforts often focus on running very large applications in batch mode while the cloud isn't a batch system and has a focus in the web application delivery space as opposed to data centers
2) grid typically requires expert knowledge in setting up and in execution (the end users are at least proficient with distributed computing) while cloud does not require any proficiency at least from the end user (he does not have to care about the mapping of task into resources, etc.)
3) it can be argued that cloud computing can be offered on top of grid computing by offering on-demand resource provisioning (http://www.ibm.com/developerworks/library/wa-cloudgrid/) but this is not always the case
Big Data is quite different since it targets to address specific challenges that arise when data have high volume, variety and velocity (IBM adds also veracity). So if cloud and grid focus on the infrastructure and platform part, big data focuses more on the platform and application domain part. Note that there is an overlap on the platform side and it is also quite possible to have cloud enabled Big Data solutions (for example to provide on-demand access to resources). In this manner they can be complementary (http://readwrite.com/2013/06/07/how-cloud-computing-democratizes-big-data#awesm=~ozQF78RtH19q6K) athough both can also exist on their own.
I think cloud computing has elasticity as main feature.
I never saw works with grid and elasticity.
Today there are many providers that rent services for cloud computing, and commercially is more common tha grid.
Scheduling appears to be a common and most important issue that needs to be addressed in Grid, Cloud, and BigData processing, as response time is dependent on the scheduling applied on participating entities, say jobs, tasks, or instances.
Grids and Clouds are distributed processing architectures that can be efficiently utilized for big data processing. As far as difference is concerned, clouds are elastic and provision of services is relatively easier than grids. Alternately, grids are internet based heterogeneous architectures therefore more complex than clouds. Big data processing could be done any of these architectures including clusters as well. Big data processing systems are the software based systems developed on top of Grid, Cloud or Cluster Infrastructure.
Cloud Computing is nothing but an advancement mode of grid computing.Big data is not a computing,it takes support of grid and cloud computing to process the data.Big data is a huge, bulk raw data which is very high in volume,Variety and Velocity.Big data needs very high Computing Capacity to process it and only Cloud Computing can Provide its need with its flexible, scaleable and cost effective nature.
Grid computing system is a widely distributed resource for a common goal. It is Brother of Cloud Computing and Sister of Supercomputer. We can think the grid is a distributed system connected to a single network. This types of computing work with the large volume of files. Basically, it is a cluster types system. So people call it cluster computing.
Source: https://www.cloudwebhostingtips.com/grid-computing-vs-cloud-computing-supercomputer/
Grid computer tends to be more geographically disperse and heterogeneous by nature. Grid network also has various types. A single grid is like dedicated connection but a common grid perform multiple tasks.
The size of the grid is large. So grid computing is like supercomputing. It consists of many network, computer, and middleware. Grid computer is dedicated to some specific function of the large volume of data. In the grid process, each task divided into a various process. All the process starts execution simultaneously on a different computer. As a result, very few seconds needs to execute and enjoy the flavor of supercomputing.
For more:https://www.cloudwebhostingtips.com/
- Badges
- Science topic
- Similar topics
- Physical Sciences
- Materials Science