Elasticity physics problems pdf

Look up elasticity in Wiktionary, the free dictionary. Elasticity as a comic book super power. This disambiguation page lists articles associated with the title Elasticity. If an internal link led you here, elasticity physics problems pdf may wish to change the link to point directly to the intended article.

This page was last edited on 31 August 2016, at 15:33. By using this site, you agree to the Terms of Use and Privacy Policy. In cloud computing, elasticity is defined as “the degree to which a system is able to adapt to workload changes by provisioning and de-provisioning resources in an autonomic manner, such that at each point in time the available resources match the current demand as closely as possible”.

Elasticity is a defining characteristic that differentiates cloud computing from previously proposed computing paradigms, such as grid computing. The dynamic adaptation of capacity, e. Let us illustrate elasticity through a simple example of a service provider who wants to run a website on an IaaS cloud. An elastic system should immediately detect this condition and provision nine additional machines from the cloud, so as to serve all web users responsively.

The ten machines that are currently allocated to the website are mostly idle and a single machine would be sufficient to serve the few users who are accessing the website. An elastic system should immediately detect this condition and deprovision nine machines and release them to the cloud. Elasticity aims at matching the amount of resource allocated to a service with the amount of resource it actually requires, avoiding over- or under-provisioning. Hence, the service provider’s expenses are higher than optimal and the profit is reduced.

In the above example, under-provisioning the website may make it seem slow or unreachable. Web users eventually give up on accessing it, thus, the service provider loses customers.

One potential problem is that elasticity takes time. VM to be ready to use. The VM startup time is dependent on factors, such as image size, VM type, data center location, number of VMs, etc. Cloud providers have different VM startup performance.

This implies any control mechanism designed for elastic applications must consider in its decision process the time needed for the elasticity actions to take effect, such as provisioning another VM for a specific application component. Ganglia or Nagios, are no longer suitable for monitoring the behavior of elastic applications.

For example, during its lifetime, a data storage tier of an elastic application might add and remove data storage VMs due to cost and performance requirements, varying the number of used VMs. Thus, additional information is needed in monitoring elastic applications, such as associating the logical application structure over the underlying virtual infrastructure.

Cloud applications can be of varying types and complexities, with multiple levels of artifacts deployed in layers. Controlling such structures must take into consideration a variety of issues, an approach in this sense being rSYBL. Nikolas Herbst, Rouven Krebs, Giorgos Oikonomou, George Kousiouris, Athanasia Evangelinou, Alexandru Iosup, and Samuel Kounev.