Energy Optimization Algorithm for Virtual Machine Scheduling in Cloud Computing

By Ram Narayan Shukla

These cloud computing environments give the impression of boundless computing

resources to cloud clients, allowing them to modify their resource consumption rate

according to demand. Cloud computing environments include two sorts of actors: cloud

providers and cloud users 1]. On the one hand, providers keep massive amounts of

computer resources in massive data centers and rent them out to customers on a per-use

basis. On the other hand, other users have apps with variable amounts of load and rent

resources from providers to run them. The connection between providers and consumers

follows the shape indicated in Figure 1.1 in the vast majority of cases. A user sends a

resource request to a provider first. When the provider receives the request, it looks for

resources to fulfill it and assigns them to the user who made the request, which are

generally virtual computers (VMs).The user then uses the assigned resources to execute

programs and pays for the resources used. Once the user has done using the resources,

they are returned to the provider. The fact that these people are usually from different

backgrounds and have different interests is a fascinating aspect of the cloud computing

ecosystem.

Most providers want to make as much money as possible with as little investment as

feasible. They may want to make the most of their computer resources for this reason,

such as hosting as many virtual machines as possible on a single system. To put it another

way, providers try to get the most out of their resources. Putting too many virtual machines

on a single computer, on the other hand, may cause them to interact, resulting in poor

and/or unpredictable performance that irritates users. Users, on the other hand, want their

duties done for the least amount of money possible, or to put it another way, they want to

maximize their cost effectiveness.

• Format: Paperback
• Language: English
• ISBN: 1835800122
• ISBN13: 9781835800126
• Release Date: October 2023
• Publisher: Self Publish
• Length: 110 Pages
• Weight: 0.35 lbs.
• Dimensions: 0.2" x 6.0" x 9.0"