Dynamic VM Scaling: Provisioning and Pricing through an Online Auction

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Dynamic VM Scaling: Provisioning and Pricing through an Online Auction

Dynamic VM Scaling: Provisioning and Pricing through an Online Auction

Abstract

Today’s IaaS clouds allow dynamic scaling of VMs allocated to a user, depending on the user’s real-time demand. There are two types of scaling: horizontal scaling (scale-out) by allocating more instances of VM to the user, and vertical scaling (scale-up) by boosting user-owned VM resources. Dynamic VM Scaling: Provisioning and Pricing through an Online Auction was a daunting issue how to efficiently allocate resources on physical servers to meet on – the-go users ‘ scaling demand, achieving the best use of the server and user utility.

Dynamic VM Scaling: Provisioning and Pricing through an Online Auction An accompanying critical challenge is how to effectively charge incremental resources so that the economic benefits of both cloud provider and cloud users are guaranteed. There has been online auction design dealing with dynamic VM provisioning where the resource bids are not related to each other, failing to handle VM scaling where later bids may rely on the same user’s earlier bids.

System Configuration

H/W System Configuration

Speed                   : 1.1 GHz
 
RAM                      : 256 MB(min)
 
Hard Disk              : 20 GB
 
Floppy Drive          : 1.44 MB
 
Key Board             : Standard Windows Keyboard
 
Mouse                  : Two or Three Button Mouse
 
Monitor                : SVGA
 
S/W System Configuration
 
 
Platform                     :  cloud computing
 
Operating system       : Windows Xp,7,
 
Server                       : WAMP/Apache
 
Working on                : Browser Like Firefox, IE

Conclusion

This work designs a truthful and efficient online auction for dynamic resource scaling and pricing, where cloud users repeatedly bid for resources in the future with increased amounts according to their scale-up / out preferences. We consider the minimization of server energy costs in social welfare maximization, and reveal an important property, submodularity, of the objective function in the resulting significantly more challenging offline problem.