Cost Optimization for Dynamic Replication and Migration of Data in Cloud Data Centers

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Cost Optimization for Dynamic Replication and Migration of Data in Cloud Data Centers

Cost Optimization for Dynamic Replication and Migration of Data in Cloud Data Centers

Abstract

Cloud Storage Providers (CSPs) offer geographically data stores with different prices for several storage classes. An important issue facing cloud users is how to exploit these storage classes to serve an application at a minimum cost with a time-varying workload on its objects. Cost Optimization for Dynamic Replication and Migration of Data in Cloud Data Centers To address this problem, we first propose the optimal offline algorithm that leverages dynamic and linear programming techniques with the assumption of accurate workload knowledge available on objects.

 

Cost Optimization for Dynamic Replication and Migration of Data in Cloud Data Centers Due to the high time complexity of this algorithm and its a priori knowledge requirement, we propose two online algorithms that make a trade-off between residential and migration costs and dynamically select storage classes across CSPs. The first online algorithm is deterministic without any knowledge of workload and incurs no more than 2-1 times of the minimum cost obtained by the optimal offline algorithm, where is the ratio of residential costs in the most expensive data store to the cheapest one in either network or storage costs.

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

Developers must optimally exploit the price difference between storage and network services across multiple CSPs to minimize the cost of data placement for time-varying workload applications. To achieve this goal, we have designed algorithms with full and partial future workload information.