Abstract
In order to achieve energy saving and reduce the total cost of ownership, green storage has become the first priority for data center. Detecting and deleting the redundant data are the key factors to the reduction of the energy consumption of CPU, while high performance stable chunking strategy provides the groundwork for detecting redundant data. The existing chunking algorithm greatly reduces the system performance when confronted with big data and it wastes a lot of energy. Factors affecting the chunking performance are analyzed and discussed in the paper and a new fingerprint signature calculation is implemented. Furthermore, a Bit String Content Aware Chunking Strategy (BCCS) is put forward. This strategy reduces the cost of signature computation in chunking process to improve the system performance and cuts down the energy consumption of the cloud storage data center. On the basis of relevant test scenarios and test data of this paper, the advantages of the chunking strategy are verified.
Highlights
Along with the development of the generation of network computing technology, such as the application of Internet and cloud computing, the scale of the data center is showing the explosive growth in the past 10 years
Most existing chunking algorithms obtain the fingerprints in the sliding window by Rabin algorithm to determine the chunk boundary
It consumes a large amount of CPU computing resources on fingerprint computing
Summary
Along with the development of the generation of network computing technology, such as the application of Internet and cloud computing, the scale of the data center is showing the explosive growth in the past 10 years. The key problem of improving chunking performance lies in cutting down the consumption of CPU resources by reducing the number of invalid signature calculations and comparisons. By replacing the comparison operation, BCCS uses bitwise operation to optimize each matching process and exclude the unmatching positions as much as possible, getting the maximum jumping distance to quicken the matching process of binary string It reduces calculation and comparison costs by taking advantage of the bit feature information brought by failure matching every time. This measure reduces the cost of signature computation in chunking process and brings down CPU resource consumption to improve the system performance.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
