The Multi-Queue Replacement Algorithm for Second Level Buffer Caches
Proceedings of the General Track: 2002 USENIX Annual Technical Conference
Massive arrays of idle disks for storage archives
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
Energy conservation techniques for disk array-based servers
Proceedings of the 18th annual international conference on Supercomputing
Interplay of energy and performance for disk arrays running transaction processing workloads
ISPASS '03 Proceedings of the 2003 IEEE International Symposium on Performance Analysis of Systems and Software
The dynamics of viral marketing
ACM Transactions on the Web (TWEB)
I tube, you tube, everybody tubes: analyzing the world's largest user generated content video system
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Dependability, access diversity, low cost: pick two
HotDep'07 Proceedings of the 3rd workshop on on Hot Topics in System Dependability
Optimizing power consumption in large scale storage systems
HOTOS'07 Proceedings of the 11th USENIX workshop on Hot topics in operating systems
Write off-loading: Practical power management for enterprise storage
ACM Transactions on Storage (TOS)
A measurement-driven analysis of information propagation in the flickr social network
Proceedings of the 18th international conference on World wide web
Buzztraq: predicting geographical access patterns of social cascades using social networks
Proceedings of the Second ACM EuroSys Workshop on Social Network Systems
| Hi-index | 0.00 |
This paper looks at optimising the energy costs for storing user-generated content when accesses are highly skewed towards a few "popular" items, but the popularity ranks vary dynamically. Using traces from a video-sharing website and a social news website, it is shown that the non-popular content, which constitute the majority by numbers, tend to have accesses which spread locally in the social network, in a viral fashion. Based on the proportion of viral accesses, popular data is separated onto a few disks on storage. The popular disks receive the majority of accesses, allowing other disks to be spun down when there are no requests, saving energy. Our technique, SpinThrift, improves upon Popular Data Concentration (PDC), which, in contrast with our binary separation between popular and unpopular items, directs the majority of accesses to a few disks by arranging data according to popularity rank. Disregarding the energy required for data reorganisation, SpinThrift and PDC display similar energy savings. However, because of the dyamically changing popularity ranks, SpinThrift requires less than half the number of data reorderings compared to PDC.