@inproceedings{ bharath2011,
  author = {Bharath Cheluvaraju and A.~S.~ Ramachandra Kousik and
                  Shrisha Rao},
  title = {Anticipatory Retrieval and Semantic Caching for Data
                  Search at Variable Bandwidths},
  booktitle = {5th Annual {IEEE} International Systems Conference
                  (IEEE SysCon 2011)},
  address = {Montreal, QC, Canada},
  abstract = {In the recent past, there have been rapid advances in
                  the technology of processors, storage and networks,
                  leading to technologies like cloud computing.
                  However, amid all these advances, performance of
                  clouds and cloud services continues to present
                  challenges.  Access latencies to the information on
                  the cloud due to variable bandwidth continues to be
                  a serious problem of research; more so in
                  environments requiring mobile devices to stay
                  connected to the cloud.  One way to smooth out bumps
                  in bandwidth available is to use anticipatory
                  retrieval of data, and to cache data that is likely
                  to be requested later.  The proposed anticipatory
                  retrieval and caching system is a solution that
                  takes this path.  It offers a better experience to
                  those mobile users who are connected to a cloud and
                  make frequent access to the cloud's datastore.  The
                  proposed method aims to provide ubiquitous access to
                  data on clouds regardless of the bandwidth levels.
                  This is done by locally caching all the one-hop
                  related item-sets $I_1, I_2, \ldots, I_k$
                  semantically belonging to (or semantically linked
                  to) a particular item-set $I'$.  Caching is done
                  asynchronously in the background during times of
                  high bandwidth.  The proposed algorithms assess the
                  semantic relevance of the data using semantic
                  distances along with user priorities and
                  availability of bandwidth, and then prioritizes
                  anticipatory data downloads on to the cloud's
                  storage based on the relevance quotient.},
  pages = {531--537},
  note = {doi:10.1109/SYSCON.2011.5929049},
  month = apr,
  year = {2011}
     }