1,428 research outputs found
A Literature Survey of Cooperative Caching in Content Distribution Networks
Content distribution networks (CDNs) which serve to deliver web objects
(e.g., documents, applications, music and video, etc.) have seen tremendous
growth since its emergence. To minimize the retrieving delay experienced by a
user with a request for a web object, caching strategies are often applied -
contents are replicated at edges of the network which is closer to the user
such that the network distance between the user and the object is reduced. In
this literature survey, evolution of caching is studied. A recent research
paper [15] in the field of large-scale caching for CDN was chosen to be the
anchor paper which serves as a guide to the topic. Research studies after and
relevant to the anchor paper are also analyzed to better evaluate the
statements and results of the anchor paper and more importantly, to obtain an
unbiased view of the large scale collaborate caching systems as a whole.Comment: 5 pages, 5 figure
A CQM-based approach to solving a combinatorial problem with applications in drug design
The use of D-Wave's Leap Hybrid solver is demonstrated here in solving a
Knapsack optimization problem: finding meal combinations from a fixed menu that
fit a diner's constraints. This is done by first formulating the optimization
problem as a Constrained Quadratic Model (CQM) and then submitting it to a
quantum annealer. We highlight here the steps needed, as well as the
implemented code, and provide solutions from a Chicken and Waffle restaurant
menu. Additionally, we discuss how this model may be generalized to find
optimal drug molecules within a large search space with many complex, and often
contradictory, structures and property constraints.Comment: 10 pages, 2 table
Discovering Valuable Items from Massive Data
Suppose there is a large collection of items, each with an associated cost
and an inherent utility that is revealed only once we commit to selecting it.
Given a budget on the cumulative cost of the selected items, how can we pick a
subset of maximal value? This task generalizes several important problems such
as multi-arm bandits, active search and the knapsack problem. We present an
algorithm, GP-Select, which utilizes prior knowledge about similarity be- tween
items, expressed as a kernel function. GP-Select uses Gaussian process
prediction to balance exploration (estimating the unknown value of items) and
exploitation (selecting items of high value). We extend GP-Select to be able to
discover sets that simultaneously have high utility and are diverse. Our
preference for diversity can be specified as an arbitrary monotone submodular
function that quantifies the diminishing returns obtained when selecting
similar items. Furthermore, we exploit the structure of the model updates to
achieve an order of magnitude (up to 40X) speedup in our experiments without
resorting to approximations. We provide strong guarantees on the performance of
GP-Select and apply it to three real-world case studies of industrial
relevance: (1) Refreshing a repository of prices in a Global Distribution
System for the travel industry, (2) Identifying diverse, binding-affine
peptides in a vaccine de- sign task and (3) Maximizing clicks in a web-scale
recommender system by recommending items to users
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