965 research outputs found
More Analysis of Double Hashing for Balanced Allocations
With double hashing, for a key , one generates two hash values and
, and then uses combinations for
to generate multiple hash values in the range from the initial two.
For balanced allocations, keys are hashed into a hash table where each bucket
can hold multiple keys, and each key is placed in the least loaded of
choices. It has been shown previously that asymptotically the performance of
double hashing and fully random hashing is the same in the balanced allocation
paradigm using fluid limit methods. Here we extend a coupling argument used by
Lueker and Molodowitch to show that double hashing and ideal uniform hashing
are asymptotically equivalent in the setting of open address hash tables to the
balanced allocation setting, providing further insight into this phenomenon. We
also discuss the potential for and bottlenecks limiting the use this approach
for other multiple choice hashing schemes.Comment: 13 pages ; current draft ; will be submitted to conference shortl
Scheduling with Predictions and the Price of Misprediction
In many traditional job scheduling settings, it is assumed that one knows the time it will take for a job to complete service. In such cases, strategies such as shortest job first can be used to improve performance in terms of measures such as the average time a job waits in the system. We consider the setting where the service time is not known, but is predicted by for example a machine learning algorithm. Our main result is the derivation, under natural assumptions, of formulae for the performance of several strategies for queueing systems that use predictions for service times in order to schedule jobs. As part of our analysis, we suggest the framework of the "price of misprediction," which offers a measure of the cost of using predicted information
Balanced Allocations and Double Hashing
Double hashing has recently found more common usage in schemes that use
multiple hash functions. In double hashing, for an item , one generates two
hash values and , and then uses combinations for to generate multiple hash values from the initial two. We
first perform an empirical study showing that, surprisingly, the performance
difference between double hashing and fully random hashing appears negligible
in the standard balanced allocation paradigm, where each item is placed in the
least loaded of choices, as well as several related variants. We then
provide theoretical results that explain the behavior of double hashing in this
context.Comment: Further updated, small improvements/typos fixe
Statistically-secure ORAM with Overhead
We demonstrate a simple, statistically secure, ORAM with computational
overhead ; previous ORAM protocols achieve only
computational security (under computational assumptions) or require
overheard. An additional benefit of our ORAM is its
conceptual simplicity, which makes it easy to implement in both software and
(commercially available) hardware.
Our construction is based on recent ORAM constructions due to Shi, Chan,
Stefanov, and Li (Asiacrypt 2011) and Stefanov and Shi (ArXiv 2012), but with
some crucial modifications in the algorithm that simplifies the ORAM and enable
our analysis. A central component in our analysis is reducing the analysis of
our algorithm to a "supermarket" problem; of independent interest (and of
importance to our analysis,) we provide an upper bound on the rate of "upset"
customers in the "supermarket" problem
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