1,943 research outputs found
ATP: a Datacenter Approximate Transmission Protocol
Many datacenter applications such as machine learning and streaming systems
do not need the complete set of data to perform their computation. Current
approximate applications in datacenters run on a reliable network layer like
TCP. To improve performance, they either let sender select a subset of data and
transmit them to the receiver or transmit all the data and let receiver drop
some of them. These approaches are network oblivious and unnecessarily transmit
more data, affecting both application runtime and network bandwidth usage. On
the other hand, running approximate application on a lossy network with UDP
cannot guarantee the accuracy of application computation. We propose to run
approximate applications on a lossy network and to allow packet loss in a
controlled manner. Specifically, we designed a new network protocol called
Approximate Transmission Protocol, or ATP, for datacenter approximate
applications. ATP opportunistically exploits available network bandwidth as
much as possible, while performing a loss-based rate control algorithm to avoid
bandwidth waste and re-transmission. It also ensures bandwidth fair sharing
across flows and improves accurate applications' performance by leaving more
switch buffer space to accurate flows. We evaluated ATP with both simulation
and real implementation using two macro-benchmarks and two real applications,
Apache Kafka and Flink. Our evaluation results show that ATP reduces
application runtime by 13.9% to 74.6% compared to a TCP-based solution that
drops packets at sender, and it improves accuracy by up to 94.0% compared to
UDP
Almost Optimal Distribution-Free Junta Testing
We consider the problem of testing whether an unknown n-variable Boolean function is a k-junta in the distribution-free property testing model, where the distance between functions is measured with respect to an arbitrary and unknown probability distribution over {0,1}^n. Chen, Liu, Servedio, Sheng and Xie [Zhengyang Liu et al., 2018] showed that the distribution-free k-junta testing can be performed, with one-sided error, by an adaptive algorithm that makes O~(k^2)/epsilon queries. In this paper, we give a simple two-sided error adaptive algorithm that makes O~(k/epsilon) queries
Pseudorandomness via the discrete Fourier transform
We present a new approach to constructing unconditional pseudorandom
generators against classes of functions that involve computing a linear
function of the inputs. We give an explicit construction of a pseudorandom
generator that fools the discrete Fourier transforms of linear functions with
seed-length that is nearly logarithmic (up to polyloglog factors) in the input
size and the desired error parameter. Our result gives a single pseudorandom
generator that fools several important classes of tests computable in logspace
that have been considered in the literature, including halfspaces (over general
domains), modular tests and combinatorial shapes. For all these classes, our
generator is the first that achieves near logarithmic seed-length in both the
input length and the error parameter. Getting such a seed-length is a natural
challenge in its own right, which needs to be overcome in order to derandomize
RL - a central question in complexity theory.
Our construction combines ideas from a large body of prior work, ranging from
a classical construction of [NN93] to the recent gradually increasing
independence paradigm of [KMN11, CRSW13, GMRTV12], while also introducing some
novel analytic machinery which might find other applications
Sparse solutions of linear Diophantine equations
We present structural results on solutions to the Diophantine system
,
with the smallest number of non-zero entries. Our tools are algebraic and
number theoretic in nature and include Siegel's Lemma, generating functions,
and commutative algebra. These results have some interesting consequences in
discrete optimization
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