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LSM-based Storage Techniques: A Survey
Recently, the Log-Structured Merge-tree (LSMtree) has been widely adopted for use in the storage layer of
modern NoSQL systems. Because of this, there have been
a large number of research efforts, from both the database
community and the operating systems community, that try
to improve various aspects of LSM-trees. In this paper, we
provide a survey of recent research efforts on LSM-trees so
that readers can learn the state-of-the-art in LSM-based storage techniques. We provide a general taxonomy to classify
the literature of LSM-trees, survey the efforts in detail, and
discuss their strengths and trade-offs. We further survey several representative LSM-based open-source NoSQL systems
and discuss some potential future research directions resulting from the survey
data-structures  lsm  nosql  storage  index  data  rocksdb  leveldb  cassandra  hbase  database  filetype:pdf  comp-sci  paper 
3 days ago by jabley
Identifying HTTPS-Protected Netflix Videos in Real-Time
After more than a year of research and development, Netflix
recently upgraded their infrastructure to provide HTTPS
encryption of video streams in order to protect the privacy of their
viewers. Despite this upgrade, we demonstrate that it is possible to
accurately identify Netflix videos from passive traffic capture in
real-time with very limited hardware requirements. Specifically,
we developed a system that can report the Netflix video being
delivered by a TCP connection using only the information
provided by TCP/IP headers.
To support our analysis, we created a fingerprint database
comprised of 42,027 Netflix videos. Given this collection of
fingerprints, we show that our system can differentiate between
videos with greater than 99.99% accuracy. Moreover, when tested
against 200 random 20-minute video streams, our system
identified 99.5% of the videos with the majority of the
identifications occurring less than two and a half minutes into the
video stream.
filetype:pdf  paper  https  data  leak  tls  security 
april 2017 by jabley
Fast Lossless Compression of Scientific Floating-Point Data
In scientific computing environments, large amounts of floating-point data often need to
be transferred between computers as well as to and from storage devices. Compression
can reduce the number of bits that need to be transferred and stored. However, the runtime
overhead due to compression may be undesirable in high-performance settings
where short communication latencies and high bandwidths are essential. This paper describes
and evaluates a new compression algorithm that is tailored to such environments.
It typically compresses numeric floating-point values better and faster than other algorithms
do. On our data sets, it achieves compression ratios between 1.2 and 4.2 as well
as compression and decompression throughputs between 2.8 and 5.9 million 64-bit double-precision
numbers per second on a 3GHz Pentium 4 machine
paper  comp-sci  compression  algorithms  data  filetype:pdf 
april 2017 by jabley
High Throughput Compression of Double-Precision Floating-Point Data
This paper describes FPC, a lossless compression algorithm for linear streams of 64-bit
floating-point data. FPC is designed to compress well while at the same time meeting the
high throughput demands of scientific computing environments. On our thirteen datasets,
it achieves a substantially higher average compression ratio than BZIP2, DFCM, FSD,
GZIP, and PLMI. At comparable compression ratios, it compresses and decompresses 8
to 300 times faster than the other five algorithms.
paper  comp-sci  compression  algorithms  data  filetype:pdf 
april 2017 by jabley
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