172 research outputs found
Coding for Fast Content Download
We study the fundamental trade-off between storage and content download time.
We show that the download time can be significantly reduced by dividing the
content into chunks, encoding it to add redundancy and then distributing it
across multiple disks. We determine the download time for two content access
models - the fountain and fork-join models that involve simultaneous content
access, and individual access from enqueued user requests respectively. For the
fountain model we explicitly characterize the download time, while in the
fork-join model we derive the upper and lower bounds. Our results show that
coding reduces download time, through the diversity of distributing the data
across more disks, even for the total storage used.Comment: 8 pages, 6 figures, conferenc
Efficient Task Replication for Fast Response Times in Parallel Computation
One typical use case of large-scale distributed computing in data centers is
to decompose a computation job into many independent tasks and run them in
parallel on different machines, sometimes known as the "embarrassingly
parallel" computation. For this type of computation, one challenge is that the
time to execute a task for each machine is inherently variable, and the overall
response time is constrained by the execution time of the slowest machine. To
address this issue, system designers introduce task replication, which sends
the same task to multiple machines, and obtains result from the machine that
finishes first. While task replication reduces response time, it usually
increases resource usage. In this work, we propose a theoretical framework to
analyze the trade-off between response time and resource usage. We show that,
while in general, there is a tension between response time and resource usage,
there exist scenarios where replicating tasks judiciously reduces completion
time and resource usage simultaneously. Given the execution time distribution
for machines, we investigate the conditions for a scheduling policy to achieve
optimal performance trade-off, and propose efficient algorithms to search for
optimal or near-optimal scheduling policies. Our analysis gives insights on
when and why replication helps, which can be used to guide scheduler design in
large-scale distributed computing systems.Comment: Extended version of the 2-page paper accepted to ACM SIGMETRICS 201
Throughput-Smoothness Trade-offs in Multicasting of an Ordered Packet Stream
An increasing number of streaming applications need packets to be strictly
in-order at the receiver. This paper provides a framework for analyzing
in-order packet delivery in such applications. We consider the problem of
multicasting an ordered stream of packets to two users over independent erasure
channels with instantaneous feedback to the source. Depending upon the channel
erasures, a packet which is in-order for one user, may be redundant for the
other. Thus there is an inter-dependence between throughput and the smoothness
of in-order packet delivery to the two users. We use a Markov chain model of
packet decoding to analyze these throughput-smoothness trade-offs of the users,
and propose coding schemes that can span different points on each trade-off.Comment: Accepted to NetCod 201
- …