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VIDEO STREAMING IN DISTRIBUTED ERASURE-CODED STORAGE SYSTEMS: STALL DURATION
ANALYSIS

 * March 2017
 * IEEE/ACM Transactions on Networking PP(99)

DOI:10.1109/TNET.2018.2851379
Authors:
Abubakr Alabbasi
 * Purdue University



Vaneet Aggarwal
 * Purdue University



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Citations (29)
References (60)
Figures (4)





ABSTRACT AND FIGURES

The demand for global video has been burgeoning across industries. With the
expansion and improvement of video streaming services, cloud-based video is
evolving into a necessary feature of any successful business for reaching
internal and external audiences. This paper considers video streaming over
distributed systems where the video segments are encoded using an erasure code
for better reliability thus being the first work to our best knowledge that
considers video streaming over erasure-coded distributed cloud systems. The
download time of each coded chunk of each video segment is characterized and
ordered statistics over the choice of the erasure-coded chunks is used to obtain
the playback time of different video segments. Using the playback times, bounds
on the moment generating function on the stall duration is used to bound the
mean stall duration. Moment generating function based bounds on the ordered
statistics are also used to bound the stall duration tail probability which
determines the probability that the stall time is greater than a pre-defined
number. These two metrics, mean stall duration and the stall duration tail
probability, are important quality of experience (QoE) measures for the end
users. Based on these metrics, we formulate an optimization problem to jointly
minimize the convex combination of both the QoE metrics averaged over all
requests over the placement and access of the video content. The non-convex
problem is solved using an efficient iterative algorithm. Numerical results show
significant improvement in QoE metrics for cloud-based video as compared to the
considered baselines.
A schematic illustrates video fragmentation and erasure-coding processes. Video
i is composed of L i segments. Each segments is partitioned into k i chunks and
then encoded using an (n i , k i ) MDS code.
… 
An Illustration of a distributed storage system equipped with m nodes and
storing 3 video files assuming (n i , k i ) erasure codes.
… 
An Example of the instantaneous queue status at server q, where q ∈ 1, 2, ...,
m.
… 
Comparison between our upper bound on download time and the upper bound proposed
in [14], [16]. We vary the arrival rate of file i from 0.5 × λ i to λ i , where
λ i is the base arrival rate. Our proposed upper bound outperforms that in [14],
[16], especially for high load.
… 

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CITATIONS (29)


REFERENCES (60)




... These articles have provided analytical results for a static (n, k)
redundancy under various simplified settings. For memoryless service, tight
numerical bounds are presented in [29], analytical bounds are provided in [23],
[24], [30], [31], tight analytical approximations in [22], and exact analysis
for small systems in [3]. An exact analysis of tail index for Pareto-distributed
file sizes is studied in [33], and an exact analysis for random independent
scheduling for asymptotically large number of servers in [32]. ...
... random execution time T i with distribution function F for each scheduled
coded subtask on this server. Recent works [15], [24], [31], [37] suggest that a
shifted exponential distribution is a good fit for modeling the service time
distribution in distributed computation networks. It is suggested that the
service time for each computation of coded subtask can be modeled by two
aggregate components; a constant server start-time and a random memoryless
component. ...

Single-Forking of Coded Subtasks for Straggler Mitigation
Article
 * Jul 2021
 * IEEE ACM T NETWORK

 * Ajay Badita
 * Parimal Parag
 * Vaneet Aggarwal

Given the unpredictable nature of the nodes in distributed computing systems,
some of the tasks can be significantly delayed. Such delayed tasks are called
stragglers. Straggler mitigation can be achieved by redundant computation. In
maximum distance separable (MDS) redundancy method, a task is divided into $k$
subtasks which are encoded to $n$ coded subtasks, such that a task is completed
if any $k$ out of $n$ coded subtasks are completed. Two important metrics of
interest are task completion time, and server utilization which is the aggregate
completed work by all servers in this duration. We consider a proactive
straggler mitigation strategy where $n_{0}$ out of $n$ coded subtasks are
started at time 0 while the remaining $n-n_{0}$ coded subtasks are launched when
$\ell _{0}\le \min \left \{{n_{0},k}\right \}$ of the initial ones finish. The
coded subtasks are halted when $k$ of them finish. For this flexible forking
strategy with multiple parameters, we analyze the mean of two performance
metrics when the random service completion time at each server is independent
and distributed identically ( i.i.d. ) to a shifted exponential. From this
study, we find a tradeoff between the metrics which provides insights into the
parameter choices. Experiments on Intel DevCloud illustrate that the shifted
exponential distribution adequately captures the random coded subtask completion
times, and our derived insights continue to hold.
View
Show abstract
... The problem remains open for systems with larger number of servers. For
exponentially distributed service times and Poisson arrivals, bounds are
presented in [11], [12], [31]- [33], [38], [39], and analytical approximations
in [15]. Exact analysis for special case of large systems is considered in [35],
[40], and small systems in [34]. ...

Modeling Performance and Energy trade-offs in Online Data-Intensive Applications
Preprint
Full-text available
 * Aug 2021

 * Ajay Badita
 * Rooji Jinan
 * Balajee Vamanan
 * Parimal Parag

We consider energy minimization for data-intensive applications run on large
number of servers, for given performance guarantees. We consider a system, where
each incoming application is sent to a set of servers, and is considered to be
completed if a subset of them finish serving it. We consider a simple case when
each server core has two speed levels, where the higher speed can be achieved by
higher power for each core independently. The core selects one of the two speeds
probabilistically for each incoming application request. We model arrival of
application requests by a Poisson process, and random service time at the server
with independent exponential random variables. Our model and analysis
generalizes to today's state-of-the-art in CPU energy management where each core
can independently select a speed level from a set of supported speeds and
corresponding voltages. The performance metrics under consideration are the mean
number of applications in the system and the average energy expenditure. We
first provide a tight approximation to study this previously intractable problem
and derive closed form approximate expressions for the performance metrics when
service times are exponentially distributed. Next, we study the trade-off
between the approximate mean number of applications and energy expenditure in
terms of the switching probability.
View
Show abstract
... Quality of Experince(QoE) for Video gushing in a distributed environment is
encoded by an erasure code. [1], [2]. A customization or contextual system is
required to dectect event based or semantic based video anaysis with less
computational power and hardware. ...

Deep Learning Based Smart Survilance Robot
Conference Paper
Full-text available
 * Jan 2021

 * Dr Vithya Ganesan
 * Smritilekha Das
 * Tamal Kundu
 * S. Bushra

View
Video File Allocation for Wear-Leveling in Distributed Storage Systems With
Heterogeneous Solid-State-Disks (SSDs)
Article
 * Jan 2022
 * IEEE T CIRC SYST VID

 * Dayoung Lee
 * Joonho Lee
 * Minseok Song

With the advent of new large-capacity solid-state disks (SSDs) such as
quad-level-cells (QLC), SSD arrays can be effectively used in video storage
systems that require large-capacity storage space. Typically, SSD manufacturers
specify a drive-writes-per-day (DWPD) metric, which is the ratio of bytes
written per day to the total capacity in bytes, to ensure an SSD’s specified
lifetime; it is important to limit the number of write operations by considering
the DWPD for each SSD. We propose a new video file allocation technique to
effectively manage the heterogeneous DWPD characteristics of SSDs in distributed
storage systems. To express the degree of wear-leveling for heterogeneous SSDs,
we first introduce the concept of ADWD, which is the actual number of bytes
written per day compared to DWPD. We then propose two algorithms for file
placement and migration. The file placement algorithm places files greedily
based on the bandwidth-to-space ratio (BSR) of each file and SSD to balance the
bandwidth usage and storage of the SSD. The file migration algorithm moves files
from overloaded to underloaded SSDs to meet bandwidth limit requirements while
minimizing the overall ADWD as a result of migration, and then migrates
additional popular files to improve SSD bandwidth utilization. To use these
algorithms in actual distributed file systems, we implemented a suite of tools
for file placement and migration in the Hadoop distributed file system (HDFS).
Experimental results show that the proposed algorithm reduces the mean of ADWD
by 35.44% and its standard deviation by 69.78% compared to the benchmark methods
on average.
View
Show abstract
EC-360: Speeding Up 360° Video Streaming Using Tile-based Online Erasure Coding
Conference Paper
 * Dec 2021

 * Jianxin Shi
 * Lingjun pu
 * Tian Zhang
 * Jingdong Xu

View
Latency Optimal Storage and Scheduling of Replicated Fragments for Memory
Constrained Servers
Article
 * Jun 2022
 * IEEE T INFORM THEORY

 * Rooji Jinan
 * Ajay Badita
 * Pradeep Kiran Sarvepalli
 * Parimal Parag

We consider the setting of a distributed storage system where a single file is
subdivided into smaller fragments of same size which are then replicated with a
common replication factor across servers of identical cache size. An incoming
file download request is sent to all the servers, and the download is completed
whenever the request gathers all the fragments. At each server, we are
interested in determining the set of fragments to be stored, and the sequence in
which fragments should be accessed, such that the mean file download time for a
request is minimized. We model the fragment download time as an exponential
random variable independent and identically distributed for all fragments across
all servers, and show that the mean file download time can be lower bounded in
terms of the expected number of useful servers summed over all distinct fragment
downloads. We present deterministic storage schemes that attempt to maximize the
number of useful servers. We show that finding the optimal sequence of accessing
the fragments is a Markov decision problem, whose complexity grows exponentially
with the number of fragments. We propose heuristic algorithms that determine the
sequence of access to the fragments which are empirically shown to perform well.
View
Show abstract
Latency-Redundancy Tradeoff in Distributed Read-Write Systems
Conference Paper
 * Jan 2022

 * Saraswathy Ramanathan
 * Gaurav Gautam
 * Vikram Srinivasan
 * Parimal Parag

View
Latency Minimization for Mobile Edge Computing Networks
Article
 * Oct 2021
 * IEEE T MOBILE COMPUT

 * Chang-Lin Chen
 * Christopher G. Brinton
 * Vaneet Aggarwal

The proliferation of data-intensive mobile applications is causing latency to
become an issue in mobile edge computing (MEC) systems. In this work, we propose
a novel methodology that optimizes communication, computation, and caching
configurations in MEC to minimize the mean latency experienced by mobile
devices. Transmission and computation processes are modeled using M/G/1 queues
to account for service rates and warm-up times. Our caching scheme includes time
variables for each file at each edge server in determining when to discard files
from storage. We theoretically analyze the latency experienced by mobile devices
due to communication, computation, and caching, showing how MEC system latency
depends on the offloading decisions of mobile devices, bandwidth and CPU
resources, and expiration times of files in the storage of edge servers. Our
method for solving the latency minimization problem consists of two main
components: iNner cOnVex Approximation (NOVA) to deal with non-convexity in the
optimization, and an online algorithm for preventing cache storage violations as
new tasks arrive and are serviced by the MEC system. Simulation results show
that our algorithm outperforms several baselines in minimizing latency, and
verify the benefit of including different resource allocation variables in our
optimization.
View
Show abstract
Latency-Redundancy Tradeoff in Distributed Read-Write Systems
Preprint
Full-text available
 * Aug 2021

 * Saraswathy Ramanathan
 * Gaurav Gautam
 * Vikram Srinivasan
 * Parimal Parag

Data is replicated and stored redundantly over multiple servers for availability
in distributed databases. We focus on databases with frequent reads and writes,
where both read and write latencies are important. This is in contrast to
databases designed primarily for either read or write applications. Redundancy
has contrasting effects on read and write latency. Read latency can be reduced
by potential parallel access from multiple servers, whereas write latency
increases as a larger number of replicas have to be updated. We quantify this
tradeoff between read and write latency as a function of redundancy, and provide
a closed-form approximation when the request arrival is Poisson and the service
is memoryless. We empirically show that this approximation is tight across all
ranges of system parameters. Thus, we provide guidelines for redundancy
selection in distributed databases.
View
Show abstract
Optimizing QoS for Erasure-Coded Wireless Data Centers
Conference Paper
 * Jun 2021

 * Srujan Thomdapu
 * Ketan Rajawat

View
Show more

Taming Tail Latency for Erasure-coded, Distributed Storage Systems
Article
Full-text available
 * Mar 2017

 * Vaneet Aggarwal
 * Abubakr Alabbasi
 * Jingxian Fan
 * Tian Lan

Distributed storage systems are known to be susceptible to long tails in
response time. In modern online storage systems such as Bing, Facebook, and
Amazon, the long tails of the service latency are of particular concern. with
99.9th percentile response times being orders of magnitude worse than the mean.
As erasure codes emerge as a popular technique to achieve high data reliability
in distributed storage while attaining space efficiency, taming tail latency
still remains an open problem due to the lack of mathematical models for
analyzing such systems. To this end, we propose a framework for quantifying and
optimizing tail latency in erasure-coded storage systems. In particular, we
derive upper bounds on tail latency in closed form for arbitrary service time
distribution and heterogeneous files. Based on the model, we formulate an
optimization problem to jointly minimize the weighted latency tail probability
of all files over the placement of files on the servers, and the choice of
servers to access the requested files. The non-convex problem is solved using an
efficient, alternating optimization algorithm. Numerical results show
significant reduction of tail latency for erasure-coded storage systems with a
realistic workload.
View
Show abstract
Taming tail latency for erasure-coded, distributee storage systems
Conference Paper
 * May 2017

 * Vaneet Aggarwal
 * Jingxian Fan
 * Tian Lan

View
The MDS Queue: Analysing the Latency Performance of Erasure Codes
Article
 * Feb 2017
 * IEEE T INFORM THEORY

 * Kangwook Lee
 * Nihar Shah
 * Longbo Huang
 * Kannan Ramchandran

In order to scale economically, data centers are increasingly evolving their
data storage methods from simple data replication to more powerful erasure
codes, which provide the same level of reliability as replication but at a
significantly lower storage cost. In particular, it is well known that Maximum-
Distance-Separable (MDS) codes, such as Reed-Solomon codes, can achieve a target
reliability with the maximum storage efficiency. While the use of codes for
providing improved reliability in archival storage systems, where data is less
frequently accessed (or so-called “cold data”), is well understood, the role of
codes in storing more frequently accessed and active “hot data”, where latency
is the key metric, is less clear. In this paper, we study data storage systems
based on MDS codes through the lens of queueing theory, and term the queueing
system arising under codes as an “MDS queue.” We provide lower and upper bounds
on the average job latency for both centralized and decentralized versions of
MDS queues. We also provide extensive simulations to corroborate our analysis as
well as obtain additional insights.
View
Show abstract
Exponential laws of computing growth
Article
 * Jan 2017
 * COMMUN ACM

 * Ted G Lewis

A new look at Moore’s Law opens an inquiry into the causes of exponential growth
in the power of computer chips and systems and their market adoptions. The
observed doubling of computational speeds relies on exponentially growing
processes at three levels of the computing ecosystem: chips, systems, and
communities. At the chip level, the exponential growth of number of components
on computer chips is enabled by the regular geometry of chip layouts. Multiple
cores were introduced when clock speeds maxed out in the 1990s; now cores double
with each chip generation. At the system level, Gustafson’s law assures us that
data intensive applications will keep the cores fully busy, thereby doubling the
computational output with every generation. And Koomey’s laws demonstrate the
continuing successes of system designers: both computation speeds of computer
systems and computations per unit of energy expenditure have doubled every 1.57
years. At the community level, the S-curve model of technology adoption is
exponential until its inflection point. Businesses try to forecast the
inflection points and jump to new technologies; technology jumping enables
exponential growth over many generations of technologies. Empirically validated
diffusion models of technology adoption account for the S-curves and show why
the initial stages of adoption grow exponentially. It is now plausible that
Moore’s-law-like exponential growth for information technologies will continue
for several more decades.
View
Show abstract
Parallel and Distributed Methods for Constrained Nonconvex Optimization􀀀Part I:
Theory
Article
 * Dec 2016
 * IEEE T SIGNAL PROCES

 * Gesualdo Scutari
 * Francisco Facchinei
 * Lorenzo Lampariello

In this two-part paper, we propose a general algorithmic framework for the
minimization of a nonconvex smooth function subject to nonconvex smooth
constraints, and also consider extensions to some structured, nonsmooth
problems. The algorithm solves a sequence of (separable) strongly convex
problems and maintains feasibility at each iteration. Convergence to a
stationary solution of the original nonconvex optimization is established. Our
framework is very general and flexible and unifies several existing Successive
Convex Approximation (SCA)- based algorithms More importantly, and differently
from current SCA approaches, it naturally leads to distributed and
parallelizable implementations for a large class of nonconvex problems. This
Part I is devoted to the description of the framework in its generality. In Part
II we customize our general methods to several multi-agent optimization problems
in communications, networking, and machine learning; the result is a new class
of centralized and distributed algorithms that compare favorably to existing
ad-hoc (centralized) schemes.
View
Show abstract
Parallel and Distributed Methods for Constrained Nonconvex Optimization--Part
II: Applications in Communications and Machine Learning
Article
 * Dec 2016
 * IEEE T SIGNAL PROCES

 * Gesualdo Scutari
 * Francisco Facchinei
 * Lorenzo Lampariello
 * Peiran Song

In Part I of this paper, we proposed and analyzed a novel algorithmic framework
for the minimization of a nonconvex objective function, subject to nonconvex
constraints, based on inner convex approximations. This Part II is devoted to
the (nontrivial) application of the framework to the following relevant
large-scale problems ranging from communications to machine learning: i)
(generalizations of) the rate profile maximization in MIMO interference
broadcast networks; ii) the max-min fair multicast multigroup beamforming
problem in a multicell environment; and iii) a general nonconvex constrained
bicriteria formulation for k-sparse variable selection in statistical learning;
the two criteria are a nonconvex loss objective function, measuring the fitness
of the model to data, and the latter is a nonconvex sparsity-inducing constraint
in the general form of difference-of-convex (DC) functions, which allows to
accomodate in a unified fashion convex and nonconvex surrogates of the '0
function. The proposed algorithms outperform current state-ofthe- art schemes
for i)-iii) both theoretically and numerically. For instance, they are the first
distributed schemes for the class of problems i) and ii); and they also lead to
subproblems enjoying closed form solutions.
View
Show abstract
Implementation of cloud based live streaming for surveillance
Conference Paper
 * Apr 2016

 * Neel Oza
 * Naitik B Gohil

Rapid technological growth made surveillance as most promising application
domain. With great extent of smart city most of the things are controlled by
internet. Security is one of the applications that everyone needs to be
controlled remotely. This paper presents cloud based surveillance system for
live video streaming that can be surveillance from anywhere and anytime. This
system provides the live streaming by using cloud; Raspberry Pi 2 module and
FFMPEG based USB Camera.
View
Show abstract
MP-DASH: Adaptive Video Streaming Over Preference-Aware Multipath
Conference Paper
 * Nov 2016

 * Bo Han
 * Feng Qian
 * Lusheng Ji
 * Vijay Gopalakrishnan

Compared with using only a single wireless path such as WiFi, leveraging
multipath (e.g., WiFi and cellular) can dramatically improve users' quality of
experience (QoE) for mobile video streaming. However, Multipath TCP (MPTCP), the
de-facto multipath solution, lacks the support to prioritize one path over
another. When applied to video streaming, it may cause undesired network usage
such as substantial over-utilization of the metered cellular link. In this
paper, we propose MP-DASH, a multipath framework for video streaming with the
awareness of network interface preferences from users. The basic idea behind
MP-DASH is to strategically schedule video chunks' delivery and thus satisfy
user preferences. MP-DASH can work with a wide range of off-the-shelf video rate
adaptation algorithms with very small changes. Our extensive field studies at 33
locations in three U.S. states suggest that MP-DASH is very effective: it can
reduce cellular usage by up to 99% and radio energy consumption by up to 85%
with negligible degradation of QoE, compared with off-the-shelf MPTCP.
View
Show abstract
The fork-join queue and related systems with synchronization constraints:
stochastic ordering and computable bounds
Article
 * Sep 1989

 * Francois Baccelli
 * Armand M Makowski
 * Adam Shwartz

A simple queueing system, known as the fork-join queue, is considered with basic
performance measure defined as the delay between the fork and join dates. Simple
lower and upper bounds are derived for some of the statistics of this quantity.
They are obtained, in both transient and steady-state regimes, by stochastically
comparing the original system to other queueing systems with a structure simpler
than the original system, yet with identical stability characteristics. In
steady-state, under renewal assumptions, the computation reduces to standard
GI/GI /1 calculations and the bounds constitute a first sizing-up of system
performance. These bounds can also be used to show that for homogeneous
fork-join queue system under assumptions, the moments of the system response
time grow logarithmically in the number of parallel processors provided the
service time distribution has rational Laplace–Stieltjes transform. The bounding
arguments combine ideas from the theory of stochastic ordering with the notion
of associated random variables, and are of independent interest to study various
other queueing systems with synchronization constraints. The paper is an
abridged version of a more complete report on the matter [6].
View
Show abstract
Cache content-selection policies for streaming video services
Conference Paper
 * Apr 2016

 * Stefan Dernbach
 * Nina Taft
 * Jim Kurose
 * Azin Ashkan

View
Show more




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