19,319 research outputs found
Leveraging Deep Learning to Improve the Performance Predictability of Cloud Microservices
Performance unpredictability is a major roadblock towards cloud adoption, and
has performance, cost, and revenue ramifications. Predictable performance is
even more critical as cloud services transition from monolithic designs to
microservices. Detecting QoS violations after they occur in systems with
microservices results in long recovery times, as hotspots propagate and amplify
across dependent services. We present Seer, an online cloud performance
debugging system that leverages deep learning and the massive amount of tracing
data cloud systems collect to learn spatial and temporal patterns that
translate to QoS violations. Seer combines lightweight distributed RPC-level
tracing, with detailed low-level hardware monitoring to signal an upcoming QoS
violation, and diagnose the source of unpredictable performance. Once an
imminent QoS violation is detected, Seer notifies the cluster manager to take
action to avoid performance degradation altogether. We evaluate Seer both in
local clusters, and in large-scale deployments of end-to-end applications built
with microservices with hundreds of users. We show that Seer correctly
anticipates QoS violations 91% of the time, and avoids the QoS violation to
begin with in 84% of cases. Finally, we show that Seer can identify
application-level design bugs, and provide insights on how to better architect
microservices to achieve predictable performance
Multicast Transmission Prefix and Popularity Aware Interval Caching Based Admission Control Policy
Admission control is a key component in multimedia servers, which will allow
the resources to be used by the client only when they are available. A problem
faced by numerous content serving machines is overload, when there are too many
clients who need to be served, the server tends to slow down. An admission
control algorithm for a multimedia server is responsible for determining if a
new request can be accepted without violating the QoS requirements of the
existing requests in the system. By caching and streaming only the data in the
interval between two successive requests on the same object, the following
request can be serviced directly from the buffer cache without disk operations
and within the deadline of the request. An admission control strategy based on
Popularity-aware interval caching for Prefix [3] scheme extends the interval
caching by considering different popularity of multimedia objects. The method
of Prefix caching with multicast transmission of popular objects utilizes the
hard disk and network bandwidth efficiently and increases the number of
requests being served.Comment: 17 pages
An Open-Source Benchmark Suite for Cloud and IoT Microservices
Cloud services have recently started undergoing a major shift from monolithic
applications, to graphs of hundreds of loosely-coupled microservices.
Microservices fundamentally change a lot of assumptions current cloud systems
are designed with, and present both opportunities and challenges when
optimizing for quality of service (QoS) and utilization. In this paper we
explore the implications microservices have across the cloud system stack. We
first present DeathStarBench, a novel, open-source benchmark suite built with
microservices that is representative of large end-to-end services, modular and
extensible. DeathStarBench includes a social network, a media service, an
e-commerce site, a banking system, and IoT applications for coordination
control of UAV swarms. We then use DeathStarBench to study the architectural
characteristics of microservices, their implications in networking and
operating systems, their challenges with respect to cluster management, and
their trade-offs in terms of application design and programming frameworks.
Finally, we explore the tail at scale effects of microservices in real
deployments with hundreds of users, and highlight the increased pressure they
put on performance predictability
Collaborative filtering via sparse Markov random fields
Recommender systems play a central role in providing individualized access to
information and services. This paper focuses on collaborative filtering, an
approach that exploits the shared structure among mind-liked users and similar
items. In particular, we focus on a formal probabilistic framework known as
Markov random fields (MRF). We address the open problem of structure learning
and introduce a sparsity-inducing algorithm to automatically estimate the
interaction structures between users and between items. Item-item and user-user
correlation networks are obtained as a by-product. Large-scale experiments on
movie recommendation and date matching datasets demonstrate the power of the
proposed method
Parallel and Distributed Collaborative Filtering: A Survey
Collaborative filtering is amongst the most preferred techniques when
implementing recommender systems. Recently, great interest has turned towards
parallel and distributed implementations of collaborative filtering algorithms.
This work is a survey of the parallel and distributed collaborative filtering
implementations, aiming not only to provide a comprehensive presentation of the
field's development, but also to offer future research orientation by
highlighting the issues that need to be further developed.Comment: 46 page
Mobile Multimedia Recommendation in Smart Communities: A Survey
Due to the rapid growth of internet broadband access and proliferation of
modern mobile devices, various types of multimedia (e.g. text, images, audios
and videos) have become ubiquitously available anytime. Mobile device users
usually store and use multimedia contents based on their personal interests and
preferences. Mobile device challenges such as storage limitation have however
introduced the problem of mobile multimedia overload to users. In order to
tackle this problem, researchers have developed various techniques that
recommend multimedia for mobile users. In this survey paper, we examine the
importance of mobile multimedia recommendation systems from the perspective of
three smart communities, namely, mobile social learning, mobile event guide and
context-aware services. A cautious analysis of existing research reveals that
the implementation of proactive, sensor-based and hybrid recommender systems
can improve mobile multimedia recommendations. Nevertheless, there are still
challenges and open issues such as the incorporation of context and social
properties, which need to be tackled in order to generate accurate and
trustworthy mobile multimedia recommendations
A Survey Paper on Recommender Systems
Recommender systems apply data mining techniques and prediction algorithms to
predict users' interest on information, products and services among the
tremendous amount of available items. The vast growth of information on the
Internet as well as number of visitors to websites add some key challenges to
recommender systems. These are: producing accurate recommendation, handling
many recommendations efficiently and coping with the vast growth of number of
participants in the system. Therefore, new recommender system technologies are
needed that can quickly produce high quality recommendations even for huge data
sets.
To address these issues we have explored several collaborative filtering
techniques such as the item based approach, which identify relationship between
items and indirectly compute recommendations for users based on these
relationships. The user based approach was also studied, it identifies
relationships between users of similar tastes and computes recommendations
based on these relationships.
In this paper, we introduce the topic of recommender system. It provides ways
to evaluate efficiency, scalability and accuracy of recommender system. The
paper also analyzes different algorithms of user based and item based
techniques for recommendation generation. Moreover, a simple experiment was
conducted using a data mining application -Weka- to apply data mining
algorithms to recommender system. We conclude by proposing our approach that
might enhance the quality of recommender systems.Comment: This paper has some typos in i
Pushing the Boundaries of Crowd-enabled Databases with Query-driven Schema Expansion
By incorporating human workers into the query execution process crowd-enabled
databases facilitate intelligent, social capabilities like completing missing
data at query time or performing cognitive operators. But despite all their
flexibility, crowd-enabled databases still maintain rigid schemas. In this
paper, we extend crowd-enabled databases by flexible query-driven schema
expansion, allowing the addition of new attributes to the database at query
time. However, the number of crowd-sourced mini-tasks to fill in missing values
may often be prohibitively large and the resulting data quality is doubtful.
Instead of simple crowd-sourcing to obtain all values individually, we leverage
the user-generated data found in the Social Web: By exploiting user ratings we
build perceptual spaces, i.e., highly-compressed representations of opinions,
impressions, and perceptions of large numbers of users. Using few training
samples obtained by expert crowd sourcing, we then can extract all missing data
automatically from the perceptual space with high quality and at low costs.
Extensive experiments show that our approach can boost both performance and
quality of crowd-enabled databases, while also providing the flexibility to
expand schemas in a query-driven fashion.Comment: VLDB201
Consumer Grade Brain Sensing for Emotion Recognition
For several decades, electroencephalography (EEG) has featured as one of the
most commonly used tools in emotional state recognition via monitoring of
distinctive brain activities. An array of datasets have been generated with the
use of diverse emotion-eliciting stimuli and the resulting brainwave responses
conventionally captured with high-end EEG devices. However, the applicability
of these devices is to some extent limited by practical constraints and may
prove difficult to be deployed in highly mobile context omnipresent in everyday
happenings. In this study, we evaluate the potential of OpenBCI to bridge this
gap by first comparing its performance to research grade EEG system, employing
the same algorithms that were applied on benchmark datasets. Moreover, for the
purpose of emotion classification, we propose a novel method to facilitate the
selection of audio-visual stimuli of high/low valence and arousal. Our setup
entailed recruiting 200 healthy volunteers of varying years of age to identify
the top 60 affective video clips from a total of 120 candidates through
standardized self assessment, genre tags, and unsupervised machine learning.
Additional 43 participants were enrolled to watch the pre-selected clips during
which emotional EEG brainwaves and peripheral physiological signals were
collected. These recordings were analyzed and extracted features fed into a
classification model to predict whether the elicited signals were associated
with a high or low level of valence and arousal. As it turned out, our
prediction accuracies were decidedly comparable to those of previous studies
that utilized more costly EEG amplifiers for data acquisition
Distributed Graphical Simulation in the Cloud
Graphical simulations are a cornerstone of modern media and films. But
existing software packages are designed to run on HPC nodes, and perform poorly
in the computing cloud. These simulations have complex data access patterns
over complex data structures, and mutate data arbitrarily, and so are a poor
fit for existing cloud computing systems. We describe a software architecture
for running graphical simulations in the cloud that decouples control logic,
computations and data exchanges. This allows a central controller to balance
load by redistributing computations, and recover from failures. Evaluations
show that the architecture can run existing, state-of-the-art simulations in
the presence of stragglers and failures, thereby enabling this large class of
applications to use the computing cloud for the first time
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