331,686 research outputs found
Experimental Performance Evaluation of Cloud-Based Analytics-as-a-Service
An increasing number of Analytics-as-a-Service solutions has recently seen
the light, in the landscape of cloud-based services. These services allow
flexible composition of compute and storage components, that create powerful
data ingestion and processing pipelines. This work is a first attempt at an
experimental evaluation of analytic application performance executed using a
wide range of storage service configurations. We present an intuitive notion of
data locality, that we use as a proxy to rank different service compositions in
terms of expected performance. Through an empirical analysis, we dissect the
performance achieved by analytic workloads and unveil problems due to the
impedance mismatch that arise in some configurations. Our work paves the way to
a better understanding of modern cloud-based analytic services and their
performance, both for its end-users and their providers.Comment: Longer version of the paper in Submission at IEEE CLOUD'1
Evaluation of Docker Containers for Scientific Workloads in the Cloud
The HPC community is actively researching and evaluating tools to support
execution of scientific applications in cloud-based environments. Among the
various technologies, containers have recently gained importance as they have
significantly better performance compared to full-scale virtualization, support
for microservices and DevOps, and work seamlessly with workflow and
orchestration tools. Docker is currently the leader in containerization
technology because it offers low overhead, flexibility, portability of
applications, and reproducibility. Singularity is another container solution
that is of interest as it is designed specifically for scientific applications.
It is important to conduct performance and feature analysis of the container
technologies to understand their applicability for each application and target
execution environment. This paper presents a (1) performance evaluation of
Docker and Singularity on bare metal nodes in the Chameleon cloud (2) mechanism
by which Docker containers can be mapped with InfiniBand hardware with RDMA
communication and (3) analysis of mapping elements of parallel workloads to the
containers for optimal resource management with container-ready orchestration
tools. Our experiments are targeted toward application developers so that they
can make informed decisions on choosing the container technologies and
approaches that are suitable for their HPC workloads on cloud infrastructure.
Our performance analysis shows that scientific workloads for both Docker and
Singularity based containers can achieve near-native performance. Singularity
is designed specifically for HPC workloads. However, Docker still has
advantages over Singularity for use in clouds as it provides overlay networking
and an intuitive way to run MPI applications with one container per rank for
fine-grained resources allocation
Cloud-based or On-device: An Empirical Study of Mobile Deep Inference
Modern mobile applications are benefiting significantly from the advancement
in deep learning, e.g., implementing real-time image recognition and
conversational system. Given a trained deep learning model, applications
usually need to perform a series of matrix operations based on the input data,
in order to infer possible output values. Because of computational complexity
and size constraints, these trained models are often hosted in the cloud. To
utilize these cloud-based models, mobile apps will have to send input data over
the network. While cloud-based deep learning can provide reasonable response
time for mobile apps, it restricts the use case scenarios, e.g. mobile apps
need to have network access. With mobile specific deep learning optimizations,
it is now possible to employ on-device inference. However, because mobile
hardware, such as GPU and memory size, can be very limited when compared to its
desktop counterpart, it is important to understand the feasibility of this new
on-device deep learning inference architecture. In this paper, we empirically
evaluate the inference performance of three Convolutional Neural Networks
(CNNs) using a benchmark Android application we developed. Our measurement and
analysis suggest that on-device inference can cost up to two orders of
magnitude greater response time and energy when compared to cloud-based
inference, and that loading model and computing probability are two performance
bottlenecks for on-device deep inferences.Comment: Accepted at The IEEE International Conference on Cloud Engineering
(IC2E) conference 201
Analysis and Strategy for the Performance Testing in Cloud Computing
The aim of this study is the analysis and presentation of some ideas on performance testing in Cloud Computing. Performance is an important factor in testing a web application. Performance testing in cloud computing is different from that of traditional applications. Our research methodology in this article includes an overview of existing works on testing performance in Cloud Computing, focusing on discussion that the traditional benchmarks are not sufficient to analyze performance testing in Cloud Computing. In this study we are focused mainly on analysis performance metrics in Cloud Computing, based on their characteristics such as elasticity, scalability, pay-per-use and fault tolerance, and then we discuss why needed new strategies for performance testing in Cloud Computing and creation of new benchmarks. From this study we conclude that the performance testing and evaluation should be performed using new models testing, which are created according to Cloud Computing characteristics and metrics
A New Efficient Cloud Model for Data Intensive Application
Cloud computing play an important role in data intensive application since it provide a consistent performance over time and it provide scalability and good fault tolerant mechanism Hadoop provide a scalable data intensive map reduce architecture Hadoop map task are executed on large cluster and consumes lot of energy and resources Executing these tasks requires lot of resource and energy which are expensive so minimizing the cost and resource is critical for a map reduce application So here in this paper we propose a new novel efficient cloud structure algorithm for data processing or computation on azure cloud Here we propose an efficient BSP based dynamic scheduling algorithm for iterative MapReduce for data intensive application on Microsoft azure cloud platform Our framework can be used on different domain application such as data analysis medical research dataminining etc Here we analyze the performance of our system by using a co-located cashing on the worker role and how it is improving the performance of data intensive application over Hadoop map reduce data intrinsic application The experimental result shows that our proposed framework properly utilizes cloud infrastructure service management overheads bandwith bottleneck and it is high scalable fault tolerant and efficien
A Process Framework for Managing Quality of Service in Private Cloud
As information systems leaders tap into the global market of cloud computing-based services, they struggle to maintain consistent application performance due to lack of a process framework for managing quality of service (QoS) in the cloud. Guided by the disruptive innovation theory, the purpose of this case study was to identify a process framework for meeting the QoS requirements of private cloud service users. Private cloud implementation was explored by selecting an organization in California through purposeful sampling. Information was gathered by interviewing 23 information technology (IT) professionals, a mix of frontline engineers, managers, and leaders involved in the implementation of private cloud. Another source of data was documents such as standard operating procedures, policies, and guidelines related to private cloud implementation. Interview transcripts and documents were coded and sequentially analyzed. Three prominent themes emerged from the analysis of data: (a) end user expectations, (b) application architecture, and (c) trending analysis. The findings of this study may help IT leaders in effectively managing QoS in cloud infrastructure and deliver reliable application performance that may help in increasing customer population and profitability of organizations. This study may contribute to positive social change as information systems managers and workers can learn and apply the process framework for delivering stable and reliable cloud-hosted computer applications
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