3,028 research outputs found
Design Architecture-Based on Web Server and Application Cluster in Cloud Environment
Cloud has been a computational and storage solution for many data centric
organizations. The problem today those organizations are facing from the cloud
is in data searching in an efficient manner. A framework is required to
distribute the work of searching and fetching from thousands of computers. The
data in HDFS is scattered and needs lots of time to retrieve. The major idea is
to design a web server in the map phase using the jetty web server which will
give a fast and efficient way of searching data in MapReduce paradigm. For real
time processing on Hadoop, a searchable mechanism is implemented in HDFS by
creating a multilevel index in web server with multi-level index keys. The web
server uses to handle traffic throughput. By web clustering technology we can
improve the application performance. To keep the work down, the load balancer
should automatically be able to distribute load to the newly added nodes in the
server
Fast Data in the Era of Big Data: Twitter's Real-Time Related Query Suggestion Architecture
We present the architecture behind Twitter's real-time related query
suggestion and spelling correction service. Although these tasks have received
much attention in the web search literature, the Twitter context introduces a
real-time "twist": after significant breaking news events, we aim to provide
relevant results within minutes. This paper provides a case study illustrating
the challenges of real-time data processing in the era of "big data". We tell
the story of how our system was built twice: our first implementation was built
on a typical Hadoop-based analytics stack, but was later replaced because it
did not meet the latency requirements necessary to generate meaningful
real-time results. The second implementation, which is the system deployed in
production, is a custom in-memory processing engine specifically designed for
the task. This experience taught us that the current typical usage of Hadoop as
a "big data" platform, while great for experimentation, is not well suited to
low-latency processing, and points the way to future work on data analytics
platforms that can handle "big" as well as "fast" data
Impliance: A Next Generation Information Management Appliance
ably successful in building a large market and adapting to the changes of the
last three decades, its impact on the broader market of information management
is surprisingly limited. If we were to design an information management system
from scratch, based upon today's requirements and hardware capabilities, would
it look anything like today's database systems?" In this paper, we introduce
Impliance, a next-generation information management system consisting of
hardware and software components integrated to form an easy-to-administer
appliance that can store, retrieve, and analyze all types of structured,
semi-structured, and unstructured information. We first summarize the trends
that will shape information management for the foreseeable future. Those trends
imply three major requirements for Impliance: (1) to be able to store, manage,
and uniformly query all data, not just structured records; (2) to be able to
scale out as the volume of this data grows; and (3) to be simple and robust in
operation. We then describe four key ideas that are uniquely combined in
Impliance to address these requirements, namely the ideas of: (a) integrating
software and off-the-shelf hardware into a generic information appliance; (b)
automatically discovering, organizing, and managing all data - unstructured as
well as structured - in a uniform way; (c) achieving scale-out by exploiting
simple, massive parallel processing, and (d) virtualizing compute and storage
resources to unify, simplify, and streamline the management of Impliance.
Impliance is an ambitious, long-term effort to define simpler, more robust, and
more scalable information systems for tomorrow's enterprises.Comment: This article is published under a Creative Commons License Agreement
(http://creativecommons.org/licenses/by/2.5/.) You may copy, distribute,
display, and perform the work, make derivative works and make commercial use
of the work, but, you must attribute the work to the author and CIDR 2007.
3rd Biennial Conference on Innovative Data Systems Research (CIDR) January
710, 2007, Asilomar, California, US
Technical considerations towards mobile user QoE enhancement via Cloud interaction
This paper discusses technical considerations of a Cloud infrastructure which interacts with mobile devices in order to migrate part of the computational overhead from the mobile device to the Cloud. The aim of the interaction between the mobile device and the Cloud is the enhancement of parameters that affect the Quality of Experience (QoE) of the mobile end user through the offloading of computational aspects of demanding applications. This paper shows that mobile user’s QoE can be potentially enhanced by offloading computational tasks to the Cloud which incorporates a predictive context-aware mechanism to schedule delivery of content to the mobile end-user using a low-cost interaction model between the Cloud and the mobile user. With respect to the proposed enhancements, both the technical considerations of the cloud infrastructure are examined, as well as the interaction between the mobile device and the Cloud
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