4,015 research outputs found
Reliable Messaging to Millions of Users with MigratoryData
Web-based notification services are used by a large range of businesses to
selectively distribute live updates to customers, following the
publish/subscribe (pub/sub) model. Typical deployments can involve millions of
subscribers expecting ordering and delivery guarantees together with low
latencies. Notification services must be vertically and horizontally scalable,
and adopt replication to provide a reliable service. We report our experience
building and operating MigratoryData, a highly-scalable notification service.
We discuss the typical requirements of MigratoryData customers, and describe
the architecture and design of the service, focusing on scalability and fault
tolerance. Our evaluation demonstrates the ability of MigratoryData to handle
millions of concurrent connections and support a reliable notification service
despite server failures and network disconnections
Dynamic Physiological Partitioning on a Shared-nothing Database Cluster
Traditional DBMS servers are usually over-provisioned for most of their daily
workloads and, because they do not show good-enough energy proportionality,
waste a lot of energy while underutilized. A cluster of small (wimpy) servers,
where its size can be dynamically adjusted to the current workload, offers
better energy characteristics for these workloads. Yet, data migration,
necessary to balance utilization among the nodes, is a non-trivial and
time-consuming task that may consume the energy saved. For this reason, a
sophisticated and easy to adjust partitioning scheme fostering dynamic
reorganization is needed. In this paper, we adapt a technique originally
created for SMP systems, called physiological partitioning, to distribute data
among nodes, that allows to easily repartition data without interrupting
transactions. We dynamically partition DB tables based on the nodes'
utilization and given energy constraints and compare our approach with physical
partitioning and logical partitioning methods. To quantify possible energy
saving and its conceivable drawback on query runtimes, we evaluate our
implementation on an experimental cluster and compare the results w.r.t.
performance and energy consumption. Depending on the workload, we can
substantially save energy without sacrificing too much performance
EbbRT: Elastic Building Block Runtime - case studies
We present a new systems runtime, EbbRT, for cloud hosted applications. EbbRT takes a different approach to the role operating systems play in cloud computing. It supports stitching application functionality across nodes running commodity OSs and nodes running specialized application specific software that only execute what is necessary to accelerate core functions of the application. In doing so, it allows tradeoffs between efficiency, developer productivity, and exploitation of elasticity and scale. EbbRT, as a software model, is a framework for constructing applications as collections of standard application software and Elastic Building Blocks (Ebbs). Elastic Building Blocks are components that encapsulate runtime software objects and are implemented to exploit the raw access, scale and elasticity of IaaS resources to accelerate critical application functionality. This paper presents the EbbRT architecture, our prototype and experimental evaluation of the prototype under three different application scenarios
Efficient classification using parallel and scalable compressed model and Its application on intrusion detection
In order to achieve high efficiency of classification in intrusion detection,
a compressed model is proposed in this paper which combines horizontal
compression with vertical compression. OneR is utilized as horizontal
com-pression for attribute reduction, and affinity propagation is employed as
vertical compression to select small representative exemplars from large
training data. As to be able to computationally compress the larger volume of
training data with scalability, MapReduce based parallelization approach is
then implemented and evaluated for each step of the model compression process
abovementioned, on which common but efficient classification methods can be
directly used. Experimental application study on two publicly available
datasets of intrusion detection, KDD99 and CMDC2012, demonstrates that the
classification using the compressed model proposed can effectively speed up the
detection procedure at up to 184 times, most importantly at the cost of a
minimal accuracy difference with less than 1% on average
LIKWID Monitoring Stack: A flexible framework enabling job specific performance monitoring for the masses
System monitoring is an established tool to measure the utilization and
health of HPC systems. Usually system monitoring infrastructures make no
connection to job information and do not utilize hardware performance
monitoring (HPM) data. To increase the efficient use of HPC systems automatic
and continuous performance monitoring of jobs is an essential component. It can
help to identify pathological cases, provides instant performance feedback to
the users, offers initial data to judge on the optimization potential of
applications and helps to build a statistical foundation about application
specific system usage. The LIKWID monitoring stack is a modular framework build
on top of the LIKWID tools library. It aims on enabling job specific
performance monitoring using HPM data, system metrics and application-level
data for small to medium sized commodity clusters. Moreover, it is designed to
integrate in existing monitoring infrastructures to speed up the change from
pure system monitoring to job-aware monitoring.Comment: 4 pages, 4 figures. Accepted for HPCMASPA 2017, the Workshop on
Monitoring and Analysis for High Performance Computing Systems Plus
Applications, held in conjunction with IEEE Cluster 2017, Honolulu, HI,
September 5, 201
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