4,636 research outputs found
MonALISA : A Distributed Monitoring Service Architecture
The MonALISA (Monitoring Agents in A Large Integrated Services Architecture)
system provides a distributed monitoring service. MonALISA is based on a
scalable Dynamic Distributed Services Architecture which is designed to meet
the needs of physics collaborations for monitoring global Grid systems, and is
implemented using JINI/JAVA and WSDL/SOAP technologies. The scalability of the
system derives from the use of multithreaded Station Servers to host a variety
of loosely coupled self-describing dynamic services, the ability of each
service to register itself and then to be discovered and used by any other
services, or clients that require such information, and the ability of all
services and clients subscribing to a set of events (state changes) in the
system to be notified automatically. The framework integrates several existing
monitoring tools and procedures to collect parameters describing computational
nodes, applications and network performance. It has built-in SNMP support and
network-performance monitoring algorithms that enable it to monitor end-to-end
network performance as well as the performance and state of site facilities in
a Grid. MonALISA is currently running around the clock on the US CMS test Grid
as well as an increasing number of other sites. It is also being used to
monitor the performance and optimize the interconnections among the reflectors
in the VRVS system.Comment: Talk from the 2003 Computing in High Energy and Nuclear Physics
(CHEP03), La Jolla, Ca, USA, March 2003, 8 pages, pdf. PSN MOET00
Performance Characterization of In-Memory Data Analytics on a Modern Cloud Server
In last decade, data analytics have rapidly progressed from traditional
disk-based processing to modern in-memory processing. However, little effort
has been devoted at enhancing performance at micro-architecture level. This
paper characterizes the performance of in-memory data analytics using Apache
Spark framework. We use a single node NUMA machine and identify the bottlenecks
hampering the scalability of workloads. We also quantify the inefficiencies at
micro-architecture level for various data analysis workloads. Through empirical
evaluation, we show that spark workloads do not scale linearly beyond twelve
threads, due to work time inflation and thread level load imbalance. Further,
at the micro-architecture level, we observe memory bound latency to be the
major cause of work time inflation.Comment: Accepted to The 5th IEEE International Conference on Big Data and
Cloud Computing (BDCloud 2015
Advances in the Design and Implementation of a Multi-Tier Architecture in the GIPSY Environment
We present advances in the software engineering design and implementation of
the multi-tier run-time system for the General Intensional Programming System
(GIPSY) by further unifying the distributed technologies used to implement the
Demand Migration Framework (DMF) in order to streamline distributed execution
of hybrid intensional-imperative programs using Java.Comment: 11 pages, 3 figure
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