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A Portal Based Approach to Viewing Aggregated Network Performance Data in Distributed Brokering Systems. By Gurhan Gunduz, Shrideep Pallickara, Geoffrey Fox Syracuse University, Indiana University, Community Grid Labs ggunduz, spallick, gcf@indiana.edu IC 2003 LAS VEGAS, NV, USA.
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A Portal Based Approach to Viewing Aggregated Network Performance Data in Distributed Brokering Systems By Gurhan Gunduz, Shrideep Pallickara, Geoffrey Fox Syracuse University, Indiana University, Community Grid Labs ggunduz, spallick, gcf@indiana.edu IC 2003 LAS VEGAS, NV, USA
Introduction • More applications, services and frameworks becoming network centric. • Network performance is important.
Efforts on network measurement • IP provider Metrics(subgroup of IETF’s Bench Marking Working Group) • CADIA(Cooperative Association for Internet Analysis Data) • NWS(Network Weather System)
NaradaBrokering • Distributed event brokering system designed to run on a large network of cooperating broker nodes. • Broker nodes are organized in a cluster-based architecture which allows the system to support large heterogeneous client configurations. • Communication in NaradaBrokering is asynchronous. • NaradaBrokering provides support for JMS, P2P interactions, audio-video conferencing while supporting communication through firewalls and proxies
NaradaBrokering Transport Framework • Transport framework aims to abstract the operations that need to be supported for enabling efficient communications between nodes. TCP, UDP, SSL, RTP and HTTP. • Operations that need to be supported between two communication endpoints are encapsulated within the “link” primitive. • A Link is an abstraction that hides details pertaining to communications between 2 communicating entities.
Link and Performance Measurement • Can expose and measure a set of performance factors. • Cooperation from the other end-point of the communication link. • Echo behavior • Can measure round trip delays, jitter, bandwidth, loss rates, etc. • Links can enable/disable the measurement of all performance factors or a specific factor.
Accumulating performance metrics for a node • Every broker incorporates a Monitoring Service(MS). • Transport Controller of a node maintains the list of the links • MS cycles through the links and retrieve performance information. • MS controls frequency of metric measurements.
Aggregating performance metrics from multiple nodes • MS report performance data to a Performance Aggregation Service(PAS). • PAS exchanges information with MS. • PAS can give simple commands to MS. • PASs can exchange information with each other
Encapsulating performance data • MS encapsulates performance data in an XML format. • Why XML; • Easy access to relevant fields in the performance data. • Description capability of the content provides intelligent data mining. • XPATH queries
Aggregating performance metrics from multiple nodes • Aggregated data is saved in a database • Currently flat file • We plan using light weight XML database for this purpose. • Apache Xindice • SourceForge exist
Accumulation of data in a portal • Information accumulated within Aggregators is accessible from a portal • Apache Jetspeed is used as a portal environment.
Accumulation of data in a portal II • Portals can display multiple HTML • Can collect content from disparate remote sources. • Can facilitate customized user groups. Restrictive user view using customized view capability of a portal • Portlets, a specialized module, Java servlet which operates in a portal, is used to view accumulated performance data within the aggregators.
Accumulation of data in a portal III • XSLT portlet is used to view aggregated performance data. • XSLT portlet converts a given XML file into HTML using the given XSL style sheet.
Accumulation of data in a portal IV • Two ways for customized user view • Having separate database file for each user group • Let portlet select it from one database file
Detecting conditions • Evaluate constraints in the aggregation node. Adv? • Use XPATH to query our database. • Check metrics for thresholds • Inform nodes to take actions to correct situation
Future Work • Trade-offs of using flat files versus light-weight databases. • Identify, circumvent, project and prevent system bottlenecks. • Aid routing algorithms.