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, 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. Easy access to relevant fields in the performance data. Description capability of the content provides intelligent data mining. Description capability of the content provides intelligent data mining. XPATH queries XPATH queries

PAS

Multiple PAS

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 Apache Xindice SourceForge exist 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.

Portal view

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 Having separate database file for each user group Let portlet select it from one database file 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.