Monalytics: Online Monitoring and Analytics for Managing Large Scale Data Centers

Speaker:	Professor Karsten SCHWAN
		College of Computing
		Georgia Institute of Technology

Title:		"Monalytics: Online Monitoring and Analytics for
		 Managing Large Scale Data Centers"

Date:		Monday, 26 July 2010

Time:		4:00pm - 5:00pm

Venue:		Lecture Theatre H (near lifts 27 & 28), HKUST


Abstract:

To effectively manage large-scale data centers and utility clouds, 
operators must understand current system and application behaviors. This 
requires continuous monitoring along with online analysis of the data 
captured by the monitoring system. As a result, there is a need to move 
to systems in which both tasks can be performed in an integrated fashion, 
thereby better able to drive online system management. Coining the term 
'monalytics' to refer to the combined monitoring and analysis systems 
used for managing large-scale data center systems, this paper articulates 
principles for monalytics systems, describes software approaches for 
implementing them, and provides experimental evaluations justifying 
principles and implementation approach. Specific technical contributions 
include consideration of scalability across both 'space' and 'time', the 
ability to dynamically deploy and adjust monalytics functionality at 
multiple levels of abstraction in target systems, and the capability to 
operate across the range of application to hypervisor layers present in 
large-scale data center or cloud computing systems. Our monalytics 
implementation targets virtualized systems and cloud infrastructures, via 
the integration of its functionality into the Xen hypervisor.


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Biography:

Karsten Schwan is a Regents' Professor in the College of Computing at the 
Georgia Institute of Technology. He also a Director of the Center for 
Experimental Research in Computer Systems (CERCS), with co-directors from 
both GT's College of Computing and School of Electrical and Computer 
Engineering. The NSF-sponsored CERCS research center's faculty conduct 
research in experimental computer systems in the domains of Enterprise, 
High Performance, and Embedded/Pervasive Systems, with members from 
industry and from federal agencies. Prof. Schwan's M.S. and Ph.D. degrees 
are from Carnegie-Mellon University in Pittsburgh, Pennsylvania, where he 
began his research in high performance computing, addressing operating 
and programming systems support for the Cm* multiprocessor. At the Ohio 
State University, he established the PArallel, Real-time Systems (PARTS) 
Laboratory, containing both custom embedded processors and commercial 
parallel machines, and conducting research on operating and programming 
system support for cluster computing and for adaptive real-time systems. 
At Georgia Tech, his work ranges from topics in operating and 
communication systems, to middleware, to parallel and distributed 
applications, focusing on information-intensive distributed applications 
in the enterprise domain (e.g., the operational information systems 
supporting large enterprises) and in the high performance domain (e.g., 
high performance I/O, remote data visualization, and online 
collaboration). Technical topics currently pursued in his research span 
(1) scalable techniques for virtualizing and managing future multi-core 
and multi-machine platforms, (2) efficient methods for managing 
applications and services across multiple machines, including new 
techniques for runtime performance and behavior monitoring and 
understanding, (3) middleware for high performance data movement, 
addressing I/O in future petascale machines and QoS-sensitive data 
streaming in pervasive and wide area systems, and (4) experimentation 
with representative applications in the HPC, enterprise, and pervasive 
domains. He can be reached at  and at 
www.cc.gatech.edu/~schwan