Seminars & Events
Mathematics and Computer Science Division
"Supporting Time-Critical Event Processing in Distributed Environments"
DATE: March 9, 2010
TIME: 10:30 AM - 11:30 AM
SPEAKER: Qian Zhu, The Ohio State University, LANS Postdoc Interviewee
LOCATION: Building 240 Seminar Room 4301, Argonne National Laboratory
HOST: Boyana Norris
Description:
Abstract
Considering the emerging large-scale scientific applications, there is a need for the system to provide a timely response to an important event. Computations need to be performed to handle the detected event. Often, this response can require significant computation and possibly communication, and can be very challenging to complete within the time-frame. The resources available for the processing may only be detected when the event occurs, and may not be known in advance. At the same time, there could be application-specific edibility in the computation that may be desired. There could be an application-specific benet function, which captures what is most desirable to compute.
The applications are usually composed of multiple components that are inherently dynamic and heterogeneous. There could be complex interactions between these components. In particular, our goal is to complete the task within the pre-specified time frame, while attempting to maximize the pre-specified benet function. Therefore, numerous performance-related parameters must be continuously tuned. Usually, there is a trade between parameter adaptation in favor of benet optimization and application execution time. This further depends on the capacity of the resources that the task was allocated. Thus, allocating appropriate resource collections to applications and setting optimal values for adjustable parameters are critical to application performance. Furthermore, as applications and resources are interacting in a complex and dynamic way, it is highly desirable that the procedure for resource allocation and parameter adaptation could be self-managing and self-optimization, requiring only a high-level objective, such as the benet function, as input to the system.
We have developed a middleware to support such functionality and to enable development and deployment of large-scale scientific applications. The main functionality of our middleware is to enable time-critical event handling to achieve the maximum benet, as per the application specific benet function, while satisfying the time constraint. This requires support for self-adaptation. Performing such optimization further leads us to a resource selection and scheduling problem. We have given a formal formulation based on optimal control theory and developed an autonomic adaptation algorithm. We did an efficiency value to react how effectively a particular service can be executed on a particular node. Based on which we have developed a greedy scheduling algorithm to schedule these service components. Our middleware is also based on the existing Grid infrastructure and Service-Oriented Architecture (SOA) concepts. Furthermore, we considered the research problems on how to successfully complete the time-critical event processing in presence of resource failures and how to maximize the benet given a resource budget in cloud computing environments. Experimental results have demonstrated the effectiveness of our proposed approaches in supporting time-critical events processing in distributed environments.
More Information:
Please take the elevator to the fourth floor and make a right, down the walk way to seminar room 4301.
Save the event to your calendar [schedule.ics]
