SmartGRID Ongoing research work in Univ. Fribourg and Univ. Applied Sciences of Western Switzerland (HES-SO) SwiNG Grid Day, Bern, Nov. 26th, 2009 Ye HUANG PhD Student, Pervasive Artificial Intelligence Group, Dept of Informatics, University of Fribourg, Switzerland Grid Group, Dept of Information and Communication Technologies, HES-SO (FR), Switzerland

2 Outline  Introduction  Smart Resource Management Layer  Smart Signaling Layer  Future Work

3 SmartGRID Overview  Swarm Agent-Based Scheduling framework for Dynamic, Reactive, Interoperable Grid Computing  Developed in:  University of Fribourg, Switzerland   University of Applied Sciences of Western Switzerland (HES-SO)   Two Phd students:  One is working on the grid scheduling (Ye)  The other is working on swarm agent-based resource discovery (Amos)

4 SmartGRID Motivation  Filling the gap between Grid applications and resources  How to achieve:  Using stable grid applications  Large scale of resources  Mixed policies  Unstable, unreliable network ?

5 SmartGRID layered architecture  Loosely coupled layered architecture.  Two layers and one internal interface. Smart Resource Management Layer Data Warehouse Interface Smart Signaling Layer

6 Smart Resource Management Layer (SRML)  Nowadays Grids  Grid nodes are well controlled by the local facilities  Job sharing is generally limited within each grid node  Our Vision  Enabling job sharing between distributed nodes is one more step towards a universal computing infrastructure  Our Goal  Serving the grid as a whole, not for individual nodes  Without centralized control

7 SRML (2) – MaGate Scheduler  SRML is comprised by a set of fully decentralized MaGate schedulers (original and implemented) Remote MaGate LRM  Self-management, matchmaking, behavior logging  Job submission and analysis  Agreement based negotiation for job sharing and other events  Job allocation on local resources  Adoption mechanism for external services (SSL, etc.) Grid Applications

8 SRML (3) - Interoperable scheduling  Reference experiment to prove the idea of Interoperable scheduling  Local suited and Local unsuited jobs are submitted to each node  Objective: to increase the “Rate of successfully executed Jobs from the entire grid Community (RJC)”  First results  Different policies applied while adopting the SRML and SSL  Different results can be obtained with same policies on grids with different scope  A general guideline should give the principle of intelligent scheduling (Community-Aware Scheduling Protocol, CASP)

9 SRML Achievements  We developed:  A fully [1, 2] decentralized interoperable MaGate scheduler;  Integrated SmartGRID prototype [3] to evaluate the idea of scheduler coordination across different nodes;  Some pilot works [4, 5] leading to exploit a more proactive collaboration mechanism;  A proposal of the Community-Aware Scheduling Protocol(CASP) [6] to integrate knowledge from previous work.  More Info & Download:  

10 Smart Signaling Layer (SSL)  Done by Amos BROCO  Goals  Connect nodes together  Overlay management middleware / algorithm  Provide information to the upper layer  Information provisioning / monitoring middleware  We developed  A middleware for the development and deployment of ant algorithms  A fully distributed algorithm to maintain an optimized P2P overlay  An efficient resource discovery protocol exploiting local caches  More Info & Download:

11 Future Work  SRML/SSL Integration  Improved Datawarehouse based internal interface  Smart Signaling Layer  Fine-tuning of ant algorithms (improve efficiency)  Smart Resource Management Layer  Refined and improved MaGate scheduler  Implementation of the Community-Aware Scheduling Protocol (CASP)  Integration upon real grid workload trace archive

Thank you! Questions?