Group Selection Grid AWeSoMe07

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A Group Selection Pattern optimizing agent-based Virtual Organizations in Grids ,[object Object],[object Object],[object Object],[object Object],AWeSOMe'07 Vilamoura, Algarve, Portugal, Nov 26 - 27, 2007

Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

The Grid (definitions) ,[object Object],[object Object],The group structure already exists in Grids

Example of Grid Virtual Organization Org1 Org2 Org3 Virtual Organization2 Virtual Organization 1

VO management in Grids ,[object Object],[object Object],[object Object],[object Object],Require policies as emergent property of the distributed policies of interacting agents

Exploiting group structure in multiagent systems (MAS) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Group Selection ,[object Object],[object Object],[object Object]

Other Group Selection apps ,[object Object],[object Object],[object Object]

Group Selection applied to the Grid ,[object Object],[object Object],[object Object]

Example of group selection High coordination Agents interact & migrate based on group identifier Uncoordinated groups t= 100 t= 40 t=1

Algorithmic realization

Group Selection pattern ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Experiments Scenario ,[object Object],[object Object],[object Object],[object Object],[object Object],Experiments conducted in an open source, agent-based Grid simulator [AgentGridSim, 2007]. p1,p2 U ↑ U ↓ U ↓ U ↑

VO policy alignment scenario Groups with P1 and groups with P2: high utility Agents interact, migrate, mutate Groups with P1 and P2 : low utility t= 100 t= 40 t=1

Coordination metric ,[object Object],[object Object]

Utility (coordination) N agents=100 ; M policies =10; migration/mutation:0.3, 0.7 sim clock ticks m/m m/m low ratio migration/mutation rate achieves better performance

Groups Distribution (I) low migration/mutation rate (when higher utilities achieved) causes large number of groups m/m m/m

Groups Distribution (II) For a migration/mutation ratio, higher utilities with smaller groups m/m = 0.3 utility = 0.55 utility = 0.72

Conclusions ,[object Object],[object Object],[object Object],[object Object],[object Object]

References ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

End Questions?

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Dubai, known for its towering skyscrapers, luxurious lifestyle, and relentless pursuit of innovation, often finds itself in the global spotlight. However, amidst the glitz and glamour, the emirate faces its own set of challenges, including the occasional threat of flooding. In recent years, Dubai has experienced sporadic but significant floods, disrupting normalcy and posing unique challenges to its infrastructure. Among the critical nodes in this bustling metropolis is the Dubai International Airport, a vital hub connecting the world. This article delves into the intersection of Dubai flood events and the resilience demonstrated by the Dubai International Airport in the face of such challenges.

Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf

Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf

Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf

presentation ICT roal in 21st century education

presentation ICT roal in 21st century education

presentation ICT roal in 21st century education

Exploring Multimodal Embeddings with Milvus

Exploring Multimodal Embeddings with Milvus

Exploring Multimodal Embeddings with Milvus

Scaling API-first – The story of a global engineering organization Ian Reasor, Senior Computer Scientist - Adobe Radu Cotescu, Senior Computer Scientist - Adobe Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

Tracing the root cause of a performance issue requires a lot of patience, experience, and focus. It’s so hard that we sometimes attempt to guess by trying out tentative fixes, but that usually results in frustration, messy code, and a considerable waste of time and money. This talk explains how to correctly zoom in on a performance bottleneck using three levels of profiling: distributed tracing, metrics, and method profiling. After we learn to read the JVM profiler output as a flame graph, we explore a series of bottlenecks typical for backend systems, like connection/thread pool starvation, invisible aspects, blocking code, hot CPU methods, lock contention, and Virtual Thread pinning, and we learn to trace them even if they occur in library code you are not familiar with. Attend this talk and prepare for the performance issues that will eventually hit any successful system. About authorWith two decades of experience, Victor is a Java Champion working as a trainer for top companies in Europe. Five thousands developers in 120 companies attended his workshops, so he gets to debate every week the challenges that various projects struggle with. In return, Victor summarizes key points from these workshops in conference talks and online meetups for the European Software Crafters, the world’s largest developer community around architecture, refactoring, and testing. Discover how Victor can help you on victorrentea.ro : company training catalog, consultancy and YouTube playlists.

Finding Java's Hidden Performance Traps @ DevoxxUK 2024

Finding Java's Hidden Performance Traps @ DevoxxUK 2024

Finding Java's Hidden Performance Traps @ DevoxxUK 2024

Three things you will take away from the session: • How to run an effective tenant-to-tenant migration • Best practices for before, during, and after migration • Tips for using migration as a springboard to prepare for Copilot in Microsoft 365 Main ideas: Migration Overview: The presentation covers the current reality of cross-tenant migrations, the triggers, phases, best practices, and benefits of a successful tenant migration Considerations: When considering a migration, it is important to consider the migration scope, performance, customization, flexibility, user-friendly interface, automation, monitoring, support, training, scalability, data integrity, data security, cost, and licensing structure Next Wave: The next wave of change includes the launch of Copilot, which requires businesses to be prepared for upcoming changes related to Copilot and the cloud, and to consolidate data and tighten governance ShareGate: ShareGate can help with pre-migration analysis, configurable migration tool, and automated, end-user driven collaborative governance

Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff

Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff

Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff

💥 You’re lucky! We’ve found two different (lead) developers that are willing to share their valuable lessons learned about using UiPath Document Understanding! Based on recent implementations in appealing use cases at Partou and SPIE. Don’t expect fancy videos or slide decks, but real and practical experiences that will help you with your own implementations. 📕 Topics that will be addressed: • Training the ML-model by humans: do or don't? • Rule-based versus AI extractors • Tips for finding use cases • How to start 👨‍🏫👨‍💻 Speakers: o Dion Morskieft, RPA Product Owner @Partou o Jack Klein-Schiphorst, Automation Developer @Tacstone Technology

DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam

DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam

DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam

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+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...

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Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf

Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf

Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf

presentation ICT roal in 21st century education

presentation ICT roal in 21st century education

presentation ICT roal in 21st century education

Exploring Multimodal Embeddings with Milvus

Exploring Multimodal Embeddings with Milvus

Exploring Multimodal Embeddings with Milvus

Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

Finding Java's Hidden Performance Traps @ DevoxxUK 2024

Finding Java's Hidden Performance Traps @ DevoxxUK 2024

Finding Java's Hidden Performance Traps @ DevoxxUK 2024

Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff

Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff

Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff

DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam

DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam

DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam

Group Selection Grid AWeSoMe07

1.

2.

3.

4. Example of Grid Virtual Organization Org1 Org2 Org3 Virtual Organization2 Virtual Organization 1

5.

6.

7.

8.

9.

10. Example of group selection High coordination Agents interact & migrate based on group identifier Uncoordinated groups t= 100 t= 40 t=1

11. Algorithmic realization

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13.

14. VO policy alignment scenario Groups with P1 and groups with P2: high utility Agents interact, migrate, mutate Groups with P1 and P2 : low utility t= 100 t= 40 t=1

15.

16. Utility (coordination) N agents=100 ; M policies =10; migration/mutation:0.3, 0.7 sim clock ticks m/m m/m low ratio migration/mutation rate achieves better performance

17. Groups Distribution (I) low migration/mutation rate (when higher utilities achieved) causes large number of groups m/m m/m

18. Groups Distribution (II) For a migration/mutation ratio, higher utilities with smaller groups m/m = 0.3 utility = 0.55 utility = 0.72

19.

20.

21. End Questions?