DIANA Scheduling Hierarchies for Optimizing Bulk Job Scheduling

The use of meta-schedulers for resource management in large-scale distributed systems often leads to a hierarchy of schedulers. In this paper, we discuss why existing meta-scheduling hierarchies are sometimes not sufficient for Grid systems due to their inability to re-organise jobs already scheduled locally. Such a job re-organisation is required to adapt to evolving loads which are common in heavily used Grid infrastructures. We propose a peer-to-peer scheduling model and evaluate it using case studies and mathematical modelling. We detail the DIANA (Data Intensive and Network Aware) scheduling algorithm and its queue management system for coping with the load distribution and for supporting bulk job scheduling. We demonstrate that such a system is beneficial for dynamic, distributed and self-organizing resource management and can assist in optimizing load or job distribution in complex Grid infrastructures.Comment: 8 pages, 9 figures. Presented at the 2nd IEEE Int Conference on eScience & Grid Computing. Amsterdam Netherlands, December 200

DIANA Scheduling Hierarchies for Optimizing Bulk Job Scheduling

The use of meta-schedulers for resource management in large-scale distributed systems often leads to a hierarchy of schedulers. In this paper, we discuss why existing meta-scheduling hierarchies are sometimes not sufficient for Grid systems due to their inability to re-organise jobs already scheduled locally. Such a job re-organisation is required to adapt to evolving loads which are common in heavily used Grid infrastructures. We propose a peer-topeer scheduling model and evaluate it using case studies and mathematical modelling. We detail the DIANA (Data Intensive and Network Aware) scheduling algorithm and its queue management system for coping with the load distribution and for supporting bulk job scheduling. We demonstrate that such a system is beneficial for dynamic, distributed and self-organizing resource management and can assist in optimizing load or job distribution in complex Grid infrastructures

Design and Evaluation of a Decentralized System for Grid-wide Fairshare Scheduling

This contribution presents a decentralized architecture for a Grid-wide fairshare scheduling system and demonstrates its potential in a simulated environment. The system, which preserves local site autonomy, enforces locally and globally scoped share policies, allowing local resource capacity as well as global Grid capacity to be logically divided across different groups of users. The policy model is hierarchical and subpolicy definition can be delegated so that, e.g., a VO that has been granted a resource share can partition its share across its projects, which in turn can divide their shares between project members. There is no need for a central coordinator as policies are enforced collectively by the resource schedulers. Each local scheduler adopts a Grid-wide view on utilization in order to steer local resource utilization to not only maintain local resource shares but also to contribute to maintaining global shares across the entire set of Grid resources. Share enforcement is addressed by an algorithm that calculates simple priority values, thus simplifying integration with local schedulers, which can remain unaware of the hierarchical share policy structure. 1

Automated Bidding in Computing Service Markets. Strategies, Architectures, Protocols

This dissertation contributes to the research on Computational Mechanism Design by providing novel theoretical and software models - a novel bidding strategy called Q-Strategy, which automates bidding processes in imperfect information markets, a software framework for realizing agents and bidding strategies called BidGenerator and a communication protocol called MX/CS, for expressing and exchanging economic and technical information in a market-based scheduling system