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The state-action space of an individual agent in a multiagent team fundamentally dictates how the individual interacts with the rest of the team. Thus, how an agent is defined in the context of its domain has a significant effect on team performance when learning to coordinate. In this work we explore the trade-offs associated with these design choices, for example, having fewer agents in the team that individually are able to process and act on a wider scope of information about the world versus a larger team of agents where each agent observes and acts in a more local region of the domain. We focus our study on a traffic management domain and highlight the trends in learning performance when applying different agent definitions. 

To interconnect research facilities across wide geographic areas, network operators deploy science networks, also referred to as Research and Education (R&E) networks. These networks allow experimenters to establish dedicated circuits between research facilities for transferring large amounts of data, by using advanced reservation systems. Intercontinental dedicated circuits typically require coordination between multiple administrative domains, which need to reach an agreement on a suitable advance reservation. The success rate of finding an advance reservation decreases as the number of participant domains increases for traditional systems because the circuit is composed over a single path. To improve provisioning of multi-domain advance reservations, we propose an architecture for end-to-end service orchestration in multi-domain science networks that leverages software-defined exchanges (SDX) for providing multi-path, multi-domain advance reservations. We have implemented an orchestrator for multi-path, multi-domain advance reservations and an SDX to support these services. Our orchestration architecture enables multi-path, multi-domain advance reservations and improves the reservation success rate from 50% in single path systems to 99% when four path are available. 

Poster Abstract: To interconnect research facilities across wide geographic areas, network operators deploy science networks, also referred to as Research and Education (R&E) networks. These networks allow experimenters to establish dedicated network connections between research facilities for transferring large amounts of data. Recently, R&E networks have started using Software-Defined Networking (SDN) and Software Defined Exchanges (SDX) for deploying these connections. AtlanticWave/SDX is a response to the growing demand to support end-to-end network services spanning multiple SDN domains. However, requesting these services is a challenging task for domain-expert scientists, because the interfaces of the R&E networks have been developed by network operators for network operators. In this paper, we propose interfaces that allow domain expert scientists to reserve resources of the scientific network using abstractions that focus on their data transfer needs for scientific workflow management. Recent trends in the networking field pursue better interfaces for requesting network services (e.g., intent-based networking). Although intents are sufficient for the needs of network operations, they are not abstract enough in most cases to be used by domain-expert scientists. This is an issue we are addressing in the AtlanticWave/SDX design: network operators and domain expert scientists will have their own interfaces focusing on their specific needs. 

To interconnect research facilities across wide geographic areas, network operators deploy science networks, also referred to as Research and Education (R&E) networks. These networks allow experimenters to establish dedicated network connections between research facilities for transferring large amounts of data. Recently, R&E networks have started using Software-Defined Networking (SDN) and Software Defined Exchanges (SDX) for deploying these connections. AtlanticWave/SDX is a response to the growing demand to support end-to-end network services spanning multiple SDN domains. However, requesting these services is a challenging task for domain-expert scientists, because the interfaces of the R&E networks have been developed by network operators for network operators. In this paper, we propose interfaces that allow domain expert scientists to reserve resources of the scientific network using abstractions that focus on their data transfer needs for scientific workflow management. Recent trends in the networking field pursue better interfaces for requesting network services (e.g., intent-based networking). Although intents are sufficient for the needs of network operations, they are not abstract enough in most cases to be used by domain-expert scientists. This is an issue we are addressing in the AtlanticWave/SDX design: network operators and domain-expert scientists will have their own interfaces focusing on their specific needs.