You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Complex Dynamic Networks can be exploited in solving problems where traditional solutions may not be sufficient. The increasing complexity of computer networks imposes problems to the current network management solutions. In this context, network management is an example of a research area that could benefit from the use of CDNs. However, the consistency of state of management data among the elements that build management CDNs (management nodes) is an important challenge. Traditional mechanisms to maintain consistency of these states are supported by some centralization which wastes some desirable properties of CDNs (e.g., robustness). In contrast to these mechanisms, we propose a distributed, scalable and robust mechanism to maintain the consistency of state of management data in management CDNs. Our mechanism introduces multi-agent truth maintenance features and communication strategies based on dynamic process to provide consistency maintenance of state of management data. We developed a model of a management CDN on Peersim simulator to perform experiments. Besides, 2 case studies are presented. The result obtained supports our scalability and robustness claims.

Cited Abstract(s):
[1]: We present a Scalable Distributed Information Management System (SDIMS) that aggregates information about large-scale networked systems and that can serve as a basic building block for a broad range of large-scale distributed applications by providing detailed views of nearby information and summary views of global information. To serve as a basic building block, a SDIMS should have four properties: scalability to many nodes and attributes, flexibility to accommodate a broad range of applications, administrative isolation for security and availability, and robustness to node and network failures. We design, implement and evaluate a SDIMS that (1) leverages Distributed Hash Tables (DHT) to create scalable aggregation trees, (2) provides flexibility through a simple API that lets applications control propagation of reads and writes, (3) provides administrative isolation through simple extensions to current DHT algorithms, and (4) achieves robustness to node and network reconfigurations through lazy reaggregation, on-demand reaggregation, and tunable spatial replication. Through extensive simulations and micro-benchmark experiments, we observe that our system is an order of magnitude more scalable than existing approaches, achieves isolation properties at the cost of modestly increased read latency in comparison to flat DHTs, and gracefully handles failures.

[2]: Scalable management and self-organizational capabilities areemerging as central requirements for a generation of large-scale,highly dynamic, distributed applications. We have developed anentirely new distributed information management system calledAstrolabe. Astrolabe collects large-scale system state, permittingrapid updates and providing on-the-fly attribute aggregation. Thislatter capability permits an application to locate a resource, andalso offers a scalable way to track system state as it evolves overtime. The combination of features makes it possible to solve a widevariety of management and self-configuration problems. This paperdescribes the design of the system with a focus upon itsscalability. After describing the Astrolabe service, we presentexamples of the use of Astrolabe for locating resources,publish-subscribe, and distributed synchronization in largesystems. Astrolabe is implemented using a peer-to-peer protocol,and uses a restricted form of mobile code based on the SQL querylanguage for aggregation. This protocol gives rise to a novelconsistency model. Astrolabe addresses several securityconsiderations using a built-in PKI. The scalability of the systemis evaluated using both simulation and experiments; these confirmthat Astrolabe could scale to thousands and perhaps millions ofnodes, with information propagation delays in the tens of seconds.
(SDIMS) [1] is a service to aggregate information about large-scale network systems. The service is built using ideas from [2] and Distributed Hash Tables (DHT). However, as in most DHT approaches, consistency and replication issues are a known challenge.