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.
The presented efforts show interesting characteristics for consistency of shared information in distributed systems. However, these efforts have vulnerabilities which make them not appropriate for management CDNs, such as centralization @cite_18 , simple data models @cite_17 , and replication issues @cite_25 .
@cite_18 @cite_25 @cite_17
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In globally distributed systems, shared state is never perfect. When communication is neither fast nor reliable, we cannot achieve strong consistency, low latency, and availability at the same time. Unfortunately, abandoning strong consistency has wide ramifications. Eventual consistency, though attractive from a performance viewpoint, is challenging to understand and reason about, both for system architects and programmers. To provide robust abstractions, we need not just systems, but also principles: we need the ability to articulate what a consistency protocol is supposed to guarantee, and the ability to prove or refute such claims.In this tutorial, we carefully examine both the what and the how of consistency in distributed systems. First, we deconstruct consistency into individual guarantees relating the data type, the conflict resolution, and the ordering, and then reassemble them into a hierarchy of consistency models that starts with linearizability and gradually descends into sequential, causal, eventual, and quiescent consistency. Second, we present a collection of consistency protocols that illustrate common techniques, and include templates for implementations of arbitrary replicated data types that are fully available under partitions. Third, we demonstrate that our formalizations serve their purpose of enabling proofs and refutations, by proving both positive results (the correctness of the protocols) and a negative result (a version of the CAP theorem for sequential consistency). Datastores today rely on distribution and replication to achieve improved performance and fault-tolerance. But correctness of many applications depends on strong consistency properties--something that can impose substantial overheads, since it requires coordinating the behavior of multiple nodes. This paper describes a new approach to achieving strong consistency in distributed systems while minimizing communication between nodes. The key insight is to allow the state of the system to be inconsistent during execution, as long as this inconsistency is bounded and does not affect transaction correctness. In contrast to previous work, our approach uses program analysis to extract semantic information about permissible levels of inconsistency and is fully automated. We then employ a novel homeostasis protocol to allow sites to operate independently, without communicating, as long as any inconsistency is governed by appropriate treaties between the nodes. We discuss mechanisms for optimizing treaties based on workload characteristics to minimize communication, as well as a prototype implementation and experiments that demonstrate the benefits of our approach on common transactional benchmarks. Systems that maintain coherence at large granularity, such as shared virtual memory systems, suffer from false sharing and extra communication. Relaxed memory consistency models have been used to alleviate these problems, but at a cost in programming complexity. Release Consistency (RC) and Lazy Release Consistency (LRC) are accepted to offer a reasonable tradeoff between performance and programming complexity. Entry Consistency (EC) offers a more relaxed consistency model, but it requires explicit association of shared data objects with synchronization variables. The programming burden of providing such associations can be substantial. Geo-replicated services need an effective way to direct client requests to a particular location, based on performance, load, and cost. This paper presents DONAR, a distributed system that can offload the burden of replica selection, while providing these services with a sufficiently expressive interface for specifying mapping policies. Most existing approaches for replica selection rely on either central coordination (which has reliability, security, and scalability limitations) or distributed heuristics (which lead to suboptimal request distributions, or even instability). In contrast, the distributed mapping nodes in DONAR run a simple, efficient algorithm to coordinate their replica-selection decisions for clients. The protocol solves an optimization problem that jointly considers both client performance and server load, allowing us to show that the distributed algorithm is stable and effective. Experiments with our DONAR prototype--providing replica selection for CoralCDN and the Measurement Lab--demonstrate that our algorithm performs well "in the wild." Our prototype supports DNS- and HTTP-based redirection, IP anycast, and a secure update protocol, and can handle many customer services with diverse policy objectives.