Turing Model for Distributed Computing

On Automated Restru turing of Distributed Algorithms for Mobile Computing

Decentralized Model Persistence for Distributed Computing

The necessity of manipulating very large amounts of data and the wide availability of computational resources on the Cloud is boosting the popularity of distributed computing in industry. The applicability of model-driven engineering in such scenarios is hampered today by the lack of an efficient model-persistence framework for distributed computing. In this paper we present NeoEMF/HBase, a per...

A Security Model for Distributed Computing

This paper presents a multi-tier model for secure computing as a teaching method platform. The security model is based on establishing the trustworthiness and role of each component in a distributed computing environment: trusted users, trusted servers, trusted administrators, untrusted client, untrusted communication media and intermediate systems, etc. The model provides a basis for teaching ...

DisTriB: Distributed Trust Management Model Based on Gossip Learning and Bayesian Networks in Collaborative Computing Systems

The interactions among peers in Peer-to-Peer systems as a distributed collaborative system are based on asynchronous and unreliable communications. Trust is an essential and facilitating component in these interactions specially in such uncertain environments. Various attacks are possible due to large-scale nature and openness of these systems that affects the trust. Peers has not enough inform...

DisTriB: Distributed Trust Management Model Based on Gossip Learning and Bayesian Networks in Collaborative Computing Systems

The interactions among peers in Peer-to-Peer systems as a distributed collaborative system are based on asynchronous and unreliable communications. Trust is an essential and facilitating component in these interactions specially in such uncertain environments. Various attacks are possible due to large-scale nature and openness of these systems that affects the trust. Peers has not enough inform...