A Utility-Based Reputation Model for Grid Resource Management System

In this paper we propose extensions to the existing utility-based reputation model for virtual organizations (VOs) in grids, and present a novel approach for integrating reputation into grid resource management system. The proposed extensions include: incorporation of statistical model of user behaviour (SMUB) to assess user reputation; a new approach for assigning initial reputation to a new entity in a VO; capturing alliance between consumer and resource; time decay and score functions. The addition of the SMUB model provides robustness and dynamics to the user reputation model comparing to the policy-based user reputation model in terms of adapting to user actions. We consider a problem of integrating reputation into grid scheduler as a multi-criteria optimization problem. A non-linear trade-off scheme is applied to form a composition of partial criteria to provide a single objective function. The advantage of using such a scheme is that it provides a Pareto-optimal solution partially satisfying criteria with corresponding weights. Experiments were run to evaluate performance of the model in terms of resource management using data collected within the EGEE Grid-Observatory project. Results of simulations showed that on average a 45 % gain in performance can be achieved when using a reputation-based resource scheduling algorithm

Tournesol: Permissionless Collaborative Algorithmic Governance with Security Guarantees

Recommendation algorithms play an increasingly central role in our societies. However, thus far, these algorithms are mostly designed and parameterized unilaterally by private groups or governmental authorities. In this paper, we present an end-to-end permissionless collaborative algorithmic governance method with security guarantees. Our proposed method is deployed as part of an open-source content recommendation platform https://tournesol.app, whose recommender is collaboratively parameterized by a community of (non-technical) contributors. This algorithmic governance is achieved through three main steps. First, the platform contains a mechanism to assign voting rights to the contributors. Second, the platform uses a comparison-based model to evaluate the individual preferences of contributors. Third, the platform aggregates the judgements of all contributors into collective scores for content recommendations. We stress that the first and third steps are vulnerable to attacks from malicious contributors. To guarantee the resilience against fake accounts, the first step combines email authentication, a vouching mechanism, a novel variant of the reputation-based EigenTrust algorithm and an adaptive voting rights assignment for alternatives that are scored by too many untrusted accounts. To provide resilience against malicious authenticated contributors, we adapt Mehestan, an algorithm previously proposed for robust sparse voting. We believe that these algorithms provide an appealing foundation for a collaborative, effective, scalable, fair, contributor-friendly, interpretable and secure governance. We conclude by highlighting key challenges to make our solution applicable to larger-scale settings.Comment: 31 pages, 5 figure

Peer-to-Peer Networks and Computation: Current Trends and Future Perspectives

This research papers examines the state-of-the-art in the area of P2P networks/computation. It attempts to identify the challenges that confront the community of P2P researchers and developers, which need to be addressed before the potential of P2P-based systems, can be effectively realized beyond content distribution and file-sharing applications to build real-world, intelligent and commercial software systems. Future perspectives and some thoughts on the evolution of P2P-based systems are also provided

A Novel Decentralized Fuzzy Based Approach for Grid Job

In this paper with the aid of fuzzy theory we present a new method for scheduling on Grid system. Grid computing is a technology to meet the growing computational requires. In fact grid computing is one of the most popular types of distributed system. Its aim is to produce an enormous, autonomous and effective virtual machine, and it is produced by collecting different nodes with the aim of sharing their data and computational power. This paper follows the identification of grid scheduling with the help of fuzzy theory and seeking to present a new method for grid scheduling with respect to exiting obstacles. In our method we use the intermediate load of nodes of each clusters, the average of computing power which determines the node premiership and job premiership as the input parameters of fuzzy system, and regarding to the output value of fuzzy system the suitable nodes determines. We evaluate the performance of our method with some grid scheduling methods. The results of the experiments show the efficiency of the proposed method in term of makespan and Standard deviation of the load of cluster

SocialCloud: Using Social Networks for Building Distributed Computing Services

In this paper we investigate a new computing paradigm, called SocialCloud, in which computing nodes are governed by social ties driven from a bootstrapping trust-possessing social graph. We investigate how this paradigm differs from existing computing paradigms, such as grid computing and the conventional cloud computing paradigms. We show that incentives to adopt this paradigm are intuitive and natural, and security and trust guarantees provided by it are solid. We propose metrics for measuring the utility and advantage of this computing paradigm, and using real-world social graphs and structures of social traces; we investigate the potential of this paradigm for ordinary users. We study several design options and trade-offs, such as scheduling algorithms, centralization, and straggler handling, and show how they affect the utility of the paradigm. Interestingly, we conclude that whereas graphs known in the literature for high trust properties do not serve distributed trusted computing algorithms, such as Sybil defenses---for their weak algorithmic properties, such graphs are good candidates for our paradigm for their self-load-balancing features.Comment: 15 pages, 8 figures, 2 table

Dht-based security infrastructure for trusted internet and grid computing

Abstract: We designed a distributed security infrastructure with self-defence capabilities to secure networked resources in Grids and internet applications. This paper reports new developments in fuzzy trust management, game-theoretic Grid models, security-binding methodology, as well as new Grid performance metrics, defence architecture and mechanisms against intrusions, worms, and low-rate pulsing Distributed Denial of Service (DDoS) attacks. The design is based on a novel Distributed Has

Fairness Emergence in Reputation Systems

Reputation systems have been used to support users in making decisions under uncertainty or risk that is due to the autonomous behavior of others. Research results support the conclusion that reputation systems can protect against exploitation by unfair users, and that they have an impact on the prices and income of users. This observation leads to another question: can reputation systems be used to assure or increase the fairness of resource distribution? This question has a high relevance in social situations where, due to the absence of established authorities or institutions, agents need to rely on mutual trust relations in order to increase fairness of distribution. This question can be formulated as a hypothesis: in reputation (or trust management) systems, fairness should be an emergent property. The notion of fairness can be precisely defined and investigated based on the theory of equity. In this paper, we investigate the Fairness Emergence hypothesis in reputation systems and prove that , under certain conditions, the hypothesis is valid for open and closed systems, even in unstable system states and in the presence of adversaries. Moreover, we investigate the sensitivity of Fairness Emergence and show that an improvement of the reputation system strengthens the emergence of fairness. Our results are confirmed using a trace-driven simulation from a large Internet auction site.Trust, Simulation, Fairness, Equity, Emergence, Reputation System

Optimisation techniques for data distribution in Volunteer Computing

Volunteer Computing is a new paradigm of distributed computing where the ordinary computer owners volunteer their computing power and storage capability to scientific projects. The increasing number of internet connected PCs allows Volunteer Computing to provide more computing power and storage capacity than what can be achieved with supercomputers, clusters and grids. However, volunteer computing projects rely on a centralized infrastructure for distributing data. This can affect the scalability of data intensive projects and when the projects participants increases. In this thesis, a new approach is proposed to incorporate P2P techniques into volunteer computing projects and apply trust management to optimize the use of P2P techniques in these projects. This approach adopted a P2P technique to form a decentralized data centres layer based on the resources of participants of volunteer computing projects. VASCODE framework is based on Attic File System to enable building the decentralized data centres and makes use of trust framework to provide the necessary data to users to select the optimum data centres for downloading data. Empirical evaluation demonstrated that the proposed approaches can achieve better scalability and performance as compared to the central server approach used in BOINC projects. In addition, it shows that clients with the support of trust framework have reliable and consistent download times because using trust allows them select the optimum data centres and avoid the malicious behaviour of data centres