Reputation-based Resource Research Articles

Stable reputation management system in P2P networks: An evolutionary perspective

In P2P networks, peers tend to “free-ride” on the contributions of others. Many reputation-based resource sharing mechanisms have been proposed to mitigate the problem of free riding. However, the evolutionary stability of these mechanisms that require peers to keep track of the reputations of other peers has not been investigated. We show that, without any extra benefit, systems with reputation-based incentive mechanisms are not evolutionary stable. We also show that if the fraction of peers that record reputation and reciprocate accordingly is above a certain threshold, then free riding can be controlled, and a reputation management system is beneficial only when the fraction of free riders is above a certain threshold. Furthermore, we show that imposing a round-based fee and dividing it among the peers as an incentive to keep track of reputation is an evolutionary stable strategy. In this process, we analyse all the possible equilibria in different settings of the system.

Adaptive Capacity Partitioning in Cooperative Computing to Maximize Received Resources

Users in a cooperative computing environment always have a tendency to free-ride. One can employ incentive mechanisms to prevent such behavior. Some of the cooperative computing scenarios have the same access link shared between upload and download. In such a situation, increasing upload capacity decreases the download capacity and vice versa. Optimal partitioning of link capacity between upload and download needs to be done by each user to maximize its gain (i.e., download) from the network. We model this link capacity partitioning problem as a feedback control system, where feedback (resources received) decides the number of resources to be uploaded by a user. The resulting algorithm called adaptive step size (ASZ) dynamically adjusts the partitioning of link capacity to an optimal value. To compare this approach with others, a metric “level of optimality (U)”is introduced. U achieved by the ASZ is closer to the optimal level than the reputation-based resource allocation policy (existing scheme), thus resulting in its better performance. The ASZ is also integrated with BitTorrent, and the simulation results show that it increases the resources received by the users. The ASZ can provide an efficient solution to the problem of optimal partitioning in real-life distributed networks due to its distributed implementation, robustness to changes in network dynamics, and compatibility with the existing partitioning schemes.

Evolutionary Stability of Reputation-Based Incentive Mechanisms in P2P Systems

Free riding is a severe problem in P2P systems. Although many incentive mechanisms based on reputation have been proposed to mitigate problem of free riding, reputation management fails to evolve as an evolutionary stable strategy (ESS) due to cost involved in keeping track of reputation. We prove that a system setting with one time entry fee, imposed on new comers and distributed among the peers that calculate reputation, makes reputation-based resource allocation an ESS. Then, we simulate the system model using evolutionary dynamics for its performance evaluation.

Reputation-Based Resource Allocation in P2P Systems: A Game Theoretic Perspective

Free-riding is a severe problem in peer-to-peer systems. It can be controlled by adopting an effective resource allocation mechanism. This paper game theoretically analyzes a resource allocation mechanism, through two non-cooperative games viz., demand game and reputation game. This mechanism ensures that demands made by peers are commensurate to their shared capacity. We show that Nash equilibrium behavior of agents in our model is cooperative, which leads to fair and efficient allocation of network resource. Our findings show that through this mechanism, the system reaches to equilibrium where peers interact in groups formed on the basis of their capacities.

Distributed Reputation Management for Secure and Efficient Vehicular Edge Computing and Networks

Vehicular edge computing (VEC) is introduced to extend computing capacity to vehicular network edge recently. With the advent of VEC, service providers directly host services in close proximity of mobile vehicles for great improvements. As a result, a new networking paradigm, vehicular edge networks is emerged along with the development of VEC. However, it is necessary to address security issues for facilitating VEC well. In this paper, we focus on reputation management to ensure security protection and improve network efficiency in the implementation of VEC. A distributed reputation management system ( DREAMS ) is proposed, wherein VEC servers are adopted to execute local reputation management tasks for vehicles. This system has remarkable features for improving overall performance: 1) distributed reputation maintenance; 2) trusted reputation manifestation; 3) accurate reputation update; and 4) available reputation usage. In particular, we utilize multi-weighted subjective logic for accurate reputation update in DREAMS. To enrich reputation usage in DREAMS, service providers optimize resource allocation in computation offloading by considering reputation of vehicles. Numerical results indicate that DREAMS has great advantages in optimizing misbehavior detection and improving the recognition rate of misbehaving vehicles. Meanwhile, we demonstrate the effectiveness of our reputation-based resource allocation algorithm.

Improving resource efficiency in data centers using reputation-based resource selection

Today data centers are consuming a lot of energy but not very efficiently. Much of energy is wasted. There are several types of energy waste at different levels including infrastructure-, machine- and system-level waste. The former two levels have been improved significantly in the last few years, however, few efforts have been put on the last level, especially the resource waste caused by failures in a data center. In this paper, we attack the problem proactively by leveraging a reputation-based resource selection scheme to reduce the number of resubmissions of tasks, resulting from the failure during the course of their execution. To capture the characteristics of resources, we introduce Opera, an OPEn ReputAtion model. Opera characterizes itself with two important novelties: a vector representation of the reputation and the just-in-time feature that represents the real-time system status, which, to our knowledge, has never been considered in conventional reputation systems. To demonstrate the effectiveness of Opera, we have integrated the Opera trust model into the scheduler of Hadoop. The experimental results showed that Opera enables the scheduler to select appropriate nodes which helped to reduce not only the number of re-executed tasks but also the execution time of Hadoop's jobs under the presence of failures and heavy workload up to 59% and 32%, respectively. This improvements, in turn, can improve the energy efficiency of the whole system and the network by 16.17% and 53.32% for the sort application respectively.

Reputation-Based Resource Allocation in P2P Systems of Rational Users

In this paper, we study p2p systems, where peers have to share their available resources between their own and other peers' needs. One such example is a system of peers who use their capacity-limited access links both for their upstream and downstream connections. In the selfish approach, each peer would like to exploit the full capacity of his access link only for his downloads. However, if all peers acted selfishly, the system would collapse. In order to motivate peers to cooperate, we propose a distributed reputation-based system according to which peers earn reputation analogous to their contributions. In this way, each peer has to trade off the capacity he will dedicate for uploading in order to increase his reputation and therefore his revenue and the capacity he will dedicate for his downloads. All peers act rationally, trying to maximize their utility. Our proposed policies lead rational peers to cooperation while promoting fairness, as peers receive resources in proportion to their contributions. Our policies outperform existing work in this area in which the slowest link becomes the bottleneck of a heterogeneous system of different link capacity peers. On the contrary, no such bottleneck appears when our policies are used, improving the performance of the system. Finally, we apply our reputation-based approach in a BitTorrent-like file sharing system and we highlight the potential performance gains.

A reputation-driven scheduler for autonomic and sustainable resource sharing in Grid computing

The obstacle for the Grid to be prevalent is the difficulty in using, configuring and maintaining it, which needs excessive IT knowledge, workload, and human intervention. At the same time, inter-operation amongst Grids is on track. To be the core of Grid systems, the resource management must be autonomic and inter-operational to be sustainable for future Grid computing. For this purpose, we introduce HOURS, a reputation-driven economic framework for Grid resource management. HOURS is designed to tackle the difficulty of automatic rescheduling, self-protection, incentives, heterogeneous resource sharing, reservation, and SLA in Grid computing. In this paper, we focus on designing a reputation-based resource scheduler, and use emulation to test its performance with real job traces and node failure traces. To describe the HOURS framework completely, a preliminary multiple-currency-based economic model is also introduced in this paper, with which future extension and improvement can be easily integrated into the framework. The results demonstrate that our scheduler can reduce the job failure rate significantly, and the average number of job resubmissions, which is the most important metric in this paper that affects the system performance and resource utilization from the perspective of users, can be reduced from 3.82 to 0.70 compared to simple sequence resource selection.