11247110
Sustaining Incentive in Grid Resource Allocation: A Reinforcement Learning Approach
Encouraging resource sharing and cooperation among different parties is one of the central goals of grid computing. In real environments, however, selfish or malicious nodes can seriously degrade the sharing and cooperation performance of a grid. To solve this problem, we propose QIA, a novel Q-learning based resource Allocation mechanism that sustains Incentive for every participating node. Exploiting an economic model, QIA recognizes the importance of trust factor when allocating resources. Each provider considers a combined metric, which is composed of the bid price and the trust value, of a requester when allocating its resources. The incomplete information is a key issue for a provider in determining the relative weight of bid price and trust value. We propose a reinforcement Q- learning technique to resolve the issue, which is able to adapt the dynamics of grid environments. We implemented QIA in a real grid test-bed, CROWN grid. Comprehensive experiments have been conducted, which demonstrate the efficacy of QIA.
17718771
Balancing Trust and Incentive in Peer-to-Peer Collaborative System
In order to maximize resource utilization as well as providing trust management in P2P computing environments, we propose a novel framework - Trust-Incentive Service Management (TIM). Having child model, club model, bid model and trust model, TIM is constructed based on role based price strategy. In this mechanism, providers set the price according to demand and supply, and consumers maximize the surplus upon budget and deadline. A weighted voting scheme is also proposed to secure the P2P system by declining the join request from malicious peers. TIM is scalable and efficient in that: (1) it is completely distributed without a central server; (2) it dynamically manages the price and service by integrating valuables of pricing, trust, and incentive. A TIM prototype has been successfully implemented in a real P2P system, CROWN. We evaluate the proposed approach through comprehensive experiments and achieve improved results in service allocation efficiency, system completion time, and aggregated resource utilization.
7761771
Access control in peer-to-peer collaborative systems
As an emerging model of communication and computation, peer-to-peer networking represents a fully distributed, cooperative network design, and has recently gained significant acceptance. Peer groups share the properties of peer-to-peer overlay network, including full decentralization, symmetric abilities, and dynamism, which make security problems more complicated. In this paper, we propose a fine-grained and attribute-based access control framework for peer-to-peer systems. This design employs a novel policy model, which extends role-based trust management language RT to satisfy security requirements of peer groups. Intend for a pure decentralized model without centralized server, our framework presents distributed delegation authorization mechanism, which could avoid single point of failure. We also introduce our implementation experience.
2698498
An Approach for Adaptive DRAM Temperature and Power Management
High-performance DRAMs are providing increasing memory access bandwidth to processors, which is leading to high power consumption and operating temperature in DRAM chips. In this paper, we propose a customized low-power technique for high-performance DRAM systems to improve DRAM page hit rate by buffering write operations that may incur page misses. This approach reduces DRAM system power consumption and temperature without any performance penalty. We combine the throughput-aware page-hit-aware write buffer (TAP) with low-power-state-based techniques for further power and temperature reduction, namely, TAP-low. Our experiments show that a system with TAP-low could reduce the total DRAM power consumption by up to 68.6% (19.9% on average). The steady-state temperature can be reduced by as much as 7.84°C and 2.55°C on average across eight representative workloads.
6412815
Automated task distribution in multicore network processors using statistical analysis
Chip multiprocessor designs are the most common types of architectures seen in Network Processors. As the Network Processors are used to implement increasingly complicated applications, task distribution among the cores is becoming an important problem. In this paper, we propose a new task allocation scheme for such architectures. This scheme relies on the inherent modular nature of the networking applications and intelligently distributes modules among different execution cores. Additionally, we selectively replicate modules to parallelize execution of tasks having longer processing time. We have developed a technique that uses the probability distribution of the execution times of different modules in the networking applications. The proposed schemes result in resource utilization of up to 95%, 89%, and 84% on average for the processors with 2, 4, and 8 cores, respectively. The schemes are highly scalable and can improve the throughput by 6.72 times for 8 core processors, aggregated over four representative applications. The combination of selective replication of modules and variation-aware task allocation result in up to 12.5% (9.9% on average) performance improvement as compared to a scheme based on just mean processing time.
17747144
Distributed access control in CROWN groups
Security in collaborative groups is an active research topic and has been recognized by many organizations in the past few years. In this paper, we propose a fine-grained and attribute-based access control framework for our key project, CROWN grid. To avoid single point of failure and enhance scalability of the system, we employ a distributed delegation authorization mechanism. We successfully implement our proposed access control in CROWN grid, and evaluate this approach by comprehensive experiments.
16162037
Ranking-Based Statistical Localization for Wireless Sensor Networks
This paper studies Ranking-Based localization method, which, as a special case of common Range-Based localization methods, statistically estimates node positions using the ranking-order constraints rather than the actual values of network measurements. Through extensive empirical evaluation, the advantages of Ranking-Based approach over traditional Range-Based approaches under extreme network conditions are verified. Ranking-Based approach turns out to be more robust to measurement insufficiency and more immune to measurement noise.
16092814
Chinese Question Classification Based on Ensemble Learning
In this paper, a method of Chinese question classification based on ensemble learning is proposed. Words and bi-gram are extracted from question as classic features and feature classifiers are constructed. The classifiers are kinds of simple classifier and their generalization ability are not strong enough, but they are a qualified base learner for ensemble because of their low computational cost, and the generalization feasibility improved with the help of ensemble learning. We translate and modify the UIUC question set and TREC2001 question set as Chinese question set. Experimental results on the corpus show that the proposed method can achieve good performance, the classification precision reaches 87.6%, under the fine grained question types.
31519303
Data-Driven Facial Feature Morphing for 3D Face Synthesis
16665207
RCCtrust: A Combined Trust Model for Electronic Community
Previous trust models are mainly focused on reputational mechanism based on explicit trust ratings. However, the large amount of user-generated content and community context published on Web is often ignored. Without enough information, there are several problems with previous trust models: first, they cannot determine in which field one user trusts in another, so many models assume that trust exists in all fields. Second some models are not able to delineate the variation of trust scales, therefore they regard each user trusts all his friends to the same extent. Third, since these models only focus on explicit trust ratings, so the trust matrix is very sparse. To solve these problems, we present RCCtrust – a trust model which combines Reputation-, Content- and Context-based mechanisms to provide more accurate, fine-grained and efficient trust management for the electronic community. We extract trust-related information from user-generated content and community context from Web to extend reputation-based trust models. We introduce role-based and behavior-based reasoning functionalities to infer users' interests and category-specific trust relationships. Following the study in sociology, RCCtrust exploits similarities between pairs of users to depict differentiated trust scales. The experimental results show that RCCtrust outperforms pure user similarity method and linear decay trust-aware technique in both accuracy and coverage for a Recommender System.
17429523
A Novel Trust-based Cooperative Node Selection Scheme in Wireless Sensor Networks
For communication between the cluster head node (CHN) and the distant data fusion center (DFC) with the cooperation of intra-cluster nodes in wireless sensor networks (WSNs), reliable handling of packets by the cooperative node (CN) is difficult to ensure. In this paper, we propose a novel CN selection scheme based on trust for WSNs. This scheme can choose one of high trust nodes to assist in the transmission of CHN, which aims to improve the transmission performance over cooperative communication in the presence of potential malicious nodes. First, we establish a trust model to improve the reliability of transmission by CN in WSNs, which is based on both the direct trust and the indirect trust of nodes. Then the CHN selects a node based on the proposed trust model to aid the CHN in transmission. Simulation results show that the proposed scheme can mitigate the impact of the potential malicious nodes on transmission performance and the simulation results also demonstrate the efficiency of the proposed scheme.