可靠的网格作业调度机制

针对网格环境的动态性特征,提出了一种可靠的网格作业调度机制(DGJS)。按照作业完成时间期限,DGJS将作业分为：高QoS级、低QoS级和无QoS级,不同QoS级作业有不同的调度优先权;基于资源可用性预测,DGJS采用基于可靠性代价的作业调度策略,将作业尽可能调度到可靠性高的资源节点;另外,DGJS对不同QoS级作业采用不同的容错策略,在保证故障容错的同时,节省网格资源。实验表明：在动态的网格环境下,较之传统的网格作业调度算法,DGJS提高了作业成功率,减少了作业完成时间。     

针对t-可诊断多处理器系统的遗传诊断算法

结合诊断模型与遗传算法,提出一种针对t-可诊断多处理器系统的遗传诊断算法。通过运用模型的诊断性质提高初始种群的质量,并定义判断结点状态的诊断方程,设计基于该方程的适应度函数。理论分析和模拟实验表明,算法具有较好的最优解搜索能力和较快的收敛速度。     

Improving the yield of NoC-based systems through fault diagnosis and adaptive routing

We propose an effective and low cost method to increase the yield and the lifetime of torus NoCs. The method consists in detecting and diagnosing NoC interconnect faults using BIST structures and activating alternative paths for the faulty links. Alternative paths use the inherent redundancy of the torus topology, thus leading to minimal performance, area, and power overhead. We assume an extended interconnect fault model comprising stuck-at and pairwise shorts within a single link or between any two links in the network. Experimental results for a 3×3 NoC show that the proposed approach can correctly diagnose 93% of all possible interconnect faults and can mitigate 42% of those faults (representing 94.4% of the solvable faults) with a worst case performance penalty of 8% and 1% of area overhead. We also demonstrate the scalability of the method by presenting its application to larger NoCs.     

Damage assessment and repair in attack resilient distributed database systems

Preventive measures sometimes fail to defect malicious attacks. With attacks on data-intensive applications becoming an ever more serious threat, intrusion tolerant database systems are a significant concern. The main objective of such systems is to detect attacks, and to assess and repair the damage in a timely manner. This paper focuses on efficient damage assessment and repair in distributed database systems. The complexity caused by data partition, distributed transaction processing, and failures makes intrusion recovery much more challenging than in centralized database systems. This paper identifies the key challenges and presents an efficient algorithm for distributed damage assessment and repair.     

Fault tolerant machine learning for nanoscale cognitive radio

We introduce a machine learning-based classifier that identifies free radio channels for cognitive radio. The architecture is designed for nanoscale implementation, under nanoscale implementation constraints; we do not describe all physical details but believe future physical implementation to be feasible. The system uses analog computation and consists of cyclostationary feature extraction and a radial basis function network for classification. We describe a model for nanoscale faults in the system, and simulate experimental performance and fault tolerance in recognizing WLAN signals, under different levels of noise and computational errors. The system performs well under expected non-ideal manufacturing and operating conditions.     

Prediction of part orientation error tolerance of a robotic gripper

This paper presents a model which predicts the part orientation error tolerance of a three-fingered robotic gripper. The concept of “self-alignment” is introduced, where the gripper uses the grasping process to bring the workpiece into its final state of orientation. The gripper and part are represented mathematically, and initial contact locations upon grasp closure determined. This information is used to solve for the contact forces present, and criteria are developed to determine if beneficial part motion resulting in self-alignment is expected. The results are visualized via a boundary projected on a reference plane below the part. The model is validated experimentally with a number of part configurations with favorable results. This method presents a useful tool by which the mechanical designer can quantitatively predict the performance of an intuitively designed gripping system.     

Analyzing the techniques that improve fault tolerance of aggregation trees in sensor networks

Sensor networks are finding significant applications in large scale distributed systems. One of the basic operations in sensor networks is in-network aggregation. Among the various approaches to in-network aggregation, such as gossip and tree, including the hash-based techniques, the tree-based approaches have better performance and energy-saving characteristics. However, sensor networks are highly prone to failures. Numerous techniques suggested in the literature to counteract the effect of failures have not been carefully analyzed. In this paper, we focus on the performance of these tree-based aggregation techniques in the presence of failures. First, we identify a fault model that captures the important failure traits of the system. Then, we analyze the correctness of simple tree aggregation with our fault model. We then use the same fault model to analyze the techniques that utilize redundant trees to improve the variance. The impact of techniques for maintaining the correctness under faults, such as rebuilding or locally fixing the tree, is then studied under the same fault model. We also do the cost-benefit analysis of using the hash-based schemes which are based on FM sketches. We conclude that these fault tolerance techniques for tree aggregation do not necessarily result in substantial improvement in fault tolerance.     

Obesity does not reduce maximum acceptable weights of lift

The maximum acceptable weights of lift (MAWL) of obese and non-obese participants were empirically investigated. Three obesity levels were considered: non-obese (18.5 kg/m(2)< or= body mass index (BMI)or= 40 kg/m(2)). Ten male and 10 female participants were recruited for each obesity level. The participants determined their MAWL for 18 different lifting task conditions (six lifting frequencies x three lifting heights). An analysis of variance (ANOVA) was conducted to determine the effects of obesity level, gender, lifting height, lifting frequency and their interactions on MAWL. Overall, the ANOVA results indicated that obesity does not reduce MAWL, and thus, suggested that the existing MAWL data can be used to accommodate both general and obese workers. However, further studies based on the biomechanical and physiological approaches are required to provide more complete understanding of obesity effects on lifting tolerance limits.     

Implementing uniform reliable broadcast with binary consensus in systems with fair-lossy links

When implementing multivalued consensus using binary consensus, previous algorithms assume the availability of uniform reliable broadcast, which is not implementable in systems with fair-lossy links. In this paper, we show that with binary consensus we can implement uniform reliable broadcast directly in systems with fair-lossy links, and thus the separate assumption of the availability of uniform reliable broadcast is not necessary. We further prove that, when binary consensus instances are available only as black boxes, any implementation of uniform reliable broadcast in the fair-lossy link model requires the invocation of an infinite number of binary consensus instances even if no process ever broadcasts any messages, and this is true even when multivalued consensus is used.     

Energy efficient redundant configurations for real-time parallel reliable servers

Modular redundancy and temporal redundancy are traditional techniques to increase system reliability. In addition to being used as temporal redundancy, with technology advancements, slack time in a system can also be used by energy management schemes to save energy. In this paper, we consider the combination of modular and temporal redundancy to achieve energy efficient reliable real-time service provided by multiple servers. We first propose an efficient adaptive parallel recovery scheme that appropriately processes service requests in parallel to increase the number of faults that can be tolerated and thus system reliability. Then we explore schemes to determine the optimal redundant configurations of the parallel servers to minimize system energy consumption for a given reliability goal or to maximize system reliability for a given energy budget. Our analysis results show that small requests, optimistic approaches, and parallel recovery favor lower levels of modular redundancy, while large requests, pessimistic approaches and restricted serial recovery favor higher levels of modular redundancy.