42916380
Policy-enabled handoffs across heterogeneous wireless networks
"Access is the killer app" is the vision of the Daedalus project at UC Berkeley. Being able to be connected seamlessly anytime anywhere to the best network still remains an unfulfilled goal. Often, even determining the "best" network is a challenging task because of the widespread deployment of overlapping wireless networks. We describe a policy-enabled handoff system that allows users to express policies on what is the "best" wireless system at any moment, and make tradeoffs among network characteristics and dynamics such as cost, performance and power consumption. We designed a performance reporting scheme estimating current network conditions, which serves as input to the policy specification. A primary goal of this work is to make it possible to balance the bandwidth load across networks with comparable performance. To avoid the problem of handoff instability, i.e., many mobile hosts making the same handoff decision at essentially the same time, we designed randomization into our mechanism. Given the current "best" network, our system determines whether the handoff is worthwhile based on the handoff overhead and potential network usage duration.
6612123
Automatic Misconfiguration Troubleshooting with PeerPressure
Technical support contributes 17% of the total cost of ownership of today's desktop PCs [25]. An important element of technical support is troubleshooting miscon-figured applications. Misconfiguration troubleshooting is particularly challenging, because configuration information is shared and altered by multiple applications. 
 
In this paper, we present a novel troubleshooting system: PeerPressure, which uses statistics from a set of sample machines to diagnose the root-cause misconfigurations on a sick machine. This is in contrast with methods that require manual identification on a healthy machine for diagnosing misconfigurations [30]. The elimination of this manual operation makes a significant step towards automated misconfiguration troubleshooting. 
 
In PeerPressure, we introduce a ranking metric for misconfiguration candidates. This metric is based on empirical Bayesian estimation. We have prototyped a PeerPressure troubleshooting system and used a database of 87 machine configuration snapshots to evaluate its performance. With 20 real-world troubleshooting cases, PeerPressure can effectively pinpoint the root-cause misconfigurations for 12 of these cases. For the remaining cases, PeerPressure significantly narrows down the number of root-cause candidates by three orders of magnitude.
695051
PeerPressure for automatic troubleshooting
Technical support contributes 17% of the total cost of ownershipof today’s desktop PCs [3]. An important element of technical sup-port is troubleshooting misconﬁgured applications. Misconﬁgura-tion troubleshooting is particularly challenging, because conﬁgura-tion information can be shared and altered by multiple applications.Maintaining healthy conﬁgurations of a computer platform with alarge installed base and numerous third-party software packageshas been recognized as a daunting task [1]. The considerable num-ber of possible conﬁgurations and the difﬁculty in specifying the“golden state” [4], the perfect conﬁguration, have made the prob-lem appear to be intractable.In this paper, we address the problem of misconﬁguration trou-bleshooting. There are two essential goals in designing such a trou-bleshooting system:1. Troubleshooting effectiveness: the system should effectivelyidentify a small set of sick conﬁguration candidates with ashort response time;2. Automation: the system should minimize the number of man-ual steps and the number of users involved.To diagnose misconﬁgurations of an application on a sick ma-chine, it is natural to ﬁnd a healthy machine to compare against [7].Then, the conﬁgurations that differ between the healthy and the sickare misconﬁguration suspects. However, it is difﬁcult to identify a
1349303
SubVirt: implementing malware with virtual machines
Attackers and defenders of computer systems both strive to gain complete control over the system. To maximize their control, both attackers and defenders have migrated to low-level, operating system code. In this paper, we assume the perspective of the attacker, who is trying to run malicious software and avoid detection. By assuming this perspective, we hope to help defenders understand and defend against the threat posed by a new class of rootkits. We evaluate a new type of malicious software that gains qualitatively more control over a system. This new type of malware, which we call a virtual-machine based rootkit (VMBR), installs a virtual-machine monitor underneath an existing operating system and hoists the original operating system into a virtual machine. Virtual-machine based rootkits are hard to detect and remove because their state cannot be accessed by software running in the target system. Further, VMBRs support general-purpose malicious services by allowing such services to run in a separate operating system that is protected from the target system. We evaluate this new threat by implementing two proof-of-concept VMBRs. We use our proof-of-concept VMBRs to subvert Windows XP and Linux target systems, and we implement four example malicious services using the VMBR platform. Last, we use what we learn from our proof-of-concept VMBRs to explore ways to defend against this new threat. We discuss possible ways to detect and prevent VMBRs, and we implement a defense strategy suitable for protecting systems against this threat
31566264
Cooperation-Based Behavior Design
Robot soccer explores such a research topic that multiple agents work together in a Real-time, Cooperative and Adversarial (RCA) environment to achieve specific objectives. It requires that each agent can not only deal with situations individually, but also present cooperation with its teammates. In this paper, we describe a robot architecture, which addresses "scaling cooperation" among robots, and meanwhile allows each robot to make decisions independently in real-time case. The architecture is based on "ideal cooperation" principle and implemented for Small Robot League in RoboCup. Experimental results prove its effectiveness and reveal several primary characteristics of behaviors in robot soccer.
15947104
Permissible Observability Relations in FSM Networks
Previous attempts to capture the phenomenon of output don't care sequences for a component in an FSM network have been incomplete. We demonstrate that output don't care sequences for a component can be expressed using a set of observability relations given that its state transition function is kept unchanged. Each observability relation is permissible in the sense that any implementation compatible with one of them is feasible. The representation for a set of permissible observability relations is not unique. We provide a method to find a set with the minimum number of permissible relations. We briefly discuss the exploitation of permissible observability relations in state minimization, circuit implementation and signal encoding. We have implemented these methods and present some preliminary results on a few small artificially constructed examples.
5982516
Robust Fitting by Adaptive-Scale Residual Consensus
Computer vision tasks often require the robust fit of a model to some data. In a robust fit, two major steps should be taken: i) robustly estimate the parameters of a model, and ii) differentiate inliers from outliers. We propose a new estimator called Adaptive-Scale Residual Consensus (ASRC). ASRC scores a model based on both the residuals of inliers and the corresponding scale estimate determined by those inliers. ASRC is very robust to multiple-structural data containing a high percentage of outliers. Compared with RANSAC, ASRC requires no pre-determined inlier threshold as it can simultaneously estimate the parameters of a model and the scale of inliers belonging to that model. Experiments show that ASRC has better robustness to heavily corrupted data than other robust methods. Our experiments address two important computer vision tasks: range image segmentation and fundamental matrix calculation. However, the range of potential applications is much broader than these.
16600904
A services oriented framework for next generation data analysis centers
Over the past decade, advances in computational and sensor technology have enabled us to dynamically collect vast amounts of data from observations, health screening tests, simulations, and experiments at an ever-increasing pace. Knowledge discovery and data mining is an iterative process concerned with deriving interesting, non-obvious, and useful patterns and models from such large volumes of data. Although inexpensive storage is conducive to maintaining said data, accessing and managing it for knowledge discovery and data mining becomes a performance issue when datasets are large, dynamic, and distributed. In this work, we present our vision of a software framework consisting of middleware services to support interactive data mining over dynamic data at data analysis centers built on top of heterogeneous clusters. The design of a sampling service for dynamic data, together with initial performance results, are also presented.
11948003
Loadstar: Load Shedding in Data Stream Mining
In this demo, we show that intelligent load shedding is essential in achieving optimum results in mining data streams under various resource constraints. The Loadstar system introduces load shedding techniques to classifying multiple data streams of large volume and high speed. Loadstar uses a novel metric known as the quality of decision (QoD) to measure the level of uncertainty in classification. Resources are then allocated to sources where uncertainty is high. To make optimum classification decisions and accurate QoD measurement, Loadstar relies on feature prediction to model the data dropped by the load shedding mechanism. Furthermore, Loadstar is able to adapt to the changing data characteristics in data streams. The system thus offers a nice solution to data mining with resource constraints.