Body-coupled communication (BCC) is a technology that is based on the use of capacitively-coupled electric fields over the human body. In this paper we highlight the design and implementation of a medium access contro...
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Body-coupled communication (BCC) is a technology that is based on the use of capacitively-coupled electric fields over the human body. In this paper we highlight the design and implementation of a medium access control (MAC) protocol specifically designed to meet the requirements of BCC-enabled body area networks (BANs). We propose a set of specific protocol enhancements over well-known MAC protocols that are based on the concept of low-power listening. We present the proposed protocol in conjunction with the embedded software implementation for the prototype hardware platform. The purpose of this approach is to demonstrate that several protocol enhancements were motivated by observations on the actual BCC hardware and its intricacies. Finally, we present a series of experiments under real conditions.
Self-adaptive and self-organizing systems must be self-monitoring. Recent research has shown that self-monitoring can be enabled by using correlations between monitoring variables (metrics). However, computersystems ...
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Self-adaptive and self-organizing systems must be self-monitoring. Recent research has shown that self-monitoring can be enabled by using correlations between monitoring variables (metrics). However, computersystems often make a very large number of metrics available for collection. Collecting them all not only reduces system performance, but also creates other overheads related to communication, storage, and processing. In order to control the overhead, it is necessary to limit collection to a subset of the available metrics. Manual selection of metrics requires a good understanding of system internals, which can be difficult given the size and complexity of modern computersystems. In this paper, assuming no knowledge of metric semantics or importance and no advance availability of fault data, we investigate automated methods for selecting a subset of available metrics in the context of correlation-based monitoring. Our goal is to collect fewer metrics while maintaining the ability to detect errors. We propose several metric selection methods that require no information beside correlations. We compare these methods on the basis of fault coverage. We show that our minimum spanning tree-based selection performs best, detecting on average 66% of faults detectable by full monitoring (i.e., using all considered metrics) with only 30% of the metrics.
Modern software systems expose management metrics to help track their health. Recently, it was demonstrated that correlations among these metrics allow faults to be detected and their causes localized. In particular, ...
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Modern software systems expose management metrics to help track their health. Recently, it was demonstrated that correlations among these metrics allow faults to be detected and their causes localized. In particular, linear regression models have been used to capture metric correlations. We show that for many pairs of correlated metrics in software systems, such as those based on Java Enterprise Edition (JavaEE), the variance of the predicted variable is not constant. This behaviour violates the assumptions of linear regression, and we show that these models may produce inaccurate results. In this paper, leveraging insight from the system behaviour, we employ an efficient variant of linear regression to capture the non-constant variance. We show that this variant captures metric correlations, while taking the changing residual variance into consideration. We explore potential causes underlying this behaviour, and we construct and validate our models using a realistic multi-tier enterprise application. Using a set of 50 fault-injection experiments, we show that we can detect all faults without any false alarm.
Given a set of n different deterministic finite state machines (DFSMs) modeling a distributed system, we examine the problem of tolerating f crash or Byzantine faults in such a system. The traditional approach to this...
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ISBN:
(纸本)9781424437511
Given a set of n different deterministic finite state machines (DFSMs) modeling a distributed system, we examine the problem of tolerating f crash or Byzantine faults in such a system. The traditional approach to this problem involves replication and requires n middot f backup DFSMs for crash faults and 2 middot n middot f backup DFSMs for Byzantine faults. For example, to tolerate two crash faults in three DFSMs, a replication based technique needs two copies of each of the given DFSMs, resulting in a system with six backup DFSMs. In this paper, we question the optimality of such an approach and present an approach called (f, m)-fusion that permits fewer backups than the replication based approaches. Given n different DFSMs, we examine the problem of tolerating f faults using just m additional DFSMs. We introduce the theory of fusion machines and provide an algorithm to generate backup DFSMs for both crash and Byzantine faults. We have implemented our algorithms in Java and have used them to automatically generate backup DFSMs for several examples.
To ensure high availability, self-managing systems require self-monitoring and a system model against which to analyze monitoring data. Characterizing relationships between system metrics has been shown to model simpl...
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ISBN:
(纸本)9781605580371
To ensure high availability, self-managing systems require self-monitoring and a system model against which to analyze monitoring data. Characterizing relationships between system metrics has been shown to model simple multi-tier transaction systems effectively, enabling failure detection and fault diagnosis. In this paper we show how to extend this invariant metric-relationships approach to clustered multitier systems. We show through analysis and experimentation that naïve application of the approach increases cost dramatically while reducing diagnosis accuracy. We demonstrate that randomization at the load balancer during the invariant-identification phase will improve diagnosis accuracy, though it neither completely eliminates the problem nor reduces the cost;indeed, it may increase the cost, as this approach will require a long learning phase to remove all accidental correlations. Finally, we argue that knowing the system structure is necessary to effectively apply invariants to the clustered environment. Copyright 2008 ACM.
A correctly functioning enterprise-software system exhibits long-term, stable correlations between many of its monitoring metrics. Some of these correlations no longer hold when there is an error in the system, potent...
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A correctly functioning enterprise-software system exhibits long-term, stable correlations between many of its monitoring metrics. Some of these correlations no longer hold when there is an error in the system, potentially enabling error detection and fault diagnosis. However, existing approaches are inefficient, requiring a large number of metrics to be monitored and ignoring the relative discriminative properties of different metric correlations. In enterprise-software systems, similar faults tend to reoccur. It is therefore possible to significantly improve existing correlation-analysis approaches by learning the effects of common recurrent faults on correlations. We present methods to determine the most significant correlations to track for efficient error detection, and the correlations that contribute the most to diagnosis accuracy. We apply machine learning to identify the relevant correlations, removing the need for manually configured correlation thresholds, as used in the prior approaches. We validate our work on a multi-tier enterprise-software system. We are able to detect and correctly diagnose 8 of 10 injected faults to within three possible causes, and to within two in 7 out of 8 cases. This compares favourably with the existing approaches whose diagnosis accuracy is 3 out of 10 to within 3 possible causes. We achieve a precision of at least 95%.
The use of multi-hop wireless networks based on 802.11 technology is extensive and growing, owing to their ease of deployment and low cost. However, such networks exhibit poor fairness, starving nodes that are too man...
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The use of multi-hop wireless networks based on 802.11 technology is extensive and growing, owing to their ease of deployment and low cost. However, such networks exhibit poor fairness, starving nodes that are too many hops distant from the gateway. The best current solution to this problem is source rate limiting. While effective in many topologies, this fails to completely address the fairness problem. In this paper we investigate this problem of residual unfairness in multi-hop wireless networks that use source-rate limiting. We identify the five necessary conditions for its occurrence, showing that elimination of any of these conditions is sufficient to remove the remaining unfairness. For cases where the conditions are unavoidable, we present two simple changes that can ameliorate the problem, providing on average 30% improvement for the least-rate flow.
Computing and communication are in flux today. On the communication side, historically there have been multiple, parallel networks servicing different types of traffic. Cost pressures are forcing convergence to a sing...
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Computing and communication are in flux today. On the communication side, historically there have been multiple, parallel networks servicing different types of traffic. Cost pressures are forcing convergence to a single IP-based network. Simultaneously the computing world has been steadily moving from monolithic applications to client/server systems, and from there to arbitrary distributed applications. In making this move, the computing world has typically viewed the network as little more than a high-speed bit-pipe, with the primary focus being on services provided by end hosts. This approach has a number of limitations, including replicated functionality, complex system coupling, and limited ability to integrate applications across different networks. By providing enabling services functionality, an integrated network can make possible both greater efficiencies and more-sophisticated distributed applications.
Modern society is heavily dependent on wireless networks for providing voice and data communications. Wireless data broadcast has recently emerged as an attractive way to disseminate dynamic data to a large number of ...
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ISBN:
(纸本)142440505X
Modern society is heavily dependent on wireless networks for providing voice and data communications. Wireless data broadcast has recently emerged as an attractive way to disseminate dynamic data to a large number of clients. In data broadcast systems, the server proactively transmits the information on a downlink channel; the clients access the data by listening to the channel. Wireless data broadcast systems can serve a large number of heterogeneous clients, minimizing power consumption as well as protecting the privacy of the clients' locations. The availability and relatively low cost of antennas resulted in a number of potential threats to the integrity of the wireless infrastructure. In particular, the data broadcast systems are vulnerable to jamming, i.e., the use of active signals to prevent data broadcast. The goal of jammers is to cause disruption, resulting in long waiting times and excessive power consumption. In this paper we investigate efficient schedules for wireless data broadcast that perform well in the presence of a jammer. We show that the waiting time of client can be reduced by adding redundancy to the schedule and establish upper and lower bounds on the achievable minimum waiting time under different requirements on the staleness of the transmitted data
Global competition has forced manufacturing enterprises to collaborate towards fulfilling market demands and satisfying customers. A major challenge in implementing enterprise collaboration is on the integration of he...
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