Abstract Various types of errors can propagate in networks, and they are usually hard to diagnose. For example, social networks spread rumors as well as useful information. Computer networks can spread Internet worms or malicious packets. In many cases, it is very hard to find the root cause (a.k.a. initial rumor spreader) of such errors without complete knowledge of the error propagation. We aim to find the root cause node when there is limited information about error propagation. We assume that there are very small number of monitor nodes in the network reporting whether error reached them or not. With this assumption, we first propose an algorithm that finds the most probable root cause node. Second, to improve the accuracy of root cause analysis, we propose another algorithm that makes use of timestamp of error reception. Finally, we study how to select monitors effectively so that root cause analysis can be accurate. With real networks from various domains, our algorithms are shown to be very effective. Copyright © 2016 John Wiley & Sons, Ltd.

Software bugs in distributed systems are notoriously hard to find due to the large number of components involved and the non-determinism introduced by race conditions between messages. This paper introduces Pop Mine, a tool for diagnosing corner-case bugs by finding the minimal causal directed acyclic graph (DAG) of events, spanning multiple processes, which captures a bug-triggering condition. Being based on causal order, a global notion of time is not required in uncovering bug-triggering distributed event patterns. Bug triggering event DAGs can be identified by comparing execution graphs from successful runs to those where bug manifestations were observed, and exposing the minimal discriminative event DAGs that may be responsible for the problem. This is a significant extension to prior debugging tools, in that prior work considered much simpler bug-triggering conditions such as single events, event sets, or ordered chains of events. To the authors' knowledge, this is the first paper that considers bug-triggering conditions in the form of distributed event graphs. To prove the effectiveness of our approach, we applied our tool to VCP, Chord and GreenGPS and diagnosed bugs. We also present performance analysis results to demonstrate the scalability of our approach.

New features are often added incrementally to avionics systems. This avoids redesign and recertification but still requires verifying the timing constraints of both new and existing applications. We introduce a new switch that facilitates this verification by bounding the latency of end-to-end communication across a network. Our clock-driven real-time switching algorithm is throughput-optimal with a bounded worst-case delay for all feasible traffic. Associated heuristics can verify whether the timing constraints of an avionics network are met, after new features have caused traffic to increase, and then search for alternative network configurations if necessary. We show how these heuristics cope with changes to an example environmental monitoring architecture within an avionics system that incorporates our switch. Our approach to analysis can be used to determine, quickly but rigorously, which system architecture meet timing constraints, and it allows the system architect to manage the cascading effects of component changes in a comprehensive manner.

The reality of multi-core hardware has made concurrent programs pervasive. Unfortunately, writing correct concurrent programs is difficult. Addressing this challenge requires advances in multiple directions, including concurrency bug detection, concurrent program testing, concurrent programming model design, etc. Designing effective techniques in all these directions will significantly benefit from a deep understanding of real world concurrency bug characteristics.This paper provides the first (to the best of our knowledge) comprehensive real world concurrency bug characteristic study. Specifically, we have carefully examined concurrency bug patterns, manifestation, and fix strategies of 105 randomly selected real world concurrency bugs from 4 representative server and client open-source applications (MySQL, Apache, Mozilla and OpenOffice). Our study reveals several interesting findings and provides useful guidance for concurrency bug detection, testing, and concurrent programming language design.Some of our findings are as follows: (1) Around one third of the examined non-deadlock concurrency bugs are caused by violation to programmers' order intentions, which may not be easily expressed via synchronization primitives like locks and transactional memories; (2) Around 34% of the examined non-deadlock concurrency bugs involve multiple variables, which are not well addressed by existing bug detection tools; (3) About 92% of the examined concurrency bugs canbe reliably triggered by enforcing certain orders among no more than 4 memory accesses. This indicates that testing concurrent programs can target at exploring possible orders among every small groups of memory accesses, instead of among all memory accesses; (4) About 73% of the examinednon-deadlock concurrency bugs were not fixed by simply adding or changing locks, and many of the fixes were not correct at the first try, indicating the difficulty of reasoning concurrent execution by programmers.

The limited number of orthogonal channels and autonomous installations of hot spots and home wireless networks often leave neighboring 802.11 basic service sets (BSSs) operating on the same or overlapping channels, therefore interfering with each other. However, the 802.11 medium access control (MAC) does not work well in resolving inter-BSS interference due to the well-known hidden/exposed-receiver problem, which has been haunting the research community for more than a decade. In this paper, we propose SELECT, an effective and efficient self-learning collision avoidance strategy to address the hidden/exposed-receiver problem in 802.11 wireless networks. SELECT is based on the observation that carrier sense with received signal strength (RSS) measurements at the sender and the receiver can be strongly correlated. A SELECT-enabled sender exploits such correlation using an automated online learning algorithm and makes an informed judgment of the channel availability at the intended receiver. SELECT achieves collision avoidance at packet-level time granularity, involves zero communication overhead, and easily integrates with the 802.11 distributed coordination function (DCF). Our evaluation in analysis, simulations, and prototype experiments show that SELECT addresses the hidden/exposed-receiver problem well. In typical hidden/exposed-receiver scenarios, SELECT improves the throughput by up to 140 percent and the channel access success ratio by up to 302 percent while almost completely eliminating contention-induced data packet drops.

The majority of existing wireless rate controls are based on the implicit assumption that frames are corrupted due to the random, arbitrary environmental and thermal noises. They generally reduce the channel rate on frame losses, trading lower efficiency in frequency band utilization for more robust modulation so that the current noise level may be tolerable. In highly interfered wireless networks where frames are lost mainly due to interference from other wireless transceivers, simply reducing the channel rate prolongs the frame transmission time and therefore aggravates frame loss ratio. This positive feedback in the rate control loop quickly diverges the interfered transceivers into a suboptimal channel rate and drives the network into a state with high interference. In the worst case, interfered transceivers can be starved. In this paper we present RAF, the rate-adaptive framing that jointly controls the channel rate and frame size according to the observed interference patterns and noise level at the receiver. Based on the inputs from physical layer carrier sense, the receiver derives the optimal channel rate and frame size that maximize throughput, and informs the transmitter of such optimal configuration in a few bits in the per-frame acknowledgement. Through intensive simulations we show that RAF consistently outperforms ARF, RBAR, and OAR in all simulated scenarios.

The limited number of orthogonal channels and the autonomous installations of hotspots and home wireless networks often leave neighboring 802.11 basic service sets (BSS's) operating on the same or overlapping channels, therefore interfering with each other. However, the 802.11 MAC does not work well in resolving inter-BSS interferences due to the well-known hidden/exposed receiver problem, which has been haunting in the research community for more than a decade. In this paper we propose SELECT, an effective and efficient self-learning collision avoidance strategy to address the open hidden/exposed receiver problem in wireless networks. SELECT is based on the observation that carrier sense with received signal strength (RSS) measurements at the sender and the receiver are strongly correlated. A SELECT-enabled sender exploits such correlation using automated on-line learning algorithm, and makes informed judgment of the channel availability at the intended receiver. SELECT achieves collision avoidance at packetlevel time granularity, involves zero communication overhead, requires no hardware support beyond what is available in offthe -shelf 802.11 devices, and easily integrates with the 802.11 DCF. Our evaluation in both analysis and simulations show that SELECT addresses the hidden/exposed receiver problem well. In typical hidden/exposed receiver scenarios SELECT improves the throughput by up to 140% and channel access success ratio by up to 302%, while almost completely eliminating contention-induced data packet drops.