The newly emerging “fail-slow” failures plague both software and hardware where the victim components are still functioning yet with degraded performance. To address this problem, this article presents Perseus , a practical fail-slow detection framework for storage devices. Perseus leverages a light regression-based model to quickly pinpoint and analyze fail-slow failures at the granularity of drives. Within a 10-month close monitoring on 248K drives, Perseus managed to find 304 fail-slow cases. Isolating them can reduce the (node-level) 99.99th tail latency by 48%. We assemble a large-scale fail-slow dataset (including 41K normal drives and 315 verified fail-slow drives) from our production traces, based on which we provide root cause analysis on fail-slow drives covering a variety of ill-implemented scheduling, hardware defects, and environmental factors. We have released the dataset to the public for fail-slow study.

Continuous monitoring and diagnosis of network performance are of crucial importance for the Internet access service and virtual private network (VPN) service providers. Various operational constraints, which are crucial to the practice, are largely ignored in previous monitoring system designs, or are simply replaced with load balancing problems which do not work for real heterogeneous networks. Given these real-world challenges, in this paper, we design a VScope monitoring system with the following contributions. First, we design a greedy-assisted linear programming algorithm to select as few monitors as possible that can monitor the whole network under the operational constraints. Secondly, VScope takes a multi-round measurement approach to further reduce monitors deployment/management cost, by scheduling the path measurements in different rounds under the operational constraints. Evaluations based on several real VPN topologies from a tier-1 ISP as well as some other synthetic topologies demonstrate that VScope is promising to solve the aforementioned challenges.

Botnets are emerging threat with hundreds of millions of computers infected. A study shows that about 40% of all computers connected to the internet in the world are infected bots and controlled by attackers. This article is a survey of recent advances in botnet research. The survey classifies the botnet research into three areas: understanding botnets, detecting and tracking botnets, and defending against botnets. While botnets are widespread, the research and solutions for botnets are still in their infancy. The paper also summarizes the existing research and proposes future directions for botnet research.