2011 UnderstandingNetworkFailuresinD

(Gill et al., 2011) ⇒ Phillipa Gill, Navendu Jain, and Nachiappan Nagappan. (2011). “Understanding Network Failures in Data Centers: Measurement, Analysis, and Implications.” In: Proceedings of the ACM SIGCOMM 2011 conference. doi:10.1145/2043164.2018477

Subject Headings: Data Center, IT System Failure.

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Cited By

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Author Keywords

data centers; network reliability

Abstract

We present the first large-scale analysis of failures in a data center network. Through our analysis, we seek to answer several fundamental questions: which devices / links are most unreliable, what causes failures, how do failures impact network traffic and how effective is network redundancy? We answer these questions using multiple data sources commonly collected by network operators. The key findings of our study are that (1) data center networks show high reliability, (2) commodity switches such as ToRs and AggS are highly reliable, (3) load balancers dominate in terms of failure occurrences with many short-lived software related faults, (4) failures have potential to cause loss of many small packets such as keep alive messages and ACKs, and (5) network redundancy is only 40% effective in reducing the median impact of failure.

1. INTRODUCTION

Demand for dynamic scaling and benefits from economies of scale are driving the creation of mega data centers to host a broad range of services such as Web search, e-commerce, storage backup, video streaming, high-performance computing, and data analytics. To host these applications, data center networks need to be scalable, efficient, fault tolerant, and easy-to-manage. Recognizing this need, the research community has proposed several architectures to improve scalability and performance of data center networks [2, 3, 12. 14, 17, 21]. However, the issue of reliability has remained unaddressed, mainly due to a dearth of available empirical data on failures in these networks.

In this paper, we study data center network reliability by analyzing network error logs collected for over a year from thousands of network devices across tens of geographically distributed data centers. Our goals for this analysis are two-fold. First, we seek to characterize network failure patterns in data centers and understand overall reliability of the network. Second, we want to leverage lessons learned from this study to guide the design of future data center networks.

Motivated by issues encountered by network operators, we study network reliability along three dimensions:

Characterizing the most failure prone network elements. To achieve high availability amidst multiple failure sources such as hardware, software, and human errors, operators need to focus on fixing the most unreliable devices and links in the network. To this end, we characterize failures to identify network elements with high impact on network reliability e.g., those that fail with high frequency or that incur high downtime.
Estimating the impact of failures. Given limited resources at hand, operators need to prioritize severe incidents for troubleshooting based on their impact to end-users and applications. In general, however, it is difficult to accurately quantify a failure.s impact from error logs, and annotations provided by operators in trouble tickets tend to be ambiguous. Thus, as a first step, we estimate failure impact by correlating event logs with recent network traffic observed on links involved in the event. Note that logged events do not necessarily result in a service outage because of failure-mitigation techniques such as network redundancy [1] and replication of compute and data [11, 27], typically deployed in data centers.
Analyzing the effectiveness of network redundancy.

Ideally, operators want to mask all failures before applications experience any disruption. Current data center networks typically provide 1:1 redundancy to allow traffic to flow along an alternate route when a device or link becomes unavailable [1]. However, this redundancy comes at a high cost.both monetary expenses and management overheads.to maintain a large number of network devices and links in the multi-rooted tree topology. To analyze its effectiveness, we compare traffic on a per-link basis during failure events to traffic across all links in the network redundancy group where the failure occurred.

For our study, we leverage multiple monitoring tools put in place by our network operators. We utilize data sources that provide both a static view (e.g., router configuration files, device procurement data) and a dynamic view (e.g., SNMP polling, syslog, trouble tickets) of the network. Analyzing these data sources, however, poses several challenges. First, since these logs track low level network events, they do not necessarily imply application performance impact or service outage. Second, we need to separate failures that potentially impact network connectivity from high volume and often noisy network logs e.g., warnings and error messages even when the device is functional. Finally, analyzing the effectiveness of network redundancy requires correlating multiple data sources across redundant devices and links. Through our analysis, we aim to address these challenges to characterize network failures, estimate the failure impact, and analyze the effectiveness of network redundancy in data centers.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2011 UnderstandingNetworkFailuresinD	Phillipa Gill Navendu Jain Nachiappan Nagappan			Understanding Network Failures in Data Centers: Measurement, Analysis, and Implications				10.1145/2043164.2018477