2010 11th IEEE/ACM International Conference on Grid Computing

Format:

Book

Author/Creator:

IEEE/ACM International Conference on Grid Computing, author.

Contributor:

IEEE Staff, Contributor.

Language:

English

Subjects (All):

Computational grids (Computer systems)--Congresses.

Computational grids (Computer systems).

Physical Description:

1 online resource

Place of Publication:

[Place of publication not identified] I E E E 2010

Language Note:

English

Summary:

The analysis and modeling of the failures bound to occur in today's large-scale production systems is invaluable in providing the understanding needed to make these systems fault-tolerant yet efficient. Many previous studies have modeled failures without taking into account the time-varying behavior of failures, under the assumption that failures are identically, but independently distributed. However, the presence of time correlations between failures (such as peak periods with increased failure rate) refutes this assumption and can have a significant impact on the effectiveness of fault-tolerance mechanisms. For example, the performance of a proactive fault-tolerance mechanism is more effective if the failures are periodic or predictable; similarly, the performance of checkpointing, redundancy, and scheduling solutions depends on the frequency of failures. In this study we analyze and model the time-varying behavior of failures in large-scale distributed systems. Our study is based on nineteen failure traces obtained from (mostly) production large-scale distributed systems, including grids, P2P systems, DNS servers, web servers, and desktop grids. We first investigate the time correlation of failures, and find that many of the studied traces exhibit strong daily patterns and high autocorrelation. Then, we derive a model that focuses on the peak failure periods occurring in real large-scale distributed systems. Our model characterizes the duration of peaks, the peak inter-arrival time, the inter-arrival time of failures during the peaks, and the duration of failures during peaks; we determine for each the best-fitting probability distribution from a set of several candidate distributions, and present the parameters of the (best) fit. Last, we validate our model against the nineteen real failure traces, and find that the failures it characterizes are responsible on average for over 50% and up to 95% of the downtime of these systems.

Notes:

Bibliographic Level Mode of Issuance: Monograph

ISBN:

9781424493494

1424493498