Recent computer systems research has proposed using redundant requests to reduce latency. The idea is to run a request on multiple servers and wait for the first completion (discarding all remaining copies of the request). However there is no exact analysis of systems with redundancy. This paper presents the first exact analysis of systems with redundancy. We allow for any number of classes of redundant requests, any number of classes of non-redundant requests, any degree of redundancy, and any number of heterogeneous servers. In all cases we derive the limiting distribution on the state of the system. In small (two or three server) systems, we derive simple forms for the distribution of response time of both the redundant classes and non-redundant classes, and we quantify the "gain" to redundant classes and "pain" to non-redundant classes caused by redundancy. We find some surprising results. First, the response time of a fully redundant class follows a simple Exponential distribution and that of the non-redundant class follows a Generalized Hyperexponential. Second, fully redundant classes are "immune" to any pain caused by other classes becoming redundant. We also compare redundancy with other approaches for reducing latency, such as optimal probabilistic splitting of a class among servers (Opt-Split) and Join-the-Shortest-Queue (JSQ) routing of a class. We find that, in many cases, redundancy outperforms JSQ and Opt-Split with respect to overall response time, making it an attractive solution.
|Original language||English (US)|
|Number of pages||14|
|Journal||Performance Evaluation Review|
|State||Published - Jun 24 2015|
|Event||ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS 2015 - Portland, United States|
Duration: Jun 15 2015 → Jun 19 2015
Bibliographical noteFunding Information:
This material is based upon work supported by the Na-tional Science Foundation Graduate Research Fellowship un-der Grant No. DGE-1252522; was funded by NSF-CMMI-1334194 and NSF-CSR-1116282, by the Intel Science and Technology Center for Cloud Computing, and by a Google Faculty Research Award 2015/16; and has been supported by the Academy of Finland in TOP-Energy project (grant no. 268992).
- Markov chain analysis