Alexander Rasmussen

Publications9
h-index5
Citations201
Highly Influential Citations8
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TritonSort: A Balanced Large-Scale Sorting System
We present TritonSort, a highly efficient, scalable sorting system. It is designed to process large datasets, and has been evaluated against as much as 100 TB of input data spread across 832 disks in…
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Themis: an I/O-efficient MapReduce
TLDR
This work presents Themis, a MapReduce implementation that reads and writes data records to disk exactly twice, which is the minimum amount possible for data sets that cannot fit in memory.
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TritonSort: A Balanced and Energy-Efficient Large-Scale Sorting System
TLDR
This article describes the hardware and software architecture necessary to operate TritonSort, a highly efficient, scalable sorting system designed to process large datasets, and is able to sort data at approximately 80% of the disks’ aggregate sequential write speed.
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Local Recovery for High Availability in Strongly Consistent Cloud Services
TLDR
The primary contribution of Zorfu is a local recovery technique that significantly increases availability of replicated strongly consistent services by reducing the recovery time by an order of magnitude, while imposing only a negligible latency overhead.
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Improving the responsiveness of internet services with automatic cache placement
TLDR
ToOL, an analysis technique that automatically optimizes cache placement, is presented, showing that near-optimal cache placements vary significantly based on input distribution.
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Themis
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Dissertation: I/O-Efficient Data-Intensive Computing
TLDR
A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Computer Science by Alexander Carlin Rasmussen.
I/O-Efficient Data-Intensive Computing /
TLDR
This dissertation endeavors to bridge the performance gap between high-performance large-scale sorting systems and the underlying capacity of the hardware infrastructure on which they are deployed by focusing on efficient I/O as a first-class architectural concern in TritonSort and Themis.
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Parallelizing the Mace Model Checker
TLDR
Preliminary results show that the parallelization of MaceMC, a model checker for distributed systems written in Mace, can increase the number of useful states explored by the modelChecker by a factor of 50 for relatively modest cluster sizes.