Works (5)
Updated: July 5th, 2023 15:38
2018 article
Micky: A Cheaper Alternative for Selecting Cloud Instances
PROCEEDINGS 2018 IEEE 11TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING (CLOUD), pp. 409–416.
TL;DR:
A collective-optimizer is created, MICKY, that reformulates the task of finding the near-optimal cloud configuration as a multi-armed bandit problem and can achieve on average 8.6 times reduction in measurement cost as compared to the state-of-the-art method while finding near-Optimal solutions.
(via Semantic Scholar)
arxiv.org (repository)
Sources: Web Of Science, NC State University Libraries
Added: January 21, 2019
2017 conference paper
Trilogy: data placement to improve performance and robustness of cloud computing
2017 IEEE International Conference on Big Data (Big Data), 2442–2451.
TL;DR:
Evaluations show that maximizing the number of unique partitions per node increases robustness to tolerate workload deviation while minimizing this number reduces storage footprint, and a surprisingly small increase in granularity is sufficient to obtain most benefits.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
9. Industry, Innovation and Infrastructure
(OpenAlex)
Source: NC State University Libraries
Added: August 6, 2018
2016 conference paper
Inside-out: Reliable performance prediction for distributed storage systems in the cloud
Proceedings of 2016 ieee 35th symposium on reliable distributed systems (srds), 127–136.
TL;DR:
In-depth evaluation shows that Inside-Out is a robust solution that enables SDS to predict end-to-end performance even in challenging conditions, e.g., changes in workload, storage configuration, available cloud resources, size of the distributed storage service, and amount of interference due to multi-tenants.
(via Semantic Scholar)
Source: NC State University Libraries
Added: August 6, 2018
2015 article
Evaluation of MapReduce in a large cluster
2015 IEEE 8TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING, pp. 461–468.
TL;DR:
The findings of running applications on Pivotal's Analytics Workbench, which consists of a 540-node Hadoop cluster, show that IO-intensive applications do not scale as data size increases and MapReduce applications require different amounts of parallelism and overlap to minimize completion time.
(via Semantic Scholar)
Source: Web Of Science
Added: August 6, 2018
2014 journal article
Maintenance of cooperative overlays in multi-overlay networks
IET Communications, 8(15), 2676–2683.
Source: NC State University Libraries
Added: August 6, 2018
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