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Horey, James L

Publications and source records attributed to Horey, James L.

Analyzing reliability of virtual machine instances with dynamic pricing in the public cloud

This study presents reliability analysis of virtual machine instances in public cloud environments in the face of dynamic pricing. Different from traditional fixed pricing, dynamic pricing allows price to dynamically fluctuate over arbitrary period of time according to external factors such as supply and demand, excess capacity, etc. This pricing option introduces a new type of fault: virtual machine instances may be unexpectedly terminated due to conflicts in the original bid price and the current offered price. This new class of fault under dynamic pricing may be more dominant than traditional faults in cloud computing environments, where resource availability associated with traditional faults is often above 99.9%. To address and understand this new type of fault, we translated two classic reliability metrics, mean time between failures and availability, to the Amazon Web Services spot market using historical price data. We also validated our findings by submitting actual bids in the spot market. We found that overall, our historical analysis and experimental validation lined up well. Based upon these experimental results, we also provided suggestions and techniques to maximize overall reliability of virtual machine instances under dynamic pricing.

Cloud computing↗

Performance implications from sizing a VM on multi-core systems: A Data analytic application s view

In this paper, we present a quantitative performance analysis of data analytics applications running on multi-core virtual machines. Such environments form the core of cloud computing. In addition, data analytics applications, such as Cassandra and Hadoop, are becoming increasingly popular on cloud computing platforms. This convergence necessitates a better understanding of the performance and cost implications of such hybrid systems. For example, the very rst step in hosting applications in virtualized environments, requires the user to con gure the number of virtual processors and the size of memory. To understand performance implications of this step, we benchmarked three Yahoo Cloud Serving Benchmark (YCSB) workloads in a virtualized multi-core environment. Our measurements indicate that the performance of Cassandra for YCSB workloads does not heavily depend on the processing capacity of a system, while the size of the data set is critical to performance relative to allocated memory. We also identi ed a strong relationship between the running time of workloads and various hardware events (last level cache loads, misses, and CPU migrations). From this analysis, we provide several suggestions to improve the performance of data analytics applications running on cloud computing environments.

Lim, Seung-Hwan↗

Enhancing Privacy in Participatory Sensing Applications with Multidimensional Data

Participatory sensing applications rely on individuals to share personal data to produce aggregated models and knowledge. In this setting, privacy concerns can discourage widespread adoption of new applications. We present a privacy-preserving participatory sensing scheme based on negative surveys for both continuous and multivariate categorical data. Without relying on encryption, our algorithms enhance the privacy of sensed data in an energy and computation efficient manner. Simulations and implementation on Android smart phones illustrate how multidimensional data can be aggregated in a useful and privacy-enhancing manner.

Forrest, Stephanie↗