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Journal ArticleDOI

Bidding Strategies for Spot Instances in Cloud Computing Markets

TLDR
This work uses data based on Amazon's Elastic Compute Cloud spot market to provide users with guidelines when considering tradeoffs between cost, wait time, and interruption rates, and recommends bidding strategies in spot markets.
Abstract
In recent times, spot pricing -- a dynamic pricing scheme -- is becoming increasingly popular for cloud services. This new pricing format, though efficient in terms of cost and resource use, has added to the complexity of decision making for typical cloud computing users. To recommend bidding strategies in spot markets, we use a simulation study to understand the implications that provider-recommended strategies have for cloud users. We use data based on Amazon's Elastic Compute Cloud spot market to provide users with guidelines when considering tradeoffs between cost, wait time, and interruption rates.

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Citations
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Book ChapterDOI

Resilient Techniques Against Disruptions of Volatile Cloud Resources

TL;DR: This chapter introduces volatile cloud Resources, their life cycle, pros and cons, and presents several resilient techniques against volatile cloud resources’ disruptions and multiple failures.
Proceedings ArticleDOI

Pricing and bidding strategies for cloud spot block instances

TL;DR: A model for Spot Block prices determination is proposed and analysis of different bidding strategies in creating Spot Blocks requests is provided and two auction-based pricing mechanisms are analyzed: Uniform price auction and Generalized Second-price auction.
Journal ArticleDOI

Improving reliability and reducing cost of task execution on preemptible VM instances using machine learning approach

TL;DR: A checkpointing algorithm has been proposed for saving the task’s progress at optimal time intervals by the use of the proposed spot price prediction algorithm, which is the first attempt of its kind in this field.
Proceedings ArticleDOI

Bidding Strategies for Amazon EC2 Spot Instances-A Comprehensive Review

TL;DR: This paper presents different spot bidding strategies proposed by the authors and provides the suitability of each of the proposed bidding strategies based on the type of application, its fault tolerance, job requirements and other constraints.
Journal ArticleDOI

Review of the quality of service scheduling mechanisms in cloud

TL;DR: A comparative study about different resource allocation, load balancing and virtual machine consolidation algorithms in cloud computing in terms of their ability to provide QoS for the tasks and Service Level Agreement (SLA) guarantee amongst the jobs served is provided.
References
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Proceedings ArticleDOI

Feature-rich part-of-speech tagging with a cyclic dependency network

TL;DR: A new part-of-speech tagger is presented that demonstrates the following ideas: explicit use of both preceding and following tag contexts via a dependency network representation, broad use of lexical features, and effective use of priors in conditional loglinear models.
Journal ArticleDOI

Predicting the Present with Google Trends

TL;DR: This paper used search engine data to forecast near-term values of economic indicators, such as automobile sales, unemployment claims, travel destination planning, and consumer confidence, and showed how to use this information to forecast future economic indicators.
Journal ArticleDOI

Predicting the Present with Google Trends

TL;DR: In this paper, the authors used Google Trends and Google Insights for Search data to predict economic activity, including automobile sales, home sales, retail sales, and travel behavior, and found that Google Trends data can help improve forecasts of the current level of activity for a number of different economic time series.
Proceedings ArticleDOI

Recommender systems in e-commerce

TL;DR: An explanation of howRecommender systems help E-commerce sites increase sales, and a taxonomy of recommender systems, including the interfaces they present to customers, the technologies used to create the recommendations, and the inputs they need from customers.
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