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Incentivizing self-capping to increase cloud utilization

TLDR
This work builds a system and pricing model that enables infrastructure providers to incentivize their tenants to use graceful degradation and shows that the proposed scheme never hurts a tenant's net profit, but can improve it by as much as 93%.
Abstract
Cloud Infrastructure as a Service (IaaS) providers continually seek higher resource utilization to better amortize capital costs Higher utilization not only can enable higher profit for IaaS providers but also provides a mechanism to raise energy efficiency; therefore creating greener cloud services Unfortunately, achieving high utilization is difficult mainly due to infrastructure providers needing to maintain spare capacity to service demand fluctuations Graceful degradation is a self-adaptation technique originally designed for constructing robust services that survive resource shortages Previous work has shown that graceful degradation can also be used to improve resource utilization in the cloud by absorbing demand fluctuations and reducing spare capacity In this work, we build a system and pricing model that enables infrastructure providers to incentivize their tenants to use graceful degradation By using graceful degradation with an appropriate pricing model, the infrastructure provider can realize higher resource utilization while simultaneously, its tenants can increase their profit Our proposed solution is based on a hybrid model which guarantees both reserved and peak on-demand capacities over flexible periods It also includes a global dynamic price pair for capacity which remains uniform during each tenant's Service Level Agreement (SLA) term We evaluate our scheme using simulations based on real-world traces and also implement a prototype using RUBiS on the Xen hypervisor as an end-to-end demonstration Our analysis shows that the proposed scheme never hurts a tenant's net profit, but can improve it by as much as 93% Simultaneously, it can also improve the effective utilization of contracts from 42% to as high as 99%

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Architectural Implications of Function-as-a-Service Computing

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Cloud Pricing Models: Taxonomy, Survey, and Interdisciplinary Challenges

TL;DR: An extensive survey of many cloud pricing models that were proposed by many researchers during the past decade is offered, which concludes that hyper-converged cloud resources pool supported by cloud orchestration, virtual machine, Open Application Programming Interface, and serverless sandbox will drive the future of cloud pricing.
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Resource Deflation: A New Approach For Transient Resource Reclamation

TL;DR: This paper proposes an alternative approach for reclaiming resources, called resource deflation, which uses a dynamic, multi-level cascading reclamation technique that allows applications, operating systems, and hypervisors to implement their own policies for handling resource pressure.
Proceedings ArticleDOI

Burstable Instances for Clouds: Performance Modeling, Equilibrium Analysis, and Revenue Maximization

TL;DR: This paper presents the first unified framework to model, analyze, and optimize the operation of burstable instances, and describes the equilibrium behind tenants’ responses to the prices offered for different burstable instance service classes.
References
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Journal ArticleDOI

A view of cloud computing

TL;DR: The clouds are clearing the clouds away from the true potential and obstacles posed by this computing capability.
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Xen and the art of virtualization

TL;DR: Xen, an x86 virtual machine monitor which allows multiple commodity operating systems to share conventional hardware in a safe and resource managed fashion, but without sacrificing either performance or functionality, considerably outperform competing commercial and freely available solutions.
Journal ArticleDOI

Thirteen ways to look at the correlation coefficient

TL;DR: In this paper, the 100th anniversary of Galton's first discussion of regression and correlation is celebrated, and 13 different formulas representing a different computational and conceptual definition of Pearson's r are presented.
Journal ArticleDOI

The Case for Energy-Proportional Computing

TL;DR: Energy-proportional designs would enable large energy savings in servers, potentially doubling their efficiency in real-life use, particularly the memory and disk subsystems.
Journal ArticleDOI

Fair end-to-end window-based congestion control

TL;DR: The existence of fair end-to-end window-based congestion control protocols for packet-switched networks with first come-first served routers is demonstrated using a Lyapunov function.
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