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Proceedings ArticleDOI

A business driven cloud optimization architecture

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TLDR
A layered cloud where various cloud layers virtualize parts of the cloud infrastructure is considered, and the architecture takes into account different stakeholders in the cloud (infrastructure providers, platform providers, application providers and end users).
Abstract
In this paper, we discuss several facets of optimization in cloud computing, the corresponding challenges and propose an architecture for addressing those challenges. We consider a layered cloud where various cloud layers virtualize parts of the cloud infrastructure. The architecture takes into account different stakeholders in the cloud (infrastructure providers, platform providers, application providers and end users). The architecture supports self-management by automating most of the activities pertaining to optimization: monitoring, analysis and prediction, planning and execution.

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Citations
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Proceedings ArticleDOI

Introducing STRATOS: A Cloud Broker Service

TL;DR: A cloud broker service (STRATOS) which facilitates the deployment and runtime management of cloud application topologies using cloud elements/services sourced on the fly from multiple providers, based on requirements specified in higher level objectives is introduced.
Journal ArticleDOI

Mechanisms for SLA provisioning in cloud-based service providers

TL;DR: The experimental evaluation performed confirms that the proposed policies are capable to properly dimension the system resources making the whole system self-adaptable respect to the workload fluctuation, allowing to save up to the 32% of resource allocation cost always in the respect of SLA constraints.
DissertationDOI

Automated Improvement of Software Architecture Models for Performance and Other Quality Attributes

Anne Koziolek
TL;DR: This thesis proposes a method and tool to automatically improve component-based software architecture (CBA) models based on quantitative quality prediction techniques to support systematic, goal-oriented software design.
Proceedings ArticleDOI

SLA-aware Resource Management for Application Service Providers in the Cloud

TL;DR: This paper formulate the ASP resource management as an optimization problem and proposes both reactive and proactive heuristic policies that approximate the optimal solution.
Proceedings ArticleDOI

Securing Cloud Servers Against Flooding Based DDOS Attacks

TL;DR: This paper presents a simple distance estimation based technique to detect and prevent the cloud from flooding based DDoS attack and thereby protect other servers and users from its adverse effects.
References
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Journal ArticleDOI

Collective dynamics of small-world networks

TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
Journal ArticleDOI

Survey Constrained model predictive control: Stability and optimality

TL;DR: This review focuses on model predictive control of constrained systems, both linear and nonlinear, and distill from an extensive literature essential principles that ensure stability to present a concise characterization of most of the model predictive controllers that have been proposed in the literature.
Journal Article

Above the Clouds: A Berkeley View of Cloud Computing

TL;DR: This work focuses on SaaS Providers (Cloud Users) and Cloud Providers, which have received less attention than SAAS Users, and uses the term Private Cloud to refer to internal datacenters of a business or other organization, not made available to the general public.
Journal ArticleDOI

The vision of autonomic computing

TL;DR: A 2001 IBM manifesto noted the almost impossible difficulty of managing current and planned computing systems, which require integrating several heterogeneous environments into corporate-wide computing systems that extend into the Internet.
Proceedings ArticleDOI

Rainbow: architecture-based self-adaptation with reusable infrastructure

TL;DR: The Rainbow framework uses software architectural models to dynamically monitor and adapt a running system and shows that the separation of a generic adaptation infrastructure from system-specific adaptation knowledge makes this reuse possible.
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