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Market-Oriented Cloud Computing: Vision, Hype, and Reality for Delivering IT Services as Computing Utilities

TLDR
The need for convergence of competing IT paradigms for delivering the 21st century vision of computing is concluded.
Abstract
This keynote paper: presents a 21 st century vision of computing; identifies various computing paradigms promising to deliver the vision of computing utilities; defines Cloud computing and provides the architecture for creating market-oriented Clouds by leveraging technologies such as VMs; provides thoughts on marketbased resource management strategies that encompass both customer-driven service management and computational risk management to sustain SLA-oriented resource allocation; presents some representative Cloud platforms especially those developed in industries along with our current work towards realising market-oriented resource allocation of Clouds by leveraging the 3 rd generation Aneka enterprise Grid technology; reveals our early thoughts on interconnecting Clouds for dynamically creating an atmospheric computing environment along with pointers to future community research; and concludes with the need for convergence of competing IT paradigms for delivering our 21 st century vision.

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Citations
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Intermediate node selection for Scatter-Gather VM migration in cloud data center

TL;DR: This paper proposes to define the problem of intermediate node selection in Scatter-Gather migration and proves that it is NP-complete and two heuristic algorithms are proposed to solve the problem.
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Cloud computing resource searching based on improved ACO

TL;DR: Using the improved ant colony optimization (ACO) algorithm, the resource of cloud is found in optimized cost in limited time which is shorter than the standard ACO algorithm under situation of cloud computing.
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Hierarchical Deployment and Control of Energy Storage Devices in Data Centers

TL;DR: Experiments have been conducted using real Google cluster workload based on realistic data center specifications, demonstrating the effectiveness of the proposed optimal design and control framework.
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Joint Multiple Resource Allocation Method for Cloud Computing Services with Different QOS to Users at Multiple Locations

TL;DR: This paper proposes to enhance the existing joint multiple resource allocation method, so that it can handle multiple heterogeneous resource-attributes, and demonstrates by simulation evaluations that the enhanced method can reduce the total amount of resources up to 30%, compared with the existing method.
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Privacy Preserving Outsourcing for FrequentItemset Mining

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References
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Journal ArticleDOI

Computing in the clouds

Aaron Weiss
TL;DR: This research presents a meta-service architecture that automates the very labor-intensive and therefore time-heavy and therefore expensive and expensive process of developing and deploying new types of services and applications.
Journal ArticleDOI

The Grid Economy

TL;DR: An extensive, service-oriented Grid architecture driven by Grid economy and an approach for its realization by leveraging various existing Grid technologies are presented and commodity and auction models for resource allocation are presented.
Journal Article

Computing in the Clouds.

TL;DR: This article was written without the aid of word processing software loaded on my computer, and was saved in a place where I will have access to the content, even if my laptop is lost, the external hard drive where I keep my backups fails, and the new version of Microsoft Office refuses to open my file format.
Journal ArticleDOI

Virtual workspaces: Achieving quality of service and quality of life in the Grid

TL;DR: While Grids offer users access to many diverse and powerful resources, they do little to ensure that once a resource is accessed, it fulfills user expectations for on-demand allocation.
Posted Content

Tycoon: an Implementation of a Distributed, Market-based Resource Allocation System

TL;DR: Tycoon as mentioned in this paper is a market based distributed resource allocation system based on proportional share, which allows users to differentiate the value of their jobs, its resource acquisition latency is limited only by communication delays and it imposes no manual bidding overhead on users.
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