Overall Objectives
Research Program
Application Domains
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
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Section: New Results

Large Scale Cloud Deployment

Participants : Eddy Caron, Marcos Dias de Assunção, Christian Perez, Pedro de Souza Bento Da Silva.

Efficient Heuristics for Placing Large-Scale Distributed Applications on Multiple Clouds

With the fast growth of the demand for Cloud computing services, the Cloud has become a very popular platform to develop distributed applications. Features that in the past were available only to big corporations, like fast scalability, availability, and reliability, are now accessible to any customer, including individuals and small companies, thanks to Cloud computing. In order to place an application, a designer must choose among VM types, from private and public cloud providers, those that are capable of hosting her application or its parts using as criteria application requirements, VM prices, and VM resources. This procedure becomes more complicated when the objective is to place large component based applications on multiple clouds. In this case, the number of possible configurations explodes making necessary the automation of the placement. In this context, scalability has a central role since the placement problem is a generalization of the NP-Hard multi-dimensional bin packing problem.

In this work [22], we propose efficient greedy heuristics based on first fit decreasing and best fit algorithms, which are capable of computing near optimal solutions for very large applications, with the objective of minimizing costs and meeting application performance requirements. Through a meticulous evaluation, we show that the greedy heuristics took a few seconds to calculate near optimal solutions to placements that would require hours or even days when calculated using state of the art solutions, namely exact algorithms or meta-heuristics.

Multi-Criteria Malleable Task Management for Hybrid-Cloud Platforms

The use of large distributed computing infrastructure is a mean to address the ever increasing resource demands of scientific and commercial applications. The scale of current large-scale computing infrastructures and their heterogeneity make scheduling applications an increasingly complex task. Cloud computing minimises the heterogeneity by using virtualization mechanisms, but poses new challenges to middleware developers, such as the management of virtualization, elasticity and economic models. In this context, we proposed algorithms for efficient scheduling and execution of malleable computing tasks with high granularity while taking into account multiple optimisation criteria such as resource cost and computation time. We focused on hybrid platforms that comprise both clusters and cloud providers. In [12] we defined and formalized the main aspects of the problem, introduced the difference between local and global scheduling algorithms and evaluate their efficiency using discrete-event simulation.