Section: New Results

Heterogeneous Resource Management

Participants : Eliya Buyukkaya, Djawida Dib, Eugen Feller, Tran Ngoc Minh, Christine Morin, Nikos Parlavantzas, Guillaume Pierre.

Cross-resource scheduling in heterogeneous cloud environments

Participants : Eliya Buyukkaya, Tran Ngoc Minh, Guillaume Pierre.

Allocating resources to applications in a heterogeneous cloud environment is harder than in a homogeneous environment. In a heterogeneous cloud some rare resources are more precious than others, and should be treated carefully to maximize their utilization. Similarly, applications may request groups of resources that exhibit certain inter-resource properties such as the available bandwidth between the assigned resources. We are currently investigating scheduling algorithms for handling such scenarios.

Maximizing private cloud provider profit in cloud bursting scenarios

Participants : Christine Morin, Djawida Dib, Nikos Parlavantzas.

Current PaaS offerings either provide no support for SLA guarantees or provide limited support targeting a restricted set of application types. To overcome this limitation, we are developing an open, SLA-driven PaaS system, called Meryn, that aims at providing SLA guarantees to diverse application types while maximizing the PaaS provider profit. Meryn supports cloud bursting and applies a decentralized protocol for selecting cloud resources, trying to minimize the cost of running applications without affecting their agreed quality properties. We have performed a preliminary evaluation of Meryn [24] and worked on optimising the system and performing further experiments on the Grid5000 testbed. This work is part of Djawida Dib's PhD thesis.

Data life-cycle management in clouds

Participants : Eugen Feller, Christine Morin.

Infrastructure as a Service (IaaS) clouds provide a flexible environment where users can choose and control various aspects of the machines of interest. However, the flexibility of IaaS clouds presents unique challenges for storage and data management in these environments. Users use manual and/or ad-hoc methods to manage storage and data in these environments. FRIEDA is a Flexible Robust Intelligent Elastic Data Management framework that employs a range of data management strategies approaches in elastic environments. In the context of the DALHIS associate team (http://project.inria.fr/dalhis ), we evaluated the importance of this framework on multiple cloud testbeds. Our evaluation showed that storage planning needs to be performed in coordination with compute planning and the specific configuration of virtual machine had a strong impact on the application (e.g., some applications performed better on small instances than large instances) [40] .