Gastón Keller

Towards an improved data centre simulation with DCSim

Computing is increasingly moving into large-scale data centres, providing resources on-demand for clients on a pay-per-usage basis. One form of such on-demand computing is an Infrastructure as a Service (IaaS) Cloud, which provides low level access to virtualized resources. Developing and evaluating data centre management techniques for such large-scale data centres presents a significant challenge. As such, most work turns to simulation tools as the test environment. We present a number of extensions and additional features to an existing simulation tool, DCSim. Our improvements to DCSim include work on the core of the simulator, improved event, communication and management mechanisms, and a more complete model of the structure of a data centre. We also present improved simulation configuration tools and output, including a unique visualization tool. We evaluate the usefulness of the simulator through a demonstration of its use in comparing dynamic VM management techniques.

A distributed approach to dynamic VM management

Computing today is increasingly moving into large-scale virtualized data centres, offering computing resources in the form of virtual machines (VMs) on a pay-per-usage basis. In order to minimize costs, VMs should be consolidated on as few physical machines (PMs) as possible, switching idle PMs into a power saving mode. It may be necessary to dynamically allocate and reallocate VMs to PMs in order to meet highly dynamic VM resource requirements. The problem of assigning VMs to PMs is known to be NP-Hard. Most solutions focus on a centralized approach, with a single management node making allocation decisions periodically. This approach suffers from poor scalability and the existence of a single point of failure. We present a fully distributed approach to dynamic VM management, and evaluate our approach using a simulation tool. Results indicate that the distributed approach can achieve similar performance to the centralized solution, while eliminating the single point of failure and reducing the network bandwidth required for management.

Replication and Migration as Resource Management Mechanisms for Virtualized Environments

Virtualization has become an essential technology in the data center. Virtualization improves resource utilization through server consolidation, but it also makes resource management more complex. Golondrina, an autonomic resource management system, was built to use virtual machine relocation to handle resource stress situations, that is, situations where the combined resource needs of the virtual machines hosted in a physical machine exceed the resource availability. Preliminary experiments show that replication offers improvements over migration, and both mechanisms offer improvements over taking no action upon detection of a resource stress situation. This work is one of the first ones in proposing a resource management system for operating system-level virtualized environments. Moreover, this is the first study that uses replication as an alternative to migration and compares both mechanisms.

Managing dynamic memory allocations in a cloud through Golondrina

In this paper, we present a policy-based framework that supports automated dynamic resource management in a virtualized environment. This allows for flexibility in how resources are allocated. We show how this framework can be used to support memory management through the use of migration and making local resource adjustments.