Scientific Publications

A methodology for efficient code optimizations and memory management

Virtual Machines (VMs) live migration is one of the important approaches to improve resource utilisation and support energy efficiency in Clouds. However, VMs live migration leads to performance loss and additional costs due to increased migration time and energy overhead. This paper introduces a Performance and Energybased Cost Prediction Framework to estimate the total cost of VMs live migration by considering the resource usage and power consumption, while maintaining the expected level of performance.

Towards Virtual Machine Energy-Aware Cost Prediction in Clouds

Pricing mechanisms employed by different service providers significantly influence the role of cloud computing within the IT industry. With the increasing cost of electricity, Cloud providers consider power consumption as one of the major cost factors to be maintained within their infrastructures. Consequently, modelling a new pricing mechanism that allow Cloud providers to determine the potential cost of resource usage and power consumption has attracted the attention of many researchers.

Energy-aware Self-Adaptation for Application Execution on Heterogeneous Parallel Architectures

Hardware in HPC environments in recent years has become ever more heterogeneous in order to improve computational performance and as an aspect of managing power and energy constraints. This increase in heterogeneity requires middleware abstractions to eliminate additional complexities that it brings. In this paper we present a self-adaptation framework which includes aspects such as automated configuration, deployment and redeployment of applications to different heterogeneous infrastructure.

Energy-aware Cost Prediction and Pricing of Virtual Machines in Cloud Computing Environments

With the increasing cost of electricity, Cloud providers consider energy consumption as one of the major cost factors to be maintained within their infrastructure. Consequently, various proactive and reactive management mechanisms are used to efficiently manage the cloud resources and reduce the energy consumption and cost. These mechanisms support energy-awareness at the level of Physical Machines (PM) as well as Virtual Machines (VM) to make corrective decisions.

Executing linear algebra kernels in heterogeneous distributed infrastructures with PyCOMPSs

Python is a popular programming language due to the simplicity of its syntax, while still achieving a good performance even being an interpreted language. The adoption from multiple scientific communities has evolved in the emergence of a large number of libraries and modules, which has helped to put Python on the top of the list of the programming languages. Task-based programming has been proposed in the recent years as an alternative parallel programming model.

Performance and Energy-based Cost Prediction of Virtual Machines Live Migration in Clouds

Virtual Machines (VMs) live migration is one of the important approaches to improve resource utilisation and support energy efficiency in Clouds. However, VMs live migration leads to performance loss and additional costs due to increased migration time and energy overhead. This paper introduces a Performance and Energybased Cost Prediction Framework to estimate the total cost of VMs live migration by considering the resource usage and power consumption, while maintaining the expected level of performance.

Performance and Energy-based Cost Prediction of Virtual Machines Auto-Scaling in Cloud

Virtual Machines (VMs) auto-scaling is an important technique to provision additional resource capacity in a Cloud environment. It allows the VMs to dynamically increase or decrease the amount of resources as needed in order to meet Quality of Service (QoS) requirements. However, the auto-scaling mechanism can be time-consuming to initiate (e.g. in the order of a minute), which is unacceptable for VMs that need to scale up/out during the computation, besides additional costs due to the increase of the energy overhead.

A Holistic Resource Management for Graphics Processing Units in Cloud Computing

The persistent development of Cloud computing attracts individuals and organisations to change their IT strategies. According to this development and the incremental demand of using Cloud computing, Cloud providers continuously update the Cloud infrastructure to fit the incremental demands. Recently, accelerator units, such as Graphics Processing Units (GPUs) have been introduced in Cloud computing. This updated existence leads to provide an increase in hardware heterogeneity in the Cloud infrastructure. With the increase in hardware heterogeneity, new issues will appear.

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