3 research outputs found
A fair comparison of VM placement heuristics and a more effective solution
Data center optimization, mainly through virtual
machine (VM) placement, has received considerable attention
in the past years. A lot of heuristics have been proposed to give
quick and reasonably good solutions to this problem. However it
is difficult to compare them as they use different datasets, while
the distribution of resources in the datasets has a big impact
on the results. In this paper we propose the first benchmark
for VM placement heuristics and we define a novel heuristic.
Our benchmark is inspired from a real data center and explores
different possible demographics of data centers, which makes it
suitable when comparing the behaviour of heuristics. Our new
algorithm, RBP, outperforms the state-of-the-art heuristics and
provides close to optimal results quickly
Managing Distributed Cloud Applications and Infrastructure
The emergence of the Internet of Things (IoT), combined with greater heterogeneity not only online in cloud computing architectures but across the cloud-to-edge continuum, is introducing new challenges for managing applications and infrastructure across this continuum. The scale and complexity is simply so complex that it is no longer realistic for IT teams to manually foresee the potential issues and manage the dynamism and dependencies across an increasing inter-dependent chain of service provision. This Open Access Pivot explores these challenges and offers a solution for the intelligent and reliable management of physical infrastructure and the optimal placement of applications for the provision of services on distributed clouds. This book provides a conceptual reference model for reliable capacity provisioning for distributed clouds and discusses how data analytics and machine learning, application and infrastructure optimization, and simulation can deliver quality of service requirements cost-efficiently in this complex feature space. These are illustrated through a series of case studies in cloud computing, telecommunications, big data analytics, and smart cities
Managing Distributed Cloud Applications and Infrastructure
The emergence of the Internet of Things (IoT), combined with greater heterogeneity not only online in cloud computing architectures but across the cloud-to-edge continuum, is introducing new challenges for managing applications and infrastructure across this continuum. The scale and complexity is simply so complex that it is no longer realistic for IT teams to manually foresee the potential issues and manage the dynamism and dependencies across an increasing inter-dependent chain of service provision. This Open Access Pivot explores these challenges and offers a solution for the intelligent and reliable management of physical infrastructure and the optimal placement of applications for the provision of services on distributed clouds. This book provides a conceptual reference model for reliable capacity provisioning for distributed clouds and discusses how data analytics and machine learning, application and infrastructure optimization, and simulation can deliver quality of service requirements cost-efficiently in this complex feature space. These are illustrated through a series of case studies in cloud computing, telecommunications, big data analytics, and smart cities