Resource boxing: Converting realistic cloud task utilization patterns for theoretical scheduling

Scheduling is a core component within distributed systems to determine optimal allocation of tasks within servers. This is challenging within modern Cloud computing systems - comprising millions of tasks executing in thousands of heterogeneous servers. Theoretical scheduling is capable of providing complete and sophisticated algorithms towards a single objective function. However, Cloud computing systems pursue multiple and oftentimes conflicting objectives towards provisioning high levels of performance, availability, reliability and energy-efficiency. As a result, theoretical scheduling for Cloud computing is performed by simplifying assumptions for applicability. This is especially true for task utilization patterns, which fluctuate in practice yet are modelled as piecewise constant in theoretical scheduling models. While there exists work for modelling dynamic Cloud task patterns for evaluating applied scheduling, such models are incompatible with the inputs needed for theoretical scheduling - which require such patterns to be represented as boxes. Presently there exist no methods capable of accurately converting real task patterns derived from empirical data into boxes. This results in a significant gap towards theoreticians understanding and proposing algorithms derived from realistic assumptions towards enhanced Cloud scheduling. This work proposes resource boxing - an approach for automated conversion of realistic task patterns in Cloud computing directly into box-inputs for theoretical scheduling. We propose four resource conversion algorithms capable of accurately representing real task utilization patterns in the form of scheduling boxes. Algorithms were evaluated using production Cloud trace data, demonstrating a difference between real utilization and scheduling boxes less than 5%. We also provide an application for how resource boxing can be exploited to directly translate research from the applied community into the theoretical community

Energy consumption in cloud computing environments

The conference aimed at supporting and stimulating active productive research set to strengthen the technical foundations of engineers and scientists in the continent, through developing strong technical foundations and skills, leading to new small to medium enterprises within the African sub-continent. It also seeked to encourage the emergence of functionally skilled technocrats within the continent.Datacentres are becoming indispensable infrastructure for supporting the services offered by cloud computing. Unfortunately, they consume a great deal of energy accounting for 3% of global electrical energy consumption. The effect of this is that, cloud providers experience high operating costs, which leading to increased Total Cost of Ownership (TCO) of datacentre infrastructure. Moreover, there is increased carbon dioxide emissions that affects the universe. This paper presents a survey on the various ways in which energy is consumed in datacentre infrastructure. The factors that influence energy consumption within a datacentre is presented as well.Strathmore University; Institute of Electrical and Electronics Engineers (IEEE

Virtual machine customization and task mapping architecture for efficient allocation of cloud data center resources

Energy usage of large-scale data centers has become a major concern for cloud providers. There has been an active effort in techniques for the minimization of the energy consumed in the data centers. However, most approaches lack the analysis and application of real cloud backend traces. In existing approaches, the variation of cloud workloads and its effect on the performance of the solutions are not investigated. Furthermore, the focus of existing approaches is on virtual machine migration and placement algorithms, with little regard to tailoring virtualmachine configuration to workload characteristics, which can further reduce the energy consumption and resource wastage in a typical data center. To address these weaknesses and challenges, we propose a new architecture for cloud resource allocation that maps groups of tasks to customized virtual machine types. This mapping is based on the task usage patterns obtained from the analysis of the historical data extracted from utilization traces. In our work, the energy consumption is decreased via efficient resource allocation based on the actual resource usage of tasks. Experimental results show that, when resources are allocated based on the discovered usage patterns, significant energy saving can be achieved

Approche heuristique pour le placement des machines virtuelles dans un environnement infonuagique de grande taille

L’informatique en nuage est un nouveau paradigme qui fait référence à la mise en disposition des ressources d’une infrastructure informatique permettant de présenter un ensemble de services à la demande. Toutefois, un centre de données pourrait participer grandement dans l’augmentation de l’empreinte carbone si le procédé utilisé par le fournisseur de services ne présente pas une bonne gestion des ressources disponibles. Un fournisseur de services cloud pense principalement aux gains que ses data centers pourraient lui engendrer. Il devra donc trouver un moyen efficace pour que ses bénéfices ne soient jamais diminués. Si de plus, ce dernier pense à la réduction de l’empreinte carbone que pourraient générer ses data centers, il devra trouver un compromis entre les bénéfices qu’ils visent à atteindre et l’empreinte écologique tout en respectant l’accord de niveau de service avec ses clients. Un moyen pour satisfaire ces compromis serait de revoir la disposition des différents composants à l’intérieur du data center. Les machines virtuelles étant une des variables qui pourraient grandement participer dans la définition des stratégies pour la réduction de l’empreinte carbone, le fournisseur de services se voit dans l’obligation de trouver une solution efficace et peu coûteuse. Ainsi, trouver un bon placement des machines virtuelles dans un environnement infonuagique est une étape importante pour les fournisseurs de services afin d’améliorer l’efficacité énergétique de leurs centres de données. Les travaux antérieurs élaborés pour la résolution du problème de placement des machines virtuelles ne regroupent souvent pas toutes les informations concernant les composants dans un centre de données. En effet, chaque composant a sa propre consommation d’énergie et participe de près ou de loin à la définition de l’empreinte écologique d’un data center. Ce mémoire présente le modèle implémenté permettant de calculer l’empreinte carbone d’un centre de données. À travers ce modèle, nous minimisons la fonction objectif à partir des algorithmes de métaheuristique. Nous concevons un algorithme mémétique, une hybridation entre l’algorithme génétique que nous implémentons d’abord seul et l’algorithme de recherche tabou. Nous implémentons ensuite l’algorithme de recuit simulé.----------ABSTRACT: Cloud computing is a new paradigm that refers to the provision of resources of an IT infrastructure allowing to present a set of services on demand. However, in this context, a data center could play a significant role in increasing the carbon footprint, if the process used by the service provider does not adequately manage the available resources. The service provider will therefore need to find the best allocation of its data centers components to improve the energy efficiency. Since virtual machines are one of the components that could play an important role in the definition of strategies for reducing the carbon footprint, the service provider is intended to find an efficient and inexpensive solution. Hence, we can consider that finding an adequate placement of virtual machines in a cloud environment is an important step for service providers. To solve the virtual machines placement problem in the cloud, we need a mathematical model that allow us to compute the carbon footprint while considering all the data center' components. In this thesis, we present the model implemented to calculate the carbon footprint of a data center. Through this latter, we minimize the objective function based on the metaheuristic algorithms. We design a memetic algorithm, a hybridization between the genetic algorithm and the tabu search algorithm. Afterwards, we implement the simulated annealing algorithm. For the evaluation of these algorithms, we compare the results obtained in term of carbon footprint cost, as well as the runtime. All the results obtained will be compared with the iterated tabu search algorithm for various problem sizes. The experiments carried out in our work, demonstrate the effectiveness of the memetic algorithm to solve large-scale problems when trying to allocate virtual machines in data centers