Modeling cloud resources using machine learning

Cloud computing is a new Internet infrastructure paradigm where management optimization has become a challenge to be solved, as all current management systems are human-driven or ad-hoc automatic systems that must be tuned manually by experts. Management of cloud resources require accurate information about all the elements involved (host machines, resources, offered services, and clients), and some of this information can only be obtained a posteriori. Here we present the cloud and part of its architecture as a new scenario where data mining and machine learning can be applied to discover information and improve its management thanks to modeling and prediction. As a novel case of study we show in this work the modeling of basic cloud resources using machine learning, predicting resource requirements from context information like amount of load and clients, and also predicting the quality of service from resource planning, in order to feed cloud schedulers. Further, this work is an important part of our ongoing research program, where accurate models and predictors are essential to optimize cloud management autonomic systems.Postprint (published version

Scalable Load Balancing Scheme for Distributed Controllers in Software Defined Data Centers

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Green demand aware fog computing : a prediction-based dynamic resource provisioning approach

Fog computing could potentially cause the next paradigm shift by extending cloud services to the edge of the network, bringing resources closer to the end-user. With its close proximity to end-users and its distributed nature, fog computing can significantly reduce latency. With the appearance of more and more latency-stringent applications, in the near future, we will witness an unprecedented amount of demand for fog computing. Undoubtedly, this will lead to an increase in the energy footprint of the network edge and access segments. To reduce energy consumption in fog computing without compromising performance, in this paper we propose the Green-Demand-Aware Fog Computing (GDAFC) solution. Our solution uses a prediction technique to identify the working fog nodes (nodes serve when request arrives), standby fog nodes (nodes take over when the computational capacity of the working fog nodes is no longer sufficient), and idle fog nodes in a fog computing infrastructure. Additionally, it assigns an appropriate sleep interval for the fog nodes, taking into account the delay requirement of the applications. Results obtained based on the mathematical formulation show that our solution can save energy up to 65% without deteriorating the delay requirement performance. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Topics in Power Usage in Network Services

The rapid advance of computing technology has created a world powered by millions of computers. Often these computers are idly consuming energy unnecessarily in spite of all the efforts of hardware manufacturers. This thesis examines proposals to determine when to power down computers without negatively impacting on the service they are used to deliver, compares and contrasts the efficiency of virtualisation with containerisation, and investigates the energy efficiency of the popular cryptocurrency Bitcoin. We begin by examining the current corpus of literature and defining the key terms we need to proceed. Then we propose a technique for improving the energy consumption of servers by moving them into a sleep state and employing a low powered device to act as a proxy in its place. After this we move on to investigate the energy efficiency of virtualisation and compare the energy efficiency of two of the most common means used to do this. Moving on from this we look at the cryptocurrency Bitcoin. We consider the energy consumption of bitcoin mining and if this compared with the value of bitcoin makes this profitable. Finally we conclude by summarising the results and findings of this thesis. This work increases our understanding of some of the challenges of energy efficient computation as well as proposing novel mechanisms to save energy

Energy management in content distribution network servers

Les infrastructures Internet et l'installation d'appareils très gourmands en énergie (en raison de l'explosion du nombre d'internautes et de la concurrence entre les services efficaces offerts par Internet) se développent de manière exponentielle. Cela entraîne une augmentation importante de la consommation d'énergie. La gestion de l'énergie dans les systèmes de distribution de contenus à grande échelle joue un rôle déterminant dans la diminution de l'empreinte énergétique globale de l'industrie des TIC (Technologies de l'information et de la communication). Elle permet également de diminuer les coûts énergétiques d'un produit ou d'un service. Les CDN (Content Delivery Networks) sont parmi les systèmes de distribution à grande échelle les plus populaires, dans lesquels les requêtes des clients sont transférées vers des serveurs et traitées par des serveurs proxy ou le serveur d'origine, selon la disponibilité des contenus et la politique de redirection des CDN. Par conséquent, notre objectif principal est de proposer et de développer des mécanismes basés sur la simulation afin de concevoir des politiques de redirection des CDN. Ces politiques prendront la décision dynamique de réduire la consommation d'énergie des CDN. Enfin, nous analyserons son impact sur l'expérience utilisateur. Nous commencerons par une modélisation de l'utilisation des serveurs proxy et un modèle de consommation d'énergie des serveurs proxy basé sur leur utilisation. Nous ciblerons les politiques de redirection des CDN en proposant et en développant des politiques d'équilibre et de déséquilibre des charges (en utilisant la loi de Zipf) pour rediriger les requêtes des clients vers les serveurs. Nous avons pris en compte deux techniques de réduction de la consommation d'énergie : le DVFS (Dynamic Voltage Frequency Scaling) et la consolidation de serveurs. Nous avons appliqué ces techniques de réduction de la consommation d'énergie au contexte d'un CDN (au niveau d'un serveur proxy), mais aussi aux politiques d'équilibre et de déséquilibre des charges afin d'économiser l'énergie. Afin d'évaluer les politiques et les mécanismes que nous proposons, nous avons mis l'accent sur la manière de rendre l'utilisation des ressources des CDN plus efficace, mais nous nous sommes également intéressés à leur coût en énergie, à leur impact sur l'expérience utilisateur et sur la qualité de la gestion des infrastructures. Dans ce but, nous avons défini comme métriques d'évaluation l'utilisation des serveurs proxy, d'échec des requêtes comme les paramètres les plus importants. Nous avons transformé un simulateur d'événements discrets CDNsim en Green CDNsim, et évalué notre travail selon différents scénarios de CDN en modifiant : les infrastructures proxy des CDN (nombre de serveurs proxy), le trafic (nombre de requêtes clients) et l'intensité du trafic (fréquence des requêtes client) en prenant d'abord en compte les métriques d'évaluation mentionnées précédemment. Nous sommes les premiers à proposer un DVFS et la combinaison d'un DVFS avec la consolidation d'un environnement de simulation de CDN en prenant en compte les politiques d'équilibre et de déséquilibre des charges. Nous avons conclu que les techniques d'économie d'énergie permettent de réduire considérablement la consommation d'énergie mais dégradent l'expérience utilisateur. Nous avons montré que la technique de consolidation des serveurs est plus efficace dans la réduction d'énergie lorsque les serveurs proxy ne sont pas beaucoup chargés. Dans le même temps, il apparaît que l'impact du DVFS sur l'économie d'énergie est plus important lorsque les serveurs proxy sont bien chargés. La combinaison des deux (DVFS et consolidation des serveurs) permet de consommer moins d'énergie mais dégrade davantage l'expérience utilisateur que lorsque ces deux techniques sont utilisées séparément.Explosive increase in Internet infrastructure and installation of energy hungry devices because of huge increase in Internet users and competition of efficient Internet services causing a great increase in energy consumption. Energy management in large scale distributed systems has an important role to minimize the contribution of Information and Communication Technology (ICT) industry in global CO2 (Carbon Dioxide) footprint and to decrease the energy cost of a product or service. Content distribution Networks (CDNs) are one of the popular large scale distributed systems, in which client requests are forwarded towards servers and are fulfilled either by surrogate servers or by origin server, depending on contents availability and CDN redirection policy. Our main goal is therefore, to propose and to develop simulation-based principled mechanisms for the design of CDN redirection policies which will do and carry out dynamic decisions to reduce CDN energy consumption and then to analyze its impact on user experience constraints to provide services. We started from modeling surrogate server utilization and derived surrogate server energy consumption model based on its utilization. We targeted CDN redirection policies by proposing and developing load-balance and load-unbalance policies using Zipfian distribution, to redirect client requests to servers. We took into account two energy reduction techniques, Dynamic Voltage Frequency Scaling (DVFS) and server consolidation. We applied these energy reduction techniques in the context of a CDN at surrogate server level and injected them in load-balance and load-unbalance policies to have energy savings. In order to evaluate our proposed policies and mechanisms, we have emphasized, how efficiently the CDN resources are utilized, at what energy cost, its impact on user experience and on quality of infrastructure management. For that purpose, we have considered surrogate server's utilization, energy consumption, energy per request, mean response time, hit ratio and failed requests as evaluation metrics. In order to analyze energy reduction and its impact on user experience, energy consumption, mean response time and failed requests are considered more important parameters. We have transformed a discrete event simulator CDNsim into Green CDNsim and evaluated our proposed work in different scenarios of a CDN by changing: CDN surrogate infrastructure (number of surrogate servers), traffic load (number of client requests) and traffic intensity (client requests frequency) by taking into account previously discussed evaluation metrics. We are the first who proposed DVFS and the combination of DVFS and consolidation in a CDN simulation environment, considering load-balance and loadunbalance policies. We have concluded that energy reduction techniques offer considerable energy savings while user experience is degraded. We have exhibited that server consolidation technique performs better in energy reduction while surrogate servers are lightly loaded. While, DVFS impact is more considerable for energy gains when surrogate servers are well loaded. Impact of DVFS on user experience is lesser than that of server consolidation. Combination of both (DVFS and server consolidation) presents more energy savings at higher cost of user experience degradation in comparison when both are used individually