Analysis of Cloud Storage Issues in Distributed Cloud Data Centres by Parameter Improved Particle Swarm Optimization (PIPSO) Algorithm

Cloud Computing environment provides several on demand services for users. The recent problem and important issues in cloud computing is to optimize cloud storage over distributed data centers. The cloud storage optimization problem is considered as one of most challenging task. To overcome storage issues, the Parameter Improved Particle Swarm Optimization (PIPSO) technique is proposed. This paper presents the objective and the requirements of the distributed cloud storage issue based on the current topology. PIPSO technique manages each other between two server centers. The simulation has been done in MATLAB programming software. The results show that proposed technique achieves better results when compared with the other existing methods

Review of Recycling of E-DATA Through Green Computing

This century has a progressive evolution in IT. New techniques gadgets and tools are being invented every day. ThisLeeds to consume energy and resources. The planet need a friendly environment in which consuming resources is balanced andtemperature is decreased. So; one of the most important responsibilities of human is providing green industry in order to get apurity environment This paper is a review of a few vital writings identified with the field of green processing that underscoresthe vitality of green registering for reasonable improvement

Online Footprint -A Serious Game for Reducing Digital Carbon Emission

Life is getting digital more than ever as technology improves. While the Internet is responsible for two percent of global greenhouse gas emissions, it is underestimated as a pollutant. Since public awareness is one of the most important preservation methods, it can contribute to protecting the environment from carbon emissions by raising people's understanding. In this regard, serious games, as a type of gamification transmitting educational content besides entertainment, immerse the player in enjoyment while teaching them a specific topic or enhancing their skills in a field. This study proposes a serious game, taking the digital unseen carbon footprint and its effects on the landscape into the topic. The game considers SDG goals provided by the United Nations Department of Economic and Social Affairs. In this regard, the research uses SDGs 4 and 7 by providing quality education for all and access to sustainable energy by changing people's everyday habits

Power Management for Cloud-Scale Data Centers

Recent years have seen the rapid growth of large and geographically distributed data centers deployed by Internet service operators to support various services such as cloud computing. Consequently, high electricity bills, as well as negative environmental implications (e.g., CO2 emission and global warming) come along. In this thesis, we first propose a novel electricity bill capping algorithm that not only minimizes the electricity cost, but also enforces a cost budget on the monthly bill for cloud-scale data centers that impact the power markets. Our solution first explicitly models the impacts of the power demands induced by cloud-scale data centers on electricity prices and the power consumption of cooling and networking in the minimization of electricity bill. In the second step, if the electricity cost exceeds a desired monthly budget due to unexpectedly high workloads, our solution guarantees the quality of service for premium customers and trades off the request throughput of ordinary customers. We formulate electricity bill capping as two related constrained optimization problems and propose efficient algorithms based on mixed integer programming. We then propose GreenWare, a novel middleware system that conducts dynamic request dispatching to maximize the percentage of renewable energy used to power a network of distributed data centers, subject to the desired cost budget of the Internet service operator. Our solution first explicitly models the intermittent generation of renewable energy, e.g., wind power and solar power, with respect to varying weather conditions in the geographical location of each data center. We then formulate the core objective of GreenWare as a constrained I optimization problem and propose an efficient request dispatching algorithm based on linear-fractional programming (LFP)

Évaluation de l'incertitude d'un modèle d'analyse de cycle de vie temporel de la production et de la consommation de l'électricité dans un contexte de gestion des centres de données

Dans le cadre d’un projet en collaboration avec le groupe synchromedia de l’ÉTS, le CIRAIG a étudié de nouvelles approches pour quantifier les impacts environnementaux de la consommation d’électricité par des centres de données. Pour cela, deux modèles temporels de prédictions ont été mis en place en utilisant l’Analyse de Cycle de Vie (ACV). Un premier utilisant une approche ACV attributionnelle et un second avec une approche ACV conséquentielle. Cet outil, qui est l’ACV, est régulé par la norme ISO 14044, qui définie les terminologies, les règles et les recommandations pour réaliser des ACV. L’une de ces recommandations est de réaliser des analyses d’incertitudes pour vérifier la fiabilité des résultats. Lors de la réalisation des modèles attributionnels et conséquentiels, cette analyse additionnelle avait été temporairement mise de côté pour être traitée de façon globale dans ce mémoire. Ce mémoire à pour objectif de renforcer la crédibilité des modèles mis en place en effectuant des analyses d’incertitudes sur les résultats produits par ces études antérieures. Pour ce faire, l’étude a été divisée en quatre sous objectifs : 1) calculer les distributions de probabilités des processus de la base de données ecoinvent utilisés dans les modèles pour les provinces de Québec, de l’Alberta et de l’Ontario ; 2) calculer les conséquences de ces distributions de probabilités sur les modèles de sélection de la province ayant l’électricité avec les impacts environnementaux les plus faibles ; 3) évaluer les différences entre les sources de données utilisées pour la construction du modèle attributionnel et évaluer la conséquence de ces écarts sur les résultats ; 4) quantifier la part de capacité de production électrique non prise en compte dans les sources de données utilisées et évaluer les conséquences de ce critère sur la composition des bouquets électriques horaires. Pour répondre à ces objectifs, des scripts informatiques de simulation de Monte-Carlo sont programmés pour générer les distributions de probabilités des nombreux bouquets électriques horaires, et ce, pour les trois provinces étudiées. De plus, des comparaisons entre les sources de données sont effectuées. Enfin, une modélisation simplifiée du réseau de production et distribution d’électricité pour la province de l’Ontario est réalisée dans un logiciel spécialisé. Ceci a pour but d’identifier l’importance des contraintes physiques du réseau électrique dans une même province et donc leurs répercussions sur les variations des impacts environnementaux entrainés par des changements marginaux de la demande électrique. Cette étude a permis d’observer que l’incertitude des résultats de ces nouveaux modèles temporels n’a pas beaucoup de conséquence sur les conclusions qu’ils apportent. Ces nouvelles approches pour calculer les impacts environnementaux de façon temporelle de la consommation d’électricité sont des améliorations pour les systèmes utilisant une grande quantité d’électricité tels que les centres de données, car ils permettent de modéliser avec plus de précision l’impact de la consommation d’électricité sur l’environnement. ---------- As part of a research program focused on finding ways to decrease the environnemental impacts of data center, the CIRAIG developed two models in order to be able to select the province with the cleanest electricity. Even if both models use the life cycle analysis methodology (LCA) they differ on their approach. The first model is based on attributional LCA and the second one on consequential LCA. However the last step of an LCA, as recommended by the ISO, is to evaluate the uncertainty of the results. This step was left aside in the previous studies to be the main subject of this research. The goal of this research is to improve the trust in the those models by doing uncertainty analysis on the results they produced. This analysis was split into four parts: 1) compute the distributions of the grid mix used by the two studies; 2) compute the consequences of those distributions on the decisions; 3) quantify the differences between the data sources and evaluate their consequences on the decisions; 4) identify and quantify the power plants not included in the data sources and evaluate their contribution on the grid-mixes. To fulfil those goals, scripts were written to compute Monte-Carlo simulations of the environnemental impacts of the multiple grid-mix used in the models for the tree provinces. Data about the electric production have been collected to identify previously not accounted for power plants. Comparisons of the data sources used in the original studies were carry out to evaluate the significance of the disparities. Finally a model of the electric grid of Ontario was implemented in a power system simulation software. This was to show the importance of some of the physical constraints inside the network. The result of this study show that the uncertainty included in the results have little to no consequences on the decision process for the studied provinces. This two new models, implemented to take into account the temporal aspect of electric consumption of electricity on the environmental impacts, are a real improvement to the previous static models

Managing the Cost, Energy Consumption, and Carbon Footprint of Internet Services

The large amount of energy consumed by Internet services represents significant and fast-growing financial and environmental costs. Increasingly, services are exploring dynamic methods to minimize energy costs while respecting their service-level agreements (SLAs). Furthermore, it will soon be important for these services to manage their usage of “brown energy ” (produced via carbon-intensive means) relative to renewable or “green ” energy. This paper introduces a general, optimization-based framework for enabling multi-data-center services to manage their brown energy consumption and leverage green energy, while respecting their SLAs and minimizing energy costs. Based on the framework, we propose policies for request distribution across the data centers. Our policies can be used to abide by caps on brown energy consumption, such as those that might arise from Kyotostyle carbon limits, from corporate pledges on carbon-neutrality, or from limits imposed on services to encourage brown energy conservation. We evaluate our framework and policies extensively through simulations and real experiments. Our results show how our policies allow a service to trade off consumption and cost. For example, using our policies, the service can reduce brown energy consumption by 24 % for only a 10 % increase in cost, while still abiding by SLAs.