Rafael Souza

Estimating sustainability impact of high dependable data centers: a comparative study between Brazilian and US energy mixes

The advent of services such as cloud computing, social networks and e-commerce has led to an increased demand for computer resources from data centers. Prominent issues for data center designers are sustainability, cost, and dependability, which are each significantly affected by the redundant architectures required to support these services. Within this context, models are important tools for designers when attempting to quantify these issues before implementing the final architecture. This paper proposes a set of models for the integrated quantification of the sustainability impact, cost, and dependability of data center power and cooling infrastructures. This is achieved with the support of an environment called ASTRO. The approach taken to perform the system dependability evaluation employs a hybrid modeling strategy which recognizes the advantages of both stochastic Petri nets and reliability block diagrams. Additionally, an energy flow model is proposed to estimate the environmental impact and cost of data center architectures whilst respecting the energy constraints of each device. This work also presents a case study which analyzes the environmental impact and dependability metrics as well as the operational energy cost of real-world data center power and cooling architectures within the context of the energy mixes of the US and Brazil.

A Petri Net-Based Approach to the Quantification of Data Center Dependability

Data center availability and reliability have accomplished greater concern due to increased dependence on Internet services (e.g., Cloud computing paradigm, social networks and e-commerce). For companies that heavily depend on the Internet for their operations, service outages can be very expensive, easily running into millions of dollars per hour [15]. A widely used design principle in fault-tolerance is to introduce redundancy to enhance availability. However, since redundancy leads to additional use of resources and energy, it is expected to have a negative impact on sustainability and the associated cost.

A cloud-based recommendation model

The recommendation systems aim to minimize information overload by helping users in searching desired information. Faced with this scenario, we investigate the use of cloud factors able to have a positive influence on generating recommendations. Thus, we present a new, simple model based on cloud features which is associated with the content-based technique of recommendation. The practical applicability of data storage environments in the cloud provides the best use of cloud resources and meets users preferences.

The Effects of Temperature Variation on Data Center IT Systems

Data centers are constantly growing in order to meet the demands of new technologies such as cloud computing and e-commerce. In such paradigms, periods of downtime can lead to financial losses of millions of dollars and permanently damage a companys reputation. Several factors affect the availability of IT systems in data centers, among them temperature variations within the data center room. This paper proposes an approach for evaluating the effect of such temperature changes on data center IT systems. In this approach, the evaluation is accomplished with stochastic Petri net (SPN) models which simulate the impact of temperature on the availability of variously specified IT and cooling architectures. A case study is included to demonstrate the applicability of the proposed models.

An Algorithm to Optimize Electrical Flows

Cloud computing has expanded in recent years due to many effects, such as accessibility, reliability, and collaboration. To provide those functionalities high availability is in demand, which implies a higher electric energy consumption by the computers that support the cloud infrastructure. Studies that pay attention to this electric energy consumption are important due to its impact on sustainability and operational costs. This paper proposes a power load distribution algorithm (PLDA) to optimize electrical flows of power infrastructures. The PLDA adopts the Energy Flow Model (EFM) as its basis. The EFM is a model that computes sustainability impacts and cost issues, while it respects the energy providing restrictions of each component. In addition, a case study illustrates the applicability of the proposed PLDA through the analysis of six private cloud power architectures. Considerable results were observed, including a reduction on energy consumption of 10.7%, and an improvement (reduction) on the environmental impact of over 140% was obtained.