Paulo Maciel | Searcharxiv

arXiv2025

Distributed Application Provisioning over Ethereum based private and permissioned Blockchain: Availability modeling, capacity, and costs planning

Blockchain and Cloud Computing are two of the main topics related to the distributed computing paradigm, and in the last decade, they have seen exponential growth in their adoption. Cloud computing has long been established as the main mechanism to test, develop, and deliver new applications and services in a distributed manner across the World Wide Web. Large data centers host many services and store petabytes of user data. Infrastructure and services owners rule the access to data and may even be able to change contents and attest to its veracity. Blockchain is a step towards a future where the user's data are considered safer, besides being public. Advances in blockchain-based technologies, now, support service provisioning over permissioned and private infrastructures. Therefore, organizations or groups of individuals may share information, service even if they do not trust each other, besides supporting infrastructure management tasks. This paper presents and evaluates models for assessing the availability and capacity-oriented availability of cloud computing infrastructures. It aims at running Blockchain's distributed applications based on the Ethereum blockchain platform and the required expenses to perform service delivery in public and private infrastructures. Most of the obtained results also apply to other blockchains based platforms.

cs.DC↗

arXiv2020

Building Energy Consumption Models Based On Smartphone User's Usage Patterns

The increasing usage of smartphones in everyday tasks has been motivated many studies on energy consumption characterization aiming to improve smartphone devices' effectiveness and increase user usage time. In this scenario, it is essential to study mechanisms capable of characterizing user usage patterns, so smartphones' components can be adapted to promote the best user experience with lower energy consumption. The goal of this study is to build an energy consumption model based on user usage patterns aiming to provide the best accurate model to be used by application developers and automated optimization. To develop the energy consumption models, we established a method to identify the components with the most influence in the smartphone's energy consumption and identify the states of each influential device. Besides that, we established a method to prove the robustness of the models constructed using inaccurate hardware and a strategy to assess the accuracy of the model built. After training and testing each strategy to model the energy consumption based on the user's usage and perform the Nemenyi test, we demonstrated that it is possible to get a Mean Absolute Error of 158.57mW when the smartphone's average power is 1970.1mW. Some studies show that the leading smartphone's workload is the user. Based on this fact, we developed an automatic model building methodology that is capable of analyzing the user's usage data and build smart models that can estimate the smartphone's energy consumption based on the user's usage pattern. With the automatic model building methodology, we can adopt strategies to minimize the usage of components that drain the battery.

cs.HC↗