Sustainable Edge Computing: Challenges and Future Directions

An increasing amount of data is being injected into the network from IoT (Internet of Things) applications. Many of these applications, developed to improve society's quality of life, are latency-critical and inject large amounts of data into the network. These requirements of IoT applications trigger the emergence of Edge computing paradigm. Currently, data centers are responsible for a global energy use between 2% and 3%. However, this trend is difficult to maintain, as bringing computing infrastructures closer to the edge of the network comes with its own set of challenges for energy efficiency. In this paper, we propose our approach for the sustainability of future computing infrastructures to provide (i) an energy-efficient and economically viable deployment, (ii) a fault-tolerant automated operation, and (iii) a collaborative resource management to improve resource efficiency. We identify the main limitations of applying Cloud-based approaches close to the data sources and present the research challenges to Edge sustainability arising from these constraints. We propose two-phase immersion cooling, formal modeling, machine learning, and energy-centric federated management as Edge-enabling technologies. We present our early results towards the sustainability of an Edge infrastructure to demonstrate the benefits of our approach for future computing environments and deployments.Comment: 26 pages, 16 figure

Predictive GPU-based ADAS management in energy-conscious smart cities

The demand of novel IoT and smart city applications is increasing significantly and it is expected that by 2020 the number of connected devices will reach 20.41 billion. Many of these applications and services manage real-time data analytics with high volumes of data, thus requiring an efficient computing infrastructure. Edge computing helps to enable this scenario improving service latency and reducing network saturation. This computing paradigm consists on the deployment of numerous smaller data centers located near the data sources. The energy efficiency is a key challenge to implement this scenario, and the management of federated edge data centers would benefit from the use of microgrid energy sources parameterized by user's demands. In this research we propose an ANN predictive power model for GPU-based federated edge data centers based on data traffic demanded by the application. We validate our approach, using real traffic for a state-of-the-art driving assistance application, obtaining 1 hour ahead power predictions with a normalized root-mean-square deviation below 7.4% when compared with real measurements. Our research would help to optimize both resource management and sizing of edge federations