The Internet of Things (IoT) is used for various domains such as monitoring the environment, health care, and smart cities. Monitoring and measuring energy consumption of these systems is a crucial step in making them energy efficient. External Hardware-based power monitoring is not always available for IoT edge nodes. An alternative is to create an accurate power model that relates easy-to-monitor parameters (e.g., instructions count, cache misses, node temperature, etc) to externally monitored power. This relationship helps to estimate the power drawn by the nodes. IoT edge nodes have several power optimization leverages like Dynamic Voltage and Frequency Scaling (DVFS). When models calibration does not consider these leverages, the gap between power estimation and actual power usage increases. In related works, several power models and corresponding Software-defined power meters do not consider CPU frequency on IoT edge nodes. These Software-defined power meters provide regression-based power models for IoT edge nodes. This work compares predictions made by these state of the art power models to accurate external power monitoring. We show that not considering CPU frequency can result in incorrect estimations. We investigate and compare several methodologies for building power models, considering the CPU frequency, power, and energy leverage. Different performance metrics and regression methods are explored to estimate power usage. We demonstrate that linear and polynomial regression-based models are able to account for various CPU frequencies on IoT edge nodes. Using these models, we can predict the power consumed by IoT edge nodes running a specific workload, with a MAPE of 2% compared to accurate Hardware-based power meters.

Disseminating data in a wireless distributed system under limited resources, where nodes have constrained energy and communications capabilities, is challenging. In such contexts, common data dissemination techniques cannot be used. Instead, simple device-to-device communication policies allow mitigating the impact of communications on nodes energy consumption. However, depending on nodes configuration (up-times duration, wireless technology capabilities and energy consumption), choosing a suitable communication policy is challenging.In this paper, we propose and study two approaches based on classification algorithms that aim at predicting the most suitable communication policy to use, for a given node configuration, to match a given coverage and energy consumption target. The first approach called in situ learning, trains the classification models during deployment. The second approach called offline learning, uses existing data that are collected from previous deployments or simulations. Results show that, for a resource constrained environment, common classification models can take several months to converge with in situ learning. Depending on the policy, in situ learning can have a significant energy consumption overhead. Results underline offline learning as an interesting alternative to in situ learning, but requires to collect data from previous deployments or simulations.

A Cyber-Physical System (CPS) deployed in remote and resource-constrained environments faces multiple challenges. It has, no or limited: network coverage, possibility of energy replenishment, physical access by humans. Cyber-physical nodes deployed to observe and interact with the Arctic tundra face these challenges. They are subject to environmental factors such as avalanches, low temperatures, snow, ice, water and wild animals. Without energy supply infrastructures and humans available, nodes must achieve long operational lifetime from a single battery charge. They must be extremely energy-efficient. To reduce energy costs and increase their energy efficiency, cyber-physical nodes sleep most of the time, and avoid to communicate when they are unreachable. But, a CPS needs to disseminate data between the nodes for multiple purposes including data reporting to a back-end service, resilient operations, safe-keeping of observational data, and propagating nodes updates. Loosely-coupled data dissemination policies offer this possibility [1] . Although, investigations should be made on their applicability to large-scale CPS. In this paper, we evaluate and discuss the efficiency in energy, time and number of successful delivery of four data dissemination policies proposed in [1] . This evaluation is based on flow-level simulations. We study small and large-scale CPS, and evaluate the effects of the number of nodes and the size of the disseminated data on the nodes energy consumption and the dissemination's delivery success. To mitigate negative effects raised on large-scale CPS and large disseminated data sizes, different strategies are proposed and evaluated. We show that energy saving strategies do not always imply energy efficiency, and better data dissemination often comes at a cost. This last result highlights the importance of simulation prior to real CPS deployments in constrained environments. • CPS deployed in remote and resource-constrained environments faces challenges. • Energy efficient and scalable approaches for data dissemination in CPS are needed. • Simulation of CPS provides trade-offs in energy and data dissemination performance.

In the context of Edge Computing or Cyber-Physical Systems, cross-functional, and cross-geographical DevOps teams are in charge of automating deployments, configuration, and management (i.e., reconfiguration) of complex, large-scale, highly dynamic, and geo-distributed service-oriented software systems. In this context, DevOps teams cannot reasonably manually coordinate their reconfiguration operations in a global manner. Furthermore, as disconnection is the norm in these paradigms, a central entity responsible for reconfiguration should be avoided, and the set of changes to apply should be as fast as possible. This paper presents Ballet, a fast tool to automate decentralized choreographies (i.e., coordination) of cross-DevOps reconfiguration. We show a gain of 42.6% for a deployment scenario and 24% for an update scenario on an OpenStack case study.

A Cyber-Physical System (CPS) deployed to a resource-constrained environment can face multiple challenges like (i) no or limited network coverage, (ii) no or limited possibility of energy replenishment, (iii) no or limited physical access by humans, (iv) nodes must cope with environmental factors including avalanches, low temperatures, snow, ice, water and wild animals. Devices being part of such a CPS must be battery powered and be energy efficient to achieve long life-time. However, the CPS still has to disseminate data to increase resiliency, safely keep results or update nodes. A trade-off between energy spent and data dissemination needs to be found, ideally by minimizing the energy usage and maximizing the relevant data dissemination performance metrics. In this paper, we evaluate and discuss the efficiency (in energy, time and number of successful distributions) of multiple data distribution policies by mean of flow-level simulations. We report on the trade-off between (i) successful data dissemination and (ii) energy and uptime overheads resulting from the usage of loosely coupled policies. To fully explore the scope of possibilities, we simulate a wide range of scenarios extracted from real measurements and previous deployments. Characteristics of CPS devices developed by the Distributed Arctic Observatory (DAO) are used as simulation platforms. Results show that an efficient policy in a given scenario can perform worse in another scenario. We also show that simple policies, especially when combined, can help in minimizing the energy consumed by most of the devices composing the CPS and maximizing the relevant dissemination performance metrics.

To do in-situ observations of the arctic tundra, many small sensor nodes are used. Reporting the data to remote back-ends is hard because backhaul networks are scarce, and a node cannot expect to see one. Also, there are typically no humans physically close to the nodes to fetch the data and to replace batteries. Consequently, the nodes need a highly available, energy-efficient long range network. While LoRaWAN provides energy-efficient communication for nodes, it does so at the cost of needing an always-on gateway with a high energy consumption. The gateway is also a single point of failure. Instead we propose LoRaLitE for Energy-Limited environments where the gateway enters sleep phases to reduce energy consumption. The sleep phases of the nodes are coordinated accordingly. For high availability, any end-node with a backhaul-network can be elected to become the gateway. We conducted a series of simulation experiments to document the performance behavior of both LoRaWanand LoRaLitE. The results show that the LoRaLitE gateway spends from 10 to 10,000 times less energy than a LoRaWAN gateway. A LoRaLitE node spends from 13% to 42% more energy than a LoRaWAN node. The achievable bandwidth for LoRaLitE is only insignificantly smaller than for LoRaWAN. A LoRaWangateway needs a relatively large, heavy battery. If the gateway fails, assuming a new gateway can be elected, several nodes must have similar large batteries to be able to take over as the gateway. This is impractical to achieve for more than a few nodes because the nodes become more expensive, harder to camouflage, and impractical to deploy. A LoRaLitE gateway on the other hand only needs a battery like any other node. Even if all nodes need larger batteries than for the LoRaWancase, the increase is in practical terms insignificant. We conclude that LoRaLitE makes it realistic to use LoRa for nodes on the arctic tundra, providing for insignificant increased energy usage at the nodes, insignificant reduced bandwidth, but with significant increase in gateway availability. LoRaLitE also provides for an easier deployment in practice because all nodes can be kept small and light,

The arctic tundra is most sensitive to climate change. The change can be quantified from observations of the fauna, flora and weather conditions. To do observations at sufficient spatial and temporal resolution, ground-based observation nodes with sensors are needed.However, the arctic tundra is resource-limited with regards to energy, data networks, and humans. There are also regulatory and practical obstacles. Consequently, observation nodes must be small and unobtrusive, have a year or longer operational lifetime from small batteries, and be able to report results and receive software updates over scarce back-haul networks.We describe the architecture, design, and implementation of prototype observation nodes deployed to the arctic tundra for the periods August 2019 to July 2020 and August 2020 to July 2021.For the 2019 deployment, ten nodes were each placed inside ten existing camera traps. A camera trap is a box with a wildlife camera taking pictures of rodents when they enter the box from tunnels under snow and ice. For the 2020 deployment, eight nodes were located pairwise inside four camera traps.Each node measures carbon dioxide level and temperature inside the camera trap during the winter season. A node reports its state and observational data each night over a commercial low power IoT telecom back-haul network, if available.We report on the issues encountered doing actual deployments of the prototype nodes. For each issue, we describe the reason for why it happened, relate it to the architecture, design and implementation, and explain what we did about it.

IoT nodes need to be updated after deployment. However, doing so for nodes deployed to resource-challenged environments, like the arctic tundra, is a challenge. Because humans as the common case cannot physically visit the nodes, updating must be done from a remote update service over a back-haul data network. However, most nodes are not in range of a back-haul data network. Even when nodes are in range, they probably sleep to conserve energy and the remote cloud back-end therefore cannot communicate with them.We report on an approach and a prototype system for distributing updates from a cloud update distribution service to nodes. We assume that nodes carry one or several local area network technologies supporting short-lived point-to-point ad-hoc communication between two nodes at a time in range of each other. We assume that a single node in each neighborhood has a back-haul network, and delegate to this node to further distribute updates inside the neighborhood.A series of performance measuring experiments were conducted on the update distribution system when it executes on nodes with behaviors from always-on to mostly-off. We document how the distribution system behaves through a set of performance metrics. The results are very sensitive to the behavior of the nodes.

In a context of Cyber-Physical Systems (CPS), energy-efficiency is a critical factor to achieve long operational life-time. The constraint of using battery-powered devices adds degrees of complexity, especially in a hard to reach environment with scarce network and energy resources. The reporting of data consumes large amount of energy, reducing the life-time of both individual nodes and the CPS as a whole. One way to reduce the energy cost of communication is to reduce the number of Bits to transmit. However, this is a viable approach only if the transmitted data remain suitable for further analysis.In this paper, we report on the effect of reducing the image dimensions on the confidence score computed by a convolutional neural network (CNN) determining the species of animals present in images. We also report on the energy consumption of transmitting full vs. reduced dimensions of images. CPS devices and CNNs developed by the Distributed Arctic Observatory (DAO) project are used as experimental platforms.The results show that the energy needed to report the images can be reduced by up to 98% while only reducing the average confidence of determining the species correctly by 0.10%.

Observing an environment is essential to its understanding. This is certainly true for the arctic tundra as it is extremely sensitive to climate change. Satellites are used to observe large areas of the arctic tundra. However, the measurements are not at sufficient spatial resolution to be used to determine the species of animals, and to measure humidity, temperature, and CO2 levels for small areas. Ground-based measurements, which represent less that one percent of the experiments carried on the arctic tundra, must be done. Deploying and maintaining ground-based instruments is hard to do because visiting the arctic tundra is expensive, time consuming, and dangerous. Instead, automation is needed, where instruments can operate independently for long periods of time, and allow interaction with remote users for reporting of data and control of the instruments. However, the arctic tundra also has, as a common case, limited data network and energy coverage. We present the Distributed Arctic Observatory, where instruments called Observation Units (OUs) are distributed across the arctic tundra to measure a range of environmental state variables, and report them to where they are needed for further analysis. The DAO project uses Unmanned Aerial Vehicles (UAVs) to provide backhaul network access and energy to OUs. The modifications applied to the UAV and to OUs are presented. We describe the experiences gathered from practical use of the UAV to provide network access and energy to OUs located on the ground at 70°N. We show that it is feasible to apply UAVs to provide network access and energy to OUs on the arctic tundra, albeit practical restrictions.

Combining computation and water warming is an efficient alternative for recycling dissipated energy. Based on a set of Computing Water Heaters, used as a distributed super computer, we introduce a computing water heater scheduling system that has been validated by simulation on extreme workloads. These simulations lead us to reach an approximate CPU usage of about 60% while minimizing the thermal resistance usage to 10% (only when necessary) on a complex steady state.

Summary Current large‐scale systems, like datacenters and supercomputers, are facing an increasing electricity consumption. These infrastructures are often dimensioned according to the workload peak. However, as their consumption is not power‐proportional when the workload is low, the power consumption is still high. Shutdown techniques have been developed to adapt the number of switched‐on servers to the actual workload. However, datacenter operators are reluctant to adopt such approaches because of their potential impact on reactivity and hardware failures, and their energy gain is often largely misjudged. In this article, we evaluate the potential gain of shutdown techniques by taking into account shutdown and boot up costs in time and energy. This evaluation is made on recent server architectures and future energy‐aware architectures. Our simulations exploit real traces collected on production infrastructures under various machine configurations with several shutdown policies, with and without workload prediction. We study the impact of future's knowledge for saving energy with such policies. Finally, we examine the energy benefits brought by suspend‐to‐disk and suspend‐to‐RAM techniques, and we study the impact of shutdown techniques on the energy consumption of prospective hardware with heterogeneous processors (big‐medium‐little paradigm).

Energy consumption has become one of the mostcritical issues in the evolution of High Performance Computingsystems (HPC). Controlling the energy consumption of HPCplatforms is not only a way to control the cost but also a stepforward on the road towards exaflops. Powercapping is a widelystudied technique that guarantees that the platform will notexceed a certain power threshold instantaneously but it givesno flexibility to adapt job scheduling to a longer term energybudget control. We propose a job scheduling mechanism that extends thebackfilling algorithm to become energy-aware. Simultaneously, we adapt resource management with a node shutdown technique to minimize energy consumption whenever needed. Thiscombination enables an efficient energy consumption budgetcontrol on a cluster during a period of time. The technique isexperimented, validated and compared with various alternativesthrough extensive simulations. Experimentation results show highsystem utilization and limited bounded slowdown along withinteresting outcomes in energy efficiency while respecting anenergy budget during a particular time period.