Energy-aware design of hardware and software for ultra-low-power systems

Future visions of the Internet of Things and Industry 4.0 demand for large scale deployments of mobile devices while removing the numerous disadvantages of using batteries: degradation, scale, weight, pollution, and costs. However, this requires computing platforms with extremely low energy consumptions, and thus employ ultra-low-power hardware, energy harvesting solutions, and highly efficient power-management hardware and software. The goal of these power management solutions is to either achieve power neutrality, a condition where energy harvest and energy consumption equalize while maximizing the service quality, or to enhance power efficiency for conserving energy reserves. To reach these goals, intelligent power-management decisions are needed that utilize precise energy data. This thesis discusses the measurement of energy in embedded systems, both online and by external equipment, and the utilization of the acquired data for modeling the power consumption states of each involved hardware component. Furthermore, a method is shown to use the resulting models by instrumenting preexisting device drivers. These drivers enable new functionalities, such as online energy accounting and energy application interfaces, and facilitate intelligent power management decisions. In order to reduce additional efforts for device driver reimplementation and the violation of the separation of concerns paradigm, the approach shown in this thesis synthesizes instrumentation aspects for an aspect oriented programming language, so that the original device-driver source code remains unaffected. Eventually, an automated process of energy measurement and data analysis is presented. This process is able to yield precise energy models with low manual effort. In combination with the instrumentation synthesis of aspect code, this method enables an accelerated creation process for energy models of ultra-low-power systems. For all proposed methods, empirical accuracy and overhead measurements are presented. To support the claims of the author, first practical energy aware and wireless-radio networked applications are showcased: An energy-neutral light sensor, a photovoltaic-powered seminar-room door plate, and a sensor network experiment testbed for research and education

Chapter Measuring Energy

Data centres are part of today's critical information and communication infrastructure, and the majority of business transactions as well as much of our digital life now depend on them. At the same time, data centres are large primary energy consumers, with energy consumed by IT and server room air conditioning equipment and also by general building facilities. In many data centres, IT equipment energy and cooling energy requirements are not always coordinated, so energy consumption is not optimised. Most data centres lack an integrated energy management system that jointly optimises and controls all its energy consuming equipments in order to reduce energy consumption and increase the usage of local renewable energy sources. In this chapter, the authors discuss the challenges of coordinated energy management in data centres and present a novel scalable, integrated energy management system architecture for data centre wide optimisation. A prototype of the system has been implemented, including joint workload and thermal management algorithms. The control algorithms are evaluated in an accurate simulation‐based model of a real data centre. Results show significant energy savings potential, in some cases up to 40%, by integrating workload and thermal management

Measuring Energy

This chapter provides an introduction to quantifying the energy consumed by software. It is written for computer scientists, software engineers, embedded system developers and programmers who want to understand how to measure the energy consumed by the code they write in order to optimize for energy efficiency. We start with an overview of the electrical foundations of energy measurement and show how these are applied by reviewing the most commonly found energy sensing techniques. This is followed by a brief discussion of the signal processing required to obtain energy consumption data from sensing. We then present two energy measurement systems that are based on sensing techniques. Both can be used to directly measure the energy consumed by software running on embedded systems without the need to modify the hardware. As an alternative, regression-based techniques can be used to infer energy consumption based on monitoring events during program execution using counters monitors offered by the hardware. We introduce the foundations of regression analysis and illustrate how an energy model for an ARM processor can be built using linear regression. In the conclusion, we offer a wider discussion on what should be considered when selecting an energy measurement technique

PhyNetLab: An IoT-Based Warehouse Testbed

Future warehouses will be made of modular embedded entities with communication ability and energy aware operation attached to the traditional materials handling and warehousing objects. This advancement is mainly to fulfill the flexibility and scalability needs of the emerging warehouses. However, it leads to a new layer of complexity during development and evaluation of such systems due to the multidisciplinarity in logistics, embedded systems, and wireless communications. Although each discipline provides theoretical approaches and simulations for these tasks, many issues are often discovered in a real deployment of the full system. In this paper we introduce PhyNetLab as a real scale warehouse testbed made of cyber physical objects (PhyNodes) developed for this type of application. The presented platform provides a possibility to check the industrial requirement of an IoT-based warehouse in addition to the typical wireless sensor networks tests. We describe the hardware and software components of the nodes in addition to the overall structure of the testbed. Finally, we will demonstrate the advantages of the testbed by evaluating the performance of the ETSI compliant radio channel access procedure for an IoT warehouse

Unikernel-Based Real-Time Virtualization Under Deferrable Servers: Analysis and Realization (Artifact)

This artifact provides the source code to validate and reproduce the numerical results of the associated paper "Unikernel-Based Real-Time Virtualization under Deferrable Servers: Analysis and Realization". Due to the nature of a close-source project with the company, i.e., EMVICORE GmbH, the source code of the case study in Section 6.2 is not included in this artifact

Unikernel-Based Real-Time Virtualization Under Deferrable Servers: Analysis and Realization

For cyber-physical systems, real-time virtualization optimizes the hardware utilization by consolidating multiple systems into the same platform, while satisfying the timing constraints of their real-time tasks. This paper considers virtualization based on unikernels, i.e., single address space kernels usually constructed by using library operating systems. Each unikernel is a guest operating system in the virtualization and hosts a single real-time task. We consider deferrable servers in the virtualization platform to schedule the unikernel-based guest operating systems and analyze the worst-case response time of a sporadic real-time task under such a virtualization architecture. Throughout synthesized tasksets, we empirically show that our analysis outperforms the restated analysis derived from the state-of-the-art, which is based on Real-Time Calculus. Furthermore, we provide insights on implementation-specific issues and offer evidence that the proposed scheduling architecture can be effectively implemented on top of the Xen hypervisor while incurring acceptable overhead