Timo Hönig

Worst-Case Energy Consumption Analysis for Energy-Constrained Embedded Systems

The fact that energy is a scarce resource in many embedded real-time systems creates the need for energy-aware task schedulers, which not only guarantee timing constraints but also consider energy consumption. Unfortunately, existing approaches to analyze the worst-case execution time (WCET) of a task usually cannot be directly applied to determine its worst-case energy consumption (WCEC) due to execution time and energy consumption not being closely correlated on many state-of-the-art processors. Instead, a WCEC analyzer must take into account the particular energy characteristics of a target platform. In this paper, we present 0g, a comprehensive approach to WCEC analysis that combines different techniques to speed up the analysis and to improve results. If detailed knowledge about the energy costs of instructions on the target platform is available, our tool is able to compute upper bounds for the WCEC by statically analyzing the program code. Otherwise, a novel approach allows 0g to determine the WCEC by measurement after having identified a set of suitable program inputs based on an auxiliary energy model, which specifies the energy consumption of instructions in relation to each other. Our experiments for three target platforms show that 0g provides precise WCEC estimates.

SEEP: exploiting symbolic execution for energy-aware programming

In recent years, there has been a rapid evolution of energy-aware computing systems (e.g., mobile devices, wireless sensor nodes), as still rising system complexity and increasing user demands make energy a permanently scarce resource. While static and dynamic optimizations for energy-aware execution have been massively explored, writing energy-efficient programs in the first place has only received limited attention. This paper proposes SEEP, a framework which exploits symbolic execution and platform-specific energy profiles to provide the basis for energy-aware programming. More specifically, the framework provides developers with information about the energy demand of their code at hand, even for the invocation of library functions and in settings with multiple possibly strongly heterogeneous target platforms. This equips developers with the necessary knowledge to take energy demand into account during the task of writing programs.

Monitoring Bats in the Wild: On Using Erasure Codes for Energy-Efficient Wireless Sensor Networks

We explore the advantages of using Erasure Codes (ECs) in a very challenging sensor networking scenario, namely, monitoring and tracking bats in the wild. The mobile bat nodes collect contact information that needs to be transmitted to stationary base stations whenever they are in communication range. We are particularly interested in improving the overall communication reliability of the wireless communication. The mobile nodes are capable of storing a few 100kB of data and to exchange contact information in aggregated form. Due to the continuous flight of the bats and the forest environment, the wireless channel quality varies quickly and, thus, the communication is in general assumed to be highly unreliable. Given the very strict energy constraints of the mobile node and the inherently asymmetric channels, conventional techniques such as full data replication or Automatic Repeat Request to improve the communication reliability are prohibitive. In this work, we investigate the tradeoff between reliability achieved and the cost in form of additional transmissions, that is, the additional energy costs. Our energy measurements on a real platform combined with larger-scale simulation of the wireless communication clearly indicate the advantages of using ECs in our scenario. The results are also applicable in other configurations when unreliable communication channels meet tight energy budgets.

GenE: A Benchmark Generator for WCET Analysis

The fact that many benchmarks for evaluating worst-case execution time (WCET) analysis tools are based on real-world applications greatly increases the value of their results. However, at the same time, the complexity of these programs makes it difficult, sometimes even impossible, to obtain all corresponding flow facts (i.e., loop bounds, infeasible paths, and input values triggering the WCET), which are essential for a comprehensive evaluation. In this paper, we address this problem by presenting GenE, a benchmark generator that in addition to source code also provides the flow facts of the benchmarks created. To generate a new benchmark, the tool combines code patterns that are commonly found in real-time applications and are challenging for WCET analyzers. By keeping track of how patterns are put together, GenE is able to determine the flow facts of the resulting benchmark based on the known flow facts of the patterns used. Using this information, it is straightforward to synthesize the accurate WCET, which can then serve as a baseline for the evaluation of WCET analyzers.

How to Make Profit: Exploiting Fluctuating Electricity Prices with Albatross, A Runtime System for Heterogeneous HPC Clusters

The ongoing evolution of the power grid towards a highly dynamic supply system poses challenges as renewables induce new grid characteristics. The volatility of electricity sources leads to a fluctuating electricity price, which even becomes negative when excess supply occurs. Operators of high-performance--computing (HPC) clusters therefore can consider the highly dynamic variations of electricity prices to provide an energy-efficient and economic operation. This paper presents Albatross, a runtime system for heterogeneous HPC clusters. To ensure an energy-efficient and economic processing of HPC workloads, our system exploits heterogeneity at the hardware level and considers dynamic electricity prices. We have implemented Albatross and evaluate it on a heterogeneous HPC cluster in our lab to show how the power demand of the cluster decreases when electricity prices are high (i.e., excess demand at the grid). When electricity prices are low or negative (i.e., excess supply to the grid), Albatross purposefully increases the workload and, thus, power demand of the HPC cluster---to make profit.

An End-to-End Toolchain: From Automated Cost Modeling to Static WCET and WCEC Analysis

Reliable and fine-grained cost-models are fundamental for real-time systems to statically predict worst-case execution time (WCET) estimates of program code in order to guarantee timeliness. Analogous considerations hold for energy-constrained systems where worst-case energy consumption (WCEC) values are mandatory to ensure meeting predefined energy budgets. These cost models are generally unavailable for commercial off-the-shelf (COTS) hardware platforms, although static worst-case analysis tools require those models in order to predict the WCET as well as the WCEC of program code. To solve this problem, we present NEO, an end-to-end toolchain to automate cost-model generation for both WCET and WCEC analyses. NEO exploits automatically generated benchmarks, which are input for 1) an instruction-level emulation and 2) automatically conducted execution-time and energy-consumption measurements on the target platform. The gathered values (i.e., occurrences per instruction, execution-time and energy-consumption per benchmark) are combined as mathematical optimization problems. The solutions to the formulated problems, which are designed to reveal the worst-case behavior, yield the respective cost models. To statically determine upper bounds of benchmarks, we integrated the cost models into the state-of-the-art WCET analyzer PLATIN. Our evaluations on COTS hardware reveal that our open-source, end-to-end toolchain NEO yields accurate worst-case bounds.

Playing Hare and Tortoise: The FigarOS Kernel for Fine-Grained System-Level Energy Optimizations

Energy has emerged to be the most important resource for computing systems. Despite the exceptional importance of energy, reducing its demand at application and system level remains a challenging task for programmers and engineers. This is aggravated by the fact that traditional energy-saving approaches are not only error-prone but even lead to adverse consequences (i.e. increased energy consumption). To address this concern, we present the FigarOS operating system for fine-grained system-level energy optimizations. The evaluation of our FigarOS implementation shows that the operating system lowers the energy consumption of processes by up to 2.9 x.

Making Profit with ALBATROSS: A Runtime System for Heterogeneous High-Performance-Computing Clusters

1 MOTIVATION AND INTRODUCTION The ongoing evolution of the power grid towards a highly dynamic supply and demand system poses challenges [1] to its operators and subscribers. The dependence on renewable electricity (e.g., wind, solar, and water) induces new grid characteristics: the volatility of electricity sources leads to an interplay of excess supply and demand, which results in fluctuating electricity prices. Thus, the operation of high-performance–computing (HPC) clusters canwork with the fluctuating electricity price to ensure cost effectiveness [2]. This poster abstract presents Albatross [3], a runtime system for heterogeneous HPC clusters. To ensure an energy-efficient and economic processing of HPC workloads, our system exploits heterogeneity at the hardware level and considers dynamic electricity prices. Early results of our Albatross prototype running on a heterogeneous HPC cluster in our lab show how the power demand of the cluster decreases when electricity prices are high (i.e., excess demand at the grid), and how our system purposefully increases the workload and, thus, power demand when electricity prices are low or even negative (i.e., excess supply to the grid)—to make profit.

X-lap: a systems approach for cross-layer profiling and latency analysis for cyber-physical networks

Networked control applications for cyber-physical networks demand predictable and reliable real-time communication. Applications of this domain have to cooperate with network protocols, the operating system, and the hardware to improve safety properties and increase resource efficiency. In consequence, a cross-layer approach is necessary for the design and holistic optimisation of cyber-physical systems and networks. This paper presents X-Lap, a cross-layer, inter-host timing analysis tool tailored to the needs of real-time communication. We use X-Lap to evaluate the timing behaviour of a reliable real-time communication protocol. Our analysis identifies parts of the protocol which are responsible for unwanted jitter. To system designers, X-Lap provides useful support for the design and evaluation of networked real-time systems.

In the Heat of Conflict: On the Synchronisation of Critical Sections

Advances in semiconductor technology greatly extend the scope of special-purpose applications as multi-core processors find the way into embedded systems. The increasing number of processor cores makes it more important than ever to have real-time operating systems process parallel threads in the most efficient way. In doing so, they have to pursue multiple (often conflicting) goals: namely being predictable as to time and energy demand. In shared-memory multi-core systems, contention at critical sections makes it inevitable for the operating system to execute competing threads with highly efficient synchronisation methods. Related research has primarily focussed on timing aspects of synchronisation methods, while the energy efficiency of the latter is an unexplored field, yet. In this paper, we implement and evaluate five distinct synchronisation methods and analyse their run-time characteristics (i.e. time, energy) in-depth. We evaluate the overall demand at application level, and empirically prove that contention increases the energy demand significantly even when competing processes are temporarily suspended. Furthermore, the evaluation reveals that choosing the right synchronisation method can decrease the energy demand by more than a factor of 5. We come to the conclusion that it is mandatory to consider the effects of process synchronisation for energy analysis and energy-efficiency optimisations.