A functional Test Framework to observe MPSoC Power Management Techniques in Virtual Platforms

e24058 Background: Breast cancer survivors on aromatase inhibitors (AIs) can develop endothelial dysfunction, a measure of increased CV risk. Stress modifying techniques such as MBSR may improve CV function and quality of life (QOL). We present data of a pilot study assessing the impact of MBSR on endothelial dysfunction and psychosocial outcomes. Methods: 31 women with locally advanced breast cancer and taking an AI were randomized 1:1 to 8 weekly MBSR classes or wait-list control. Subjects with tobacco use, known hypertension or hyperlipidemia were excluded. Participants completed surveys and vascular testing at baseline and 10 weeks. With the COVID-19 pandemic, MBSR classes were held on a virtual platform. Endothelial function was measured using the Endo-PAT2000 system; reductions in EndoPAT ratio (<1.6) are indicative of worsening endothelial function and increased CV risk. Questionnaires included the CDC’s 4-item Healthy Days Core Module (HRQOL), Patient Health Questionnaire (PHQ)-8 for depression, the Perceived Stress Scale, and the Generalized Anxiety Disorder Scale (GAD-7). Functional test markers were compared between groups using t-tests. Results: Baseline mean age was 59 years, body mass index was 28.4 kg/m2, mean systolic blood pressure was 128.1 mmHg and cholesterol was 226.9 mg/dL. These measures did not differ between groups. Baseline EndoPAT ratio (0.60) and follow up (0.52) were low in all participants. As statewide COVID-19 cases spiked, 3/31 participants did not return for a second assessment, and for another 5 participants, one EndoPAT ratio measurement was not interpretable. Both groups showed a decline in EndoPat ratio pre-to-post, but the decline among MBSR participants (all of whom completed the intervention) was five times less than that observed among controls [Table]. Groups did not differ on QOL or psychosocial outcomes. Conclusions: Breast cancer survivors on AIs have endothelial dysfunction, a measure of increased CV risk. Stress reduction techniques, using virtual platforms, are feasible and acceptable to this patient population, and may help improve endothelial function. Further research and longer follow up is warranted in understanding the long-term impact of these interventions on CV risk and psychosocial well-being. [Table: see text]

The use of high-end multicore processors today can incur high power density with significant variability in spatial and temporal usage of resources by workloads. This situation leads to power and temperature hotspots, which in turn may lead to non-uniform ageing and accelerated chip failure. These drawbacks can be mitigated by online tuning of system performance and adopting closed-loop thermal and reliability management policies. The development and evaluation of these policies cannot be performed solely on real hardware - due to observability and flexibility limitations or just by relying on trace-driven simulation, due to dependencies present among power, thermal effects, reliability and performance. We present a complete and virtual platform to develop, simulate and evaluate power, temperature and reliability management control strategies for high-performance multicores. The accuracy and effectiveness of our solution are ensured by integrating a established system simulator (Simics) with models for power consumption, temperature distribution and aging. The models are based on characterization on real hardware. Control strategies exploration and design are carried out in the MATLAB/Simulink framework allowing the use of control theory tools. Fast prototyping is achieved by developing a suitable interface between Simics and MATLAB/Simulink, enabling co-simulation of hardware platforms and controllers.

With the predicted device, core, and multi-core scaling, a recent study revealed that regardless of chip organization and topology, multi-core scaling is power limited. It has been predicted that at 22 nm, 21 % of a fixed-size chip must be powered off, and at 8 nm, even more than 50 % (Esmaeilzadeh et al., News 39(3):365–376, 2011). Especially for mixed-criticality systems, which consist of a mixture of safety and non-safety relevant applications, this is of major concern. Safety-critical applications cannot be simply switched on and off or migrated during run-time. A system engineer should be aware of any possible cross-application interferences with respect to timing, power, and thermal properties as soon as possible in the design process. Introduction of power and temperature management must be planned and realized without violating freedom from interference. For this reason, the extra-functional properties need to be modeled and analyzed at the system level, because they can strongly affect the overall quality of service (performance, battery lifetime) or even cause the system to fail meeting its real-time and safety requirements.In this chapter, we present our vision of a SystemC-based simulation framework for capturing extra-functional properties in virtual platforms, currently under development in the CONTREX project. This covers the specification of platform properties (extra-functional model) as well as the dynamic capturing, processing, and extraction of power/temperature information during the simulation. Especially closing the loop back to the application and run-time services is an important feature for complex heterogeneous hardware platforms and software stacks. As an example, we will present a battery-powered mixed-critical avionics system, running a safety-critical flight control application and a performance critical image processing application on the same multi-core System on Chip.

Power management techniques are applied to several types of computer systems, such as desktops, servers and embedded systems. For all of those systems, the use of a power management technique is expected to decrease power consumption without degrading performance. Therefore, a variety of power management techniques tailored for specific hardware architectures and the power management mechanisms for computer systems have been investigated. Feedback control constitutes a very promising technique for power management, because it provides some theoretical guarantees in terms of tracking accuracy and system stability. However, a computer system is subjected to unknown and nonstationary workloads. Therefore a feedback control based power management policy must be capable of adapting itself to the fluctuations of the workload, and still guarantee optimal performance with minimum power consumption. This paper proposes an adaptive control technique for power management considering such requirements. The experimental results have shown that the proposed adaptive control technique outperformed other state-of-the art policies both in power and system performance.

Formal software development begins with formal specification of the user requirements or design of a system. Hence, formal specification languages are used to resolve ambiguities in user requirements or detect design errors at the early software life cycle. Furthermore, specification-based functional testing derives test inputs from its formal specification. However, formal specifications are expressed using set theory or predicate logics which are non-executable. Thus, functional test execution over abstract expressions would be impossible. Therefore, there is a need to refine abstract specification into a form that can be executable. In this paper, an automatic functional testing framework using object constraint language (OCL) formal specification has been proposed. The major objective of this paper is to describe how the refinement processes are integrated into the specification-based testing framework.

Availability was considered a critical factor of Network Function Virtualization(NFV) platforms. And many existing testing tools with fault injection features for NFV, only aim at availability, while now more and more attention have been paid to the performance degradation when failure happens. Moreover, performance data on hardware/software resources, like CPU consumption, memory usage, are required to collect in performance and fault-tolerant testing. To fulfill the requirements above, this paper proposes a service-based testing framework for NFV platform performance evaluation, under workloads and fault loads. By taking advantage of the Microservice Architecture, the proposed framework will integrate some existing open-source testing frameworks, including performance testing tools, fault injection tools, and monitoring tools, and build a more comprehensive testing scenario. A case study is conducted on Clearwater, a widely used open-source NFV application, to validate the efficiency and usability of the proposed framework.

Traditional network resident functions (e.g., firewalls, network address translation) and middleboxes (caches, load balancers) have moved from purpose-built appliances to software-based components. However, L2/L3 network functions (NFs) are being implemented on Network Function Virtualization (NFV) platforms that extensively exploit kernel-bypass technology. They often use DPDK for zero-copy delivery and high performance. On the other hand, L4/L7 middleboxes, which usually require full network protocol stack support, take advantage of a full-fledged kernel-based system with a greater emphasis on functionality. Thus, L2/L3 NFs and middleboxes continue to be handled by distinct platforms on different nodes.This paper proposes MiddleNet that seeks to overcome this dichotomy by developing a unified network resident function framework that supports L2/L3 NFs and L4/L7 middleboxes. MiddleNet supports function chains that are essential in both NFV and middlebox environments. MiddleNet uses DPDK for zero-copy packet delivery without interrupt-based processing, to enable the ‘bump-in-the-wire’ L2/L3 processing performance required of NFV. To support L4/L7 middlebox functionality, MiddleNet utilizes a consolidated, kernel-based protocol stack processing, avoiding a dedicated protocol stack for each function. MiddleNet fully exploits the event-driven capabilities provided by the extended Berkeley Packet Filter (eBPF) and seamlessly integrates it with shared memory for high-performance communication in L4/L7 middlebox function chains. The overheads for MiddleNet are strictly load-proportional, without needing the dedicated CPU cores of DPDK-based approaches. MiddleNet supports flow-dependent packet processing by leveraging Single Root I/O Virtualization (SR-IOV) to dynamically select packet processing needed (Layer 2 to Layer 7). Our experimental results show that MiddleNet can achieve high performance in such a unified environment.

The development of all electric ship (AES) configurations necessitates the use of multiple power sources to support a wide variety of load profiles. The latter are diverse ranging from propulsion loads to high power pulsed loads while the operational status of the ship varies complicating even further the power and energy management process. The high level of complexity is giving rise to modern sophisticated ship Electrical Power Management and Control Systems (EPMACS) resembling the ones adopted in land-based hybrid systems. This paper discusses the adoption of intelligent control methodologies on an EPMACS which can substantially reduce complexity, increase integrability and improve the overall efficiency and reliability.

New advancements in computers and information technologies have yielded novel ideas to create more effective virtual collaboration platforms for multiple enterprises. Virtual enterprise (VE) is a collaboration model between multiple independent business partners in a value chain and is particularly suited to small and medium-sized enterprises (SMEs). The most challenging problem in implementing VE systems is ineffcient and inflexible data storage and management techniques for VE systems. In this research, an ontology-based multi-agent virtual enterprise (OMAVE) system is proposed to help SMEs shift from the classical trend of manufacturing part pieces to producing high-value-added, high-tech, innovative products. OMAVE targets improvement in the flexibility of VE business processes in order to enhance integration with available enterprise resource planning (ERP) systems. The architecture of OMAVE supports the requisite flexibility and enhances the reusability of the data and knowledge created in a VE system. In this article, a detailed description of system features along with the rule-based reasoning and decision support capabilities of OMAVE system are presented. To test and verify the functionality and operation of this system, a sample product was manufactured using OMAVE applications and tools with the contribution of three SMEs.

Optimization studies for the energy management systems of hybrid electric powertrains have critical importance as an effective measure for vehicle manufacturers to reduce greenhouse gas emissions and fuel consumption due to increasingly stringent emission regulations in the automotive industry, strict fuel economy legislation, continuously rising oil prices, and increasing consumer awareness of global warming and environmental pollution. In this study, firstly, the mathematical model of the powertrain and the rule-based energy management system of the vehicle with a power-split hybrid electric vehicle configuration are developed in the Matlab/Simulink environment and verified with real test data from the vehicle dynamometer for the UDDS drive cycle. In this way, a realistic virtual test platform has been developed where the simulation results of the energy management systems based on discrete dynamic programming and Pontryagin’s minimum principle optimization can be used to train the artificial neural network-based energy management algorithms for hybrid electric vehicles. The average fuel consumption in relation to the break specific fuel consumption of the internal combustion engine and the total electrical energy consumption of the battery in relation to the operating efficiency of the electrical machines, obtained by comparing the simulation results at the initial battery charging conditions of the vehicle using different driving cycles, will be analyzed and the advantages of the different energy management techniques used will be evaluated.

The proliferation of Internet of Things (IoT) applications has created an unprecedented demand for autonomous, battery-operated sensor nodes capable of extended operational lifetimes. This paper presents a comprehensive analysis of ultra-low power design methodologies for IoT sensors, with particular emphasis on energy harvesting techniques and advanced power management strategies. Through systematic examination of circuit-level optimizations, energy harvesting mechanisms, and intelligent power management protocols, this research demonstrates potential energy consumption reductions of up to 95% compared to conventional designs. The study evaluates various energy harvesting sources including solar, thermal, vibration, and radio frequency energy, while proposing novel power management architectures that dynamically adapt to environmental conditions and application requirements. Experimental results indicate that properly designed ultra-low power IoT sensors can achieve operational lifetimes exceeding 10 years with minimal maintenance, making them viable for large-scale deployment in smart city infrastructure, environmental monitoring, and industrial automation applications.

As the hardware and software complexity grows, it is unlikely for the power management hardware/software to have a full observation of the entire system status. In this paper, we propose a new modeling and optimization technique based on partially observable Markov decision process (POMDP) for robust power management, which can achieve near-optimal power savings, even when only partial system information is available. Three scenarios of partial observations that may occur in an embedded system are discussed and their modeling techniques are presented. The experimental results show that, compared with power management policy derived from traditional Markov decision process model that assumes the system is fully observable, the new power management technique gives significantly better performance and energy tradeoff.

Dynamic power management (DPM) is a design methodology for dynamically reconfiguring systems to provide the requested services and performance levels with a minimum number of active components or a minimum load on such components. DPM encompasses a set of techniques that achieves energy-efficient computation by selectively turning off (or reducing the performance of) system components when they are idle (or partially unexploited). In this paper, we survey several approaches to system-level dynamic power management. We first describe how systems employ power-manageable components and how the use of dynamic reconfiguration can impact the overall power consumption. We then analyze DPM implementation issues in electronic systems, and we survey recent initiatives in standardizing the hardware/software interface to enable software-controlled power management of hardware components.

Recently proposed techniques for peak power management [4] involve centralized decision-making and assume quick evaluation of the various power management states. These techniques do not prevent instantaneous power from exceeding the peak power budget, but instead trigger corrective action when the budget has been exceeded. Similarly, they are not suitable for many-core architectures (processors with tens or possibly hundreds of cores on the same die) due to an exponential explosion in the number of global power management states. In this paper, we look at a hierarchical and a gradient ascent-based technique for decentralized peak power management for many-core architectures. The proposed techniques prevent power from exceeding the peak power budget and enable the placement of several more cores on a die than what the power budget would normally allow. We show up to 47% (33% on average) improvements in throughput for a given power budget. Our techniques outperform the static oracle by 22%.

Serine integrases catalyze the integration of bacteriophage DNA into a host genome by site-specific recombination between ‘attachment sites’ in the phage (attP) and the host (attB). The reaction is highly directional; the reverse excision reaction between the product attL and attR sites does not occur in the absence of a phage-encoded factor, nor does recombination occur between other pairings of attachment sites. A mechanistic understanding of how these enzymes achieve site-selectivity and directionality has been limited by a lack of structural models. Here, we report the structure of the C-terminal domains of a serine integrase bound to an attP DNA half-site. The structure leads directly to models for understanding how the integrase-bound attP and attB sites differ, why these enzymes preferentially form attP × attB synaptic complexes to initiate recombination, and how attL × attR recombination is prevented. In these models, different domain organizations on attP vs. attB half-sites allow attachment-site specific interactions to form between integrase subunits via an unusual protruding coiled-coil motif. These interactions are used to preferentially synapse integrase-bound attP and attB and inhibit synapsis of integrase-bound attL and attR. The results provide a structural framework for understanding, testing and engineering serine integrase function.