Energy Management via PI Control for Data Parallel Applications with Throughput Constraints

International audienceThis paper presents a new proportional-integral (PI) controller that sets the operating point of computing tiles in a system on chip (SoC). We address data-parallel applications with throughput constraints. The controller settings are investigated for application configurations with different QoS levels and different buffer sizes. The control method is evaluated on a test chip with four tiles executing a realistic HMAX object recognition application. Experimental results suggest that the proposed controller outperforms the state-of-the-art results: it attains, on average, 25% less number of frequency switches and has slightly higher energy savings. The reduction in number of frequency switches is important because it decreases the involved overhead. In addition, the PI controller meets the throughput constraint in cases where other approaches fail

Energy-Efficient Concurrency Control for Dynamic-Priority Real-Time Tasks with Abortable Critical Sections

In this paper, we are interested in energy-efficient concurrency control for real-time tasks on a non-ideal DVS processor. Based on well-known ceiling-based concurrency control protocols (such as priority ceiling protocol (PCP) and stack resource policy (SRP)), researchers have proposed energy-efficient approaches to mange concurrent accesses to shared resources so that the energy consumption can be reduced. However, ceiling-based protocols have a problem of ceiling blocking which imposes a great impact on the performance of real-time systems. In order to achieve sufficient performance, we propose a new protocol, called conditional abortable stack resource policy (CA-SRP), to resolve the ceiling blocking problem for dynamic-priority real-time tasks by incorporating a conditional abort rule into SRP. Based on the schedulability analysis of CA-SRP, we also propose a method, called dynamic speed assignment (DSA), to dynamically calculate and assign proper processor speeds for task execution so that the energy consumption can be reduced further. The capabilities of our proposed CA-SRP and DSA have been evaluated by a series of experiments, for which we have encouraging results

Green computing: power optimisation of VFI-based real-time multiprocessor dataflow applications (extended version)

Execution time is no longer the only performance metric for computer systems. In fact, a trend is emerging to trade raw performance for energy savings. Techniques like Dynamic Power Management (DPM, switching to low power state) and Dynamic Voltage and Frequency Scaling (DVFS, throttling processor frequency) help modern systems to reduce their power consumption while adhering to performance requirements. To balance flexibility and design complexity, the concept of Voltage and Frequency Islands (VFIs) was recently introduced for power optimisation. It achieves fine-grained system-level power management, by operating all processors in the same VFI at a common frequency/voltage.This paper presents a novel approach to compute a power management strategy combining DPM and DVFS. In our approach, applications (modelled in full synchronous dataflow, SDF) are mapped on heterogeneous multiprocessor platforms (partitioned in voltage and frequency islands). We compute an energy-optimal schedule, meeting minimal throughput requirements. We demonstrate that the combination of DPM and DVFS provides an energy reduction beyond considering DVFS or DMP separately. Moreover, we show that by clustering processors in VFIs, DPM can be combined with any granularity of DVFS. Our approach uses model checking, by encoding the optimisation problem as a query over priced timed automata. The model-checker Uppaal Cora extracts a cost minimal trace, representing a power minimal schedule. We illustrate our approach with several case studies on commercially available hardware

A survey of offline algorithms for energy minimization under deadline constraints

Modern computers allow software to adjust power management settings like speed and sleep modes to decrease the power consumption, possibly at the price of a decreased performance. The impact of these techniques mainly depends on the schedule of the tasks. In this article, a survey on underlying theoretical results on power management, as well as offline scheduling algorithms that aim at minimizing the energy consumption under real-time constraints, is given

On a reduction for a class of resource allocation problems

In the resource allocation problem (RAP), the goal is to divide a given amount of resource over a set of activities while minimizing the cost of this allocation and possibly satisfying constraints on allocations to subsets of the activities. Most solution approaches for the RAP and its extensions allow each activity to have its own cost function. However, in many applications, often the structure of the objective function is the same for each activity and the difference between the cost functions lies in different parameter choices such as, e.g., the multiplicative factors. In this article, we introduce a new class of objective functions that captures the majority of the objectives occurring in studied applications. These objectives are characterized by a shared structure of the cost function depending on two input parameters. We show that, given the two input parameters, there exists a solution to the RAP that is optimal for any choice of the shared structure. As a consequence, this problem reduces to the quadratic RAP, making available the vast amount of solution approaches and algorithms for the latter problem. We show the impact of our reduction result on several applications and, in particular, we improve the best known worst-case complexity bound of two important problems in vessel routing and processor scheduling from $O(n^2)$ to $O(n \log n)$

Sistema Dinâmico de Economia de Energia em RTOS.

Este documento apresenta a utilização de diferentes modos de economia de energia aplicados em sistemas operacionais de tempo real, quando o mesmo não possui tarefas a serem executadas. Espera-se que, ao entrar em modos de baixo consumo oferecidos pelo próprio microcontrolador, haja uma diminuição no consumo de energia, proporcional ao número de módulos internos que são desativados. Entretanto, a economia pode não se mostrar válida de acordo com a carga de processamento que o sistema possui, pois a corrente instantânea consumida após a transição do estado de baixo consumo para o modo ativo é superior ao consumo do sistema em operação constante. Desta forma, será apresentada uma política de economia de energia e como aplicá-la, simultaneamente minimizando a carga energética e balanceando a exigência de processamento

Towards More Efficient 5G Networks via Dynamic Traffic Scheduling

Department of Electrical EngineeringThe 5G communications adopt various advanced technologies such as mobile edge computing and unlicensed band operations, to meet the goal of 5G services such as enhanced Mobile Broadband (eMBB) and Ultra Reliable Low Latency Communications (URLLC). Specifically, by placing the cloud resources at the edge of the radio access network, so-called mobile edge cloud, mobile devices can be served with lower latency compared to traditional remote-cloud based services. In addition, by utilizing unlicensed spectrum, 5G can mitigate the scarce spectrum resources problem thus leading to realize higher throughput services. To enhance user-experienced service quality, however, aforementioned approaches should be more fine-tuned by considering various network performance metrics altogether. For instance, the mechanisms for mobile edge computing, e.g., computation offloading to the edge cloud, should not be optimized in a specific metric's perspective like latency, since actual user satisfaction comes from multi-domain factors including latency, throughput, monetary cost, etc. Moreover, blindly combining unlicensed spectrum resources with licensed ones does not always guarantee the performance enhancement, since it is crucial for unlicensed band operations to achieve peaceful but efficient coexistence with other competing technologies (e.g., Wi-Fi). This dissertation proposes a focused resource management framework for more efficient 5G network operations as follows. First, Quality-of-Experience is adopted to quantify user satisfaction in mobile edge computing, and the optimal transmission scheduling algorithm is derived to maximize user QoE in computation offloading scenarios. Next, regarding unlicensed band operations, two efficient mechanisms are introduced to improve the coexistence performance between LTE-LAA and Wi-Fi networks. In particular, we develop a dynamic energy-detection thresholding algorithm for LTE-LAA so that LTE-LAA devices can detect Wi-Fi frames in a lightweight way. In addition, we propose AI-based network configuration for an LTE-LAA network with which an LTE-LAA operator can fine-tune its coexistence parameters (e.g., CAA threshold) to better protect coexisting Wi-Fi while achieving enhanced performance than the legacy LTE-LAA in the standards. Via extensive evaluations using computer simulations and a USRP-based testbed, we have verified that the proposed framework can enhance the efficiency of 5G.clos