ENERGY-AWARE OPTIMIZATION FOR EMBEDDED SYSTEMS WITH CHIP MULTIPROCESSOR AND PHASE-CHANGE MEMORY

Over the last two decades, functions of the embedded systems have evolved from simple real-time control and monitoring to more complicated services. Embedded systems equipped with powerful chips can provide the performance that computationally demanding information processing applications need. However, due to the power issue, the easy way to gain increasing performance by scaling up chip frequencies is no longer feasible. Recently, low-power architecture designs have been the main trend in embedded system designs. In this dissertation, we present our approaches to attack the energy-related issues in embedded system designs, such as thermal issues in the 3D chip multiprocessor (CMP), the endurance issue in the phase-change memory(PCM), the battery issue in the embedded system designs, the impact of inaccurate information in embedded system, and the cloud computing to move the workload to remote cloud computing facilities. We propose a real-time constrained task scheduling method to reduce peak temperature on a 3D CMP, including an online 3D CMP temperature prediction model and a set of algorithm for scheduling tasks to different cores in order to minimize the peak temperature on chip. To address the challenging issues in applying PCM in embedded systems, we propose a PCM main memory optimization mechanism through the utilization of the scratch pad memory (SPM). Furthermore, we propose an MLC/SLC configuration optimization algorithm to enhance the efficiency of the hybrid DRAM + PCM memory. We also propose an energy-aware task scheduling algorithm for parallel computing in mobile systems powered by batteries. When scheduling tasks in embedded systems, we make the scheduling decisions based on information, such as estimated execution time of tasks. Therefore, we design an evaluation method for impacts of inaccurate information on the resource allocation in embedded systems. Finally, in order to move workload from embedded systems to remote cloud computing facility, we present a resource optimization mechanism in heterogeneous federated multi-cloud systems. And we also propose two online dynamic algorithms for resource allocation and task scheduling. We consider the resource contention in the task scheduling

Battery Aware Dynamic Scheduling for Periodic Task Graphs

Battery lifetime is a primary design constraint for mobile embedded systems. It has been shown to depend heavily on the load current profile (i.e. evolution of the current drawn over time). However, up to now, very few low-power scheduling policies take this fact into account. We explore how scheduling guidelines drawn from battery models can help in the extension of battery capacity. We proposed a 'Battery-Aware Scheduling' methodology for periodically arriving task-graphs (Directed Acyclic Graph) with real time deadlines and precedence constraints. The methodology presented divides the problem into two steps. First, a good DVS algorithms dynamically determines the minimum frequency of execution. Then, a greedy algorithm allows a near optimal priority function to choose the task which would maximize slack recovery. Battery simulations carried out on the profile generated by our approach for a large set of task-graphs show that battery life time is extended up to 23.3% compared to existing dynamic scheduling schemes

Energy-Aware Scheduling for Streaming Applications

Streaming applications have become increasingly important and widespread,with application domains ranging from embedded devices to server systems.Traditionally, researchers have been focusing on improving the performanceof streaming applications to achieve high throughput and low response time.However, increasingly more attention is being shifted topower/performance trade-offbecause power consumption has become a limiting factor on system designas integrated circuits enter the realm of nanometer technology.This work addresses the problem of scheduling a streaming application(represented by a task graph)with the goal of minimizing its energy consumptionwhile satisfying its two quality of service (QoS) requirements,namely, throughput and response time.The available power management mechanisms are dynamic voltage scaling (DVS),which has been shown to be effective in reducing dynamic power consumption, andvary-on/vary-off, which turns processors on and off to save static power consumption.Scheduling algorithms are proposed for different computing platforms (uniprocessor and multiprocessor systems),different characteristics of workload (deterministic and stochastic workload),and different types of task graphs (singleton and general task graphs).Both continuous and discrete processor power models are considered.The highlights are a unified approach for obtaining optimal (or provably close to optimal)uniprocessor DVS schemes for various DVS strategies anda novel multiprocessor scheduling algorithm that exploits the differencebetween the two QoS requirements to perform processor allocation,task mapping, and task speedscheduling simultaneously

Energy Awareness and Scheduling in Mobile Devices and High End Computing

In the context of the big picture as energy demands rise due to growing economies and growing populations, there will be greater emphasis on sustainable supply, conservation, and efficient usage of this vital resource. Even at a smaller level, the need for minimizing energy consumption continues to be compelling in embedded, mobile, and server systems such as handheld devices, robots, spaceships, laptops, cluster servers, sensors, etc. This is due to the direct impact of constrained energy sources such as battery size and weight, as well as cooling expenses in cluster-based systems to reduce heat dissipation. Energy management therefore plays a paramount role in not only hardware design but also in user-application, middleware and operating system design. At a higher level Datacenters are sprouting everywhere due to the exponential growth of Big Data in every aspect of human life, the buzz word these days is Cloud computing. This dissertation, focuses on techniques, specifically algorithmic ones to scale down energy needs whenever the system performance can be relaxed. We examine the significance and relevance of this research and develop a methodology to study this phenomenon. Specifically, the research will study energy-aware resource reservations algorithms to satisfy both performance needs and energy constraints. Many energy management schemes focus on a single resource that is dedicated to real-time or nonreal-time processing. Unfortunately, in many practical systems the combination of hard and soft real-time periodic tasks, a-periodic real-time tasks, interactive tasks and batch tasks must be supported. Each task may also require access to multiple resources. Therefore, this research will tackle the NP-hard problem of providing timely and simultaneous access to multiple resources by the use of practical abstractions and near optimal heuristics aided by cooperative scheduling. We provide an elegant EAS model which works across the spectrum which uses a run-profile based approach to scheduling. We apply this model to significant applications such as BLAT and Assembly of gene sequences in the Bioinformatics domain. We also provide a simulation for extending this model to cloud computing to answers “what if” scenario questions for consumers and operators of cloud resources to help answers questions of deadlines, single v/s distributed cluster use and impact analysis of energy-index and availability against revenue and ROI

Providing QoS with Reduced Energy Consumption via Real-Time Voltage Scaling on Embedded Systems

Low energy consumption has emerged as one of the most important design objectives for many modern embedded systems, particularly the battery-operated PDAs. For some soft real-time applications such as multimedia applications, occasional deadline misses can be tolerated. How to leverage this feature to save more energy while still meeting the user required quality of service (QoS) is the research topic this thesis focuses on. We have proposed a new probabilistic design methodology, a set of energy reduction techniques for single and multiple processor systems by using dynamic voltage scaling (DVS), the practical solutions to voltage set-up problem for multiple voltage DVS system, and a new QoS metric. Most present design space exploration techniques, which are based on application's worst case execution time, often lead to over-designing systems. We have proposed the probabilistic design methodology for soft real-time embedded systems by using detailed execution time information in order to reduce the system resources while delivering the user required QoS probabilistically. One important phase in the probabilistic design methodology is the offline/online resource management. As an example, we have proposed a set of energy reduction techniques by employing DVS techniques to exploit the slacks arising from the tolerance to deadline misses for single and multiple processor systems while meeting the user required completion ratio statistically. Multiple-voltage DVS system is predicted as the future low-power system by International Technology Roadmap for Semiconductors (ITRS). In order to find the best way to employ DVS, we have formulated the voltage set-up problem and provided its practical solutions that seek the most energy efficient voltage setting for the design of multiple-voltage DVS systems. We have also presented a case study in designing energy-efficient dual voltage soft real-time system with (m, k)-firm deadline guarantee. Although completion ratio is widely used as a QoS metric, it can only be applied to the applications with independent tasks. We have proposed a new QoS metric that differentiates firm and soft deadlines and considers the task dependency as well. Based on this new metric, we have developed a set of online scheduling algorithms that enhance quality of presentation (QoP) significantly, particularly for overloaded systems

The Interplay of Reward and Energy in Real-Time Systems

This work contends that three constraints need to be addressed in the context of power-aware real-time systems: energy, time and task rewards/values. These issues are studied for two types of systems. First, embedded systems running applications that will include temporal requirements (e.g., audio and video). Second, servers and server clusters that have timing constraints and Quality of Service (QoS) requirements implied by the application being executed (e.g., signal processing, audio/video streams, webpages). Furthermore, many future real-time systems will rely on different software versions to achieve a variety of QoS-aware tradeoffs, each with different rewards, time and energy requirements.For hard real-time systems, solutions are proposed that maximize the system reward/profit without exceeding the deadlines and without depleting the energy budget (in portable systems the energy budget is determined by the battery charge, while in server farms it is dependent on the server architecture and heat/cooling constraints). Both continuous and discrete reward and power models are studied, and the reward/energy analysis is extended with multiple task versions, optional/mandatory tasks and long-term reward maximization policies.For soft real-time systems, the reward model is relaxed into a QoS constraint, and stochastic schemes are first presented for power management of systems with unpredictable workloads. Then, load distribution and power management policies are addressed in the context of servers and homogeneous server farms. Finally, the work is extended with QoS-aware local and global policies for the general case of heterogeneous systems

Energy Efficient Scheduling for Real-Time Systems

The goal of this dissertation is to extend the state of the art in real-time scheduling algorithms to achieve energy efficiency. Currently, Pfair scheduling is one of the few scheduling frameworks which can optimally schedule a periodic real-time taskset on a multiprocessor platform. Despite the theoretical optimality, there exist large concerns about efficiency and applicability of Pfair scheduling in practical situations. This dissertation studies and proposes solutions to such efficiency and applicability concerns. This dissertation also explores temperature aware energy management in the domain of real-time scheduling. The thesis of this dissertation is: the implementation efficiency of Pfair scheduling algorithms can be improved. Further, temperature awareness of a real-time system can be improved while considering variation of task execution times to reduce energy consumption. This thesis is established through research in a number of directions. First, we explore the applicability of Dynamic Voltage and Frequency Scaling (DVFS) feature in the underlying platform, within Pfair scheduled systems. We propose techniques to reduce energy consumption in Pfair scheduling by using DVFS. Next, we explore the problem of quantum size selection in Pfair scheduled system so that runtime overheads are minimized. We also propose a hardware design for a central Pfair scheduler core in a multiprocessor system to minimized the overheads and energy consumption of Pfair scheduling. Finally, we propose a temperature aware energy management scheme for tasks with varying execution times

Block level voltage

Over the past years, state-of-art power optimization methods move towards higher abstraction levels that result in more efficient power savings. Among existing power optimization approaches, dynamic power management (DPM) is considered to be one of the most effective strategies. Depending on abstraction levels, DPM can be implemented in different formats but here we focus on scheduling that is more suitable for real-time system design use. This differs from the concurrent scheduling approaches that start from either the HLS (High-Level Synthesis) or RTS (Real-Time System) point of view, we propose a synergy solution of both approaches, namely block-level voltage/frequency scheduling (BLVFS). The presented block-level voltage/ frequency scheduling approach shows a generic solution for low power SoC (System on Chip) system design while the approaches which belong to the HLS and RTS categories have a strong dependency on the system functionalities. Consider a SoC as a combination of heterogeneous functional blocks, our approach provides efficient power savings by dynamically scheduling the scaling of voltage and frequency at the same time. Simulation results indicate that by using heuristic based strategies significant power savings can be achieved

Developing an energy efficient real-time system

Increasing number of battery operated devices creates a need for energy-efficient real-time operating system for such devices. Designing a truly energy-efficient system is a multi-staged effort; this thesis consists of three main tasks that address different aspects of energy efficiency of a real-time system (RTS). The first chapter introduces an energy-efficient algorithm that alternates processor frequency using DVFS to schedule tasks on cores. Speed profiles is calculated for every task that gives information about how long a task would run for and at what processor speed. We pair tasks with similar speed profiles to give us a resultant merged speed profile that can be efficient scheduled on a cluster. Experiments carried out on ODROID-XU3 are compared with a reference approach that provides energy saving of up to 20%. The second chapter proposes power-aware techniques to segregate a task set over a heterogeneous platform such that the overall energy consumption is minimized. With the help of calculated speed profiles, second contribution of this work feasibly partitions a given task set into individual sets for a cluster based homogeneous platform. Various heuristics are proposed that are compared against a baseline approach with simulation results. The final chapter of this thesis focuses on the importance of having an underlying energy-efficient operating system. We discuss an energy-efficient way of porting a real-time operating system (RTOS), QP, over TMS320F28377S along with modifications to make the Operating System (OS) consume minimal energy for its operation --Abstract, page iii

Modelling energy efficiency for computation

In the last decade, efficient use of energy has become a topic of global significance, touching almost every area of modern life, including computing. From mobile to desktop to server, energy efficiency concerns are now ubiquitous. However, approaches to the energy problem are often piecemeal and focus on only one area for improvement. I argue that the strands of the energy problem are inextricably entangled and cannot be solved in isolation. I offer a high-level view of the problem and, building from it, explore a selection of subproblems within the field. I approach these with various levels of formality, and demonstrate techniques to make improvements on all levels.Clare College Domestic Research Scholarshi