Slow down or race to halt: towards managing complexity of real-time energy management decisions

Existing work in the context of energy management for real-time systems often ignores the substantial cost of making DVFS and sleep state decisions in terms of time and energy and/or assume very simple models. Within this paper we attempt to explore the parameter space for such decisions and possible constraints faced

Control-theoretic dynamic voltage scaling for embedded controllers

For microprocessors used in real-time embedded systems, minimizing power consumption is difficult due to the timing constraints. Dynamic voltage scaling (DVS) has been incorporated into modern microprocessors as a promising technique for exploring the trade-off between energy consumption and system performance. However, it remains a challenge to realize the potential of DVS in unpredictable environments where the system workload cannot be accurately known. Addressing system-level power-aware design for DVS-enabled embedded controllers, this paper establishes an analytical model for the DVS system that encompasses multiple real-time control tasks. From this model, a feedback control based approach to power management is developed to reduce dynamic power consumption while achieving good application performance. With this approach, the unpredictability and variability of task execution times can be attacked. Thanks to the use of feedback control theory, predictable performance of the DVS system is achieved, which is favorable to real-time applications. Extensive simulations are conducted to evaluate the performance of the proposed approach.Comment: Accepted for publication in IET Computers and Digital Techniques. doi:10.1049/iet-cdt:2007011

On the integration of application level and resource level QoS control for real-time applications

We consider a dynamic set of soft real-time applications using a set of shared resources. Each application can execute in different modes, each one associated with a level of Quality of Service (QoS). Resources, in their turn, have different modes, each one with a speed and a power consumption, and are managed by a Reservation Based scheduler enabling a dynamic allocation of the fraction of resources (bandwidth) assigned to each application. To cope with dynamic changes of the application, we advocate an adaptive resource allocation policy organised in two nested feedback loops. The internal loop operates on the scheduling parameter to obtain a resource allocation that meets the temporal constraints of the applications. The external loop operates on the QoS level of the applications and on the power level of the resources to strike a good trade-off between the global QoS and the energy consumption. This loop comes into play whenever the workload of the application exceeds the bounds that permit the internal loop to operate correctly, or whenever it decreases below a level that permit more aggressive choices for the QoS or substantial energy saving

A Three Phase Scheduling for System Energy Minimization of Weakly Hard Real Time Systems

This paper aims to present a three phase scheduling algorithm that offers lesser energy consumption for weakly hard real time systems modeled with (1D55E;1D55E;1D55E;1D55E;, 1D55C;1D55C;1D55C;1D55C;) constraint. The weakly hard real time system consists of a DVS processor (frequency dependent) and peripheral devices (frequency independent) components. The energy minimization is done in three phase taking into account the preemption overhead. The first phase partitions the jobs into mandatory and optional while assigning processor speed ensuring the feasibility of the task set. The second phase proposes a greedy based preemption control technique which reduces the energy consumption due to preemption. While the third phase refines the feasible schedule received from the second phase by two methods, namely speed adjustment and delayed start. The proposed speed adjustment assigns optimal speed to each job whereas fragmented idle slots are accumulated to provide better opportunity to switch the component into sleep state by delayed start strategy as a result leads to energy saving. The simulation results and examples illustrate that our approach can effectively reduce the overall system energy consumption (especially for systems with higher utilizations) while guaranteeing the (1D55E;1D55E;1D55E;1D55E;, 1D55C;1D55C;1D55C;1D55C;) at the same time

On the integration of application level and resource level QoS control for realtime applications

Abstract We consider a dynamic set of soft real-time applications using a set of shared resources. Each application can execute in different modes, each one associated with a level of Quality of Service (QoS). Resources, in their turn, have different modes, each one with a speed and a power consumption, and are managed by a Reservation Based scheduler enabling a dynamic allocation of the fraction of resources (bandwidth) assigned to each application. To cope with dynamic changes of the application, we advocate an adaptive resource allocation policy organised in two nested feedback loops. The internal loop operates on the scheduling parameter to obtain a resource allocation that meets the temporal constraints of the applications. The external loop operates on the QoS level of the applications and on the power level of the resources to strike a good trade-off between the global QoS and the energy consumption. This loop comes into play whenever the workload of the application exceeds the bounds that permit the internal loop to operate correctly, or whenever it decreases below a level that permit more aggressive choices for the QoS or substantial energy saving

Power-Aware Resilience for Exascale Computing

To enable future scientific breakthroughs and discoveries, the next generation of scientific applications will require exascale computing performance to support the execution of predictive models and analysis of massive quantities of data, with significantly higher resolution and fidelity than what is possible within existing computing infrastructure. Delivering exascale performance will require massive parallelism, which could result in a computing system with over a million sockets, each supporting many cores. Resulting in a system with millions of components, including memory modules, communication networks, and storage devices. This increase in number of components significantly increases the propensity of exascale computing systems to faults, while driving power consumption and operating costs to unforeseen heights. To achieve exascale performance two challenges must be addressed: resilience to failures and adherence to power budget constraints. These two objectives conflict insofar as performance is concerned, as achieving high performance may push system components past their thermal limit and increase the likelihood of failure. With current systems, the dominant resilience technique is checkpoint/restart. It is believed, however, that this technique alone will not scale to the level necessary to support future systems. Therefore, alternative methods have been suggested to augment checkpoint/restart -- for example process replication. In this thesis, we present a new fault tolerance model called shadow replication that addresses resilience and power simultaneously. Shadow replication associates a shadow process with each main process, similar to traditional replication, however, the shadow process executes at a reduced speed. Shadow replication reduces energy consumption and produces solutions faster than checkpoint/restart and other replication methods in limited power environments. Shadow replication reduces energy consumption up to 25 depending upon the application type, system parameters, and failure rates. The major contribution of this thesis is the development of shadow replication, a power-aware fault tolerant computational model. The second contribution is an execution model applying shadow replication to future high performance exascale-class systems. Next, is a framework to analyze and simulate the power and energy consumption of fault tolerance methods in high performance computing systems. Lastly, to prove the viability of shadow replication an implementation is presented for the Message Passing Interface

Software Approaches to Manage Resource Tradeoffs of Power and Energy Constrained Applications

Power and energy efficiency have become an increasingly important design metric for a wide spectrum of computing devices. Battery efficiency, which requires a mixture of energy and power efficiency, is exceedingly important especially since there have been no groundbreaking advances in battery capacity recently. The need for energy and power efficiency stretches from small embedded devices to portable computers to large scale data centers. The projected future of computing demand, referred to as exascale computing, demands that researchers find ways to perform exaFLOPs of computation at a power bound much lower than would be required by simply scaling today's standards. There is a large body of work on power and energy efficiency for a wide range of applications and at different levels of abstraction. However, there is a lack of work studying the nuances of different tradeoffs that arise when operating under a power/energy budget. Moreover, there is no work on constructing a generalized model of applications running under power/energy constraints, which allows the designer to optimize their resource consumption, be it power, energy, time, bandwidth, or space. There is need for an efficient model that can provide bounds on the optimality of an application's resource consumption, becoming a basis against which online resource management heuristics can be measured. In this thesis, we tackle the problem of managing resource tradeoffs of power/energy constrained applications. We begin by studying the nuances of power/energy tradeoffs with the response time and throughput of stream processing applications. We then study the power performance tradeoff of batch processing applications to identify a power configuration that maximizes performance under a power bound. Next, we study the tradeoff of power/energy with network bandwidth and precision. Finally, we study how to combine tradeoffs into a generalized model of applications running under resource constraints. The work in this thesis presents detailed studies of the power/energy tradeoff with response time, throughput, performance, network bandwidth, and precision of stream and batch processing applications. To that end, we present an adaptive algorithm that manages stream processing tradeoffs of response time and throughput at the CPU level. At the task-level, we present an online heuristic that adaptively distributes bounded power in a cluster to improve performance, as well as an offline approach to optimally bound performance. We demonstrate how power can be used to reduce bandwidth bottlenecks and extend our offline approach to model bandwidth tradeoffs. Moreover, we present a tool that identifies parts of a program that can be downgraded in precision with minimal impact on accuracy, and maximal impact on energy consumption. Finally, we combine all the above tradeoffs into a flexible model that is efficient to solve and allows for bounding and/or optimizing the consumption of different resources