Machine speed scaling by adapting methods for convex optimization with submodular constraints

In this paper, we propose a new methodology for the speed-scaling problem based on its link to scheduling with controllable processing times and submodular optimization. It results in faster algorithms for traditional speed-scaling models, characterized by a common speed/energy function. Additionally, it efficiently handles the most general models with job-dependent speed/energy functions with single and multiple machines. To the best of our knowledge, this has not been addressed prior to this study. In particular, the general version of the single-machine case is solvable by the new technique in O(n2) time

Dynamic Power Management for Reactive Stream Processing on the SCC Tiled Architecture

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.Dynamic voltage and frequency scaling} (DVFS) is a means to adjust the computing capacity and power consumption of computing systems to the application demands. DVFS is generally useful to provide a compromise between computing demands and power consumption, especially in the areas of resource-constrained computing systems. Many modern processors support some form of DVFS. In this article we focus on the development of an execution framework that provides light-weight DVFS support for reactive stream-processing systems (RSPS). RSPS are a common form of embedded control systems, operating in direct response to inputs from their environment. At the execution framework we focus on support for many-core scheduling for parallel execution of concurrent programs. We provide a DVFS strategy for RSPS that is simple and lightweight, to be used for dynamic adaptation of the power consumption at runtime. The simplicity of the DVFS strategy became possible by sole focus on the application domain of RSPS. The presented DVFS strategy does not require specific assumptions about the message arrival rate or the underlying scheduling method. While DVFS is a very active field, in contrast to most existing research, our approach works also for platforms like many-core processors, where the power settings typically cannot be controlled individually for each computational unit. We also support dynamic scheduling with variable workload. While many research results are provided with simulators, in our approach we present a parallel execution framework with experiments conducted on real hardware, using the SCC many-core processor. The results of our experimental evaluation confirm that our simple DVFS strategy provides potential for significant energy saving on RSPS.Peer reviewe

Energy Saving Exploiting the Limited Preemption Task Mode

Limited preemptive scheduling has been shown to dominate both non-preemptive and fully preemptive scheduling under fixed priority systems, as far as schedulability is concerned. This paper suggests the use of DVS and DMP techniques under limited preemptive scheduling to further reduce energy consumption with respect to a fully preemptive or non-preemptive approach

Energy Management for Tiny Real-Time Kernels

In battery operated embedded systems, an efficient energy management is a key feature for increasing the system lifetime, as well as for controlling the application performance. In this paper, we present a power management module designed for tiny embedded operating systems and implemented in the Erika Enterprise, an OSEK-compliant kernel. The obtained results show both the effectiveness of the presented component and the impact of operating mode changes on global performance

On the Energy-Aware Partitioning of Real-Time Tasks on Homogeneous Multi-Processor Systems

In high-performance computing systems, efficient energy management is a key feature for keeping energy bills low and avoiding thermal dissipation problems, as well as for controlling the application performance. This paper considers the problem of partitioning and scheduling a set of real-time tasks on a realistic hardware platform consisting of a number of homogeneous processors. Several well-known heuristics are compared to identify the approach that better reduces the overall energy consumption of the entire system. Despite the actual state Another well-known approach is the Dynamic Power Man- of art, the approach which minimizes the number of active cores is the most energy efficient

Energy-aware algorithms for tasks and bandwidth co-allocation under real-time and redundancy constraints

The energy consumption in distributed systems depends on several inter-related factors, including task partitioning, process redundancy, fault tolerance, task and message scheduling, and communication bandwidth allocation. Although some of these issues have been considered in the literature in isolation, a systematic approach considering all the constraints is still missing. This paper addresses the problem of allocating a task set and the required communication bandwidth on a distributed embedded system, aiming at reducing energy consumption while guaranteeing timing and redundancy constraints. Two heuristic approaches are proposed and compared against a complete method and simulated annealing. Simulation results show the effectiveness of the proposed approaches