Energy-Efficient Scheduling for Homogeneous Multiprocessor Systems

We present a number of novel algorithms, based on mathematical optimization formulations, in order to solve a homogeneous multiprocessor scheduling problem, while minimizing the total energy consumption. In particular, for a system with a discrete speed set, we propose solving a tractable linear program. Our formulations are based on a fluid model and a global scheduling scheme, i.e. tasks are allowed to migrate between processors. The new methods are compared with three global energy/feasibility optimal workload allocation formulations. Simulation results illustrate that our methods achieve both feasibility and energy optimality and outperform existing methods for constrained deadline tasksets. Specifically, the results provided by our algorithm can achieve up to an 80% saving compared to an algorithm without a frequency scaling scheme and up to 70% saving compared to a constant frequency scaling scheme for some simulated tasksets. Another benefit is that our algorithms can solve the scheduling problem in one step instead of using a recursive scheme. Moreover, our formulations can solve a more general class of scheduling problems, i.e. any periodic real-time taskset with arbitrary deadline. Lastly, our algorithms can be applied to both online and offline scheduling schemes.Comment: Corrected typos: definition of J_i in Section 2.1; (3b)-(3c); definition of \Phi_A and \Phi_D in paragraph after (6b). Previous equations were correct only for special case of p_i=d_

Simulation of Efficient Real-Time Scheduling and Power Optimisation

International audienceSophisticated applications turn out to be executed upon more than one CPU for practical and economic reasons. Due to advances in circuit technology and performance limitation, multi-core technology has become the mainstream in CPU designs. However, the most serious limitation of these devices is the battery lifetime since battery technology is not keeping up with the rest of the power-hungry processors and peripherals used in today's mobile devices. As a solution, many investigations have turned toward the algorithms of power management combined with some scheduling policies. They can make significant energy saving while preserving the temporal constraints of these embedded systems. Reducing energy, especially, affect not only the battery lifetime, but also aim to reduce the heat generated by real-time embedded controller in various products or even to decrease the conditions of cooling and the costs, in the large scale, of giant multiprocessor computers. To assess the behavior and performance of the strategy of scheduling a flexible multiprocessor scheduling simulation and evaluation platform is needed. This paper puts forth the claim that the STORM simulator improves application quality both in terms of execution time and energy consumption for a high performance mobile computing embedded system design

The Impact of Parallel Processing on Operating Systems

The base entity in computer programming is the process or task. The parallelism can be achieved by executing multiple processes on different processors. Distributed systems are managed by distributed operating systems that represent the extension for multiprocessor architectures of multitasking and multiprogramming operating systems.

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