3E: Energy-Efficient Elastic Scheduling for Independent Tasks in Heterogeneous Computing Systems

Reducing energy consumption is a major design constraint for modern heterogeneous computing systems to minimize electricity cost, improve system reliability and protect environment. Conventional energy-efficient scheduling strategies developed on these systems do not sufficiently exploit the system elasticity and adaptability for maximum energy savings, and do not simultaneously take account of user expected finish time. In this paper, we develop a novel scheduling strategy named energy-efficient elastic (3E) scheduling for aperiodic, independent and non-real-time tasks with user expected finish times on DVFS-enabled heterogeneous computing systems. The 3E strategy adjusts processors’ supply voltages and frequencies according to the system workload, and makes trade-offs between energy consumption and user expected finish times. Compared with other energy-efficient strategies, 3E significantly improves the scheduling quality and effectively enhances the system elasticity

A Survey of Techniques For Improving Energy Efficiency in Embedded Computing Systems

Recent technological advances have greatly improved the performance and features of embedded systems. With the number of just mobile devices now reaching nearly equal to the population of earth, embedded systems have truly become ubiquitous. These trends, however, have also made the task of managing their power consumption extremely challenging. In recent years, several techniques have been proposed to address this issue. In this paper, we survey the techniques for managing power consumption of embedded systems. We discuss the need of power management and provide a classification of the techniques on several important parameters to highlight their similarities and differences. This paper is intended to help the researchers and application-developers in gaining insights into the working of power management techniques and designing even more efficient high-performance embedded systems of tomorrow

Joint Computation Offloading and Prioritized Scheduling in Mobile Edge Computing

With the rapid development of smart phones, enormous amounts of data are generated and usually require intensive and real-time computation. Nevertheless, quality of service (QoS) is hardly to be met due to the tension between resourcelimited (battery, CPU power) devices and computation-intensive applications. Mobileedge computing (MEC) emerging as a promising technique can be used to copy with stringent requirements from mobile applications. By offloading computationally intensive workloads to edge server and applying efficient task scheduling, energy cost of mobiles could be significantly reduced and therefore greatly improve QoS, e.g., latency. This paper proposes a joint computation offloading and prioritized task scheduling scheme in a multi-user mobile-edge computing system. We investigate an energy minimizing task offloading strategy in mobile devices and develop an effective priority-based task scheduling algorithm with edge server. The execution time, energy consumption, execution cost, and bonus score against both the task data sizes and latency requirement is adopted as the performance metric. Performance evaluation results show that, the proposed algorithm significantly reduce task completion time, edge server VM usage cost, and improve QoS in terms of bonus score. Moreover, dynamic prioritized task scheduling is also discussed herein, results show dynamic thresholds setting realizes the optimal task scheduling. We believe that this work is significant to the emerging mobile-edge computing paradigm, and can be applied to other Internet of Things (IoT)-Edge applications

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_

POSTER: Exploiting asymmetric multi-core processors with flexible system sofware

Energy efficiency has become the main challenge for high performance computing (HPC). The use of mobile asymmetric multi-core architectures to build future multi-core systems is an approach towards energy savings while keeping high performance. However, it is not known yet whether such systems are ready to handle parallel applications. This paper fills this gap by evaluating emerging parallel applications on an asymmetric multi-core. We make use of the PARSEC benchmark suite and a processor that implements the ARM big.LITTLE architecture. We conclude that these applications are not mature enough to run on such systems, as they suffer from load imbalance. Furthermore, we explore the behaviour of dynamic scheduling solutions on either the Operating System (OS) or the runtime level. Comparing these approaches shows us that the most efficient scheduling takes place in the runtime level, influencing the future research towards such solutions.This work has been supported by the Spanish Government (SEV2015-0493), by the Spanish Ministry of Science and Innovation (contracts TIN2015-65316-P), by Generalitat de Catalunya (contracts 2014-SGR-1051 and 2014-SGR-1272), by the RoMoL ERC Advanced Grant (GA 321253) and the European HiPEAC Network of Excellence. The Mont-Blanc project receives funding from the EU's Seventh Framework Programme (FP7/2007-2013) under grant agreement number 610402 and from the EU's H2020 Framework Programme (H2020/2014-2020) under grant agreement number 671697. M. Moretó has been partially supported by the Ministry of Economy and Competitiveness under Juan de la Cierva postdoctoral fellowship number JCI-2012-15047. M. Casas is supported by the Secretary for Universities and Research of the Ministry of Economy and Knowledge of the Government of Catalonia and the Cofund programme of the Marie Curie Actions of the 7th R&D Framework Programme of the European Union (Contract 2013 BP B 00243).Peer ReviewedPostprint (author's final draft