Energy saving and optimization play an increasingly important role in industrial electronic systems. A heterogeneous embedded system is composed of a general-purpose central processing unit with an enhanced module of graphics processing units (GPU). This article explores the effective strategies of task granularity and software prefetching for energy optimization. In this article, we propose a novel energy optimization model for GPU-based embedded systems by harnessing a communication-based pipeline spatial and temporal relation. We analyze the characteristics of a multiple thread execution of parallel GPUs. We present an effective algorithm for the dynamic power optimization with the adaptively adjusted distance of software prefetching. The experimental results show that the dynamic energy consumption can be saved by 22.1% and 21.8%, respectively, under two prefetching strategies (register and shared memory) without loss of performance. We demonstrate the effectiveness of the proposed methods for energy saving and consumption reduction of performance driven computing in industrial scenarios.

Energy Optimization by Software Prefetching for Task Granularity in GPU-Based Embedded Systems

As mobile devices of embedded systems prevail in our daily life, energy saving emerges as one very important issue and power management techniques are desirable. This paper presents the design and the implementation of a dynamic power management mechanism (DPMM) for embedded systems running micro-kernel operating systems. The DPMM is composed of policy manager, DVFS (Dynamic Voltage Frequency Scaling) controller, DPM (Dynamic Power Management) server, resource management flags, and DPM library. They are imple-mented to execute on an embedded system platform equipped with an XScale PXA270 processor and various I/O interfaces or devices running Zinix micro-kernel operating system. Testing results indicate that this DPMM effectively achieves power management in the system.

A Dynamic Power Management Mechanism for Embedded System with Micro-Kernel Operating System

To reduce the increasing cost of building and operating cloud data centers, cloud providers are seeking various mechanisms to achieve higher resource effectiveness. For example, cloud operators are leveraging dynamic resource management techniques to consolidate a higher density of application workloads into commodity physical servers to maximize server resource utilization. However, higher workload density is a major source of performance interference problems in multi-tenant clouds. Existing performance isolation techniques such as dedicated CPU cores for specific workloads are not enough as there are still common resource (e.g., last-level cache and memory bandwidth in memory subsystem) on the processor that are shared among all CPUs on the same NUMA node. While prior work has proposed a variety of resource partitioning techniques, it still remains unexplored to characterize the impact of memory subsystem resource partitioning for the consolidated workloads with different priorities and investigate software support to dynamically manage memory subsystem resource sharing in a real-time manner. To bridge the gap, we propose Themis, a feedback-based controller that enables a priority-aware and fairness-aware memory subsystem resource management strategy to guarantee the performance of high-priority workloads while maintaining fairness across all colocated workloads in high-density clouds. Themis is evaluated with multiple typical cloud applications in our data center environment. The results show that Themis improves the performance of various workloads by up to 3.15%, and fairness by more than 70% in memory subsystem resource allocation compared to existing state-of-the-art work.

Themis: Fair Memory Subsystem Resource Sharing with Differentiated QoS in Public Clouds

Energy consumption in cloud data centers has become an increasingly important contributor to greenhouse gas emissions and operation costs. To reduce energy-related costs and improve environmental sustainability, most modern data centers consolidate Virtual Machine (VM) workloads belonging to different application classes, some being latency-critical (LC) and others being more tolerant to performance changes, known as best-effort (BE). However, in public cloud scenarios, the real classes of applications are often opaque to data center operators. The heterogeneous applications from different cloud tenants are usually consolidated onto the same hosts to improve energy efficiency, but it is not trivial to guarantee decent performance isolation among colocated workloads. We tackle the above challenges by introducing Demeter, a QoS-aware power management controller for heterogeneous black-box workloads in public clouds. Demeter is designed to work without offline profiling or prior knowledge about black-box workloads. Through the correlation analysis between network throughput and CPU resource utilization, Demeter automatically classifies black-box workloads as either LC or BE. By provisioning differentiated CPU management strategies (including dynamic core allocation and frequency scaling) to LC and BE workloads, Demeter achieves considerable power savings together with a minimum impact on the performance of all workloads. We discuss the design and implementation of Demeter in this work, and conduct extensive experimental evaluations to reveal its effectiveness. Our results show that Demeter not only meets the performance demand of all workloads, but also responds quickly to dynamic load changes in our cloud environment. In addition, Demeter saves an average of 10.6% power consumption than state of the art mechanisms.

Yu Yung Ke

Papers

A Dynamic Power Management Mechanism for Embedded System with Micro-Kernel Operating System

Themis: Fair Memory Subsystem Resource Sharing with Differentiated QoS in Public Clouds

Demeter: QoS-aware CPU scheduling to reduce power consumption of multiple black-box workloads

Demeter