An Energy Optimization Technique for Latency and Quality Constrained Video Applications

이 논문은 지연 시간 및 화질 제약이 있는 비디오 응용을 위한 에너지 최적 화 기법을 제안한다.이는 두 가지 핵심 기법-프레임 생략 기법 및 버퍼링 기법 - 으로 구성되어있다.버퍼링은 운영 체제 수준에서 유휴 시간 이용률을 증가시키고, 프레임 생략은 응용 수준에서 유휴 시간 자체를 증가시키며, 양쪽 모두 동적 전압 조 절 기법의 효과를 향상시킨다.이 논문에서는 제안한 기법을 적용하기 위해H.263 부호기 응용을 사용한다. 실험에서는 제안한 기법이 주어진 지연 시간 및 화질 제약 을 만족하면서 괄목할만한 에너지 절감을 얻을 수 있음을 보인다

DyPS: Dynamic Processor Switching for Energy-Aware Video Decoding on Multi-core SoCs

In addition to General Purpose Processors (GPP), Multicore SoCs equipping modern mobile devices contain specialized Digital Signal Processor designed with the aim to provide better performance and low energy consumption properties. However, the experimental measurements we have achieved revealed that system overhead, in case of DSP video decoding, causes drastic performances drop and energy efficiency as compared to the GPP decoding. This paper describes DyPS, a new approach for energy-aware processor switching (GPP or DSP) according to the video quality . We show the pertinence of our solution in the context of adaptive video decoding and describe an implementation on an embedded Linux operating system with the help of the GStreamer framework. A simple case study showed that DyPS achieves 30% energy saving while sustaining the decoding performanc

A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing

Edge computing is promoted to meet increasing performance needs of data-driven services using computational and storage resources close to the end devices, at the edge of the current network. To achieve higher performance in this new paradigm one has to consider how to combine the efficiency of resource usage at all three layers of architecture: end devices, edge devices, and the cloud. While cloud capacity is elastically extendable, end devices and edge devices are to various degrees resource-constrained. Hence, an efficient resource management is essential to make edge computing a reality. In this work, we first present terminology and architectures to characterize current works within the field of edge computing. Then, we review a wide range of recent articles and categorize relevant aspects in terms of 4 perspectives: resource type, resource management objective, resource location, and resource use. This taxonomy and the ensuing analysis is used to identify some gaps in the existing research. Among several research gaps, we found that research is less prevalent on data, storage, and energy as a resource, and less extensive towards the estimation, discovery and sharing objectives. As for resource types, the most well-studied resources are computation and communication resources. Our analysis shows that resource management at the edge requires a deeper understanding of how methods applied at different levels and geared towards different resource types interact. Specifically, the impact of mobility and collaboration schemes requiring incentives are expected to be different in edge architectures compared to the classic cloud solutions. Finally, we find that fewer works are dedicated to the study of non-functional properties or to quantifying the footprint of resource management techniques, including edge-specific means of migrating data and services.Comment: Accepted in the Special Issue Mobile Edge Computing of the Wireless Communications and Mobile Computing journa