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Energy management for energy harvesting-based embedded systems (EHES) is an emerging field, which aims to collect renewable energy from the environment to power an embedded system. In this work, we use the systematic mapping method to study the relevant literature, with the objective of exploring and analysing the state of the art in energy management for EHES, as well as to provide assistance for subsequent literature reviews. To this end, we conducted extensive searches to find articles related to energy harvesting, embedded systems, energy consumption, and energy management. We searched for papers from January 2005 to July 2019 from three mainstream databases, ACM, IEEE Xplore, and Web of Science, and found more than 3000 papers about EHES. Finally, we selected 142 eligible papers. We have completed the system mapping research from five aspects, namely, (1) research type (validation research, evaluation research, solution proposal, philosophical paper, opinion, and experience), (2) research goals (application or theory), (3) application scenarios, (4) tools or methods, and (5) paper distribution, such as publication year and authors’ nationality. The results showed that the major research type of the EHES papers is validation research, accounting for 65%, which indicated research is still in the theoretical stage and many researchers focus on how to improve the efficiency of harvesting energy, develop a reasonable energy supply plan, and adapt EHES for real-world requirements. Furthermore, this work reviews the tools used for EHES. As the future development direction, it is indispensable to provide tools to EHES for research, testing, development, and so on. The results of our analysis provide significant contributions to understanding the existing knowledge and highlighting potential future research opportunities in the EHES field.

In order to achieve energy saving and reduce the total cost of ownership, green storage has become the first priority for data center. Detecting and deleting the redundant data are the key factors to the reduction of the energy consumption of CPU, while high performance stable chunking strategy provides the groundwork for detecting redundant data. The existing chunking algorithm greatly reduces the system performance when confronted with big data and it wastes a lot of energy. Factors affecting the chunking performance are analyzed and discussed in the paper and a new fingerprint signature calculation is implemented. Furthermore, a Bit String Content Aware Chunking Strategy (BCCS) is put forward. This strategy reduces the cost of signature computation in chunking process to improve the system performance and cuts down the energy consumption of the cloud storage data center. On the basis of relevant test scenarios and test data of this paper, the advantages of the chunking strategy are verified.

Energy consumption is a fundamental barrier in taking full advantage of today and future semiconductor manufacturing technologies. The paper presents our recent research activities and results on characterizing and reducing the energy consumption in embedded systems. Firstly, a technique for characterizing the energy consumption of embedded processors during an application execution is presented. The technique trains a per-processor linear approximation model for fitting it to the energy consumption of the processor obtained by postlayout simulation. Secondly, based on the energy model mentioned above, the paper shows techniques for reducing the energy consumption by optimally mapping program code, stack frames, and data items to the scratch-pad memory (SPM) of the processor memory space.

Current data centers networks rely on electronic switching and point-to-point interconnects. When considering future data center requirements, these solutions will raise issues in terms of flexibility, scalability, performance, and energy consumption. For this reason several optical switched interconnects, which make use of optical switches and wavelength division multiplexing (WDM), have been recently proposed. However, the solutions proposed so far suffer from low flexibility and are not able to provide service differentiation. In this paper we introduce a novel data center network based on hybrid optical switching (HOS). HOS combines optical circuit, burst, and packet switching on the same network. In this way different data center applications can be mapped to the optical transport mechanism that best suits their traffic characteristics. Furthermore, the proposed HOS network achieves high transmission efficiency and reduced energy consumption by using two parallel optical switches. We consider the architectures of both a traditional data center network and the proposed HOS network and present a combined analytical and simulation approach for their performance and energy consumption evaluation. We demonstrate that the proposed HOS data center network achieves high performance and flexibility while considerably reducing the energy consumption of current solutions.

As cloud data center consumes more and more energy, both researchers and engineers aim to minimize energy consumption while keeping its services available. A good energy model can reflect the relationships between running tasks and the energy consumed by hardware and can be further used to schedule tasks for saving energy. In this paper, we analyzed linear and nonlinear regression energy model based on performance counters and system utilization and proposed a support vector regression energy model. For performance counters, we gave a general linear regression framework and compared three linear regression models. For system utilization, we compared our support vector regression model with linear regression and three nonlinear regression models. The experiments show that linear regression model is good enough to model performance counters, nonlinear regression is better than linear regression model for modeling system utilization, and support vector regression model is better than polynomial and exponential regression models.