Due to the increase of data volumes expected for the LHC Run 3 and Run 4, the ALICE Collaboration designed and deployed a new, energy efficient, computing model to run online and Offline O 2 data processing within a s...
详细信息
Due to the increase of data volumes expected for the LHC Run 3 and Run 4, the ALICE Collaboration designed and deployed a new, energy efficient, computing model to run online and Offline O 2 data processing within a single software framework. The ALICE O 2 Event Processing Nodes (EPN) project performs onlinedata reconstruction using GPUs (Graphic Processing Units) instead of CPUs and applies an efficient, entropy-based, online data compression to cope with Pb-Pb collision data at a 50 kHz hadronic interaction rate. Also, the O 2 EPN farm infrastructure features an energy efficient, environmentally friendly, adiabatic cooling system which allows for operational and capital cost savings.
As a typical energy-cyber-physical system (e-CPS), home energy management system (HEMS) plays a critical role in power systems by accommodating higher levels of renewable generation, reducing power costs, and decreasi...
详细信息
As a typical energy-cyber-physical system (e-CPS), home energy management system (HEMS) plays a critical role in power systems by accommodating higher levels of renewable generation, reducing power costs, and decreasing consumer energy bills. HEMS can help understand the home appliances energy use and learn the users' preference so as to optimize home appliances operation and achieve higher energy efficiency. HEMS needs massive historical and real-time data for the above applications. Since HEMS is always based on a wireless sensor network, a more effective online data compression approach is necessary. The efficient datacompression methods can not only relieve data transmission pressure and reduce data storage overhead, but also enhance data analysis efficiency. This paper proposes an online pattern-based datacompression approach for the data generated by home appliances. The proposed approach first discovers the patterns of the time series data and then utilizes these patterns for the online data compression. The pattern discovery method in the proposed approach includes an online adaptive segmenting algorithm with incremental processing technique and a similarity metric based on piecewise statistic distance. The key issues of parameter selection and data reconstruction are also presented. Real-world common home appliance datasets are employed for comparing the performance of the proposed approach with those of six state-of-the-art algorithms. The experimental results demonstrate the outperformance of the proposed approach. Further complexity analysis shows that the proposed approach has linear time complexity. To the best of our knowledge, this is the first paper that performs online data compression based on the extracted patterns of the time series.
The Internet of Things (IoT) has reduced the distance between one point and another and between people by connecting multiple devices to the web. However, the volume and speed of data creation and transmission have al...
详细信息
ISBN:
(纸本)9781665410939
The Internet of Things (IoT) has reduced the distance between one point and another and between people by connecting multiple devices to the web. However, the volume and speed of data creation and transmission have also increased. In this scenario, some challenges start to emerge, such as potentially irrelevant or redundant data transmission, that is, generating a more significant expenditure of energy and processing, in addition to the unnecessary use of the communication channel. Thus, to mitigate these, a solution for IoT devices would be datacompression techniques. However, such devices available in the market today have severe limitations in terms of storage and processing power. Therefore, to overcome these limitations, the TinyML can be used to seek ways to implement machine learning models in low-power devices. In this context, this article aims to evaluate the impact of the compression algorithm (Tiny Anomaly Compress - TAC) on the performance of a microcontroller applied to the context of vehicles in a real scenario. As a result, it was found that even with the embedded algorithm, the microcontroller processing time is not affected in a meaningful way.
Currently, the applications of the Internet of Things (IoT) generate a large amount of sensor data at a very high pace, making it a challenge to collect and store the data. This scenario brings about the need for effe...
详细信息
Currently, the applications of the Internet of Things (IoT) generate a large amount of sensor data at a very high pace, making it a challenge to collect and store the data. This scenario brings about the need for effective datacompression algorithms to make the data manageable among tiny and battery-powered devices and, more importantly, shareable across the network. Additionally, considering that, very often, wireless communications (e.g., low-power wide-area networks) are adopted to connect field devices, user payload compression can also provide benefits derived from better spectrum usage, which in turn can result in advantages for high-density application scenarios. As a result of this increase in the number of connected devices, a new concept has emerged, called TinyML. It enables the use of machine learning on tiny, computationally restrained devices. This allows intelligent devices to analyze and interpret data locally and in real time. Therefore, this work presents a new datacompression solution (algorithm) for the IoT that leverages the TinyML perspective. The new approach is called the Tiny Anomaly Compressor (TAC) and is based on data eccentricity. TAC does not require previously established mathematical models or any assumptions about the underlying data distribution. In order to test the effectiveness of the proposed solution and validate it, a comparative analysis was performed on two real-world datasets with two other algorithms from the literature (namely Swing Door Trending (SDT) and the Discrete Cosine Transform (DCT)). It was found that the TAC algorithm showed promising results, achieving a maximum compression rate of 98.33%. Additionally, it also surpassed the two other models regarding the compression error and peak signal-to-noise ratio in all cases.
Large-scale hosting infrastructures have become the fundamental platforms for many real-world systems such as cloud computing infrastructures, enterprise data centers, and massive data processing systems. However, it ...
详细信息
Large-scale hosting infrastructures have become the fundamental platforms for many real-world systems such as cloud computing infrastructures, enterprise data centers, and massive data processing systems. However, it is a challenging task to achieve both scalability and high precision while monitoring a large number of intranode and internode attributes (e. g., CPU usage, free memory, free disk, internode network delay). In this paper, we present the design and implementation of a Resilient self-Compressive Monitoring (RCM) system for large-scale hosting infrastructures. RCM achieves scalable distributed monitoring by performing online data compression to reduce remote data collection cost. RCM provides failure resilience to achieve robust monitoring for dynamic distributed systems where host and network failures are common. We have conducted extensive experiments using a set of real monitoring data from NCSU's virtual computing lab (VCL), PlanetLab, a Google cluster, and real Internet traffic matrices. The experimental results show that RCM can achieve up to 200 percent higher compression ratio and several orders of magnitude less overhead than the existing approaches.
This correspondence shows that learning automata techniques, which have been useful in developing weak estimators, can be applied to datacompression applications in which the data distributions are nonstationary. The...
详细信息
This correspondence shows that learning automata techniques, which have been useful in developing weak estimators, can be applied to datacompression applications in which the data distributions are nonstationary. The adaptive coding scheme utilizes stochastic learning-based weak estimation techniques to adaptively update the probabilities of the source symbols, and this is done without resorting to either maximum likelihood, Bayesian, or sliding-window methods. The authors have incorporated the estimator in the adaptive Fano coding scheme and in an adaptive entropy-based scheme that "resembles" the well-known arithmetic coding. The empirical results obtained for both of these adaptive methods are obtained on real-life files that possess a fair degree of non-stationarity. From these results, it can be seen that the proposed schemes compress nearly 10% more than their respective adaptive methods that use maximum-likelihood estimator-based estimates.
暂无评论