【摘要】：能源是是人類社會(huì)發(fā)展必不可少的物質(zhì)基礎(chǔ)，但能源緊缺的態(tài)勢(shì)日趨嚴(yán)峻，因此社會(huì)和國(guó)家政策積極倡導(dǎo)節(jié)能減排。華南理工大學(xué)能耗監(jiān)管平臺(tái)順應(yīng)國(guó)家節(jié)能政策這一大趨勢(shì)，按照《高等學(xué)校校園建筑節(jié)能監(jiān)管系統(tǒng)建設(shè)技術(shù)導(dǎo)則》進(jìn)行建設(shè)，完成了南北校區(qū)總計(jì)129棟近100萬(wàn)平方米各類建筑能耗計(jì)量工程，實(shí)現(xiàn)了能耗智能實(shí)時(shí)監(jiān)控、能耗統(tǒng)計(jì)等功能。 能耗監(jiān)管平臺(tái)物理架構(gòu)中的數(shù)據(jù)中轉(zhuǎn)站主要負(fù)責(zé)對(duì)末端水電冷數(shù)據(jù)采集器組成的物聯(lián)網(wǎng)（Internet Of Things, IOT）進(jìn)行數(shù)據(jù)采集、數(shù)據(jù)緩存、數(shù)據(jù)發(fā)布等，運(yùn)行于傳統(tǒng)的PC服務(wù)器上，而PC服務(wù)器硬件成本高，占空間大，能耗大，故研究嘗試將其移植到成本低廉省空間能耗小的Linux嵌入式設(shè)備中。但由于數(shù)據(jù)中轉(zhuǎn)站要緩存的數(shù)據(jù)量大以及程序中多線程的資源消耗多，嵌入式設(shè)備的內(nèi)存資源往往不夠，經(jīng)常發(fā)生內(nèi)存交換，即磁盤I/O，導(dǎo)致內(nèi)存的訪問(wèn)效率低下，制約了整個(gè)系統(tǒng)的性能，使得數(shù)據(jù)中轉(zhuǎn)站中轉(zhuǎn)數(shù)據(jù)的響應(yīng)變得非常慢。為緩解這個(gè)嚴(yán)重問(wèn)題，提高嵌入設(shè)備的系統(tǒng)性能，本文引入了內(nèi)存壓縮的機(jī)制，設(shè)計(jì)和實(shí)現(xiàn)了一個(gè)可動(dòng)態(tài)裝載卸載的內(nèi)存壓縮系統(tǒng)。 內(nèi)存壓縮系統(tǒng)的主要思想是，在內(nèi)存中預(yù)分配一塊區(qū)域作為虛擬交換區(qū)，將換出的頁(yè)以壓縮的形式存放在該虛擬交換區(qū)。當(dāng)發(fā)生請(qǐng)求調(diào)頁(yè)時(shí)，系統(tǒng)從虛擬交換區(qū)中找到該頁(yè)，，將其解壓縮后換入內(nèi)存，從而避免了頁(yè)的換入換出這兩個(gè)硬盤訪問(wèn)的低速過(guò)程。該系統(tǒng)是以塊設(shè)備驅(qū)動(dòng)程序的形式來(lái)實(shí)現(xiàn)的，其好處是無(wú)需修改內(nèi)核的源碼，而且模塊可以在無(wú)需重新啟動(dòng)系統(tǒng)的情況下動(dòng)態(tài)的裝載和卸載。此外，根據(jù)能耗數(shù)據(jù)局部和全局重復(fù)性都很高并且以字符串形式存儲(chǔ)的特點(diǎn)，本文設(shè)計(jì)出了一種基于字典的自適應(yīng)無(wú)損壓縮算法MLZ（Mixed LZ）。它是一種基于LZ77和LZW的混合型改進(jìn)算法，以LZW算法為主LZ77算法為輔，利用了它們的互補(bǔ)特性，減少了掃描時(shí)間以及滑動(dòng)窗口中字符串比較匹配時(shí)間，具有更好的全局與局部自適應(yīng)性、更高的壓縮率。 最后本文對(duì)該嵌入式內(nèi)存壓縮系統(tǒng)進(jìn)行了性能測(cè)試，基于華南理工大學(xué)能耗監(jiān)管平臺(tái)，將華南理工大學(xué)所有的能耗數(shù)據(jù)點(diǎn)作為測(cè)試工作集，在相同的系統(tǒng)配置下，在本地調(diào)用webservice接口分別從使用和未使用內(nèi)存壓縮系統(tǒng)的嵌入式設(shè)備以及未使用內(nèi)存壓縮系統(tǒng)的PC機(jī)上的數(shù)據(jù)中轉(zhuǎn)站中獲取能耗數(shù)據(jù)點(diǎn)的值，并計(jì)算花費(fèi)的總時(shí)間，以此判斷使用之后內(nèi)存壓縮機(jī)制是否提升了系統(tǒng)性能以及是否滿足實(shí)際部署需求。實(shí)驗(yàn)結(jié)果表明，使用了本文實(shí)現(xiàn)的內(nèi)存壓縮系統(tǒng)達(dá)到了預(yù)期目標(biāo)。
[Abstract]:Energy is an indispensable material foundation for the development of human society, but the situation of energy shortage is becoming more and more serious. Therefore, social and national policies actively advocate energy conservation and emission reduction. Construction, completed the North-South campus of a total of 129 buildings nearly 1 million square meters of various building energy consumption measurement projects, to achieve intelligent real-time monitoring of energy consumption, energy consumption statistics and other functions.
The data transfer station in the physical architecture of the energy consumption monitoring platform is mainly responsible for data acquisition, data caching and data publishing of the Internet of Things (IOT), which is composed of the terminal water and electricity cooled data collector. It runs on the traditional PC server. The PC server has high hardware cost, large space occupation and large energy consumption, so the research tries to move it. Because of the large amount of data to be cached by the data transfer station and the consumption of multi-thread resources in the program, the memory resources of the embedded devices are often insufficient. Memory exchange, i.e. disk I/O, often occurs, which leads to the inefficiency of memory access and restricts the performance of the whole system. In order to alleviate this serious problem and improve the system performance of embedded devices, this paper introduces the mechanism of memory compression, designs and implements a memory compression system which can be loaded and unloaded dynamically.
The main idea of the memory compression system is to pre-allocate an area in memory as a virtual swap area and store the pages in the virtual swap area in the form of compression. The system is implemented in the form of a block device driver, which has the advantage of not modifying the source code of the kernel, and that modules can be loaded and unloaded dynamically without restarting the system. In this paper, a dictionary-based adaptive lossless compression algorithm MLZ (Mixed LZ) is designed. It is a hybrid improved algorithm based on LZ77 and LZW, supplemented by LZW algorithm as the main LZ77 algorithm, using their complementary characteristics, it reduces the scanning time and the matching time of string comparison in sliding window, and has better global and global performance. Self adaptability and higher compression ratio.
Finally, the performance of the embedded memory compression system is tested. Based on the energy consumption monitoring platform of South China University of Technology, all the energy consumption data points of South China University of Technology are taken as test worksets. Under the same system configuration, the embedded devices of using and not using memory compression system are invoked by local web service interface, respectively. The experimental results show that the memory compression system implemented in this paper achieves the performance of the system and meets the actual deployment requirements. Expected target.
【學(xué)位授予單位】：華南理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：TP368.1

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本文編號(hào)：2240413