【摘要】：能源是是人類社會發(fā)展必不可少的物質(zhì)基礎(chǔ)，但能源緊缺的態(tài)勢日趨嚴峻，因此社會和國家政策積極倡導(dǎo)節(jié)能減排。華南理工大學(xué)能耗監(jiān)管平臺順應(yīng)國家節(jié)能政策這一大趨勢，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》進行建設(shè)，完成了南北校區(qū)總計129棟近100萬平方米各類建筑能耗計量工程，實現(xiàn)了能耗智能實時監(jiān)控、能耗統(tǒng)計等功能。 能耗監(jiān)管平臺物理架構(gòu)中的數(shù)據(jù)中轉(zhuǎn)站主要負責(zé)對末端水電冷數(shù)據(jù)采集器組成的物聯(lián)網(wǎng)（Internet Of Things, IOT）進行數(shù)據(jù)采集、數(shù)據(jù)緩存、數(shù)據(jù)發(fā)布等，運行于傳統(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)存的訪問效率低下，制約了整個系統(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。為緩解這個嚴重問題，提高嵌入設(shè)備的系統(tǒng)性能，本文引入了內(nèi)存壓縮的機制，設(shè)計和實現(xiàn)了一個可動態(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)，將換出的頁以壓縮的形式存放在該虛擬交換區(qū)。當(dāng)發(fā)生請求調(diào)頁時，系統(tǒng)從虛擬交換區(qū)中找到該頁，，將其解壓縮后換入內(nèi)存，從而避免了頁的換入換出這兩個硬盤訪問的低速過程。該系統(tǒng)是以塊設(shè)備驅(qū)動程序的形式來實現(xiàn)的，其好處是無需修改內(nèi)核的源碼，而且模塊可以在無需重新啟動系統(tǒng)的情況下動態(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的特點，本文設(shè)計出了一種基于字典的自適應(yīng)無損壓縮算法MLZ（Mixed LZ）。它是一種基于LZ77和LZW的混合型改進算法，以LZW算法為主LZ77算法為輔，利用了它們的互補特性，減少了掃描時間以及滑動窗口中字符串比較匹配時間，具有更好的全局與局部自適應(yīng)性、更高的壓縮率。 最后本文對該嵌入式內(nèi)存壓縮系統(tǒng)進行了性能測試，基于華南理工大學(xué)能耗監(jiān)管平臺，將華南理工大學(xué)所有的能耗數(shù)據(jù)點作為測試工作集，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機上的數(shù)據(jù)中轉(zhuǎn)站中獲取能耗數(shù)據(jù)點的值，并計算花費的總時間，以此判斷使用之后內(nèi)存壓縮機制是否提升了系統(tǒng)性能以及是否滿足實際部署需求。實驗結(jié)果表明，使用了本文實現(xiàn)的內(nèi)存壓縮系統(tǒng)達到了預(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)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TP368.1

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