【摘要】：近些年來(lái),隨著云計(jì)算的普及,網(wǎng)絡(luò)需求得以快速發(fā)展,由數(shù)據(jù)中心或者數(shù)據(jù)服務(wù)器產(chǎn)生的電力花費(fèi)也持續(xù)增長(zhǎng),并且呈現(xiàn)一種急劇增長(zhǎng)的趨勢(shì)。一項(xiàng)調(diào)查報(bào)告指出,全球范圍內(nèi)服務(wù)器的電力花費(fèi)可能已經(jīng)超過(guò)了服務(wù)器等硬件設(shè)備的花費(fèi)。因此,對(duì)降低電力花費(fèi)進(jìn)行研究就迫在眉睫了。同時(shí),隨著智能電網(wǎng)的發(fā)展,越來(lái)越多的電網(wǎng)開(kāi)始施行動(dòng)態(tài)電價(jià)機(jī)制,特別是分時(shí)電價(jià)和實(shí)時(shí)電價(jià)。動(dòng)態(tài)電價(jià)不僅在時(shí)間上具有差異,而且在空間上也具有差異性。當(dāng)數(shù)據(jù)中心或者數(shù)據(jù)服務(wù)器配備了電能存儲(chǔ)設(shè)備,例如電池,就可以在低電價(jià)時(shí)存儲(chǔ)電能,在高電價(jià)時(shí)釋放電能。于是在這種背景下,研究怎樣利用電價(jià)的特點(diǎn)對(duì)電力進(jìn)行調(diào)度分配以減少整個(gè)數(shù)據(jù)中心或服務(wù)器的電力成本就顯得尤為可行和重要了。 一方面,對(duì)數(shù)據(jù)中心能耗的研究已經(jīng)取得了很多重要的成果。實(shí)際上,它也是一種間接降低電力成本的方法。比較成熟的方法有動(dòng)態(tài)電壓調(diào)整、動(dòng)態(tài)電壓與頻率調(diào)整、動(dòng)態(tài)調(diào)整服務(wù)器狀態(tài)、虛擬機(jī)技術(shù)等等。另一方面,智能電網(wǎng)的發(fā)展帶來(lái)的動(dòng)態(tài)電價(jià)機(jī)制,使得數(shù)據(jù)中心可以遷移網(wǎng)絡(luò)負(fù)載到低電價(jià)階段(或地點(diǎn))執(zhí)行或者儲(chǔ)存低電價(jià)階段(或地點(diǎn))電能在高電價(jià)階段(或地點(diǎn))使用,從而直接降低數(shù)據(jù)中心的電力成本。 本文基于動(dòng)態(tài)電價(jià)重點(diǎn)探討了三個(gè)層面的問(wèn)題(由淺入深,由簡(jiǎn)單到復(fù)雜)：?jiǎn)螖?shù)據(jù)服務(wù)器的電力成本優(yōu)化、服務(wù)器集群的電力成本優(yōu)化以及分布式數(shù)據(jù)中心的電力成本優(yōu)化。針對(duì)單數(shù)據(jù)服務(wù)器的電力成本優(yōu)化問(wèn)題,考慮了網(wǎng)絡(luò)負(fù)載的隨機(jī)特性和分時(shí)電價(jià)的時(shí)域性差異,提出動(dòng)態(tài)規(guī)劃的解決方案,克服了將網(wǎng)絡(luò)負(fù)載作為確定性負(fù)載進(jìn)行處理的缺陷；針對(duì)服務(wù)器集群的電力成本優(yōu)化問(wèn)題,考慮結(jié)合通過(guò)直接減少電力成本(利用分時(shí)電價(jià)的時(shí)域性差異)和通過(guò)節(jié)能間接減少電力成本兩個(gè)方面,提出建立馬爾科夫決策過(guò)程的解決方案,將以往研究的兩種方法進(jìn)行了針對(duì)性的結(jié)合；針對(duì)分布式數(shù)據(jù)中心的電力成本優(yōu)化問(wèn)題,考慮到了電價(jià)的空間和時(shí)間差異和網(wǎng)絡(luò)負(fù)載的調(diào)度以及網(wǎng)絡(luò)帶寬的限制,同時(shí)保障服務(wù)質(zhì)量的需求,提出了優(yōu)化網(wǎng)絡(luò)負(fù)載調(diào)度的解決方案。需要說(shuō)明的一點(diǎn)：我們所指的電力成本僅是指服務(wù)器的電力花費(fèi)。
[Abstract]:In recent years, with the popularity of cloud computing, network demand has developed rapidly, and the power cost generated by data centers or data servers has continued to increase, and has shown a trend of rapid growth. Worldwide, servers may be spending more on power than on hardware such as servers, according to a survey. Therefore, it is urgent to study the power cost reduction. At the same time, with the development of smart grid, more and more power grids begin to implement dynamic pricing mechanism, especially time-sharing price and real-time price. Dynamic electricity price is not only different in time, but also different in space. When a data center or a data server is equipped with a power storage device, such as a battery, it can store electricity at a low price and release it at a high price. Under this background, it is particularly feasible and important to study how to make use of the characteristics of electricity price to dispatch and distribute electricity to reduce the power cost of the whole data center or server. On the one hand, the research on data center energy consumption has made a lot of important achievements. In fact, it is also an indirect way to reduce the cost of electricity. More mature methods include dynamic voltage adjustment, dynamic voltage and frequency adjustment, dynamic adjustment of server state, virtual machine technology and so on. On the other hand, the dynamic pricing mechanism brought about by the development of smart grid, The data center can migrate the network load to the low price stage (or location) to execute or store the low electricity price stage (or location) for use in the high price stage (or location), thus directly reducing the power cost of the data center. Based on dynamic electricity pricing, this paper focuses on three aspects (from simple to complex): single data server power cost optimization, server cluster power cost optimization and distributed data center power cost optimization. Considering the stochastic characteristics of network load and time-domain difference of time-sharing price, a dynamic programming solution is proposed to solve the power cost optimization problem of single data server, which overcomes the defect of treating network load as deterministic load. Aiming at the problem of power cost optimization in server cluster, we consider combining two aspects: direct reduction of power cost (using time-domain difference of time-sharing price) and indirect reduction of power cost through energy saving. This paper proposes a solution to establish Markov decision process, combining the two methods studied in the past, aiming at the power cost optimization problem of distributed data center. Considering the space and time difference of electricity price, the scheduling of network load and the limitation of network bandwidth, and at the same time guaranteeing the requirement of quality of service, the solution of optimizing network load scheduling is put forward. One point to note: we refer to the cost of electricity only for the server.
【學(xué)位授予單位】：中國(guó)科學(xué)技術(shù)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP308

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本文編號(hào)：2278545