本文選題：云計算 + 數(shù)據(jù)中心�。� 參考：《電子科技大學(xué)》2012年碩士論文

【摘要】：隨著數(shù)據(jù)中心規(guī)模的不斷擴(kuò)大，數(shù)據(jù)中心服務(wù)器的性能越來越受人們的關(guān)注，性能低在很大程度上是由于服務(wù)器負(fù)載過高而效率低下。同時，能源消耗成為日益嚴(yán)重和備受關(guān)注的問題，負(fù)載均衡使得數(shù)據(jù)中心不會因為某些機(jī)器負(fù)載太低而浪費(fèi)不必要的資源。本文首先介紹了云計算的背景和負(fù)載均衡的研究意義。然后重點(diǎn)設(shè)計了最小綜合負(fù)載優(yōu)先：一種高可變環(huán)境下的云計算數(shù)據(jù)中心動態(tài)調(diào)度算法。云計算數(shù)據(jù)中心的一個挑戰(zhàn)性的調(diào)度問題是分配和遷移虛擬機(jī)，以及物理主機(jī)的集成功能。動態(tài)負(fù)載均衡調(diào)度算法是一個NP-hard問題。傳統(tǒng)的負(fù)載均衡調(diào)度算法普遍只考慮一個因素，比如物理服務(wù)器的CPU。綜合負(fù)載均衡度最小優(yōu)先算法(lowest integrated load first，以下簡稱LILF)同時考慮了多個維度的資源，，包括：CPU、內(nèi)存和網(wǎng)絡(luò)帶寬，將他們整合進(jìn)了物理服務(wù)器和虛擬服務(wù)器進(jìn)行調(diào)度，用綜合負(fù)載值用來作為分配虛擬機(jī)的依據(jù)。本文還研究了云計算數(shù)據(jù)中心整體的多維度不均衡度的衡量、物理服務(wù)器的平均不均衡度的衡量，以及CPU、內(nèi)存和網(wǎng)絡(luò)帶寬的平均利用率。這些指標(biāo)用來比較算法之間的優(yōu)劣。最后的模擬結(jié)果顯示了LILF在數(shù)據(jù)中心總體不均衡度、物理服務(wù)器不均衡度，以及平均利用率三個指標(biāo)上均有非常好的性能。作為應(yīng)對預(yù)訂任務(wù)的業(yè)務(wù)需求的擴(kuò)展，本論文還詳細(xì)的設(shè)計和實現(xiàn)了靜態(tài)離線負(fù)載均衡調(diào)度算法。靜態(tài)離線負(fù)載均衡調(diào)度算法的目標(biāo)是使得未來一段時間內(nèi)的物理服務(wù)器平均負(fù)載均衡。調(diào)度系統(tǒng)知曉一段時間內(nèi)前來的所有任務(wù)以及其生命周期，從而可以計算出一段時間內(nèi)平均每一臺物理機(jī)應(yīng)該承擔(dān)的負(fù)載，然后以平均負(fù)載作為標(biāo)準(zhǔn)分配，使得所有物理機(jī)的負(fù)載在一段時間內(nèi)接近該平均負(fù)載。
[Abstract]:With the expansion of data center scale, people pay more and more attention to the performance of data center server. At the same time, energy consumption is becoming more and more serious and concerned. Load balancing makes data center not waste unnecessary resources because of low load of some machines. This paper first introduces the background of cloud computing and the significance of load balancing. Then we design a dynamic scheduling algorithm for cloud computing data center in high variable environment. A challenging scheduling problem for cloud computing data centers is the allocation and migration of virtual machines and the integration of physical hosts. Dynamic load balancing scheduling algorithm is a NP-hard problem. Traditional load balancing scheduling algorithms generally only consider one factor, such as physical server CPU. Integrated load balancing minimum priority algorithm (lowest integrated load first,) takes into account multiple dimensions of resources, including: CPU, memory and network bandwidth, and integrates them into physical servers and virtual servers for scheduling. The synthetic load value is used as the basis for the allocation of virtual machines. This paper also studies the measurement of multi-dimensional imbalance of cloud computing data center, the measurement of average imbalance of physical server, and the average utilization of CPU, memory and network bandwidth. These indexes are used to compare the advantages and disadvantages of the algorithms. Finally, the simulation results show that LILF has very good performance in the data center overall imbalance, physical server imbalance, and the average utilization rate. As an extension to meet the business requirements of reservation tasks, this paper also designs and implements a static off-line load balancing scheduling algorithm in detail. The aim of static off-line load balancing scheduling algorithm is to balance the load of physical servers in the future. The scheduling system knows all the tasks that come over a period of time and its life cycle, so that it can calculate the average load per physical machine over a period of time, and then distribute the average load as a standard. The load of all physical machines is close to the average load for a period of time.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：TP308

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本文編號：2072093