本文選題：移動云計算 + 數(shù)據(jù)中心　； 參考：《新疆大學(xué)》2014年碩士論文

【摘要】：隨著近年來移動互聯(lián)網(wǎng)的快速發(fā)展，云計算的概念被引入移動網(wǎng)絡(luò)，從而構(gòu)造了新型云服務(wù)模型—移動云計算。海量接入終端、爆炸性增長的計算任務(wù)和移動終端電量受限等特點給移動云計算的發(fā)展帶來了新挑戰(zhàn)。本文著重關(guān)注移動云環(huán)境下的全局節(jié)能問題。首先，近年來數(shù)據(jù)中心能耗的急劇增高使全球用電壓力驟然上升，大量研究致力于提高數(shù)據(jù)中心能效比，降低空載數(shù)據(jù)中心服務(wù)器的比例；其次，由于移動設(shè)備電量的局限性，移動端設(shè)備節(jié)能也成為近年來的研究熱點。大量研究表明，可將移動設(shè)備的計算任務(wù)上傳到資源服務(wù)器（例如，附近其它計算節(jié)點或云端數(shù)據(jù)中心）以減小終端用戶的計算能耗。然而，在某些特定場景下，云端數(shù)據(jù)中心的服務(wù)可用性較差。因此，具有較強(qiáng)靈活性的移動私有云被廣泛研究。產(chǎn)生計算任務(wù)的移動設(shè)備可利用其網(wǎng)絡(luò)拓?fù)涓浇亩嗯_其它移動設(shè)備完成存在依賴關(guān)系的并行任務(wù)工作流集群，以達(dá)到節(jié)能計算的目標(biāo)。 有向無環(huán)圖（DAG）能反映出工作流中各任務(wù)間的相互約束關(guān)系，因而被普遍應(yīng)用于討論面向并行任務(wù)集群的工作流調(diào)度問題。然而，隨著移動應(yīng)用和集群任務(wù)數(shù)量的爆炸式增長，現(xiàn)有基于單DAG工作流的調(diào)度方案有如下問題：（1）在多種形態(tài)的多DAG工作流復(fù)雜場景下無法兼顧所有情況和全面考慮整體節(jié)能效果；（2）對于多DAG工作流的性能優(yōu)化效果尚可，卻帶來了大量的能耗，無法兼顧性能提升與能耗優(yōu)化；（3）適用場景局限性較大，算法普適性較差。 針對上述問題，，本文分別針對移動云環(huán)境下的數(shù)據(jù)中心節(jié)能和移動私有云環(huán)境下的終端設(shè)備節(jié)能提出了兩種基于多DAG工作流的節(jié)能調(diào)度算法—MREO和EAMRS。MREO以減少數(shù)據(jù)中心處理器的占用數(shù)目為目的，整合獨立任務(wù)后，進(jìn)一步對計算密集型和通信密集型的任務(wù)特點進(jìn)行分析，并采用回溯和分支限界進(jìn)行整合路徑的動態(tài)優(yōu)化選擇。EAMRS算法合理利用工作流中各任務(wù)間的松弛時間，減少了多DAG工作流下移動計算節(jié)點的占用數(shù)目，通過任務(wù)復(fù)制有效控制傳輸能耗，以降低移動私有云的整體能耗。此外，文章采用了兼顧使用率、尾功耗和網(wǎng)絡(luò)影響因子的智能手機(jī)能耗評估模型，提高了算法評估的準(zhǔn)確性和有效性。模擬實驗表明，兩種算法分別在移動云數(shù)據(jù)中心和移動私有云場景下有效降低了系統(tǒng)的計算和傳輸能耗，確保了多DAG工作流的性能，并采用PowerMonitor等能耗測量設(shè)備較準(zhǔn)確的評估了算法的能耗優(yōu)化有效性。
[Abstract]:With the rapid development of mobile Internet in recent years, the concept of cloud computing has been introduced into mobile network, and a new cloud service model, mobile cloud computing, has been constructed. The characteristics of massive access terminals, explosively growing computing tasks and limited power of mobile terminals have brought new challenges to the development of mobile cloud computing. This paper focuses on global energy conservation in mobile cloud environment. First, the sharp increase in energy consumption in data centers in recent years has led to a sharp rise in global power pressure. A great deal of research has been devoted to improving the energy efficiency ratio of data centers and reducing the proportion of no-load data center servers; secondly, due to the limitations of mobile devices, In recent years, energy saving of mobile devices has become a hot topic. A large number of studies have shown that computing tasks of mobile devices can be uploaded to resource servers (for example, other computing nodes in the vicinity or cloud data centers) to reduce the computing energy consumption of end users. However, in some specific scenarios, the service availability of cloud data centers is poor. Therefore, mobile private cloud with strong flexibility has been widely studied. Mobile devices that generate computing tasks can use several other mobile devices near their network topology to complete a dependent parallel task workflow cluster in order to achieve the goal of energy-saving computing. The directed acyclic graph (DAG) can reflect the inter-constraint relation among the tasks in the workflow, so it is widely used to discuss the workflow scheduling problem for the parallel task cluster. However, as the number of mobile applications and cluster tasks explodes, The existing scheduling scheme based on single DAG workflow has the following problem: 1) under the complex scenario of multi-form multi- workflow, it can not take into account all situations and consider the overall energy-saving effect.) the performance optimization effect of multi- workflow can be achieved. However, it brings a lot of energy consumption, which can not take into account the performance improvement and energy consumption optimization. In response to the above problems, In this paper, we propose two energy-saving scheduling algorithms, MREO and EAMRS.MREO, for data center energy saving in mobile cloud environment and terminal device energy saving in mobile private cloud environment, respectively, in order to reduce the number of data center processors. After integrating independent tasks, the characteristics of computation-intensive and communication-intensive tasks are further analyzed, and the dynamic optimization selection of integration path using backtracking and branch limits. EAMRS algorithm makes reasonable use of the relaxation time between tasks in workflow. In order to reduce the total energy consumption of mobile private cloud, the number of mobile computing nodes in multi- workflow is reduced, and the transmission energy consumption is effectively controlled by task replication. In addition, a smart phone energy consumption evaluation model which takes account of utilization rate, tail power consumption and network impact factors is used to improve the accuracy and effectiveness of the algorithm evaluation. Simulation experiments show that the two algorithms can effectively reduce the calculation and transmission energy consumption of the system in the mobile cloud data center and the mobile private cloud scene, and ensure the performance of the multi- workflow. The energy consumption optimization efficiency of the algorithm is evaluated accurately by using PowerMonitor and other energy consumption measurement equipment.
【學(xué)位授予單位】：新疆大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP393.09

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