【摘要】：隨著無線通信及信息技術(shù)領(lǐng)域的迅猛發(fā)展,移動設(shè)備可以安裝豐富的應(yīng)用程序,為人們的日常生活提供了許多便利。然而這些復(fù)雜的應(yīng)用極大地消耗了移動設(shè)備的能量,降低了電池續(xù)航時間,同時,電池技術(shù)在短期內(nèi)無法有大的突破。因此,降低移動設(shè)備的能耗,成為一個迫切需要解決的問題。云計算的出現(xiàn)為移動設(shè)備節(jié)能及能力擴展提供了一個新的思路。 針對移動設(shè)備的節(jié)能問題,本文提出一種基于云協(xié)助的節(jié)能任務(wù)調(diào)度策略。在移動設(shè)備中的應(yīng)用可以被細分成一系列順序任務(wù)及并行任務(wù)。這些任務(wù)可以被卸載到云端執(zhí)行,高性能云端服務(wù)器可以加速任務(wù)執(zhí)行,并節(jié)約移動設(shè)備的執(zhí)行能耗。然而,由于這些任務(wù)需要通過無線傳輸通道被傳輸?shù)皆贫?因此在節(jié)約執(zhí)行能耗的同時,會引起附加的傳輸能耗。由于執(zhí)行能耗及傳輸能耗的對立,任務(wù)卸載到云端之前,需要先判斷是否能降低總能耗,因此,合適且有效的節(jié)能調(diào)度策略顯得非常必要。本文首先對此任務(wù)調(diào)度問題通過任務(wù)模型、執(zhí)行模型及傳輸模型三個方面進行系統(tǒng)建模,得到優(yōu)化函數(shù),其優(yōu)化目標為在總完成時間約束內(nèi),最小化移動設(shè)備的能量消耗。然后將系統(tǒng)模型映射到圖論中,從而將任務(wù)調(diào)度問題轉(zhuǎn)化成為一個有約束最短路問題,采用LARAC (Lagrangian Relaxation Based Aggregated Cost)算法進行求解,得到其近似最優(yōu)解。 仿真實驗表明,當與只在移動設(shè)備上執(zhí)行的純策略相比,本文所提出的基于云協(xié)助的任務(wù)調(diào)度策略最多降低了82.47%的能耗以及25.70%的時間消耗。另外,本文還進一步在多種時間約束下,對不同類型的應(yīng)用仿真驗證了所提算法的有效性及適用性。
[Abstract]:With the rapid development of wireless communication and information technology, mobile devices can install a wealth of applications, which provides a lot of convenience for people's daily life. However, these complex applications greatly consume the energy of mobile devices and reduce battery life. At the same time, battery technology can not make a big breakthrough in the short term. Therefore, reducing the energy consumption of mobile devices has become an urgent problem to be solved. The emergence of cloud computing provides a new idea for energy saving and capability expansion of mobile devices. In order to solve the problem of energy saving in mobile devices, this paper proposes an energy saving task scheduling strategy based on cloud assistance. Applications in mobile devices can be subdivided into a series of sequential tasks and parallel tasks. These tasks can be unloaded to the cloud, and the high performance cloud server can speed up the task execution and save the execution energy of the mobile device. However, since these tasks need to be transmitted to the cloud via wireless transmission channels, additional transmission energy may be caused while saving energy for execution. Due to the opposition between execution energy consumption and transmission energy consumption, it is necessary to determine whether the total energy consumption can be reduced before the task is unloaded to the cloud. Therefore, a suitable and effective energy saving scheduling strategy is very necessary. In this paper, the task scheduling problem is first modeled in three aspects: task model, execution model and transmission model, and the optimization function is obtained. The optimization goal is to minimize the energy consumption of mobile devices within the total completion time constraints. Then the system model is mapped to graph theory, and the task scheduling problem is transformed into a constrained shortest path problem, which is solved by LARAC (Lagrangian Relaxation Based Aggregated Cost) algorithm, and the approximate optimal solution is obtained. Simulation results show that the proposed cloud-assisted task scheduling strategy can reduce the energy consumption by 82.47% and the time consumption by 25.70% compared with the pure strategy only implemented on mobile devices. In addition, the effectiveness and applicability of the proposed algorithm are verified by simulation of different types of applications under various time constraints.
【學(xué)位授予單位】：廈門大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP393.05

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本文編號：2241389