移動(dòng)Ad Hoc云環(huán)境中基于移動(dòng)性預(yù)測(cè)的計(jì)算卸載算法研究
發(fā)布時(shí)間：2018-08-28 11:23
【摘要】：以智能手機(jī)和平板電腦為代表的移動(dòng)設(shè)備的快速普及給人們的日常生活帶來(lái)了極大的便利。但是,由于受到CPU性能、電池容量、存儲(chǔ)容量等因素的限制,移動(dòng)設(shè)備在處理計(jì)算密集型的任務(wù)時(shí)卻表現(xiàn)欠佳,比如運(yùn)算速度緩慢、掉電迅速等。移動(dòng)云計(jì)算技術(shù)的出現(xiàn)為該問(wèn)題的解決提供了一個(gè)很好的思路：通過(guò)把移動(dòng)客戶端上的計(jì)算密集型任務(wù)卸載到目標(biāo)代理中去執(zhí)行不僅可以大大縮減任務(wù)的處理時(shí)間而且還可以最大限度的降低移動(dòng)設(shè)備的能耗。 在移動(dòng)Ad Hoc云環(huán)境下,移動(dòng)客戶端節(jié)點(diǎn)和目標(biāo)代理節(jié)點(diǎn)的位置都時(shí)刻處于動(dòng)態(tài)變化的狀態(tài),受無(wú)線網(wǎng)絡(luò)有效覆蓋范圍的限制和節(jié)點(diǎn)移動(dòng)性的影響,客戶端節(jié)點(diǎn)和代理節(jié)點(diǎn)之間的網(wǎng)絡(luò)連接也是間歇性的。因此,當(dāng)把計(jì)算任務(wù)從移動(dòng)客戶端節(jié)點(diǎn)卸載到代理節(jié)點(diǎn)中去執(zhí)行時(shí)就有可能會(huì)出現(xiàn)計(jì)算卸載失敗的問(wèn)題。傳統(tǒng)的計(jì)算卸載算法雖然在靜態(tài)卸載環(huán)境下表現(xiàn)出色,但是在移動(dòng)Ad Hoc云環(huán)境下卻難以克服計(jì)算卸載失敗所帶來(lái)的問(wèn)題。 為了解決移動(dòng)Ad Hoc云環(huán)境下計(jì)算卸載失敗的問(wèn)題,本文在已有的靜態(tài)卸載算法MET、MCT、MinMin、MaxMin、Sufferage的基礎(chǔ)上提出了五個(gè)適用于動(dòng)態(tài)卸載環(huán)境的新算法DynMETComm DynMCTComm、DynMinMinComm、DynMaxMinComm和DynSufferageComm。與傳統(tǒng)的靜態(tài)卸載算法相比,這些新算法不僅考慮了任務(wù)在卸載過(guò)程中的通信開(kāi)銷而且增加了對(duì)任務(wù)卸載失敗后的處理,即當(dāng)卸載到代理節(jié)點(diǎn)上的任務(wù)執(zhí)行失敗時(shí),更新任務(wù)的到達(dá)時(shí)間為失敗時(shí)間點(diǎn),從而參與后續(xù)的調(diào)度。為了最大限度的避免任務(wù)卸載失敗所產(chǎn)生的開(kāi)銷,本文又提出了基于移動(dòng)性預(yù)測(cè)的新算法DynPredict。該算法在預(yù)測(cè)到任務(wù)執(zhí)行失敗時(shí),會(huì)從有能力執(zhí)行成功的代理節(jié)點(diǎn)中選擇一個(gè)次優(yōu)的代理重新進(jìn)行卸載,從而保證任務(wù)順利執(zhí)行完成。仿真結(jié)果表明：帶預(yù)測(cè)的算法DynPredict在大多數(shù)性能指標(biāo)上都能表現(xiàn)出最優(yōu)的性能,頁(yè)DynMETComm則表現(xiàn)最差；在線算法DynMCTComm則通常接近甚至超過(guò)其他三個(gè)比較復(fù)雜的批調(diào)度算法(包括DynMinMinComm、DynMaxMinComm和DynSufferageComm)的性能。 本文的研究成果可以很好地應(yīng)用于移動(dòng)Ad Hoc云環(huán)境下的計(jì)算卸載,也為下一步任務(wù)遷移方向的研究奠定了基礎(chǔ)。文中所采用的研究方法和思路對(duì)于移動(dòng)云計(jì)算環(huán)境下的計(jì)算卸載的深入研究也具有一定的參考價(jià)值。
[Abstract]:The rapid popularization of mobile devices, represented by smart phones and tablets, brings great convenience to people's daily life. However, due to the limitations of CPU performance, battery capacity, storage capacity and other factors, mobile devices perform poorly in processing computationally intensive tasks, such as slow computing speed, rapid power loss, and so on. The emergence of mobile cloud computing technology provides a good way to solve this problem: by uninstalling computationally intensive tasks on mobile clients to target agents to perform tasks that can significantly reduce the processing time It can also minimize the energy consumption of mobile devices. In mobile Ad Hoc cloud environment, the location of mobile client node and target proxy node is always in a dynamic state, which is affected by the limit of effective coverage of wireless network and the mobility of nodes. The network connection between client node and proxy node is also intermittent. Therefore, when the computing task is unloaded from the mobile client node to the proxy node, the problem of computing uninstall failure may occur. Although the traditional computing unload algorithm performs well in static uninstall environment, it is difficult to overcome the problem of computing uninstall failure in mobile Ad Hoc cloud environment. In order to solve the problem of computing unload failure in mobile Ad Hoc cloud environment, this paper proposes five new algorithms, DynMETComm DynMCTComm,DynMinMinComm,DynMaxMinComm and DynSufferageComm., which are suitable for dynamic uninstall environment, based on the existing static uninstall algorithm MET,MCT,MinMin,MaxMin,Sufferage. Compared with the traditional static unload algorithm, these new algorithms not only consider the communication overhead of the task during the uninstall process, but also increase the processing of the task unload failure, that is, when the task unloaded to the proxy node fails to execute, The time of arrival of the update task is the point of failure, so as to participate in the subsequent scheduling. In order to avoid the overhead caused by unload failure, a new algorithm DynPredict. based on mobility prediction is proposed in this paper. When the task execution failure is predicted, the algorithm selects a sub-optimal agent from the agent node with the ability to execute successfully, so as to ensure the smooth completion of the task. The simulation results show that the DynPredict algorithm with prediction can show the best performance on most performance indexes, while the page DynMETComm shows the worst performance. DynMCTComm, an online algorithm, usually approaches or exceeds the performance of the other three more complex batch scheduling algorithms (including DynMinMinComm,DynMaxMinComm and DynSufferageComm). The research results in this paper can be applied to computing unload in mobile Ad Hoc cloud environment, and it also lays a foundation for further research on the direction of task migration. The research methods and ideas used in this paper also have a certain reference value for the further study of computing uninstall in mobile cloud computing environment.
【學(xué)位授予單位】：云南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TN929.5
本文編號(hào)：2209251
[Abstract]:The rapid popularization of mobile devices, represented by smart phones and tablets, brings great convenience to people's daily life. However, due to the limitations of CPU performance, battery capacity, storage capacity and other factors, mobile devices perform poorly in processing computationally intensive tasks, such as slow computing speed, rapid power loss, and so on. The emergence of mobile cloud computing technology provides a good way to solve this problem: by uninstalling computationally intensive tasks on mobile clients to target agents to perform tasks that can significantly reduce the processing time It can also minimize the energy consumption of mobile devices. In mobile Ad Hoc cloud environment, the location of mobile client node and target proxy node is always in a dynamic state, which is affected by the limit of effective coverage of wireless network and the mobility of nodes. The network connection between client node and proxy node is also intermittent. Therefore, when the computing task is unloaded from the mobile client node to the proxy node, the problem of computing uninstall failure may occur. Although the traditional computing unload algorithm performs well in static uninstall environment, it is difficult to overcome the problem of computing uninstall failure in mobile Ad Hoc cloud environment. In order to solve the problem of computing unload failure in mobile Ad Hoc cloud environment, this paper proposes five new algorithms, DynMETComm DynMCTComm,DynMinMinComm,DynMaxMinComm and DynSufferageComm., which are suitable for dynamic uninstall environment, based on the existing static uninstall algorithm MET,MCT,MinMin,MaxMin,Sufferage. Compared with the traditional static unload algorithm, these new algorithms not only consider the communication overhead of the task during the uninstall process, but also increase the processing of the task unload failure, that is, when the task unloaded to the proxy node fails to execute, The time of arrival of the update task is the point of failure, so as to participate in the subsequent scheduling. In order to avoid the overhead caused by unload failure, a new algorithm DynPredict. based on mobility prediction is proposed in this paper. When the task execution failure is predicted, the algorithm selects a sub-optimal agent from the agent node with the ability to execute successfully, so as to ensure the smooth completion of the task. The simulation results show that the DynPredict algorithm with prediction can show the best performance on most performance indexes, while the page DynMETComm shows the worst performance. DynMCTComm, an online algorithm, usually approaches or exceeds the performance of the other three more complex batch scheduling algorithms (including DynMinMinComm,DynMaxMinComm and DynSufferageComm). The research results in this paper can be applied to computing unload in mobile Ad Hoc cloud environment, and it also lays a foundation for further research on the direction of task migration. The research methods and ideas used in this paper also have a certain reference value for the further study of computing uninstall in mobile cloud computing environment.
【學(xué)位授予單位】：云南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TN929.5
【參考文獻(xiàn)】
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，本文編號(hào)：2209251
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