软件学报
軟件學報
연건학보
JOURNAL OF SOFTWARE
2014年
10期
2373-2384
,共12页
林俊宇%王慧强%马春光%卢旭%吕宏武
林俊宇%王慧彊%馬春光%盧旭%呂宏武
림준우%왕혜강%마춘광%로욱%려굉무
认知网络%QoS%服务迁移%有向无环图%随机失效
認知網絡%QoS%服務遷移%有嚮無環圖%隨機失效
인지망락%QoS%복무천이%유향무배도%수궤실효
cognitive network%QoS%service migration%directed acyclic graph%randomness of failure
针对认知网络高度动态性带来的服务随机失效问题,提出了一种服务迁移方法以保障认知网络的 QoS.首先,采用先迁移、后优化的思想,重新生成关联服务有向无环图(directed acyclic graph,简称DAG),并在此基础上提出 DAG 动态重构算法,将关联服务转化为层次化 DAG 服务;其次,计算关键服务迁移路径,并给出可迁移服务死锁避免理论分析,将迁移服务提前迁移到当前网络空闲资源运行,以缩短服务的执行时间.仿真实验测试了3种故障注入类型下网络服务迁移方案的服务性能.实验结果显示,该方法在弹性网络负载与未知故障情况下具有较好的 QoS保障效果.
針對認知網絡高度動態性帶來的服務隨機失效問題,提齣瞭一種服務遷移方法以保障認知網絡的 QoS.首先,採用先遷移、後優化的思想,重新生成關聯服務有嚮無環圖(directed acyclic graph,簡稱DAG),併在此基礎上提齣 DAG 動態重構算法,將關聯服務轉化為層次化 DAG 服務;其次,計算關鍵服務遷移路徑,併給齣可遷移服務死鎖避免理論分析,將遷移服務提前遷移到噹前網絡空閒資源運行,以縮短服務的執行時間.倣真實驗測試瞭3種故障註入類型下網絡服務遷移方案的服務性能.實驗結果顯示,該方法在彈性網絡負載與未知故障情況下具有較好的 QoS保障效果.
침대인지망락고도동태성대래적복무수궤실효문제,제출료일충복무천이방법이보장인지망락적 QoS.수선,채용선천이、후우화적사상,중신생성관련복무유향무배도(directed acyclic graph,간칭DAG),병재차기출상제출 DAG 동태중구산법,장관련복무전화위층차화 DAG 복무;기차,계산관건복무천이로경,병급출가천이복무사쇄피면이론분석,장천이복무제전천이도당전망락공한자원운행,이축단복무적집행시간.방진실험측시료3충고장주입류형하망락복무천이방안적복무성능.실험결과현시,해방법재탄성망락부재여미지고장정황하구유교호적 QoS보장효과.
According to randomness of service failure for high dynamicity of cognitive networks, a service migration method is proposed to ensure QoS of cognitive networks. Firstly, with the principle of optimization-after-migration, the directed acyclic graph (DAG) of correlated service is regenerated according to the proposed DAG dynamic reconstruction algorithm to transform the correlated service to layered DAG service. Secondly, the critical service migration route is computed and the analysis of migration service deadlock avoidance is provided. By migrating critical service to current idle resources, service execution time can be reduced markedly. Finally, simulation experiments are conducted to test the service speedup performance of both service migration method and waiting-recovery method with three kinds of faults injected. The experiment results show that service migration method can achieve better QoS assurance quality under the flexible network load and unknown fault injection.