边缘网络中不断出现的计算密集和延迟敏感型业务推动了任务迁移技术的快速发展.然而,任务迁移过程中存在应用场景复杂多变、问题建模难度高等技术瓶颈.尤其是考虑用户移动时,如何保证用户服务的稳定性和连续性,设计合理的任务迁移策略仍是一个值得深入探讨的问题.因此,提出了一种移动感知的服务预缓存模型和任务预迁移策略,将任务迁移问题转化为最优分簇与边缘服务预缓存的组合优化问题.首先,基于用户的移动轨迹对当前执行任务状态进行预测,引入动态协作簇和迁移预测半径的概念,提出了 一种面向移动和负载两种任务场景的预迁移模型,解决了何时何地迁移的问题.然后,针对需要迁移的任务,基于最大容忍时延约束分析协作簇半径和簇内 目标服务器数量的极限值,提出了以用户为中心的分布式多服务器间动态协作分簇算法(Distributed Dynamic Multi-server Cooperative Clustering Algorithm,DDMC)以及面向服务缓存的深度强化学习算法(Cache Based Double Deep Q Network,C-DDQN),解决了最优分簇和服务缓存问题.最后,利用服务缓存的因果关系,设计了一种低复杂度的交替最小化服务缓存位置更新算法,求解出了最佳迁移 目标服务器集合,实现了任务迁移中的服务器协作及网络负载均衡.实验结果表明,提出的迁移选择算法具有良好的鲁棒性和系统性能,相比其他迁移算法所消耗的总成本降低了至少12.06％,所消耗的总时延降低了至少31.92％.
Study on Cache-oriented Dynamic Collaborative Task Migration Technology
Task migration technology has been propelled by the continuous emergence of compute-intensive and delay-sensitive services in edge networks.However,the process of task migration is hindered by technical bottlenecks such as complex and time-varying application scenarios,as well as the high difficulty in problem modeling.Especially when considering user movement,de-signing a reasonable task migration strategy that ensures the stability and the continuity of user service remains a persistent chal-lenge.Therefore,a mobile-aware service pre-caching model and task pre-migration strategy are proposed to transform the problem of task migration into an optimization problem that combines optimal clustering strategies with edge service pre-caching.First of all,the current state of the task is initially predicted based on the user's movement trajectory.To solve the problem of when and where to migrate,a pre-migration model for two task scenarios,namely mobile and load,is proposed by introducing the concept of dynamic cooperation cluster and migration prediction radius.And then,according to the tasks that need to be migrated,the maxi-mum tolerant delay constraint is utilized to derive the limit value of cooperative cluster radius and target server quantity in a clus-ter.Subsequently,a user-centric distributed dynamic multi-server cooperative clustering algorithm(DDMC)and a cache-based double deep Q network algorithm(C-DDQN)for service are proposed to solve the problem of optimal clustering and service ca-ching.Finally,a low-complexity alternate minimization service cache location update algorithm is designed using the causality of service caches to achieve the optimal set of migration target servers,which realize server collaboration and network load balancing in task migration.Experimental results demonstrate the robustness and the system performance of the proposed migration selec-tion algorithm.Compared with other algorithms,the total cost consumed is reduced by at least 12.06％,the total latency con-sumed is reduced by at least 31.92％.
Mobile edge computingService cacheDynamic collaborative clusterTask migrationDeep reinforcement learning
万方数据
维普
