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一种时延能耗感知的在轨边缘计算任务卸载调度方法

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全球智能设备的迅速增长引发了对计算资源下沉至边缘的巨大需求,催生了边缘计算范式的出现.同时,计算资源稀缺的偏远地区用户对算力的需求又推动了在轨边缘计算(Orbit Edge Computing,OEC)概念的提出和发展.在OEC场景下,偏远地区用户可以通过星地和星间通信链路将计算任务卸载至部署在低轨卫星上的边缘服务器,以此突破地面计算通信基础设施的限制,为偏远地区的用户提供低时延和高可靠的服务.然而,OEC场景中卫星算力受有限载荷和太阳能转化效率约束,同时还存在低轨卫星绕地导致的高度动态的星地连接造成的可用时隙有限的限制,面临着计算资源稀缺和可用通信时间有限所带来的挑战.因此,需要高质高效的任务卸载决策算法来保证OEC系统的高效运行.然而,目前在OEC场景下任务卸载方法大多在处理任务时无法兼顾计算任务卸载时延与能耗,此外传统方法还缺少对任务多样性的考量.针对上述问题,提出了一种基于自适应大邻域搜索的在轨边缘计算任务卸载方法OEC-ALNS,该方法以任务类型加权的任务处理成本为优化目标,并针对性地提出了基于最小化时延的破坏算子和修复算子来进一步提升搜索效率和卸载调度质量.基于Walker Delta低轨卫星星座和真实计算任务数据的实验结果表明,与传统的OEC-TA(OEC Task Allocation)方法相比,提出的OEC-ALNS方法在多个任务集异构的OEC场景中最多能够减少42.22％的加权任务处理成本和降低42.46％的平均时延.
Delay and Energy-aware Task Offloading Approach for Orbit Edge Computing
The rapid growth of smart devices around the world has created a huge demand for computing resources to sink to the edge,giving rise to the emergence of the edge computing paradigm.At the same time,the demand for computing power in remote areas where computing resources are scarce has driven the concept of orbit edge computing(OEC).In the OEC scenario,users in remote areas can offload computing tasks to edge servers deployed on LEO satellites for processing and execution through the communication link between ground station and satellite and the communication link between satellites in constellation,so as to provide low-latency and high-reliability services for users in remote areas by utilizing satellite computing resources.However,the satellite arithmetic in the OEC scenario is constrained by the limited load and solar energy conversion efficiency,and there is also the limitation of limited available time slots due to highly dynamic satellite-ground connection caused by LEO satellites circling around the earth,which is faced with the challenge of the scarcity of computational resources and the limited available communi-cation latency.Therefore,excellent task offloading decision algorithms are needed to ensure the efficient operation of OEC sys-tems.However,most of the current task offloading approaches for OEC scenario are unable to take into account the delay cost and energy cost when processing tasks,and the traditional approaches also lack the consideration of task diversity.To address the above problems,an adaptive large neighborhood search-based task offloading method for orbit edge computing,OEC-ALNS,is proposed,which takes the task processing cost weighted by task type as the optimization objective,and consists of destruction and repair operators based on the minimization of latency.Experimental results based on Walker Delta LEO satellite constellation and real computing task data show that,compared with the traditional OEC-TA(OEC task allocation)approach,the proposed OEC-ALNS approach could achieve at most 42.22％reduction on the weighted task processing cost and most 42.46％reduction of the average latency cost in OEC scenarios with heterogeneous multiple task sets.

Orbit edge computingLow-orbit satellite constellationComputation task offloadingAdaptive large neighborhood search

王众晓、彭青蓝、孙若骁、徐锡峰、郑万波、夏云霓

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河南大学人工智能学院 郑州 475004

重庆大学计算机学院 重庆 400044

昆明理工大学理学院 昆明 650031

在轨边缘计算 低轨卫星星座 计算任务卸载 自适应大邻域搜索

国家自然科学基金国家自然科学基金河南省重点研发专项河南省自然科学基金青年基金河南省高等学校重点科研项目

621720626216203623111121190024230042170024A520005

2024

10.11896/jsjkx.240100188

计算机科学
重庆西南信息有限公司（原科技部西南信息中心）

计算机科学

CSTPCD北大核心
影响因子：0.944
ISSN：1002-137X
年,卷(期)：2024.51(z1)
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