Cache Oriented Migration Decision and Resource Allocation in Edge Computing
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摘要: 边缘计算通过在网络边缘侧为用户提供计算资源和缓存服务,可以有效降低执行时延和能耗。由于用户的移动性和网络的随机性，缓存服务和用户任务会频繁地在边缘服务器之间迁移，增加了系统成本。该文构建了一种基于预缓存的迁移计算模型，研究了资源分配、服务缓存和迁移决策的联合优化问题。针对这一混合整数非线性规划问题，通过分解原问题，分别采用库恩塔克条件和二分搜索法对资源分配进行优化，并提出一种基于贪婪策略的迁移决策和服务缓存联合优化算法(JMSGS)获得最优迁移决策和缓存决策。仿真结果验证了所提算法的有效性，实现系统能耗和时延加权和最小。Abstract: Edge computing provides computing resources and caching services at the network edge, effectively reducing execution latency and energy consumption. However, due to user mobility and network randomness, caching services and user tasks frequently migrate between edge servers, increasing system costs. The migration computation model based on pre-caching is constructed and the joint optimization problem of resource allocation, service caching and migration decision-making is investigated. To address this mixed-integer nonlinear programming problem, the original problem is decomposed to optimize the resource allocation using Karush-Kuhn-Tucker condition and bisection search iterative method. Additionally, a Joint optimization algorithm for Migration decision-making and Service caching based on a Greedy Strategy (JMSGS) is proposed to obtain the optimal migration and caching decisions. Simulation results show the effectiveness of the proposed algorithm in minimizing the weighted sum of system energy consumption and latency.
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Key words:
- Edge computing /
- Migrate strategy /
- Service cache /
- Resource allocation
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1 二分搜索的上行传输功率分配算法
初始化：传输功率$ {p_i} $范围，收敛阈值$r$ (1) 根据式(21)计算得出$ \phi (p_i^{{\text{max}}}) $ (2) if $ \phi (p_i^{{\text{max}}}) \lt 0 $ then (3) $ p_i^* = p_i^{{\text{max}}} $ (4) else (5) 初始化参数$ {p_l} = p_i^{{\text{min}}} $, $ {p_h} = p_i^{{\text{max}}} $ (6) end if (7) if $ \phi ({p_m}) < 0 $ then (8) ${p_l} = {p_m}$ (9) else (10) $ {p_h} = {p_m} $ (11) end if (12) until $({p_h} - {p_l}) \le r$ (13) $ p_i^* = ({p_l} + {p_h})/2 $ 
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2 基于贪婪决策的迁移缓存联合优化算法
初始化：${N_{{\text{local}}}}{\text{ = }}{N_{\text{0}}}$, ${N_{{\text{mec}}}} = \phi $ (1) for $i{\text{ = 1:}}N$ (2) for $m{\text{ = 1:}}M_i^{{\text{sort}}}$ (3) 计算用户的代价增益函数$\Delta C(m)$ (4) end for (5) 将每个用户的代价增益函数倒序排列，加入序列$N_i^{{\text{sort}}}$ (6) for $ i{\text{ = 1:}}N_i^{{\text{sort}}} $ 计算目标函数值 (7) if ${\text{ET}}{{\text{C}}_{o + i}}{\text{ \lt ET}}{{\text{C}}_o}$ (8) $ \alpha = 1 $, $ \vartheta = 1 $ or $ \varpi $=1 (9) else (10) 保持原有模式 (11) end if (12) if $ \varpi = 1 $, $ C_m^{\text{b}} + C_m^{\text{a}} \le C_m^{\max } $ (13) 将应用程序缓存至服务器 (14) else if $ X_m^{\min } < {X_i} $ (15) 更新服务器状态 (16) else (17) 本地执行 (18) end if
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表 1 仿真参数
参数 数值 任务大小$\lambda $(Mb) 10～20 应用程序大小$b$(Gb) 1～5 本地计算能力${f_{{\text{loc}}}}$(GHz) 0.5～1.5 边缘服务器数目(个) 5～20 噪声功率谱密度${N_{\text{0}}}$(dBm/Hz) –174 系统带宽B(MHz) 1～2 服务器计算能力${f_{{\text{es}}}}$(GHz) 15～25 服务器缓存容量(Gb) 20-30
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