Mobile edge computing (MEC) has emerged as an attractive solution by executing computation-intensive services at a powerful edge server instead of mobiles. Two types of data are necessary to this end. One is user-specific data acquired from mobiles, called computation offloading (CO). The other is service-specific data downloaded from a central cloud, called service caching (SC). It is noteworthy that CO and SC decisions are coupled when each user’s service preference (SP) is personalized. Specifically, noting that the optimal SC is to cache services likely to be requested more frequently, the resultant SC tends to be biased to the SP of the user whose offloading rate is high. On the other hand, such an SC decision causes longer computing latency of users with a relatively low offloading rate, which ultimately limits a CO decision for agile MEC services. This work tackles this issue from a sum-utility maximization perspective under radio-resource and computation-latency constraints. The average computation latency is first derived in closed-form by modeling a computation as a stochastic process following a hyper-exponential distribution. Based on it, we first consider the case for homogeneous SP where CO and SC decisions are decoupled. Thus, SC can be deterministically controlled using the homogeneous SP, while CO decision is independently determined, lying between water-filling and channel-inversion allocations. Next, we design a joint CO-and-SC policy for heterogeneous SP. CO and SC decisions are iteratively optimized with the other fixed by leveraging the homogeneous SP’s result. The optimal stopping rules are derived, guaranteeing the sum-utility enhancement. The proposed algorithm’s effectiveness is verified by simulations that the proposed CO-and-SC design for heterogenous SP always outperforms that for homogeneous SP.

中文翻译：

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个性化服务偏好下移动边缘计算的计算卸载和服务缓存

通过在强大的边缘服务器而不是移动设备上执行计算密集型服务，移动边缘计算(MEC) 已成为一种有吸引力的解决方案。为此需要两种类型的数据。一种是从手机获取的用户特定数据，称为计算卸载(CO)。另一种是从中央云下载的特定于服务的数据，称为服务缓存(SC)。值得注意的是，当每个用户的服务偏好时，CO 和 SC 决策是耦合的(SP) 是个性化的。具体地，注意到最优SC是缓存可能被更频繁地请求的服务，由此产生的SC倾向于偏向于卸载率高的用户的SP。另一方面，这样的SC决策会导致卸载率相对较低的用户计算延迟较长，最终限制了敏捷MEC服务的CO决策。这项工作从无线电资源和计算延迟约束下的和效用最大化的角度解决了这个问题。平均计算延迟首先是通过将计算建模为遵循超指数分布的随机过程以封闭形式得出的。在此基础上，我们首先考虑 CO 和 SC 决策解耦的同质 SP 的情况。因此，可以使用同质 SP 确定性地控制 SC，而 CO 决策是独立确定的，介于注水和渠道反转分配之间。接下来，我们为异构 SP 设计了一个联合 CO-和-SC 策略。通过利用同质 SP 的结果，CO 和 SC 决策与另一个固定的迭代优化。导出了最优停止规则，保证了和效用的增强。所提出算法的有效性通过仿真得到验证，所提出的异质 SP 的 CO-and-SC 设计总是优于同质 SP 的设计。保证和效用的提高。所提出算法的有效性通过仿真得到验证，所提出的异质 SP 的 CO-and-SC 设计总是优于同质 SP 的设计。保证和效用的提高。所提出算法的有效性通过仿真得到验证，所提出的异质 SP 的 CO-and-SC 设计总是优于同质 SP 的设计。