Coalitional Game-Based Cooperative Computation Offloading in MEC for Reusable Tasks
Coalitional Game-Based Cooperative Computation Offloading in MEC for Reusable Tasks
复制标题
MEC 中基于联合游戏的协作计算卸载以实现可重用任务
DOI：
10.1109/jiot.2021.3064186
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发表时间：
2021
影响因子：
10.6
通讯作者：
Guizani Mohsen
中科院分区：
文献类型：
--
作者：
Yang Xuemei;Luo Hong;Sun Yan;Zou Junwei;Guizani Mohsen
移动边缘计算(MEC)已经成为物联网(IoT)应用减少延迟和节能的一种很有前途的解决方案。针对松散耦合的应用，多个设备可以使用相同的任务代码和不同的输入参数来获得不同的结果。这促使我们研究设备之间的协作，以消除重复的数据传输。利用联盟博弈理论，我们将可重用任务的合作卸载过程形式化为联盟博弈，以最大限度地节省成本。特别地，我们首先针对单任务模型提出了一种高效的基于联盟博弈的合作卸载(CGCO)算法，然后将其扩展为针对多任务模型的CGCO-M算法，并联合应用两阶段流水作业调度方法，从而帮助获得最优的任务调度。证明了我们的CGCO和CGCO-M算法能够在保证收敛的前提下获得纳什稳定解，并且CGCO算法能够获得最优解。仿真结果表明，CGCO算法与最优穷举搜索法(ES)是等价的，而CGCO-M算法在代价比上与ES算法相近。与本地执行相比，CGCO和CGCO-M的成本比分别下降了41.08%和83.70%。同时，当重用因子为0.1和1时，CGCO-M分别减少了41.46%和89.74%，这意味着CGCO-M可以以更高的重用密度节省更多的成本。
Mobile-edge computing (MEC) has been a promising solution for Internet-of-Things (IoT) applications to obtain latency reduction and energy savings. In view of the loosely coupled application, multiple devices can use the same task code and different input parameters to obtain diverse results. This motivates us to study the cooperation between devices for eliminating the repeated data transmission. Leveraging coalitional game theory, we formalize the cooperative offloading process of a reusable task into a coalitional game to maximize the cost savings. In particular, we first propose an efficient coalitional game-based cooperative offloading (CGCO) algorithm for the single-task model, and then expand it into a CGCO-M algorithm for the multiple-task model with jointly applying a two-stage flow shop scheduling approach, which helps to obtain an optimal task schedule. It is proved that our CGCO and CGCO-M can achieve the Nash-stable solution with convergence guarantee, and CGCO can obtain an optimal solution. The simulations show that CGCO is equal to the optimal exhaustive search (ES) method and CGCO-M is close to ES in terms of cost ratios. Cost ratios of CGCO and CGCO-M are significantly down by 41.08% and 83.70% compared to local executions, respectively. Meanwhile, CGCO-M obtains 41.46% and 89.74% reductions when reuse factors are 0.1 and 1, which means CGCO-M can save more cost with higher reuse density.