A Novel Multi-Edge Cooperative Offloading Genetic Algorithm towards Environmental Monitoring System

In environmental monitor architecture, the usual way of processing data is centralized. On the one hand, this leads to a long time of data processing, and with the increasing number of monitoring sensors, the pressure of data processing in data centers increases sharply.  The edge computing provides a feasible solution to relieve the processing pressure and decrease the delay of data processing in data center. However, in the environmental monitor system, the edge servers are often load unbalance because of the nonuniform distribution of sensors. While, it is usually ignored in recently researches. In this paper, we proposed a cooperative offloading strategy (MECOGA) based on genetic algorithm (GA). It aims to ease the data processing pressure and reduce the data processing time in the data center.  In addition, the load balance of edge servers is also one of our optimization goals.  In this paper, we aim at decreasing task processing delay and improving the system load balancing. Contrast to other algorithms, our MECOGA decreases the delay near 20% before collaborative loading. And the balance variance is 15% smaller than the Balance strategy.