This paper presents a novel online task scheduling strategy called SATS, designed for a hierarchical Mobile Edge Computing (MEC) architecture. SATS utilizes a Simulated Annealing-based method for scheduling tasks and demonstrates that Simulated Annealing can be a viable solution for online task scheduling, not just for offline task scheduling. However, the paper also emphasizes that the effectiveness of SATS depends on the precision of service request predictions. The paper evaluates three types of predictors: neutral, conservative, and optimistic. It concludes that when using a conservative predictor that overestimates the number of service requests, SATS performs the best in terms of higher acceptance rates and shorter processing times. In fact, when using a conservative predictor, SATS can offer an acceptance ratio that is only 5% lower than what it could have been if SATS had known the frequency of service request arrivals beforehand and deviates less than 20% from this acceptance ratio in all conducted experiments.