  Mobile edge computing (MEC) is emerging to support delay-sensitive 5G
applications at the edge of mobile networks. When a user moves erratically
among multiple MEC nodes, the challenge of how to dynamically migrate its
service to maintain service performance (i.e., user-perceived latency) arises.
However, frequent service migration can significantly increase operational
cost, incurring the conflict between improving performance and reducing cost.
To address these mis-aligned objectives, this paper studies the performance
optimization of mobile edge service placement under the constraint of long-term
cost budget. It is challenging because the budget involves the future uncertain
information (e.g., user mobility). To overcome this difficulty, we devote to
leveraging the power of prediction and advocate predictive service placement
with predicted near-future information. By using two-timescale Lyapunov
optimization method, we propose a T-slot predictive service placement (PSP)
algorithm to incorporate the prediction of user mobility based on a frame-based
design. We characterize the performance bounds of PSP in terms of cost-delay
trade-off theoretically. Furthermore, we propose a new weight adjustment scheme
for the queue in each frame named PSP-WU to exploit the historical queue
information, which greatly reduces the length of queue while improving the
quality of user-perceived latency. Rigorous theoretical analysis and extensive
evaluations using realistic data traces demonstrate the superior performance of
the proposed predictive schemes.
