  Due to the limited resource capacity of edge servers and the high purchase
costs of edge resources, service providers are facing the new challenge of how
to take full advantage of the constrained edge resources for Internet of Things
(IoT) service hosting and task scheduling to maximize system performance. In
this paper, we study the joint optimization problem on service placement,
resource provisioning, and workloads scheduling under resource and budget
constraints, which is formulated as a mixed integer non-linear programming
problem. Given that the frequent service placement and resource provisioning
will significantly increase system configuration costs and instability, we
propose a two-timescale framework for resource management and workloads
scheduling, named RMWS. RMWS consists of a Gibbs sampling algorithm and an
alternating minimization algorithm to determine the service placement and
resource provisioning on large timescales. And a sub-gradient descent method
has been designed to solve the workload scheduling challenge on small
timescales.We conduct comprehensive experiments under different parameter
settings. The RMWS consistently ensures a minimum 10% performance enhancement
compared to other algorithms, showcasing its superiority. Theoretical proofs
are also provided accordingly.
