  This paper considers an energy harvesting (EH) based multiuser mobile edge
computing (MEC) system, where each user utilizes the harvested energy from
renewable energy sources to execute its computation tasks via computation
offloading and local computing. Towards maximizing the system's weighted
computation rate (i.e., the number of weighted users' computing bits within a
finite time horizon) subject to the users' energy causality constraints due to
dynamic energy arrivals, the decision for joint computation offloading and
local computing over time is optimized {\em over time}. Assuming that the
profile of channel state information and dynamic task arrivals at the users is
known in advance, the weighted computation rate maximization problem becomes a
convex optimization problem. Building on the Lagrange duality method, the
well-structured optimal solution is analytically obtained. Both the users'
local computing and offloading rates are shown to have a monotonically
increasing structure. Numerical results show that the proposed design scheme
can achieve a significant performance gain over the alternative benchmark
schemes.
