  This paper considers a network of collaborating agents for local resource
allocation subject to nonlinear model constraints. In many applications, it is
required (or desirable) that the solution be anytime feasible in terms of
satisfying the sum-preserving global constraint. Motivated by this, sufficient
conditions on the nonlinear mapping for anytime feasibility (or non-asymptotic
feasibility) are addressed in this paper. For the two proposed distributed
solutions, one converges over directed weight-balanced networks and the other
one over undirected networks. In particular, we elaborate on uniform
quantization and discuss the notion of {\epsilon}-accurate solution, which
gives an estimate of how close we can get to the exact optimizer subject to
different quantization levels. This work, further, handles general (possibly
non-quadratic) strictly convex objective functions with application to CPU
allocation among a cloud of data centers via distributed solutions. The results
can be used as a coordination mechanism to optimally balance the tasks and CPU
resources among a group of networked servers while addressing quantization or
limited server capacity.
  Index Terms: multi-agent systems, sum-preserving resource allocation,
distributed optimization, anytime feasibility
