Online resource allocation problem with budget constraints has a wide range of applications in network science and operation research. In this problem, the decision maker needs to make actions that consume resources to accumulate rewards. Contrary to prior work, we introduce a non-linear and non-separable regularizer to this problem that acts on the total resource consumption. The motivation for introducing the regularizer is allowing the decision maker to tradeoff the reward maximization and metrics optimization such as load-balancing and fairness that appears frequently in practical needs. Our goal is to simultaneously maximize additively separable rewards and the value of a non-separable regularizer without violating resource budget constraints. We develop a primal-dual-type online algorithm for this problem in the online learning setting and confirm its no-regret guarantee and zero constraint violations for stochastic i.i.d. input models. Furthermore, the general convex resource consumption functions allow our model to be more applicable. Numerical experiments are conducted to demonstrate the theoretical guarantee of our algorithm.

Network address translation (NAT) is a basic functionality in cloud gateways. With the increasing traffic volume and number of flows introduced by the cloud tenants, the NAT gateway needs to be implemented on a cluster of servers. We propose to scale up the gateway servers, which could reduce the number of servers so as to reduce the capital expense and operation expense. We design HyperNAT, which leverages smartNICs to improve the server's processing capacity. In HyperNAT, the NAT functionality is distributed on multiple NICs, and the flow space is divided and assigned accordingly. HyperNAT overcomes the challenge that the packets in two directions of one connection need to be processed by the same NAT rule (named two-direction consistency, TDC) by cloning the rule to both data paths of the two directions. Our implementation and evaluation of HyperNAT show that HyperNAT could scale up cloud gateway effectively with low overhead.