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Service function chaining (SFC), consisting of a sequence of virtual network functions (VNFs), is the de-facto service provisioning mechanism in VNF-enabled data centers (VDCs). However, for the SFC, the dynamic and diverse virtual machine (VM) traffic must traverse a sequence of VNFs possibly installed at different locations at VDCs, resulting in prolonged network delay, redundant network traffic, and large consumption of cloud resources (e.g., bandwidth and energy). Such adverse effects of the SFC, which we refer to as SFC traffic storm, significantly impede its efficiency and practical implementation.In this paper, we solve the SFC traffic storm problem by proposing AggVNF, a framework wherein the VNFs of an SFC are implemented into one aggregate VNF while multiple instances of aggregate VNFs are available in the VDC. AggVNF adaptively allocates and migrates aggregate VNFs to optimize cloud resources in dynamic VDCs while achieving the load balance of VNFs. At the core of the AggVNF are two graph-theoretical problems that have not been adequately studied. We solve both problems by proposing optimal, approximate, and heuristic algorithms. Using real traffic patterns in Facebook data centers, we show that a) our VNF allocation algorithms yield traffic costs 56.3% smaller than the latest research using the SFC design, b) our VNF migration algorithms yield 84.2% less traffic than the latest research using the SFC design, and c) VNF migration is an effective technique in mitigating dynamic traffic in VDCs, reducing the total traffic cost by up to 24.8%. 

We study a new variation of the Traveling Salesman Problem (TSP) called the Budget-Constrained Traveling Salesman Problem (BC-TSP). BC-TSP is inspired by a few emerging network applications, such as robotic sensor networks. We design a prize-driven multi-agent reinforcement learning (MARL) framework to solve the BC-TSP. The main novelty of the framework, named P-MARL, is that it makes a connection between the prize maximization in BC-TSP and the cumulative reward maximization in reinforcement learning (RL) to design a more efficient MARL algorithm. In particular, P-MARL integrates the prizes available at nodes into the reward model of the MARL to guide the cooperative effort of multiple learning agents. Via extensive simulations using synthetic data of state capital cities of the U.S., we show that a) the P-MARL outperforms the existing prize-oblivious MARL work by collecting 28.8 % of more prizes under the same budget constraints, b) it takes two orders of magnitudes of shorter training time than the state-of-the-art deep reinforcement learning-based approach while collecting 45.3 % more prizes under the same budgets, and c) P-MARL collects prizes at least 91.9% of optimal obtained by the Integer Linear Programming (ILP) under different network parameters.