Serverless computing is emerging as an enabling technology for elastic and low-cost AI applications in the edge of core networks. It allows AI developers to decompose a complex training and time-sensitive inference task into multiple functions with dependency, and upload the task to a Multi-access Edge Computing platform (MEC) for execution. Serverless computing adopts a popular design principle: the disaggregation of storage and computation, making the functions ‘stateless’. However, most AI applications are ‘stateful’ and rely on an external storage service to manage their states (ephemeral data). This will incur a prohibitively long delay for delay-sensitive AI applications if external services storing the states are far from the serverless functions. Motivated by this critical issue, in this paper we investigate a fundamental problem in serverless computing – the stateful serverless application placement problem, for which, we first propose an efficient heuristic algorithm, and then devise an approximation algorithm with a provable approximation ratio for one of its special cases. We also consider the online version of the problem, and develop an online learning-driven algorithm with a bounded regret. The crux of the online algorithm is the adoption of the multi-armed bandits technique for dynamic admissions of inference requests, under the uncertainty of both data volumes of requests and network delays. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results show that the proposed algorithms outperform their counterparts, reducing at least 32% in the total cost and 27% of the average delay.

More and more 5G and AI applications demand flexible and low-cost processing of their traffic through diverse virtualized network functions (VNFs) to meet their security and privacy requirements. As such, the Network Function Virtualization (NFV) market has been emerged as a major service market that allows network service providers to trade their network services among customers. Since each service market usually involves complex interplays among players with different roles, efficient mechanisms that guarantee stable and efficient operations of the NFV market are urgently needed. One fundamental problem in the NFV market is how to maximize the social welfare of all players so that all players have incentives to participate in the activities of the market. In this paper, we first formulate a novel social welfare maximization problem in an NFV market of a multi-tier edge cloud network, with the aim to maximize the total revenue collected from all players, and we implement VNF services on Virtual Machines (VMs) leased by service providers to fulfill customers with service requests, where the edge cloud network consists of both cloudlets in edge networks and remote data centers in the core network. We then design an efficient incentive-compatible mechanism for the problem, and analyze the existence of a Nash equilibrium of the mechanism. Also, we consider an online social welfare maximization problem with uncertain values of customers and without the knowledge of future request arrivals, for which we devise an online learning algorithm by adopting the Multi-Armed Bandits (MAB) method with a bounded regret. We finally evaluate the performance of the proposed mechanisms through simulations and a testbed. Results show that the proposed mechanisms deliver up to 27% higher social welfare than those of existing studies

Network Function Virtualization (NFV), characterized by implementing hardware network functions as software in virtual machines (VMs), is a promising technique to provide flexible and agile network services. Meanwhile, mobile edge cloud (MEC) is envisioned as an enabling technology for providing extreme low-latency network services by deploying cloudlets within the proximity of mobile users. However, it is challenging to find an optimal placement for VNFs when considering service chaining requirements in an MEC with cloudlets having limited computing capacity and users having stringent delay requirements. In addition, security requirements of network services in the MEC makes the problem even harder. In this paper, we investigate the problem of trust-aware throughput maximization problem in an MEC, by incorporating QoS, trust requirements of user requests and allowing VNF being shared among mobile users. We first devise an exact solution to the problem by formulating an Integer Linear Program. Due to the inscalability of ILPs for large problem sizes, we propose an efficient heuristic algorithm based on a randomized rounding technique. We finally evaluate the performance of our proposed algorithms against the state-of-the-art by simulations. Results show that the proposed algorithms achieve higher system throughput than their counterparts by 10%.

With the fast development of next-generation networking techniques, a Network Function Virtualization (NFV) market is emerging as a major market that allows network service providers to trade various network services among consumers. Therefore, efficient mechanisms that guarantee stable and efficient operations of the NFV market are urgently needed. One fundamental problem in the NFV market is how to maximize the social welfare of all players, so they have incentives to participate in activities of the market. In this paper, we first formulate the social welfare maximization problem, with an aim to maximize the total revenue of all players in the NFV market. For the social welfare maximization problem, we design an efficient incentive-compatible mechanism and analyze the existence of a Nash equilibrium of the mechanism. We also consider an online social welfare maximization problem without the knowledge of future request arrivals. We devise an online learning algorithm based on Multi-Armed Bandits (MAB) to allow both customers and network service providers to make decisions with uncertainty of customers' strategy. We evaluate the performance of the proposed mechanisms by both simulations and test-bed implementations, and the results show that the proposed mechanisms obtain at most 23% higher social welfare than existing studies.

Stringent delay requirements of many mobile applications have led to the development of mobile edge clouds, to offer low latency network services at the network edges. Most conventional network services are implemented via hardware-based network functions, including firewalls and load balancers, to guarantee service security and performance. However, implementing hardware-based network functions usually incurs both a high capital expenditure (CAPEX) and operating expenditure (OPEX). Network Function Virtualization (NFV) exhibits a potential to reduce CAPEX and OPEX significantly, by deploying software-based network functions in virtual machines (VMs) on edge-clouds. We consider a fundamental problem of NFV-enabled multicasting in a mobile edge cloud, where each multicast request has both service function chain and end-to-end delay requirements. Specifically, each multicast request requires chaining of a sequence of network functions (referred to as a service function chain) from a source to a set of destinations within specified end-to-end delay requirements. We devise an approximation algorithm with a provable approximation ratio for a single multicast request admission if its delay requirement is negligible; otherwise, we propose an efficient heuristic. Furthermore, we also consider admissions of a given set of the delay-aware NFV-enabled multicast requests, for which we devise an efficient heuristic such that the system throughput is maximized, while the implementation cost of admitted requests is minimized. We finally evaluate the performance of the proposed algorithms in a real test-bed, and experimental results show that our algorithms outperform other similar approaches reported in literature.