Provisioning In-band Network Telemetry as a service for INT-based and INT follow-up applications in an online manner suffers multiple challenges, including control decisions of different INT path algorithms, resources provisioning coupled over time, and the unforeseeable INT query workloads. To overcome these challenges, this study formulates an online non-linear time-varying integer programming problem that maximizes the overall quality of service through algorithm selection and INT query workloads distribution. Specifically, an online learning algorithm is proposed to make the fractional decisions and it uses a primal–dual mechanism by simultaneously minimizing a convex problem and maximizing a concave problem, based on the previously observed inputs. Furthermore, we design a randomized rounding strategy to convert these fractional decisions to integers with an expectation guarantee. We implement our system prototype based on INTCollector upon real devices, i.e., Barefoot Wedge100BF and Inspur Rack. Our rigorous theoretical analysis shows that our INTaaS achieves sub-linear growth on dynamic regret for the optimal loss and incurs sub-linear growth on dynamic fit for the long-term constraint violations. Finally, extensive evaluations on real-world data indicate that INTaaS exhibits up-lift performance up to 40% over other state-of-the-art algorithms.

In-band Network Telemetry (INT) is proposed to detect networks via injecting specific probes to collect the hop-by-hop metadata within programmable switches. But there exist multiple challenges to conducting INT at Computing Power Network, such as control decisions of different INT frequencies, and the unforeseeable INT query workloads. In this study, we formulate an online non-linear time-varying integer programming problem that aims to maximize the overall quality of service through both frequency selection and INT query workload distribution. To achieve this, we propose an online learning, INTService, which utilizes a primal-dual mechanism to make fractional decisions. At last, extensive evaluations show that our proposed INTService exhibits up-lift performance 40% on average over other state-of-the-art algorithms.

Provisioning machine learning inference near the users at the network’s edge is emerging as a promising area for Edge AI. Due to the excessive energy consumption of edge devices, their owners often lack the motivation to actively contribute to their edge resources. To tackle this issue, we propose an incentive mechanism based on auctions, which enables edge device owners to submit their bids and compensates such bids via reward payments, ensuring the minimization of inference accuracy loss. We formulate a nonlinear mixed-integer program problem with the objective of minimizing the social cost, including accuracy loss cost, edge device cost, and service provider cost in the edge inference system. Then an Online Learning algorithm is devised to find the solutions, based on the primal-dual. To calculate the remuneration, we design a payment allocation algorithm based on the bid-winning probabilities. Our rigorous theoretical analysis shows that our algorithm designed achieves sub-linear growth on dynamic regret and dynamic fit over time while preserving the economic properties of truthfulness and individual rationality. Finally, multiple experiments validate the efficacy of the proposed auction mechanism algorithm from various perspectives compared with three other existing algorithms.

Network visualization is essential for network operators to diagnose ongoing network failures and understand the quality of the network. In-Band Network Telemetry (INT) supports network visualization by inserting P4 switch state information (e.g., queue length, hop latency, and link utilization) into the specific INT packets. In order to achieve network-wide coverage, paths of INT packets need to be delicately designed to ensure non-overlapping, high performance, and low overhead. However, existing INT path planning solutions ignore capturing the dynamic network status and fail to obtain the optimum. In this paper, we model the INT path planning based on the directed Edge Cover problem upon the dynamic network status, with the objective of minimizing the INT path latency. Although it is actually a non-linear integer program problem, by adopting delicate transformations, we design approximation algorithms with performance guarantees. We implement our system prototype INTView based on INTCollector upon real devices, i.e., Barefoot Wedge100BF and Inspur Rack. Extensive evaluation upon realistic settings shows that our proposed algorithm achieves 2x performance improvement regarding the completion time, compared with state-of-the-art schemas.

Recently, the existing distributed systems such as big data processing systems (Spark, Hadoop, etc.) and distributed training systems (Pytorch, Parameter Server, Tensorflow, etc.) need to send heartbeat packets to monitor the operation of tasks and detect the status of nodes. This paper studies the problem of how to design an efficient heartbeat mechanism for cloud datacenter, and we provide a new vision for solving this issue through the programmable switch. We firstly analyzed the format of the heartbeat packet and the aggregation process of heartbeat packets. Based on these, we designed an adaptive heartbeat mechanism by establishing an optimization problem with the goal of minimizing the aggregation cost. Furthermore, a heuristic scheduling algorithm is proposed and implemented on the simulation platform. The simulation results show that our proposed algorithm performs much better than the default heartbeat mechanism. That is, our proposed algorithm can effectively reduce the processing overhead and the network load.