Packet sampling is crucial for managing datacenter networks, serving fault diagnosis, traffic measurement, and intrusion detection functions. However, traditional sampling techniques, such as those based on sketches or ports, either lack packet–level granularity or provide insufficient visibility, leading to functional performance degradation. Recent research has employed the software-defined networking (SDN) model to enable flow-based packet sampling. However, these approaches often introduce substantial control and computation overhead, limiting their scalability. This paper presents Viper, a novel priority-based, high-visibility per-flow packet sampling mechanism tailored to address these challenges. Specifically, Viper leverages existing priority-based traffic scheduling mechanisms to prioritize shorter flows over longer ones. Then, a logical centralized controller orchestrates sampling policies for packets of different priorities. In-depth analysis indicates that the orchestration performed by the controller significantly impacts Viper’s performance. Consequently, we model this process as a nonlinear optimization problem, seeking to maximize the utility of sampling. Then, we propose an online primal–dual interior–point algorithm to address this optimization problem and prove the algorithm’s convergence, optimality, and efficiency. Experimental results show that Viper increases visibility by 3.83% to 8.3%, with negligible control overhead and a substantial reduction in sampling load by at least 20.51%.

Website Fingerprinting (WF) attack can be mitigated throughrandom camouflageorpair camouflage.Random camouflageinserts random dummy packets into the traces according to pre-defined rules. It can be compromised easily by machine learning-based WF attacks.Pair camouflageobfuscates the distinguishing features of paired websites by inserting elaborated perturbations into raw traces, thereby misleading the attacker. It is costly in maintaining a perturbation generator for each pair of websites. Based on these insights, we proposeStinger, a novel data poisoning based WF defense, which enables effective defense against WF attacks with low bandwidth overhead and only maintains one generator for all websites.Stingerexploits the idea of poisoning by contaminating the model directly in such a way that the WF attacks only classify based on the inserted poison sequences, thus being low overhead and website independent. We experimentally evaluateStingerusing the DF and AWF datasets. The results show that Stinger improves the successful defending rate by an average of 20.37% and 22.83% while reducing overhead by 85.88% and 81.35%, respectively.

Inter-datacenter network applications generate massive coflows for purposes, e.g., backup, synchronization, and analytics, with deadline requirements. Decentralized coflow scheduling frameworks are desirable for their scalability in cross-domain deployment but grappling with the challenge of information agnosticism for lack of cross-domain privileges. Current information-agnostic coflow scheduling methods are incompatible with decentralized frameworks for relying on centralized controllers to continuously monitor and learn from coflow global transmission states to infer global coflow information. Alternative methods propose mechanisms for decentralized global coflow information gathering and synchronization. However, they require dedicated physical hardware or control logic, which could be impractical for incremental deployment. This article proposes Slark, a decentralized deadline-aware coflow scheduling framework, which meets coflows’ soft and hard deadline requirements using only local traffic information. It eschews requiring global coflow transmission states and dedicated hardware or control logic by leveraging multiple software-implemented scheduling agents working independently on each node and integrating such information agnosticism into node-specific bandwidth allocation by modeling it as a robust optimization problem with flow information on the other nodes represented as uncertain parameters. Subsequently, we validate the performance robustness of Slark by investigating how perturbations in the optimal objective function value and the associated optimal solution are affected by uncertain parameters. Finally, we propose a firebug-swarm-optimization-based heuristic algorithm to tackle the non-convexity in our problem. Experimental results demonstrate that Slark can significantly enhance transmission revenue and increase soft and hard deadline guarantee ratios by 10.52% and 7.99% on average.

As Internet of Things (IoT) keeps growing, IoT-side intelligence services, such as intelligent personal assistant, healthcare surveillance, and smart home service, offload more and more complex machine-learning (ML) inference workloads to cloud clusters. GPUs have been widely adopted to accelerate the execution of these ML inference workloads. However, current cluster management systems guarantee low tail latency for ML inferences using resource over-provisioning and small batch sizes, resulting in a serious waste of GPU resources and increasing the service costs greatly. To mitigate poor GPU utilization, we present AutoInfer, a self-driving cluster management system for ML inference serving in GPU clusters, where users express only the latency and accuracy requirements for their workloads without needing to specify the model variant, GPU provisioning strategy, and batching mechanism. AutoInfer extends the matrix factorization model to automatically recommend model variants for each new incoming ML inference workload with respect to latency and accuracy requirements, by identifying similarities to previously scheduled workloads. During runtime, AutoInfer leverages online telemetry data and deep reinforcement learning to adaptively adjust the GPU allocation and batch size to account for load variations while minimizing the effects on tail latency service level objectives (SLOs). Testbed experiments show that AutoInfer is able to improve the average GPU utilization by up to 77% and keep the tail latency SLO violations under 5.5%.

Mobile edge computing (MEC) is becoming increasingly popular due to its remarkable computing capacities in close proximity to end users or devices. With the widespread use of Industrial Internet of Things, more and more cloud service providers move their services to the edge of the network for a better quality of service and become MEC providers. These MEC providers require to rent wide area network (WAN) connections to transfer industrial data, which is a considerable expense. In this article, we propose a framework calledSublessorto reduce the WAN transmission cost for a group of cooperative MEC providers. The key idea ofSublessoris allowing some specific MEC providers to act as Internet transit brokers, transmitting not only their own network traffic but also the traffic of their partners under a reasonable reselling price. This article formulates the problem as a mixed-integer programming and finds the most suitable broker number and corresponding reselling price without damaging the profit of both brokers and partners by a deep-reinforcement-learning-based algorithm. Experimental results show that our algorithm can significantly reduce the traffic transmission cost by up to 35%.

In the era of cloud computing, network services are deployed on geographically distributed cloud platforms, which results in a large amount of inter-datacenter traffic. Multi-tier pricing schemes are widely adopted by cloud service providers (CSPs) to charge cloud users for inter-datacenter transmission services. To avoid a severe penalty associated with missing a deadline, cloud users are prone to selecting a sufficiently high service level. However, they are usually unaware of the total traffic volume before accessing the network; hence, a high transmission cost is introduced. In this paper, we propose an online cost-efficient transmission scheme for cloud users with information-agnostic traffic. The basic idea is to split a long-term transmission request into a series of short-term ones. In this scheme, we take into account the CSP’s countermeasures, and model the interactions between the cloud users and the CSP as a Stackelberg game. We show that the optimal number of short-term requests and the associated transmission service levels can be determined with an online algorithm based on Lyapunov optimization. The experimental results reveal that the CSP and the cloud users can achieve a win-win outcome, whereby the transmission cost of cloud users can be reduced by 59 percent.

The cloud resource provision policy of a content provider in the presence of competitors on globally distributed cloud platforms plays a significant role in maximizing its profit. However, developing an optimal resource provision policy is quite challenging, due to the difficulty to capture the competition relationship between two competitive CPs and to obtain the budget of the competitors which is usually kept private. To solve this problem, in this article, we propose a learning-driven cloud resource provision policy for a CP with competitors. We formulate the competition between the CPs as alottery Colonel Blottogame in which the payoff of each region is positively related to the resource advantage achieved by the CP, formulate the budget allocation problem as a Markov decision process, and obtain the sub-optimal resource provision policy by reinforcement learning and deep reinforcement learning-based algorithms. We also prove the convergence of the sub-optimal solution. Finally, we validate our proposed method using real-world CPs statistics. The results show that the budget information is critical for a CP to make policy decisions, and it is better for CPs with smaller budget to focus their budget resources in regions with higher values.

Geographically distributed cloud is a promising technique to achieve high performance for service providers. For inter-datacenter transfers, deadline guarantee and fairness are the two most important requirements. On the one hand, to ensure more transfers finish before their deadlines, preemptive scheduling policies are widely used, leading to the transfer starvation problem and is hence unfair. On the other hand, to ensure fairness, inter-datacenter bandwidth is fairly shared among transfers with per-flow bandwidth allocation, which leads to deadline missing problem. A mechanism that achieves these two seemingly conflicting objectives simultaneously is still missing. In this paper, we propose TINA to schedule network transfers fairly while providing deadline guarantees. TINA allows each transfer to compete freely with each other for bandwidth. More specifically, each transfer is assigned a probability to indicate whether to transmit or not. We formulate the competition among the transfers as an El Farol game while keeping the traffic load under a threshold to avoid congestion. We then prove that the Nash Equilibrium is the optimal strategy and propose a light-weight algorithm to derive it. Finally, both simulations and testbed experiments results show that TINA achieves superior performance than state-of-art methods in terms of fairness and deadline guarantee rate.

As more and more applications are deployed in data centers, they rely on high performance data center networks (DCNs) to meet users' increasing quality of the experience (QoE) requirements. Hence, minimizing the average flow completion time (FCT) has been one of the most important goals for DCNs. However, existing traffic scheduling methods assume either prior knowledge of flows (i.e., sizes and deadlines) or symmetric topologies (i.e., Fat-Tree or Bcube). In practice, it is difficult to obtain the information of flows. Moreover, even with symmetric topology design, the DCNs will become asymmetric due to the inevitable link failure and congestion. In this case, it is a great challenge to minimize the average FCT in DCNs. In this paper, we propose a flowlet based information-agnostic traffic scheduling mechanism. The key idea of our method is leveraging multiple priority queues in switches to demote the priority of flows dynamically on the flowlet level. More specifically, the priority of a flow will be demoted according to the number of flowlets it has sent, which follows the shortest job first discipline. We formulate the average FCT minimization problem as a nonlinear Sum-of-Ratios problem and design two heuristic methods to derive the sub-optimal demotion thresholds. Experiment results show that our method can reduce the average FCT by up to 15.35% with a realistic workload, as compared to the state-of-the-art traffic scheduling methods.

Traditional schemes achieving load balancing in asymmetric topology, which need to maintain global or local congestion information, turn out to be complicated to implement. One recent research has verified that flowlet switching is more simple and efficient to achieve adaptive load balancing in asymmetric topology. Nevertheless, one tricky problem lies in determining the flowlet timeout value, δ. Setting it too small would risk reordering issue while setting it too large would reduce flowlet opportunities. In this paper, by formulating the timeout setting problem with a stationary distribution of Markov chain, we give a theoretical reference for setting an appropriate timeout value in flowlet switching based load balancing scheme. Then, we implement a flowlet switching based load balancing scheme, called EasyLB, by extending OpenFlow protocol. Experiment results show that, by setting timeout value following the preceding theoretical reference, EasyLB is adaptive to asymmetric topology and achieves fast convergence of load balancing after link failures.