Yikai Xiao

Huazhong University of Science and Technology

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Fangming Liu

Huazhong University of Science and Technology

Jian Guo

Shenzhen University

Qixia Zhang

UiT The Arctic University of Norway

Panpan Jin

Huazhong University of Science and Technology

Yipei Niu

Huazhong University of Science and Technology

Chen Qian

University of California, Santa Cruz

Rongpei Shi

Harbin Institute of Technology

Miao Zhao

Chengdu University of Information Technology

Jia Wang

Yunnan University of Finance And Economics

Yang Wang

Wuhan University

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Huazhong University of Science and Technology

Harbin Institute of Technology

Zhejiang Ocean University

Dalian University of Technology

Nanjing University of Information Science and Technology

Wuhan University

The Ohio State University

Huawei Technologies (China)

Beijing Jiaotong University

Shenzhen University

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PostMan: Rapidly Mitigating Bursty Traffic via On-Demand Offloading of Packet Processing

IEEE Transactions on Parallel and Distributed Systems (2021)

Yipei Niu Panpan Jin Jian Guo Yikai Xiao Rongpei Shi

Unexpected bursty traffic brought by certain sudden events, such as news in the spotlight on a social network or discounted items on sale, can cause severe load imbalance in backend services. Migrating hot data - the standard approach to achieve load balance - meets a challenge when handling such unexpected load imbalance, because migrating data will slow down the server that is already under heavy pressure. This article proposes PostMan, an alternative approach to rapidly mitigate load imbalance for services processing small requests. Motivated by the observation that processing large packets incurs far less CPU overhead than processing small ones, PostMan deploys a number of middleboxes called helpers to assemble small packets into large ones for the heavily-loaded server. This approach essentially offloads the overhead of packet processing from the heavily-loaded server to helpers. To minimize the overhead, PostMan activates helpers on demand, only when bursty traffic is detected. The heavily-loaded server determines when clients connect/disconnect to/from helpers based on the real-time load statistics. To tolerate helper failures, PostMan can migrate connections across helpers and can ensure packet ordering despite such migration. Driven by real-world workloads, our evaluation shows that, with the help of PostMan, a Memcached server can mitigate bursty traffic within hundreds of milliseconds, while migrating data takes tens of seconds and increases the latency during migration.

Packet loss

10.1109/tpds.2021.3092266

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Joint Optimization of Chain Placement and Request Scheduling for Network Function Virtualization

Qixia Zhang Yikai Xiao Fangming Liu John C. S. Lui Jian Guo

Compared with executing Network Functions (NFs) on dedicated hardwares, the recent trend of Network Function Virtualization (NFV) holds the promise for operators to flexibly deploy software-based NFs on commodity servers. However, virtual NFs (VNFs) are normally "chained" together to provide a specific network service. Thus, an efficient scheme is needed to place the VNF chains across the network and effectively schedule requests to service instances, which can maximize the average resource utilization of each node in service and simultaneously minimize the average response latency of each request. To this end, we formulate first VNF chains placement problem as a variant of bin-packing problem, which is NP-hard, and we model request scheduling problem based on the key concepts from open Jackson network. To jointly optimize the performance of NFV, we propose a priority-driven weighted algorithm to improve resource utilization and a heuristic algorithm to reduce response latency. Through extensive trace-driven simulations, we show that our methods can indeed enhance performance in diverse scenarios. In particular, we can improve the average resource utilization by 33.4% and can reduce the average total latency by 19.9% as compared with the state-of-the-art methods.

Network Functions Virtualization

Virtual network

Network Virtualization

10.1109/icdcs.2017.232

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NFVdeep

Yikai Xiao Qixia Zhang Fangming Liu Jia Wang Miao Zhao

With the evolution of network function virtualization (NFV), diverse network services can be flexibly offered as service function chains (SFCs) consisted of different virtual network functions (VNFs). However, network state and traffic typically exhibit unpredictable variations due to stochastically arriving requests with different quality of service (QoS) requirements. Thus, an adaptive online SFC deployment approach is needed to handle the real-time network variations and various service requests. In this paper, we firstly introduce a Markov decision process (MDP) model to capture the dynamic network state transitions. In order to jointly minimize the operation cost of NFV providers and maximize the total throughput of requests, we propose NFVdeep, an adaptive, online, deep reinforcement learning approach to automatically deploy SFCs for requests with different QoS requirements. Specifically, we use a serialization-and-backtracking method to effectively deal with large discrete action space. We also adopt a policy gradient based method to improve the training efficiency and convergence to optimality. Extensive experimental results demonstrate that NFVdeep converges fast in the training process and responds rapidly to arriving requests especially in large, frequently transferred network state space. Consequently, NFVdeep surpasses the state-of-the-art methods by 32.59% higher accepted throughput and 33.29% lower operation cost on average.

Virtual network

10.1145/3326285.3329056

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Citations (163)

PostMan: Rapidly Mitigating Bursty Traffic by Offloading Packet Processing

USENIX Annual Technical Conference (2019)

Panpan Jin Jian Guo Yikai Xiao Rongpei Shi Yipei Niu

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Citations (11)