IEEE INFOCOM 2021
Containers and Data Centers
Exploring Layered Container Structure for Cost Efficient Microservice Deployment
Lin Gu (Huazhong University of Science and Technology, China); Deze Zeng (China University of Geosciences, China); Jie Hu and Hai Jin (Huazhong University of Science and Technology, China); Song Guo (Hong Kong Polytechnic University, Hong Kong); Albert Zomaya (The University of Sydney, Australia)
In this paper, we propose a layer sharing microservice deployment and image pulling strategy which explores the advantage of layer sharing to speedup microservice startup and lower image storage consumption. The problem is formulated into an Integer Linear Programming (ILP) form. An Accelerated Distributed Augmented Lagrangian (ADAL) based distributed algorithm executed cooperatively by registries and servers is proposed. Through extensive trace driven experiments, we validate the high efficiency of our ADAL based algorithm as it accelerates the microservice startup by 2.30 times in average and reduces the storage consumption by 55.33%.
NetMARKS: Network Metrics-AwaRe Kubernetes Scheduler Powered by Service Mesh
Łukasz Wojciechowski (Samsung R&D Institute Poland, Poland); Krzysztof Opasiak and Jakub Latusek (Warsaw University of Technology & Samsung R&D Institute Poland, Poland); Maciej Wereski (Samsung R&D Institute Poland, Poland); Victor Morales (Samsung Research America, USA); Taewan Kim (Samsung Research, Samsung Electronics Co., Ltd., Korea (South)); Moonki Hong (Samsung Electronics, Co., Ltd., Korea (South))
Optimal Rack-Coordinated Updates in Erasure-Coded Data Centers
Guowen Gong, Zhirong Shen and Suzhen Wu (Xiamen University, China); Xiaolu Li and Patrick Pak-Ching Lee (The Chinese University of Hong Kong, Hong Kong)
Primus: Fast and Robust Centralized Routing for Large-scale Data Center Networks
Guihua Zhou, Guo Chen, Fusheng Lin, Tingting Xu, Dehui Wei and Jianbing Wu (Hunan University, China); Li Chen (Huawei, Hong Kong); Yuanwei Lu and Andrew Qu (Tencent, China); Hua Shao (Tsinghua University & Tencent, China); Hongbo Jiang (Hunan University, China)
Wei Wang (Hong Kong University of Science and Technology)
Sea, Space and Quantum Networks
PolarTracker: Attitude-aware Channel Access for Floating Low Power Wide Area Networks
Yuting Wang, Xiaolong Zheng, Liang Liu and Huadong Ma (Beijing University of Posts and Telecommunications, China)
results reveal the reason behind this is due to the polarization and directivity of the antenna. The dynamic attitude of a floating node incurs varying signal strength losses, which is ignored by the attitude-oblivious link model adopted in most of the existing methods. When accessing the channel at a misaligned attitude, packet errors can happen. In this paper, we propose an attitude-aware link model that explicitly quantifies the impact of node attitude on link quality. Based on the new model, we propose PolarTracker, a novel channel access method for floating LPWAN. PolarTracker tracks the node attitude alignment state and schedules the transmissions into the aligned periods with better link quality. We implement a prototype of PolarTracker on commercial LoRa platforms and extensively evaluate its performance in various real-world environments. The experimental results show that PolarTracker can efficiently improve the packet reception ratio by 48.8%, compared with ALOHA in LoRaWAN.
Mobility- and Load-Adaptive Controller Placement and Assignment in LEO Satellite Networks
Long Chen, Feilong Tang and Xu Li (Shanghai Jiao Tong University, China)
However, existing work on controller placement and assignment is not applicable to LEO satellite networks with highly dynamic topology and randomly fluctuating load. In this paper, we first formulate the adaptive controller placement and assignment (ACPA) problem and prove its NP-hardness. Then, we propose the control relation graph (CRG) to quantitatively capture the control overhead in LEO satellite networks. Next, we propose the CRG-based controller placement and assignment (CCPA) algorithm with a bounded approximation ratio. Finally, using the predicted topology and estimated traffic load, a lookahead-based improvement algorithm is designed to further decrease the overall management costs. Extensive emulation results demonstrate that the CCPA algorithm outperforms related schemes in terms of response time and load balancing.
Time-Varying Resource Graph Based Resource Model for Space-Terrestrial Integrated Networks
Long Chen and Feilong Tang (Shanghai Jiao Tong University, China); Zhetao Li (Xiangtan University, China); Laurence T. Yang (St. Francis Xavier University, Canada); Jiadi Yu and Bin Yao (Shanghai Jiao Tong University, China)
In this paper, we propose the time-varying resource graph (TVRG) to model STINs from the resource perspective. Firstly, we propose the STIN mobility model to uniformly model different movement patterns in STINs. Then, we propose a layered Resource Modeling and Abstraction (RMA) approach, where evolutions of node resources are modeled as Markov processes, by encoding predictable topologies and influences of fluctuating loads as states. Besides, we propose the low-complexity domain resource abstraction algorithm by defining two mobility-based and load-aware partial orders on resource abilities. Finally, we propose an efficient TVRG-based Resource Scheduling (TRS) algorithm for time-sensitive and bandwidth-intensive data flows, with the multi-level on-demand scheduling ability. Comprehensive simulation results demonstrate that RMA-TRS outperforms related schemes in terms of throughput, end-to-end delay and flow completion time.
Redundant Entanglement Provisioning and Selection for Throughput Maximization in Quantum Networks
Yangming Zhao and Chunming Qiao (University at Buffalo, USA)
In this paper, we propose Redundant Entanglement Provisioning and Selection (REPS) to maximize the throughput for multiple source-destination (SD) pairs in a circuit-switched, multi-hop quantum network. REPS has two distinct features: (i). It provisions backup resources for extra entanglement links between adjacent nodes for failure-tolerance; and (ii). It provides flexibility in selecting successfully created entanglement links to establish entanglement connections for the SD pairs to achieve network-wide optimization. Extensive analysis and simulations show that REPS can achieve optimal routing with a high probability, and improves the throughput by up to 68.35% over the highest-performing algorithms in existence. In addition, it also improves the fairness among the SD pairs in the networks.
Ana Aguiar (University of Porto, Portugal)
Social Networks and Applications
Medley: Predicting Social Trust in Time-Varying Online Social Networks
Wanyu Lin and Baochun Li (University of Toronto, Canada)
Conventional methods for predicting social trust often accept static graphs as input, oblivious of the fact that social interactions are time-dependent. In this work, we propose Medley, to explicitly model users' time-varying latent factors and to predict social trust that varies over time. We propose to use functional time encoding to capture continuous-time features and employ attention mechanisms to assign higher importance weights to social interactions that are more recent. By incorporating topological structures that evolve over time, our framework can infer pairwise social trust based on past interactions. Our experiments on benchmarking datasets show that Medley is able to utilize time-varying interactions effectively for predicting social trust, and achieves an accuracy that is up to 26% higher over its alternatives.
Setting the Record Straighter on Shadow Banning
Erwan Le Merrer (Inria, France); Benoit Morgan (IRIT-ENSEEIHT, University of Toulouse, France); Gilles Tredan (LAAS-CNRS, France)
MIERank: Co-ranking Individuals and Communities with Multiple Interactions in Evolving Networks
Shan Qu (Shanghai Jiaotong University, China); Luoyi Fu (Shanghai Jiao Tong University, China); Xinbing Wang (Shanghai Jiaotong University, China)
ProHiCo: A Probabilistic Framework to Hide Communities in Large Networks
Xuecheng Liu and Luoyi Fu (Shanghai Jiao Tong University, China); Xinbing Wang (Shanghai Jiaotong University, China); John Hopcroft (Cornell University, USA)
Fabricio Murai (Universidade Federal de Minas Gerais, Brasil)
Adaptive Batch Update in TCAM: How Collective Optimization Beats Individual Ones
Ying Wan (Tsinghua University, China); Haoyu Song (Futurewei Technologies, USA); Yang Xu (Fudan University, China); Chuwen Zhang (Tsinghua University, China); Yi Wang (Southern University of Science and Technology, China); Bin Liu (Tsinghua University, China)
TCAM placement for whole batches throughout. By applying the topology grouping and maintaining the group order invariance in TCAM, ABUT achieves substantial computing time reduction yet still yields the best-in-class placement cost. Our evaluations show that ABUT is ideal for low-latency and high-throughput batch TCAM updates in modern high-performance switches.
HAVS: Hardware-accelerated Shared-memory-based VPP Network Stack
Shujun Zhuang and Jian Zhao (ShangHaiJiaoTong University, China); Jian Li (Shanghai Jiao Tong University, China); Ping Yu and Yuwei Zhang (Intel, China); Haibing Guan (Shanghai Jiao Tong University, China)
This paper adopts a hardware-accelerated solution and proposes HAVS which integrates Intel I/O Acceleration Technology into the VPP network stack to achieve high-performance memory copy offloading. An asynchronous copy architecture is introduced in HAVS to free up CPU resources. Moreover, an abstract memcpy accelerator layer is constructed in HAVS to ease the use of different types of hardware accelerators and sustain high availability with a fault-tolerance mechanism. The comprehensive evaluation shows that HAVS can provide an average 50%-60% throughput improvement over the original VPP stack when accelerating the nginx and SPDK iSCSI target application.
Maximizing the Benefit of RDMA at End Hosts
Xiaoliang Wang (Nanjing University, China); Hexiang Song (NJU, China); Cam-Tu Nguyen (Nanjing University, Vietnam); Dongxu Cheng and Tiancheng Jin (NJU, China)
Xinwen Fu (U. Massachussets, Lowell)