Shouxi Luo

Shouxi Luo
Southwest Jiaotong University · School of Computing and Artificial Intelligence

PhD

About

57
Publications
3,292
Reads
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619
Citations
Citations since 2016
48 Research Items
595 Citations
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2016201720182019202020212022050100150
2016201720182019202020212022050100150

Publications

Publications (57)
Conference Paper
With the rise of stateful programmable data planes, a lot of the network functions that used to be implemented in the controller or at the end-hosts are now moving to the data plane to benefit from line-rate processing. Unfortunately, stateful data planes also mean more complex network updates as not only flows, but also the associated states, must...
Article
Updating network configurations responding to dynamic changes is a error-prone task in SDN. During the update process, in-flight packets might misuse different versions of rules, and “hot” links could be overloaded due to the unplanned update order. As for the problem of misusing rules, recently proposed suggestions like two-phase mechanism and Cus...
Article
In today’s data center networks (DCN), cloud applications commonly disseminate files from a single source to a group of receivers for service deployment, data replication, software upgrade, and etc. For these group communication tasks, recent advantages of software-defined networking (SDN) provide bandwidth-efficient ways—they enable DCN to establi...
Article
As the key infrastructure for emerging 5G and IoT applications, micro data centers would be widely deployed at network edges to provide high-bandwidth low-latency cloud service. In these systems, applications would deliver large-size data objects among servers for various purposes like service deployment, application scale-up, and data duplication...
Article
Recently, the abstraction of coflow is introduced to capture the collective data transmission patterns among modern distributed data-parallel applications. During processing, coflows generally act as barriers; accordingly, time-sensitive applications prefer their coflows to complete within deadlines, and deadline-aware coflow scheduling becomes ver...
Article
This paper studies the trajectory control and task offloading (TCTO) problem in an unmanned aerial vehicle (UAV)-assisted mobile edge computing system, where a UAV flies along a planned trajectory to collect computation tasks from smart devices (SDs). We consider a scenario that SDs are not directly connected by the base station (BS) and the UAV ha...
Article
Full-text available
Over the years, a number of semisupervised deep-learning algorithms have been proposed for time-series classification (TSC). In semisupervised deep learning, from the point of view of representation hierarchy, semantic information extracted from lower levels is the basis of that extracted from higher levels. The authors wonder if high-level semanti...
Preprint
Full-text available
Multi-access edge computing (MEC) and network function virtualization (NFV) are promising technologies to support emerging IoT applications, especially those computation-intensive. In NFV-enabled MEC environment, service function chain (SFC), i.e., a set of ordered virtual network functions (VNFs), can be mapped on MEC servers. Mobile devices (MDs)...
Preprint
Full-text available
This paper studies the trajectory control and task offloading (TCTO) problem in an unmanned aerial vehicle (UAV)-assisted mobile edge computing system, where a UAV flies along a planned trajectory to collect computation tasks from smart devices (SDs). We consider a scenario that SDs are not directly connected by the base station (BS) and the UAV ha...
Article
Full-text available
Spectrum sensing is an efficient technology for addressing the shortage of spectrum resources. Widely used methods usually employ model-based features as the test statistics, such as energies and eigenvalues, ignoring the temporal correlation aspect. Deep learning based methods have the potential to focus on various aspects, including temporal corr...
Article
Full-text available
Training a neural network requires retraining the same model many times to search for the configuration of hyper-parameters with the best training result. It is common to launch multiple training jobs and evaluate them in stages. At the completion of each stage, jobs with unpromising configurations will be terminated and jobs with new configuration...
Article
In this article, we revisit the problem of how to provide "native" IP multicast support to enterprise distributed applications in today's clouds, without touching either the application implementation or the underlying network hardware. We propose SDM, Software-Defined IP Multicast, to explore the idea of performing Multicast-to-Unicast (M2U) and U...
Article
This paper formulates a virtual machine placement (VMP) problem, where the total power consumption of physical machines (PMs) and switches and the total network bandwidth resource consumption among VMs are jointly minimized. To address the problem, we present an energy- and traffic-aware ant colony optimization (ETA-ACO) algorithm. Three novel sche...
Article
Full-text available
This paper studies the problem of offloading an application consisting of dependent tasks in multi-access edge computing (MEC). This problem is challenging because multiple conflicting objectives exist, e.g., the completion time, energy consumption, and computation overhead should be optimized simultaneously. Recently, some reinforcement learning (...
Article
Network function virtualization (NFV) is an emerging network paradigm that decouples softwarized network functions from proprietary hardware. Nowadays, resource allocation has become one of the hot topics in the NFV domain. In this paper, we formulate a service function chain (SFC) mapping problem in the context of multicast, which is also referred...
Article
Full-text available
Time series data usually contains local and global patterns. Most of the existing feature networks focus on local features rather than the relationships among them. The latter is also essential, yet more difficult to explore because it is challenging to obtain sufficient representations using a feature network. To this end, we propose a novel robus...
Article
Full-text available
Distributed machine learning is a mainstream system to learn insights for analytics and intelligence services of many fronts (e.g., health, streaming and business) from their massive operational data. In such a system, multiple workers train over subsets of data and collaboratively derive a global prediction/inference model by iteratively synchroni...
Preprint
Time series data usually contains local and global patterns. Most of the existing feature networks pay more attention to local features rather than the relationships among them. The latter is, however, also important yet more difficult to explore. To obtain sufficient representations by a feature network is still challenging. To this end, we propos...
Article
Full-text available
Large companies operate tens of data centers (DCs) across the globe to serve their customers and store data. On the other hand, many machine learning applications need a global view of such global data to pursue high model accuracy. However, for this Geo-distributed machine learning (Geo-DML), it is infeasible to move all data together over wide-ar...
Article
Full-text available
In mobile edge computing (MEC), smart mobile devices (SMDs) with limited computation resources and battery lifetime can offload their computing-intensive tasks to MEC servers, thus to enhance the computing capability and reduce the energy consumption of SMDs. Nevertheless, offloading tasks to the edge incurs additional transmission time and thus hi...
Article
Full-text available
Networking has become a well-known performance bottleneck for distributed machine learning (DML). Although lots of works have focused on accelerating the communication process of DML, they ignore the impact of the physical network on the DML performance. Concurrently, optical circuit switches (OCSes) are increasingly applied in data centers and clu...
Article
The bottleneck of Distributed Machine Learning (DML) has shifted from computation to communication. Lots of works have focused on speeding up communication phase from perspective of Parameter Server (PS) architecture, for example resource scheduling. Nonetheless, the performance improvement of these schemes is limited due to the agnostic of the phy...
Preprint
Full-text available
Dynamic routing in software-defined networking (SDN) can be viewed as a centralized decision-making problem. Most of the existing deep reinforcement learning (DRL) agents can address it, thanks to the deep neural network (DNN)incorporated. However, fully-connected feed-forward neural network (FFNN) is usually adopted, where spatial correlation and...
Article
Deep learning (DL) is an increasingly important tool for large-scale data analytics and DL workloads are also common in today's production clusters due to the increasing number of deep-learning-driven services (e.g., online search and speech recognition). To handle ever-growing training datasets, it is common to conduct distributed DL (DDL) trainin...
Article
Full-text available
Explicit congestion notification (ECN) has been widely adopted by recent proposals to build up high-throughput and low-latency datacenter network transport. In these ECN-based proposals, when the queue length of a switch exceeds a pre-defined threshold, the switch would mark all arriving packets with ECN to explicitly notify their senders to slow d...
Article
In this paper, we revisit the optimization problem of constructing multicast trees for cloud applications in the context of leaf-spine data center network (DCN). On one hand, we find that, the maximum multicast rate a network can provide equals to the minimum of the maximum unsplit throughput it can provide to each of the receivers. On the other ha...
Conference Paper
This paper investigates the virtual network function placement (VNF-P) problem in network function virtualization (NFV), with load balancing and delay issues considered. In this problem, utilization of server and link resources is jointly minimized for load balancing purpose while end-to-end delay is constrained for better user experience. An integ...
Article
In this paper, we present a scalable PAth COntrol (PACO) approach to meet the increasing demands of fine-grained and explicit paths in Software-Defined Networks. PACO builds upon Segment Routing and adopts the pathlet segment as the building blocks for constructing network paths. By proactively generating and pre-installing a small collection of se...
Article
Full-text available
While software-defined networking (SDN) has been widely applied in various networking domains including datacenters, WANs (Wide Area Networks), QoS (Quality of Service) provisioning, service function chaining, etc., it also has foreseeable applications in energy internet (EI), which envisions an intelligent energy industry on the basis of (informat...
Article
This paper studies the virtual network function placement (VNF-P) problem in the context of network function virtualization (NFV), where the end-to-end delay of a requested service function chain (SFC) is minimized and the compute, storage, I/O and bandwidth resources are considered. To address this problem, an integer encoding grey wolf optimizer...
Article
Full-text available
The OpenFlow-based SDN is widely studied to better network performance through planning fine-grained paths. However, being designed to configure path hop-by-hop, it faces the scalability issue that both the flow table overhead and path setup delay are unacceptable for large-scale networks. In this paper, we propose PACO, a framework based on Source...
Article
In current data centers, an application (e.g., MapReduce, Dryad, search platform, etc.) usually generates a group of parallel flows to complete a job. These flows compose a coflow and only completing them all is meaningful to the application. Accordingly, minimizing the average Coflow Completion Time (CCT) becomes a critical objective of flow sched...
Conference Paper
Updating network configurations responding to dynamic changes is still a tricky task in SDN. During the update process, in-flight packets might misuse different versions of rules, and “hot” links could be overloaded due to the unplanned update order. As for the problem of misusing rule, recently proposed suggestions like two-phase mechanism and Cus...
Article
In OpenFlow-driven SDN, flow tables are TCAM-hungry; commodity switches suffer from limited concrete flow table size. One method for coping with the limitations is to use aggregation schemes to reduce the number of flow entries required to express the same forwarding semantics. Unfortunately, the aggregation of rules would retard table updates and...
Conference Paper
In recent years there has been an exponential growth in the traffic of cloud data centers networks (DCNs). More and more users are outsourcing their computing demands and services to DCNs. To handle such increases in the workload and traffic in a scalable manner, many technologies have been proposed to provide dynamic topology to the network so as...
Article
The recent proposed two-phase mechanism is a provable theory to achieve consistent updates for SDN. However, how to make it work for practical rules is important yet unsolved—(1) two-phase mechanism requires that rules in the new configuration after an update are assigned with a distinct version number from rules in the old configuration before an...
Article
In recent years, there has been an exponential growth in the traffic of cloud data centers networks (DCNs). More and more users are outsourcing their computing demands and services to DCNs. To handle such increases in the workload and traffic in a scalable manner, many technologies have been proposed to provide dynamic topology to the network so as...
Conference Paper
In OpenFlow-based SDN, flow tables are TCAM-hungry and commodity switches suffer from limited concrete flow table size. One method for coping with the limitations is to use aggregation schemes to reduce the number of flow entries required to represent the same forwarding semantics. Unfortunately, the aggregation retards table updates and lengthens...
Article
Virtualization is a common technology for resource sharing in data center. To make efficient use of data center resources, the key challenge is to map customer demands (modeled as virtual data center, VDC) to the physical data center effectively. In this paper, we focus on this problem. Distinct with previous works, our study of VDC embedding probl...

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Projects

Projects (2)
Project
Schedule coflows respecting to the requirements of distributed application in data center networks