In recent years, vehicular networks have seen a proliferation of applications and services such as image tagging, lane detection, and speech recognition. Many of these applications rely on Deep Neural Networks (DNNs) and demand low-latency computation. To meet these requirements, Vehicular Edge Computing (VEC) has been introduced to augment the abundant computation capacity of vehicular networks to complement limited computation resources on vehicles. Nevertheless, offloading DNN tasks to MEC (Multi-access Edge Computing) servers effectively and efficiently remains a challenging topic due to the dynamic nature of vehicular mobility and varying loads on the servers. In this paper, we propose a novel and efficient distributed DNN Partitioning And Offloading (DPAO), leveraging the mobility of vehicles and the synergy between vehicle–edge and edge–edge computing. We exploit the variations in both computation time and output data size across different layers of DNN to make optimized decisions for accelerating DNN computations while reducing the transmission time of intermediate data. In the meantime, we dynamically partition and offload tasks between MEC servers based on their load differences. We have conducted extensive simulations and testbed experiments to demonstrate the effectiveness of DPAO. The evaluation results show that, compared to offloaded all tasks to MEC server, DPAO reduces the latency of DNN tasks by 2.4x. DPAO with queue reservation can further reduce the task average completion time by 10%.
IN3: A Framework for In-Network Computation of Neural Networks in the Programmable Data Plane
Xiaoquan Zhang , Lin Cui , Fung Po Tso , Wenzhi Li , and Weijia Jia
The advent of Network Function Virtualization (NFV) and Service Function Chains (SFCs) unleashes the power of dynamic creation of network services using Virtual Network Functions (VNFs). This is of great interest to network operators since poor service quality and resource wastage can potentially hurt their revenue in the long term. However, the study shows with a set of test-bed experiments that packet loss at certain positions (i.e., different VNFs) in an SFC can cause various degrees of resource wastage and performance degradation because of repeated upstream processing and transmission of retransmitted packets. To overcome this challenge, this study focuses on resource scheduling and deployment of SFCs while considering packet loss positions. This study developed a novel SFC packet dropping cost model and formulated an SFC scheduling problem that aims to minimize overall packet dropping cost as a Mixed-Integer Linear Programming (MILP) and proved that it is NP-hard. In this study, Palosis proposed as an efficient scheme in exploiting the functional characteristics of VNFs and their positions in SFCs for scheduling resources and deployment to optimize packet dropping cost. Extensive experiment results show that Palos can achieve up to 42.73% improvement on packet dropping cost and up to 33.03% reduction on average SFC latency when compared with two other state-of-the-art schemes.
Compiling Service Function Chains via Fine-Grained Composition in the Programmable Data Plane
With the ever-growing demand for low-latency network applications, edge computing emerges as a new paradigm that provides computation and storage resources in close proximity to end-users. Many research efforts have resorted to network function virtualization, wherein network applications are provisioned as service function chains at edge clouds. However, due to the traffic dynamics and limited resource capacity at the network edge, how to efficiently embed service chains with latency optimization and resource efficiency remains as a challenging problem. As most existing research efforts largely overlook the bottlenecked resources of VNFs in the VNF scaling, we seek a more realistic approach to provisioning VNF instances across multiple edge clouds. Also, given the limited resources at the edge, it is of significant importance to improve the VNF utilization rate. Specifically, we formulate the VNF scaling problem as an integer linear programming (ILP) problem, aiming to minimize the endto-end latency for service function chains. To solve this problem, we devise a novel bottleneck-aware algorithm that manages the number and deployment of newly created instances. After that, we propose an online algorithm for traffic steering to improve the utilization rates of VNF instances and avoid congestion on hotspot links. The proposed algorithm is shown to provide good performance by trace-driven simulation in real-world topologies.
Distributed federated service chaining: A scalable and cost-aware approach for multi-domain networks
Future networks are expected to support cross-domain, cost-aware and fine-grained services in an efficient and flexible manner. Service Function Chaining (SFC) has been introduced as a promising approach to deliver these services. In the literature, centralized resource orchestration is usually employed to process SFC requests and manage computing and network resources. However, centralized approaches inhibit the scalability and domain autonomy in multi-domain networks. They also neglect location and hardware dependencies of service chains. In this paper, we propose Distributed Federated Service Chaining (DFSC), a framework for orchestrating and maintaining SFC placement in a distributed fashion while sharing only a minimal amount of domain information and control. First, a deployment cost minimization problem is formulated as an Integer Linear Programming (ILP) problem with fine-grained constraints for location and hardware dependencies. We show that this problem is NP-hard. Then, a placement algorithm is devised to use information only on inter-domain paths and border nodes. Our extensive experimental results demonstrate that DFSC efficiently optimizes the deployment cost, supports domain autonomy and enables faster decision-making. The results also show that DFSC finds solutions within a factor 1.15 of the optimal solution on average. Compared to a centralized approach in the literature, DFSC reduces the deployment cost by up to 20% and uses 70% less decision-making time.
Low-latency service function chain migration in edge-core networks based on open Jackson networks
Multi-access Edge Computing (MEC) offers cloud computing capabilities at the edge of the network. Growing demand for low?latency services requires Service Function Chains (SFCs) to be scaled up beyond MEC network to core network. To adapt to network dynamics and provide low-latency services, being able to migrate SFCs when needed is of paramount importance. However, migration of SFCs among edge and core networks such that average latency is optimized as well as considering resource consumption is an intractable challenge because improper migration of Virtual Network Functions (VNFs) results in failure of meeting the requirements of network policies. In this paper, we investigate SFCs in edge-core networks and model the Latency-aware Edge-Core SFCs Migration problem based on open Jackson networks. Two SFC migration algorithms, i.e., Profit-driven Heuristic Search (PHS) and Average Utilization Based (AUB), are proposed to efficiently optimize average latency of all SFCs in edge-core networks. Extensive evaluation results show that PHS optimizes average latency by 19.5%, while AUB can further reduce average latency by up to 36.9% by allowing a marginally higher number of VNF migrations.
dDrops: Detecting silent packet drops on programmable data plane
Silent packet drops are common in data center networks, and are a major cause of network performance anomalies (NPAs) that have significant impacts on application performance and network management. However, existing solutions using coarse-grained statistics and flow-level telemetry either fail to provide precise location of packet drops or incur large overhead. This paper presents dDrops, a packet-level telemetry based on programmable data plane to detect and retrieve details of silent packet drops immediately when they happen. dDrops can dynamically adapt to varying ratios of silent packet drops for different ports on a switch to improve performance of silent packet drops detection. Moreover, a dynamic memory management scheme is also designed to efficiently use the limited memory on the data plane of switch. dDrops has been implemented on both P4 hardware programmable switches (based on Intel Tofino ASIC) and BMv2. Extensive experiment results show that dDrops is able to detect and locate the silent packet drops within 5 ms (including detailed information of dropped packets), and reduce the memory consumption by up to 50%.
Mitigating Cyber Threats at the Network Edge
T Sofoluwe , FP Tso , and I Phillips
In Proceedings of the ACM SIGCOMM Internet Measurement Conference, IMC , Oct 2022
The easy exploitation of IoT devices with limited security, compute and processing power has enabled hackers to carry out sophisticated attacks. Many research studies have highlighted the benefits of utilising artificial-intelligence based models in DDoS detection, but emphasis has not been placed on quantitative measurements of compute requirements for Machine Learning and Deep Learning algorithms used for DDoS detection, especially in the inference or detection stage. This research aims to fill the gap by performing quantitative measurement and comparison of various lightweight ML and DL algorithms, as well as design a lightweight collaborative framework capable of DDoS detection close to the source of the attack.
Optimizing multipath QUIC transmission over heterogeneous paths
As a novel UDP-based transport protocol which supports stream multiplexing, QUIC is faster, more lightweight and flexible than TCP. With the prevalence of multi-homed devices such as smartphones with both WiFi and 4G/5G cellular connectivity, Multipath QUIC (MPQUIC) can effectively utilize multiple network interfaces (i.e., multiple paths) to improve transmission efficiency. Current MPQUIC implementation adopts the Lowest-RTT-First (LRF) scheduler which always selects the path with the lowest smoothed RTT among all available paths. However, we show that in networks with heterogeneous paths where network characteristics (e.g., RTT, loss rate) differ considerably, such scheduling scheme leads to unnecessary waiting on fast paths and bufferbloat, degrading overall transmission performance significantly. To use heterogeneous paths efficiently (i.e., to reduce the overall file transfer completion time), this paper proposes a novel scheduling mechanism that assigns data to paths with transfer simulation without causing much additional overhead. Extensive experiment results in Mininet demonstrate that the proposed scheduling mechanism can reduce the transfer completion time by up to 29.6% as compared to existing MPQUIC implementation.
pSFC: Fine-grained composition of service function chains in the programmable data plane
Dynamic service function chains (SFC) are enabled by network function virtualization on general purpose servers. The emergence of programmable data planes (PDP) has offered a new way for the deployment of SFC. However, the implementation of network functions is constrained by resource limitations in PDPs (e.g., compute and memory resource). Moreover, most of existing works do not consider the optimization of state information (e.g., registers), which is essential for stateful network functions. In this paper, we propose pSFC which provides a fine-grained SFC deployment scheme in the PDP to tackle the problem. We first model network functions as control flow graphs (CFG) and the process of deployment as a one big switch (OBS) problem, and then propose an ILP (Integer Linear Programming) model for resource optimization for the OBS problem, which is NP-hard. To solve this problem efficiently, pSFC first composes multiple SFCs for eliminating redundant resources, decomposes the compound CFG based on the resource limitation per stage, and finally maps OBS into the substrate network. We have implemented pSFC in both bmv2 software switch and P4 hardware switch (i.e., Intel Tofino). Evaluation shows that pSFC reduces switch costs 45.7% and average latency 15% while providing the correctness of the process of SFC.
2021
Distributed federated service chaining for heterogeneous network environments
Future networks are expected to support cross-domain, cost-aware and fine-grained services in an efficient and flexible manner. Service Function Chaining (SFC) has been introduced as a promising approach to deliver these services. In the literature, centralized resource orchestration is usually employed to process SFC requests and manage computing and network resources. However, centralized approaches inhibit the scalability and domain autonomy in multi-domain networks. They also neglect location and hardware dependencies of service chains. In this paper, we propose federated service chaining, a distributed framework which orchestrates and maintains the SFC placement while sharing a minimal amount of domain information and control. We first formulate a deployment cost minimization problem as an Integer Linear Programming (ILP) problem with fine-grained constraints for location and hardware dependencies, which is NPhard. We then devise a Distributed Federated Service Chaining placement approach (DFSC) using inter-domain paths and border nodes information. Our extensive experiments demonstrate that DFSC efficiently optimizes the deployment cost, supports domain autonomy and enables faster decision-making. The results show that DFSC finds solutions within a factor 1.15 of the optimal solution. Compared to a centralized approach in the literature, DFSC reduces the deployment cost by 12% while being one order of magnitude faster.
Multi-agent reinforcement learning based 3D trajectory design in aerial-terrestrial wireless caching networks
Y-J Chen , K-M Liao , M-L Ku , FP Tso , and G-Y Chen
IEEE Transactions on Vehicular Technology, Jul 2021
This paper investigates a dynamic 3D trajectory design of multiple cache-enabled unmanned aerial vehicles (UAVs) in a wireless device-to-device (D2D) caching network with the goal of maximizing the long-term network throughput. By storing popular content at the nearby mobile user devices, D2D caching is an efficient method to improve network throughput and alleviate backhaul burden. With the attractive features of high mobility and flexible deployment, UAVs have recently attracted significant attention as cache-enabled flying base stations. The use of cache-enabled UAVs opens up the possibility of tracking the mobility pattern of the corresponding users and serving them under limited cache storage capacity. However, it is challenging to determine the optimal UAV trajectory due to the dynamic environment with frequently changing network topology and the coexistence of aerial and terrestrial caching nodes. In response, we propose a novel multi-agent reinforcement learning based framework to determine the optimal 3D trajectory of each UAV in a distributed manner without a central coordinator. In the proposed method, multiple UAVs can cooperatively make flight decisions by sharing the gained experiences within a certain proximity to each other. Simulation results reveal that our algorithm outperforms the traditional single- and multi-agent Q-learning algorithms. This work confirms the feasibility and effectiveness of cache-enabled UAVs which serve as an important complement to terrestrial D2D caching nodes.
pHeavy: Predicting heavy flows in the programmable data plane
X Zhang , L Cui , FP Tso , and W Jia
IEEE Transactions on Network and Service Management, Jul 2021
Since heavy flows account for a significant fraction of network traffic, being able to predict heavy flows has benefited many network management applications for mitigating link congestion, scheduling of network capacity, exposing network attacks and so on. Existing machine learning based predictors are largely implemented on the control plane of Software Defined Networking (SDN) paradigm. As a result, frequent communication between the control and data planes can cause unnecessary overhead and additional delay in decision making. In this paper, we present pHeavy, a machine learning based scheme for predicting heavy flows directly on the programmable data plane, thus eliminating network overhead and latency to SDN controller. Considering the scarce memory and limited computation capability in the programmable data plane, pHeavy includes a packet processing pipeline which deploys pre-trained decision tree models for in-network prediction. We have implemented pHeavy in both bmv2 software switch and P4 hardware switch (i.e., Barefoot Tofino).Evaluation results demonstrate that pHeavy has achieved 85% and 98% accuracy after receiving the first 5 and 20 packets of a flow respectively, while being able to reduce the size of decision tree by 5.4x on average. More importantly, pHeavy can predict heavy flows at line rate on the P4 hardware switch.
2020
Federated Service Chaining: Architecture and Challenges
Unmanned aerial vehicles (UAVs), aka drones, are widely used civil and commercial applications. A promising one is to use the drones as relying nodes to extend the wireless coverage. However, existing solutions only focus on deploying them to predefined locations. After that, they either remain stationary or only move in predefined trajectories throughout the whole deployment. In the open outdoor scenarios such as search and rescue or large music events, etc., users can move and cluster dynamically. As a result, network demand will change constantly over time and hence will require the drones to adapt dynamically. In this paper, we present a proof of concept implementation of an UAV access point (AP) which can dynamically reposition itself depends on the users movement on the ground. Our solution is to continuously keeping track of the received signal strength from the user devices for estimating the distance between users devices and the drone, followed by trilateration to localise them. This process is challenging because our on-site measurements show that the heterogeneity of user devices means that change of their signal strengths reacts very differently to the change of distance to the drone AP. Our initial results demonstrate that our drone is able to effectively localise users and autonomously moving to a position closer to them.
A survey on stateful data plane in software defined networks
X Zhang , L Cui , K Wei , FP Tso , Y Ji , and W Jia
It’s said that the pleasure is in the giving, not the receiving. This belief is validated by how humans interact with their family, friends and society as well as their gardens, homes, and pets. Yet for ubiquitous devices, this dynamic is reversed with devices as the donors and owners as the recipients. This paper explores an alternative paradigm where these devices are elevated, becoming members of Data Hungry Homes, allowing us to build relationships with them using the principles that we apply to family, pets or houseplants. These devices are developed to fit into a new concept of the home, can symbiotically interact with us and possess needs and traits that yield unexpected positive or negative outcomes from interacting with them. Such relationships could enrich our lives through our endeavours to “feed” our Data Hungry Homes, possibly leading us to explore new avenues and interactions outside and inside the home.
What can we expect from navigating? Exploring navigation, wearables and data through critical design concepts
M Lee-Smith , T Ross , M Maguire , FP Tso , J Morley , and S Cavazzi
What is it to navigate or to be navigated? How, and through what, is information communicated to us? Do our interactions with space need to be limited to when we are moving through it? This paper describes a collection of design concepts generated as part of the initial stages of a research project that combines a critical design mindset and research through design process to explore these types of questions. The project seeks to problematise and diversify the discussion and understanding around pedestrian navigation, wearable technology, crowdsourcing and human data interaction. The goal is to develop one of the concepts using research through design as part of PhD research studies, leading to possible future applications.
Extensive evaluation on the performance and behaviour of TCP congestion control protocols under varied network scenarios
In recent decades, many TCP Congestion Control (CC) protocols have been proposed to improve the performance and reliability of TCP in various network scenarios. However, CC protocols are usually closely coupled with network conditions such as latency and packet loss. Considering that networks with different properties are common, e.g., wired/wireless LAN and Long Fat Networks (LFNs), investigating both performance and behaviors of CC protocols under varied network scenarios becomes crucial for both network management and development. In this paper, we conduct a comprehensive measurement study on the goodput, RTT, retransmission, friendliness, fairness, convergence time and stability of most widely-used CC protocols over wired LAN/WAN and wireless LAN (both 2.4GHz and 5GHz Wi-Fi). We also conduct comparative studies with respect to transmission cost, congested reverse path and bottleneck queue size in network simulator. Based on our analysis, we reveal several interesting and original observations. We found that the goodput of BBR is at least 22.5% lower than other CC protocols in wireless LAN due to insufficient pacing rate, even though it can always fully utilize the bottleneck bandwidth with low RTT in wired networks. We also observed that the total on-wire data volume of BBR is higher than CUBIC (e.g., 2.37% higher when RTT = 100ms and loss rate = 0.01%). In addition, BBR can fully utilize the bottleneck bandwidth in most queue sizes (≥ 20packets). Surprisingly, we noticed that as the default CC protocol in most modern operating systems, CUBIC is too aggressive and unfriendly in both LAN and wireless LAN, greatly suppressing the goodput of other competing CC protocols. More specifically for CUBIC in wireless LAN, it generates 129% more retransmissions than other CC protocols. Nevertheless, we have also seen that, in scenario with heavily-congested reverse path, CUBIC can provide full utilization on bottleneck bandwidth. Lastly, we also observed that BBR converges very quickly in all evaluated scenarios, while other CC protocols present varied results, e.g., Westwood+ and Veno converge faster in 5GHz Wi-Fi networks than 2.4GHz networks.
Cloud futurology
B Varghese , P Leitner , S Ray , K Chard , A Barker , Y Elkhatib , and 6 more authors
The Cloud has become integral to most Internet-based applications and user gadgets. This article provides a brief history of the Cloud and presents a researcher’s view of the prospects for innovating at the infrastructure, middleware, and application and delivery levels of the already crowded Cloud computing stack.
Mystique: a fine-grained and transparent congestion control enforcement scheme
Y Zhang , L Cui , FP Tso , Q Guan , W Jia , and J Zhou
IEEE Transactions on Network and Service Management, Aug 2019
TCP congestion control is a vital component for the latency of Web services. In practice, a single congestion control mechanism is often used to handle all TCP connections on a Web server, e.g., Cubic for Linux by default. Considering complex and ever-changing networking environment, the default congestion control may not always be the most suitable one. Adjusting congestion control to meet different networking scenarios usually requires modification of TCP stacks on a server. This is difficult, if not impossible, due to various operating system and application configurations on production servers. In this paper, we propose Mystique, a light-weight, flexible, and dynamic congestion control switching scheme that allows network or server administrators to deploy any congestion control schemes transparently without modifying existing TCP stacks on servers. We have implemented Mystique in Open vSwitch (OVS) and conducted extensive testbed experiments in both public and private cloud environments. Experiment results have demonstrated that Mystique is able to effectively adapt to varying network conditions, and can always employ the most suitable congestion control for each TCP connection. More specifically, Mystique can significantly reduce latency by 18.13% on average when compared with individual congestion controls.
2018
Live migration on ARM-based micro-datacentres
I Avramidis , M Mackay , FP Tso , T Fukai , and T Shinagawa
In CCNC 2018 - 2018 15th IEEE Annual Consumer Communications and Networking Conference , Mar 2018
Proliferation of the number of smart devices and user applications has generated a tremendous volume of data traffic from/to a cellular network. With a traditional cellular network, a user may experience many drawbacks such as low throughput, large latencies and network outages due to overload of data traffic. The software defined network (SDN) and network function virtualization (NFV) rise as a promising solution to overcome such issues of traditional network architecture. In this paper, we introduce a new network architecture for LTE and WiFi slicing networks taking into account the advantage of SDN and NFV concepts. We propose an IPFlow management controller in a slicing network to offload and balance the flow data traffic. By utilizing the P-GW and Wireless Access Gateway, we can handle the IP-Flow between LTE and WiFi networks. The P-GW works as an IP-Flow anchor to maintain the flow seamlessly during the offloading and balancing IP-Flow. Within WiFi networks, we leverage the Light Virtual Access Point (LVAP) approach to abstract the WiFi protocol stack for a programming capability of centralized control of WiFi network through the WiFi controller. By creating a client virtual port and assigning a specific Service Set Identifier (SSID), we give a capability to slice an operator’s network to control over his clients within a WiFi coverage area network.
Build Trust in the Cloud Computing - Isolation in Container Based Virtualisation
I Alobaidan , M Mackay , and P Tso
In Proceedings - 2016 9th International Conference on Developments in eSystems Engineering, DeSE 2016 , May 2017
Cloud computing is revolutionizing many IT ecosystems through offering scalable computing resources that are easy to configure, use and inter-connect. However, this model has always been viewed with some suspicion as it raises a wide range of security and privacy issues that need to be negotiated. This research focuses on the construction of a trust layer in cloud computing to build a trust relationship between cloud service providers and cloud users. In particular, we address the rise of container-based virtualisation has a weak isolation compared to traditional VMs because of the shared use of the OS kernel and system components. Therefore, we will build a trust layer to solve the issues of weaker isolation whilst maintaining the performance and scalability of the approach. This paper has two objectives. Firstly, we propose a security system to protect containers from other guests through the addition of a Role-based Access Control (RBAC) model and the provision of strict data protection and security. Secondly, we provide a stress test using isolation benchmarking tools to evaluate the isolation in containers in term of performance.
Heterogeneous network policy enforcement in data centers
With the emergence of network function virtualization, data center start to deploy a variety of network function boxes (NFBs) in both physical and virtual form factors in order to combines inherent efficiency offered by physical NFBs with the agility and flexibility of virtual ones. However, existing schemes are limited to exclusively consider physical or virtual NFBs, which may reduce the performance efficiency of services running atop. In this paper, we propose a Heterogeneous NetwOrk Policy Enforcement scheme (HOPE) to overcome these challenges. An efficient algorithm that can closely approximate optimal latencywise NF service chaining is proposed. The experimental results have also shown that HOPE can outperform greedy algorithm by 25% in terms of network latency and is 56x more efficient than naive depth-first search algorithm.
Experimental evaluation of SDN-controlled, joint consolidation of policies and virtual machines
W Hajji , FP Tso , L Cui , and DP Pezaros
In Proceedings - IEEE Symposium on Computers and Communications , Sep 2017
In order to minimise their energy use, data centre operators are constantly exploring new ways to construct computing infrastructures. As low power CPUs, exemplified by ARM-based devices, are becoming increasingly popular, there is a growing trend for the large scale deployment of low power servers in data centres. For example, recent research has shown promising results on constructing small scale data centres using Raspberry Pi (RPi) single-board computers as their building blocks. To enable larger scale experimentation and feasibility studies, cloud simulators could be utilised. Unfortunately, stateof-the-art simulators often need significant modification to include such low power devices as core data centre components. In this paper, we introduce models and extensions to estimate the behaviour of these new components in the DISSECT-CF cloud computing simulator. We show that how a RPi based cloud could be simulated with the use of the new models. We evaluate the precision and behaviour of the implemented models using a Hadoop-based application scenario executed both in real life and simulated clouds.
Machine learning approaches to the application of disease modifying therapy for sickle cell using classification models
M Khalaf , AJ Hussain , R Keight , D Al-Jumeily , P Fergus , R Keenan , and 1 more author
This paper discusses the use of machine learning techniques for the classification of medical data, specifically for guiding disease modifying therapies for Sickle Cell. Extensive research has indicated that machine learning approaches generate significant improvements when used for the pre-processing of medical time-series data signals and have assisted in obtaining high accuracy in the classification of medical data. The aim of this paper is to present findings for several classes of learning algorithm for medically related problems. The initial case study addressed in this paper involves classifying the dosage of medication required for the treatment of patients with Sickle Cell Disease. We use different machine learning architectures in order to investigate the accuracy and performance within the case study. The main purpose of applying classification approach is to enable healthcare organisations to provide accurate amount of medication. The results obtained from a range of models during our experiments have shown that of the proposed models, recurrent networks produced inferior results in comparison to conventional feedforward neural networks and the Random Forest model. For our dataset, it was found that the Random Forest Classifier produced the highest levels of performance overall.
PROTECT: Container process isolation using system call interception
TY Win , FP Tso , Q Mair , and H Tianfield
In Proceedings - 14th International Symposium on Pervasive Systems, Algorithms and Networks, I-SPAN 2017, 11th International Conference on Frontier of Computer Science and Technology, FCST 2017 and 3rd International Symposium of Creative Computing, ISCC 2017 , Nov 2017
TCon: A transparent congestion control deployment platform for optimizing WAN transfers
Y Zhang , L Cui , FP Tso , Q Guan , and W Jia
In , Jan 2017
This is a pre-copyedited version of a contribution published in Shi X. ... et al (eds). Network and Parallel Computing published by Springer. The definitive authenticated version is available online via http://dx.doi.org/10.1007/978-3-319-68210-5_5. This paper was also presented at the IFIP International Conference on Network and Parallel Computing (NPC 2017), Hefei, China, 20th-21st October 2017.
Nowadays, many web services (e.g., cloud storage) are deployed inside datacenters and may trigger transfers to clients through WAN. TCP congestion control is a vital component for improving the performance (e.g., latency) of these services. Considering complex networking environment, the default congestion control algorithms on servers may not always be the most efficient, and new advanced algorithms will be proposed. However, adjusting congestion control algorithm usually requires modification of TCP stacks of servers, which is difficult if not impossible, especially considering different operating systems and configurations on servers. In this paper, we propose TCon, a light-weight, flexible and scalable platform that allows administrators (or operators) to deploy any appropriate congestion control algorithms transparently without making any changes to TCP stacks of servers. We have implemented TCon in Open vSwitch (OVS) and conducted extensive test-bed experiments by transparently deploying BBR congestion control algorithm over TCon. Test-bed results show that the BBR over TCon works effectively and the performance stays close to its native implementation on servers, reducing latency by 12.76% on average.
Track: Tracerouting in SDN networks with arbitrary network functions
Y Zhang , L Cui , FP Tso , and Y Zhang
In Proceedings of the 2017 IEEE 6th International Conference on Cloud Networking, CloudNet 2017 , Oct 2017
In modern Cloud Data Centers (DC)s, correct implementation of network policies is crucial to provide secure, efficient and high performance services for tenants. It is reported that the inefficient management of network policies accounts for 78% of DC downtime, challenged by the dynamically changing network characteristics and by the effects of dynamic Virtual Machine (VM) consolidation. While there has been significant research in policy and VM management, they have so far been treated as disjoint research problems. In this paper, we explore the simultaneous, dynamic VM and policy consolidation, and formulate the Policy-VM Consolidation (PVC) problem, which is shown to be NP-Hard. We then propose Sync, an efficient and synergistic scheme to jointly consolidate network policies and virtual machines. Extensive evaluation results and a testbed implementation of our controller show that policy and VM migration under Sync significantly reduces flow end-to-end delay by nearly 40%, and network-wide communication cost by 50% within few seconds, while adhering strictly to the requirements of network policies.
SDN-Based Virtual Machine Management for Cloud Data Centers
R Cziva , S Jouet , D Stapleton , FP Tso , and DP Pezaros
IEEE Transactions on Network and Service Management, Jun 2016
Nowadays, Internet-of-Things (IoT) devices generate data at high speed and large volume. Often the data require real-time processing to support high system responsiveness which can be supported by localised Cloud and/or Fog computing paradigms. However, there are considerably large deployments of IoT such as sensor networks in remote areas where Internet connectivity is sparse, challenging the localised Cloud and/or Fog computing paradigms. With the advent of the Raspberry Pi, a credit card-sized single board computer, there is a great opportunity to construct low-cost, low-power portable cloud to support real-time data processing next to IoT deployments. In this paper, we extend our previous work on constructing Raspberry Pi Cloud to study its feasibility for real-time big data analytics under realistic application-level workload in both native and virtualised environments. We have extensively tested the performance of a single node Raspberry Pi 2 Model B with httperf and a cluster of 12 nodes with Apache Spark and HDFS (Hadoop Distributed File System). Our results have demonstrated that our portable cloud is useful for supporting real-time big data analytics. On the other hand, our results have also unveiled that overhead for CPU-bound workload in virtualised environment is surprisingly high, at 67.2%. We have found that, for big data applications, the virtualisation overhead is fractional for small jobs but becomes more significant for large jobs, up to 28.6%.
Network and server resource management strategies for data centre infrastructures: A survey
The advent of virtualisation and the increasing demand for outsourced, elastic compute charged on a pay-as-you-use basis has stimulated the development of large-scale Cloud Data Centres (DCs) housing tens of thousands of computer clusters. Of the significant capital outlay required for building and operating such infrastructures, server and network equipment account for 45 and 15% of the total cost, respectively, making resource utilisation efficiency paramount in order to increase the operators’ Return-on-Investment (RoI). In this paper, we present an extensive survey on the management of server and network resources over virtualised Cloud DC infrastructures, highlighting key concepts and results, and critically discussing their limitations and implications for future research opportunities. We highlight the need for and benefits of adaptive resource provisioning that alleviates reliance on static utilisation prediction models and exploits direct measurement of resource utilisation on servers and network nodes. Coupling such distributed measurement with logically centralised Software Defined Networking (SDN) principles, we subsequently discuss the challenges and opportunities for converged resource management over converged ICT environments, through unifying control loops to globally orchestrate adaptive and load-sensitive resource provisioning.
Understanding The Network I/O Performance of Heterogenous Virtualisation in Cloud Data Centres
FP Tso
Sep 2016
date: 2016-03-08 keywords: publications pubstate: published tppubtype: techreport
Virtual Machine (VM) management is a powerful mechanism for providing elastic services over Cloud Data Centers (DC)s. At the same time, the resulting network congestion has been repeatedly reported as the main bottleneck in DCs, even when the overall resource utilization of the infrastructure remains low. However, most current VM management strategies are traffic-agnostic, while the few that are traffic-aware only concern a static initial allocation, ignore bandwidth oversubscription, or do not scale. In this paper we present S-CORE, a scalable VM migration algorithm to dynamically reallocate VMs to servers while minimizing the overall communication footprint of active traffic flows. We formulate the aggregate VM communication as an optimization problem and we then define a novel distributed migration scheme that iteratively adapts to dynamic traffic changes. Through extensive simulation and implementation results, we show that S-CORE achieves significant (up to 87%) communication cost reduction while incurring minimal overhead and downtime.
2013
Blind detection of spread spectrum flow watermarks
W Jia , FP Tso , Z Ling , X Fu , D Xuan , and W Yu
Data Center (DC) networks exhibit much more centralized characteristics than the legacy Internet, yet they are operated by similar distributed routing and control algorithms that fail to exploit topological redundancy to deliver better and more sustainable performance. Multipath protocols, for example, use node-local and heuristic information to only exploit path diversity between shortest paths. In this paper, we use a measurement-based approach to schedule flows over both shortest and non-shortest paths based on temporal network-wide utilization. We present the Baatdaat flow scheduling algorithm which uses spare DC network capacity to mitigate the performance degradation of heavily utilized links. Results show that Baatdaat achieves close to optimal Traffic Engineering by reducing network-wide maximum link utilization by up to 18% over Equal-Cost Multi-Path (ECMP) routing, while at the same time improving flow completion time by 41% - 95%.
The Glasgow Raspberry Pi Cloud: A Scale Model for Cloud Computing Infrastructures
FP Tso , DR White , S Jouet , J Singer , and D Pezaros
In The First International Workshop on Resource Management of Cloud Computing (Co-located with ICDCS 2013) , Jan 2013
date: 2013-06-24 keywords: publications pubstate: published tppubtype: conference
NA Twigg , M Fayed , C Perkins , D Pezaros , and P Tso
In SIGCOMM’12 - Proceedings of the ACM SIGCOMM 2012 Conference Applications, Technologies, Architectures, and Protocols for Computer Communication , Sep 2012
The 3G-324M is an umbrella standard of the Third Generation Partnership Project (3GPP) for wireless video communications, which was developed to satisfy the stringent requirements of real-time interactive video and audio services. In practice, 3G-324M has been employed in 3G networks today to enable the multimedia services with messaging and streaming. However, the design of the supporting architecture for the unification of the diverse streams with 3G-324M poses lots of challenges. This chapter introduces a new supporting
Blind Detection of Spread Spectrum Flow Watermarks
W Jia , FP Tso , X Fu , Z Lin , D Xuan , and W Yu
In IEEE International Conference on Computer Communications (INFOCOM) , Jan 2009