没有合适的资源?快使用搜索试试~ 我知道了~
CFR: A cooperative link failure recovery scheme in software-defi...
0 下载量 176 浏览量
2021-02-08
12:22:52
上传
评论
收藏 1.21MB PDF 举报
温馨提示
CFR: A cooperative link failure recovery scheme in software-defined networks
资源推荐
资源详情
资源评论
Received: 00 Month 0000 Revised: 00 Month 0000 Accepted: 00 Month 0000
DOI: xxx/xxxx
ARTICLE TYPE
CFR: A Cooperative Link Failure Recovery Scheme in Software
Defined Networks
Likun Wang
1,2
| Lin Yao*
1,2
| Zichuan Xu
1,2
| Guowei Wu
1,2
| Mohammad S. Obaidat
3
1
School of Software, Dalian University of
Technology, Liaoning, China
2
Key Laboratory for Ubiquitous Network
and Service Software of Liaoning Porvince,
China
3
Department of Computer Science &
Software Engineering, Monmouth
University, NJ 07764, USA
Correspondence
Lin Yao. Email: yaolin@dlut.edu.cn
Present Address
School of Software, Dalian University of
Technology, Dalian 116620, China
Summary
Software Defined Networking (SDN) that separates the control plane from the data
plane is envisioned as a promising technology to enable resilient and flexible net-
work management. Tolerating link failures is a fundamental problem in enhancing
such network resilience in SDNs. Reactive and proactive fault tolerant schemes for
conventional networks may not well balance the fault recovery time and network
performance, since the proactive scheme typically under-utilizes resources and the
reactive scheme usually incurs a longer recovery time. In this paper, we propose
a Cooperative link Failure Recovery scheme (CFR) to find a fine-grained trade-off
between resource utilization and recovery time by combining reactive and proac-
tive methods. We formalize the problem of link failure recovery as a multi-objective
optimization problem and devise a two-stage algorithm for it. The first stage of
the algorithm guarantees connectivity restoration in an acceptable recovery inter-
val based on fast failover feature supported in OpenFlow protocol, meanwhile it
assigns virtual local area network (VLAN) tags to backup paths for achieving a lower
memory consumption. The second stage of the algorithm guarantees the Quality of
Service (QoS) for different applications by adjusting the backup paths after rapid
connectivity restoration. Extensive simulations highlight that CFR can satisfy both
the carrier-grade recovery requirements and QoS requirements in terms of delay and
network bandwidth.
KEYWORDS:
Software-Defined Networking, Failure recovery, QoS guarantee, Multi-objective optimization
1 INTRODUCTION
Software-Defined Networking enables flexible and efficient network management by separating the control plane from the data
plane
1
. As shown in Figure 1 , a logically centralized controller in SDNs manages the whole network by communicating with
the switches in the data plane, where the interface connecting the control plane with data plane is called southbound interface.
Among the existing southbound interfaces, OpenFlow
2
sponsored by Open Networking Foundation (ONF)
3
is most widely
adopted. In the OpenFlow network, the network traffic is routed by following the flow entries installed into the switches. When
a new packet is transmitted to the switch, it will be forwarded to its destination if the packet head can match any flow rule;
otherwise, a request will be sent to the controller which is responsible for the transmission path.
2 Likun Wang ET AL
Network Applications
Network Controller
(OpenFlow)
Switch
Southbound Interface
Northbound Interface
Data Plane
Switch
Switch
Switch
FIGURE 1 Architecture of SDN.
Link failure recovery is a fundamental and critical functionality to guarantee the normal operations in communication net-
works. As long as link failures are detected in SDNs, the controller will recover the failed links by installing backup rules into
the switches. One challenge of recovering such link failures is how to perform the fault recovery timely. Another challenge is
how to carefully allocate network resources during the recovery process, because various network applications have different
QoS requirements such as delay and bandwidth.
To solve the above challenges, proactive and reactive recovery schemes designed for conventional networks can be adopted in
SDNs
4,5
. The proactive scheme computes the backup paths and reserves recovery resources before the failure, which achieves
rapid recovery locally without signaling a notification to the controller. Since the backup paths are dynamically allocated to
data traffic after the link failures are detected in the reactive scheme, it takes more time to accomplish the recovery without
reserving the network resources in advance, which makes it difficult to meet the carrier-grade 50 milliseconds (ms) interval
6
in
the recovery. Current recovery schemes including proactive-based and reactive-based recovery ones may be difficult to satisfy
the increasing demands during the recovery due to ignoring the trade-off between resource utilization and recovery time. On
the one hand, the proactive recovery scheme needs additional Ternary Content Addressable Memory (TCAM)
7
resources for
achieving local recovery. Since an SDN switch can only support limited rules in its TCAM
8
, the storage resources reserved
for backup paths will be wasted if there is not any failure. Furthermore, the computed backup paths before failure without
considering real-time network status may be inappropriate for the network applications with QoS requirements. On the other
hand, the reactive recovery scheme calculates the backup paths after detecting the link failures, which may cause serious packet
loss due to the delay caused by calculation and communication.
Compared with the existing schemes in the literature, we propose a cooperative link failure recovery scheme (CFR) to recover
the failed network, aiming to find a fine-grained trade-off between resource utilization and recovery time. In our approach, we
formalize the problem of link failure recovery as a multi-objective optimization problem and devise a two-stage algorithm to
achieve the connectivity restoration and QoS requirements.
Our main contributions of the paper are listed as follows:
• We are the first that proposes a cooperative link failure recovery scheme to achieve a fine trade-off between resource
utilization and recovery time by taking advantage of the fast recovery in the proactive scheme and low overhead in the
reactive scheme.
• We use a multi-objective optimization model to formalize the link recovery problem in SDNs. To address the problem, we
propose a two-stage algorithm, rapid connectivity restoration and backup path adjustment, aiming to reduce the recovery
time and guarantee the QoS requirements.
• We apply fast failover and VLAN features supported in OpenFlow protocol to enhance the performance of CFR, which
guarantees the connectivity restoration rapidly by aggregating the disrupted flows to the same egress.
Likun Wang ET AL 3
• We implement extensive simulations based on Mininet, and evaluate the performance of CFR by comparing with two
approaches PRS
9
and TI-LFA
10
. Simulation results show that CFR can satisfy both the carrier-grade recovery requirement
and QoS requirements with less memory consumption.
The rest of this paper is organized as follows. Section 2 discusses the related works about link failure recovery in SDNs.
Section 3 introduces the network model and problem statement. Section 4 introduces our failure recovery scheme CFR in detail.
Simulation results and discussions are presented in Section 5 and this paper is concluded in Section 6.
2 RELATED WORK
According to whether the controller intervenes in recovery, existing strategies to recover the link failure can be classified into
two categories: reactive strategy and proactive strategy
11
.
Reactive Strategy. Reactive strategy is the most common recovery strategy used to recover the link failure and node failure
for normal operations. In the link failure recovery, reactive strategy installs backup flow entries into the corresponding flow
tables which locate in the switch after detecting the failure. Sharma et al.
12
introduced the difference between proactive and
reactive strategies, and proposed a reactive recovery scheme to compute the backup paths based on NOX, a OpenFlow supported
controller. The proposed scheme emanated all existing paths to detect the failed link, which reduced the computation efficiency
and increased the burden of the controller. Kim et al.
13
proposed a failure recovery framework which could recover the failure
caused by multiple links. Park et al.
14
proposed a fast recovery scheme for in-band OpenFlow networks based on detouring
strategy. To reduce the number of messages between the controller and switches, Zhang et al.
15
aggregated the affected flows to a
single flow with a new label, and then calculated the restoration paths to recover the network. Waleed et al.
?
used Bellman Ford
and Dijkstra algorithms to restore the network from a link failure. The presented model provided a shortest path with reducing
delay and offered more efficient bandwidth through redundant links. Although reactive restoration strategy has a better failure
recovery with dynamic path computing, it is hard to meet the carrier-grade recovery within 50 ms
6
requirement in large and
complex networks. In real-time complex networks, Li et al.
16
devised a scheme to build the backup paths through selecting a
safe point. Zhang et al.
17
analyzed the dynamic network link and used different recovery strategies to recover the network with
the aim to conserve the TCAM space.
Proactive Strategy. Proactive strategy recovers the failed network locally without the intervention of the controller, which
can reduce recovery time. Sharma et al.
18
compared two types of recovery schemes by simulators and proved that it was difficult
to meet 50 ms recovery requirement for reactive strategy, while proactive strategy could recover the network rapidly with a
certain level of network pre-configuration. Kempf et al.
19
expanded the switch ability supported by OpenFlow 1.1, which could
detect the failure and detour the disrupted flows to the backup paths locally. Van et al.
20
exploited the group table feature to
accomplish rapid failure recovery. The proposed scheme was inspired of crankback signaling, a backtracking procedure defined
in RFC, and it could avoid the problem where the neighbor of failed node had no backup routing paths. Although this kind
of method reduced packet loss, it might generate a forwarding loop. Padma et al.
21
proposed a link protection scheme, which
achieved a local link failure recovery within 50 ms. Although the proposed scheme degraded the burden of controller, it occupied
large resources due to generating large backup rules. Considering the limited TCAM space in OpenFlow supported switches,
Mohan et al.
22
proposed a TCAM-aware recovery scheme to save the TCAM space by reusing the primary paths and aggregating
backup paths to the same egress, however this method may cause additional resource occupation if data traffic has different
destinations. In hybrid network including traditional IP routers and SDN switches, Chu et al.
23
proposed a congestion-aware
link failure recovery scheme through pre-configured IP tunnels. To overcome the shortcomings in original fast failover features,
Capone et al.
24
proposed OpenState based on OpenFlow to achieve localized recovery according to network status. Thorat et
al.
9
proposed a proactive recovery scheme based on VLAN-tags to relieve the flow storage resource limitation. However, the
node-based routing method may occupy more storage space if there are large flows in the failed link.
Summary of Related Work. To improve the recovery efficiency, we devise a cooperative link failure recovery scheme inspired
by proactive and reactive schemes. Compared with existing works, we focus on the recovery time, storage space and QoS
requirements during the recovery, we first recover the connectivity with less storage space by calculating the shortest backup
path for flows and detouring the flows to the same egress, we then adjust the backup path to achieve the QoS requirements after
rapid connectivity restoration.
剩余16页未读,继续阅读
资源评论
weixin_38539018
- 粉丝: 6
- 资源: 941
上传资源 快速赚钱
- 我的内容管理 展开
- 我的资源 快来上传第一个资源
- 我的收益 登录查看自己的收益
- 我的积分 登录查看自己的积分
- 我的C币 登录后查看C币余额
- 我的收藏
- 我的下载
- 下载帮助
最新资源
- Go 中 JOSE 标准 (JWE、JWS、JWT) 的实现.zip
- 整理过后的蘑菇数据集.csv
- java仿win10界面的酒店管理系统源码数据库 MySQL源码类型 WebForm
- Go Twitter REST 和 Streaming API v1.1.zip
- tomcat跨域解决方案(亲测可用)
- Go Memcached 客户端库 #golang.zip
- Go Manager - Go 软件包.zip
- Futurice 开发人员针对 iOS 开发提出的好主意 .zip
- franz-go 包含一个功能齐全的纯 Go 库,用于与 Kafka 0.8.0 到 3.8+ 进行交互 生产、消费、交易、管理等 .zip
- STM32 HAL库 + LM2904运算放大器 + ADC + VDO温度传感器:电路设计及代码实现
资源上传下载、课程学习等过程中有任何疑问或建议,欢迎提出宝贵意见哦~我们会及时处理!
点击此处反馈
安全验证
文档复制为VIP权益,开通VIP直接复制
信息提交成功