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A Scalable Hybrid Network Control Plane for Cloud Computing

                            Shreyash Sharma1, Neha Kamble2,
Anand khandare3

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1 ( ME student Computer Engineering, Thakur College of
Engineering and Technology / Mumbai University, India)

2(ME student Computer Engineering, Thakur College of
Engineering and Technology / Mumbai University, India)

3(Associate Professor,Thakur College of Engineering and
Technology / Mumbai University, India)

 

Abstract—
One aspect that many of us tend to forget about the cloud is
that it’s not completely digital. At one or the other place in the world, there
has to be a data center or physical server
that works like the backbone of cloud computing. In a software-defined network, a network administrator
can shape traffic
from a centralized
control console without having to touch individual switches, and can deliver services to wherever they
are needed in the network, without regard to what specific devices a server or other hardware components are connected to. SDN
focused solely on separation of the control plane of the network,
which makes decisions about how packets should flow
through the network from the data plane of the network,
which actually moves packets from place to place. Enhanced
flexibility and agility will permit more efficient usage of networking
resources, while the decrease in operating costs could possibly result in even
greater innovation and significant savings on the client’s part. SDN is increasingly accepted as the path to “cloud networking,” meaning
the transformation of networks and services to support the use of cloud
computing on a massive scale. Navigating the various missions and technology
models of SDNs is critical to properly position cloud services and realize
advantages of cloud computing. For cloud users, knowing their cloud providers’ SDN plans,
as well as the plans of private cloud software stack vendors, is the most
critical element in assessing these providers’ long-term value.

Keywords—software defined networking (SDN); backbone; switches;
control plane ; cloud networking

                                                                                                                                                               
I.     Introduction

 

The cloud computing paradigm has
been widely adopted in recent years. It provides a cost effective solution for
deploying new applications, removing the upfront hardware cost as well as the
burden of system maintenance. The cloud computing model allows resources to be
allocated on demand and provides the ability to elastically scale-out processing.
With the advent of the cloud, a plethora of web applications have also emerged.
S DN decouples the network’s control plane and data plane , and extracts
complex control functions from network devices. It also supports a fine-grained
flow-based management based on the OpenFlow 
protocol  to enable highly
programmable and flexible networks. Currently, almost all commercial switches
support the Open Flow protocol; other south-bound interface protocols, such as
Cisco one PK API, also support the fine-grained flow control feature of SDN. We
consider the fine-grained flow control to be a very important feature for
supporting innovative applications, but also an inherent problem of flow-based
SDN, because it introduces a great communication overhead between the data
plane and the control plane, which limits the scalability . Many solutions to
this problem have been explored. Some researchers design different control
plane structures to extend the control plane’s processing ability. For example,
some studies construct a flat control plane architecture to improve the control
plane scalability and reduce the delay caused by geographical distance, as in
HyperFlow, Onix and ONOS . Alternatively, some studies build a centralized
hierarchical control plane of SDN, in which the top layer controller is
responsible for global applications service. For example, Kandoo has a two
layer hierarchical architecture, in which the bottom layer controllers run
local control applications based on the local network view, and the top layer
controller runs global applications based on the global network-wide view .
Logical xBar, on the other hand, introduces a recursive building block to
construct a centralized logical hierarchical SDN network . Though the above
control plane architectures can improve the scalability of SDN networks, both
the flat and the centralized hierarchical architecture have limitations. Since
routing is an essential control operation of a SDN network, we take the
Dijkstra algorithm as an example to illustrate the problem. 1) The flat control
plane architecture cannot solve the super-linear computational complexity
growth of the control plane when a SDN network scales to large size. To
illustrate this problem, we use the source IP address and the destination IP address
together to identify a flow. We take an example to illustrate the problem. 2)
The centralized logical hierarchical control plane architecture brings path
stretch problem.

 

                                                                                                                                                              
II.    related
work

The
control plane can scale in two directions: out or up. In the scale-out
approach, the control plane functions are separated and distributed across
physical or virtual servers.  In the scale-up approach, the server’s
processing power is augmented by adding extra compute resources, such as x86
processors. In both the scale-out and scale-up architectures, performance
can be further enhanced by providing function-specific hardware acceleration.

                                                                                                                                                        
III.   proposed
system

 we propose Orion, a hybrid distributed
hierarchical control plane for large scale networks. The proposed architecture
combines the advantages of flat and centralized hierarchical control planes,
and addresses the two unresolved problems discussed above. This paper has four
contributions: First we design Orion, a hybrid hierarchical control plane which
can reduce the computational complexity growth of the SDN control plane by
constructing abstracted hierarchical network views. Second, we design an
abstracted hierarchical routing method to address the path stretch problem by
constructing abstract intra-area links and pre-calculating all intra-area
abstracted links hops. Third, we propose a hierarchical fast reroute method to
illustrate how to achieve fast rerouting in the proposed hybrid hierarchical
control plane. Finally, we implement Orion to verify the feasibility of the
hybrid hierarchical approach, and we verify its effectiveness from both the
theoretical and experimental aspects

                                                                                                                                                          
IV.   existing
system

The Flood light OpenFlow
controller provides a rich set of components. The central controller in control
packet  is implemented based on the
existing Floodlight controller by introducing the following extensions.

Encap action realizes packet
encapsulation in edge switches by extending the existing OpenFlow v1.0
protocol. In the  architecture, packet
forwarding in the data plane overlay replies on a packet like encapsulation.
When a rule with this action is applied to a flow, the switch will encapsulate
the packets with a new header targeting a given remote IP address.

                                                                                                                                                                         
V.    design

We outline the design of Orion, a
hybrid hierarchical control plane architecture of SDN focusing on the
intra-domain control and management of large-scale networks. Throughout this
paper, a domain is a complete network which can be controlled and managed by
one administrator. It can be divided into several areas, which are regions that
can each be controlled by a single SDN controller.

Control Plane Scale-out Architecture:
In the scale-out architecture, the basic platform is implemented with
generic processors augmented by separate communications processors with
specialized hardware accelerators that can offload control plane
functions. The control plane tasks are divided into sub-tasks, such as
discovery, dissemination, and recovery, and are then distributed across the
data center. Because the various tasks can execute on any server in the network
or in the cloud, the scale-out architecture lends itself well to Software
Defined Networking (SDN). Owing to its distributed arrangement, the
architecture requires robust communications between the control plane and the
data planes using APIs for the network protocol, such as OpenFlow.

 

 

 

Depending
on the network size and configuration, hardware acceleration of these
networking functions may be necessary to achieve satisfactory
performance.  Protocol-aware communications processors are designed to
handle specific control plane tasks and/or network management functions,
including packet analysis and routing, security, ARP offload, OAM offload, IGMP
messages, networking statistics, application-aware firewalling, QoS, etc.

 

Control Plane Scale-up
Architecture : In the scale-up
architecture, the existing network control platforms are supplemented by
additional and/or more powerful compute engines to help execute the network
control stack. These supplemental resources free up server CPU cycles for
other tasks, and result in an overall improvement in the network
performance.  Because general-purpose processors are not optimized for
packet processing functions, however, they are not an ideal solution for the
scale-up architecture.  As with the scale-out architecture, performance
can be improved dramatically using function-specific, protocol-aware
communications processors.

 

 

                                                                                                                                                              
VI.   performance

 The interconnect infrastructure  is formed by connecting the cores in the
subnets with each other and to the central hub through traditional metal wires.
The hubs are then connected by wires and wireless links such that the second
level of the network has the small-world property. The placement of the
wireless links between a particular pair of source and destination hubs is
important as this is responsible for establishing high speed, low-energy
interconnects on the network, which will eventually result in performance gains.

 

 

 

                         

 

Device Management Module: This
module has two parts to deal with the area device information and domain device
information.

 1) Area Device Management. This sub-module
obtains the host information through the ARP packet sent by the host. When a
host sends an ARP packet, the switch that connects to the host sends a
Packet-In message to the area controller. The sub-module decapsulates the
PacketIn message, acquires the host information and collects switch
information. 2) Domain Device Management. In order to provide interarea host
information, this module works as an ARP Proxy in Orion. In order to prevent a
broadcast storm, we use an algorithm similar to Spanning Tree to avoid a
broadcast loop. This sub-module also manages the global edge switches
information.

 

                                                                                                                                                               
VII.  conclusion

Therefore, in this paper, we have
presented a novel architecture for A Scalable Hybrid Network Control Plane for
Cloud Computing , which considers the concept of control and data plane
separation. . Orion can effectively reduce the computational
complexity of a flow based SDN control plane by several orders of magnitude,
and solve the path stretch problem brought by the logical hierarchical control
plane architecture. Further, we evaluate the effectiveness of Orion
theoretically and experimentally. Our results show the efficiency and
feasibility of Orion.

                              
Acknowledgment

Sincere thanks to our guide Professor Anand khadare who has supported
and encouraged us to complete this paper.

 

                                          reference

 

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