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Clustering Unikernel Microservices in Networking
University of Utah
As the scale of web services is growing, Microser-
vices architecture is becoming a trend to develop
large-scale web applications. Web services consists
of a lot of more scalable, less coupling small appli-
cations that can be combined into a large system.
As the Microservices becomes popular, the compat-
ibility between platforms is one of the crucial fac-
tors in deciding the quality of the application. There
are several ways to deploy applications as Microser-
vices. First, an application itself can be deployed on a
host. Although it is straightforward and efﬁcient when
it comes to dealing with resources, it is not optimal
when it comes to portability. In other words, it would
not work if the platform or operating system of host is
different from developed environment.
One of the solution is using container like Docker.
Container consists of required libraries and applica-
tions. The advantage of the container is fast and com-
patible on the same sort of kernel. However, it shares
the kernel with guest OS which would be vulnerable
to attacks aiming host kernel.
Running applications on virtual machine can also
be a solution. The difference from the container is
that each VM runs on separate kernel unlike the con-
tainers sharing kernel. By doing so, it would be se-
curer from kernel attacks at the cost of performance.
Unikernel is a new approach that merges the advan-
tages of container and virtual machine. It is basically
based on the virtual machine but it is packaged with
the only required frameworks and libraries. There-
fore, it minimizes the attack surface by excluding un-
necessary parts such as terminal input or ssh. Al-
though this may make difﬁcult to debug and test appli-
cations, it will make safer than containers and other
external tools will help the development.
In this project, I focused on the clustering of uniker-
nel Microservices. Although unikernel is secure and
lightweight, deﬁning networks for a bunch of uniker-
nel application in a host is not an easy work. To sim-
plify this, I suggested a way to abstract the internal
networks of unikernels working closely. It converts
multiple unikernel instances into a host in network
layer. For deployment of the service to hosts, python
script and a conﬁguration ﬁle are necessary to repli-
cate the Openﬂow network. The ﬂows not only de-
ﬁnes which guest OS is accessible from outside, but
also offers secure networking inside.
In a nutshell, the objective of the project is to ab-
stract the internal networking structure and to make
a group of unikernels look a host in the higher level of
networking. Microservices using unikernel is secure
and easy to deploy. However, it sometimes involves
a lot of work to integrate or duplicate web applica-
tions. Here’s an example of the real world problem. In
FIG. 1. Example of integrating services
FIG. 1, assume that web services that are developed
in different company or host. The problem is that it
requires to set up a new network structure and re-
build virtual machine images including IP address in
application code. Although it would be simple in this
example, more complicated and long process would
be involved as the number of applications in a host
FIG. 2. Pre-conﬁgured VM images for Web service
Let’s start from simpler example. As seen in FIG.
2, three virtual machines, which are nginx, PHP, and
MySQL servers, are running. It would make the prob-
lem clear. Assume that an administrator of a host
wants to run multiple services using the images. We
can dynamically change the IP of each instance by set-
ting option parameters. The problem is caused by the
ﬁxed IP addresses in web application code and server
conﬁguration ﬁle. In this case, nginx image includes
the IP addresses of PHP and MySQL servers and we
should rebuild the image to modify them. However, tt
is not always necessary to modify the code. The FIG. 3
shows no need to rebuild images for serving multiple
services. In this case, it is enough to change the IP of
FIG. 3. A case of no IP conﬂiction between services
nginx servers to avoid IP conﬂiction. However, it may
not much secure because every user should share PHP
and MySQL servers. Thus, each user would ask to
create separate VMs from others. To avoid IP conﬂic-
tion and run a virtual machine per user, Linux network
namespace helps to accomplish it. The solution is cre-
ating a network name space for each user and running
instances inside of it in FIG. 4. Then, each virtual ma-
chine that have the same IP can run on a host if layer 2
networking is carefully handled. In some cases, multi-
FIG. 4. An example requiring network namespaces to avoid
ple Microservices are required to share resources and
to work together. In FIG.5, the MySQL server would
not be able to generate ARP table for each php server
if the IP of two servers is the same. So, we need to
allocate a virtual IP for each group of virtual instaces.
By doing so, we don’t have to care about IP conﬂiction,
but also different subnet from that of virtual machines
can be used in host OS.
FIG. 5. Sharing a service from different hosts
III. RELATED WORKS
Unikernel is still in early stage compared to con-
tainers and thus, there are few project on clustering
unikernel. On the other hand, microservices on con-
tainers are being used for production. There are many
project related to clustering containers. One of the
most popular projects is Kubernetes. It deﬁnes a
group of containers working together as a pod. It’s a
large-scale project working on scaling, scheduling and
orchestration of containers. In addition, Tectonic
is a project from CentOS for cluster management of
containers. It supports docker and CentOS Rocker
containers. Docker project is also working on Docker
Swarm to cluster containers using docker API. On
top of the clustering, Apache Mesos is dealing with
scaling and automated deployment. Amazon EC2
is supporting the management and clustering of con-
tainers on the cloud platform. Containers are required
to replicated for scaling and load balancing. It is an
important factor for Microservices.
In spite of the popularity and maturity of containers,
unikernel is emerging as a new platform for Microser-
vices. Docker which is one of the most famous con-
tainer already decided to support unikernel. Uniker-
nel Microservices will make the system securer by iso-
lating the kennel of instances from each other.
A. Threat Model
Unikernel is rather secure since it runs on sepa-
rate kernel from others. Therefore, even if the ker-
nel is compromised by an attacker, it is hard to at-
tack the next virtual machine directly. Nevertheless,
we still need to carefully deal with packet ﬂow be-
tween virtual machines. Instead of setting ﬁrewalls
for each guest OS, we can utilize Openﬂow for allow-
ing or block path between two hosts. In FIG. 6, PHP
FIG. 6. Packet ﬂows allowed and blocked
server needs to access MySQL to retrieve data. How-
ever, the nginx server doesn’t need to directly access
the database. If the path is allowed, an attacker may
be able to try SQL injection attack through the nginx
One of the drawbacks for unikernel Microservice
is that it is hard to run rootkit or virus vaccine be-
cause virtual machines have limited amount of CPU
and memory. In addition, if there are tens or hundreds
of instances are running on a host, monitoring pack-
ets on each host would cost a lot of resources. For-
tunately, the booting time for unikernel is extremely
short and thus, it would be better to reboot if the
kernel is compromised or an attack is detected. To
do this, Intrusion Detection System(IDS) connected
to Software Deﬁned Networks(SDN) controller would
help to monitor packets from outside. When it detects
a suspicious packets to a VM, we can choose an ac-
tion to handle it such as, rebooting the VM, checking
by rootkit tool, or rolling back data.
Rump kernel, Openvswitch, Floodlight SDN
controller, and python scripts are used for con-
structing network. After booting rump kernels with
openvswitch bridges, openﬂows are added for layer
2 and 3 networking. This process is automated by
python script with json conﬁguration ﬁle.
A. Rump Kernels
Rumpkernel is one of the unikernel project using
NetBSD kernel. The advantage is that the kernel is
rather stable and light. In general, the booting time
is in seconds and thus, it is much faster than gen-
eral VMs. In security, it may secure than containers
like Docker because guest OS doesn’t share the ker-
nel with Domain 0.
One of the challenges in implementation is that
some guest OS should have two IP addresses for in-
ternal and external networking. In this project, I de-
cided to add an additional network interface to avoid
changing routing table in guest OS. In this case, two
virtual machines may have the same external IP. Thus,
ﬂow rules in network layer 2 and 3 should be carefully
B. Open vSwitch
Open vSwitch provides bridges for virtual ma-
chines. It is working in layer 2 network, however,
layer 3 network can also be controllable with Open-
ﬂow protocol. In this project, there is a central
bridge and it is connected to other bridges for each
group of virtual machines. Even if the bridges are
connected directly, we can generate any networking
topology by managing Openﬂow.
Bridges for each group of unikernels should be di-
rectly connected the central bridge, which means ev-
ery bridge can access each other without Openﬂow.
However, thanks to the Openﬂow, we can create any
network topology by deﬁning ﬂows between bridges.
FIG. 7 shows possible topologies when 4 bridges are
connected to a central bridge.
FIG. 7. Examples of network topologies
Floodlight is a SDN controller that can push static
ﬂows on a bridge. It also helps to monitor trafﬁc on
network and manage ﬂows. We can connect Intrusion
Detection System to the controller in order to monitor
packets and decide a host that should reboot if the
kernel is compromised.
it is difﬁcult to manage ﬂows on a lot of bridges,
even if a SDN controller is used. To automate the pro-
cess, a python script with a conﬁguration ﬁle is devel-
oped for this project. As a result, only necessary ﬁles
for the deployment are virtual machines and a conﬁg-
The deployment process is simple. First, create
bridges for each group of virtual machines and con-
nect time to the central bridge to make accessible. Af-
ter that, add extra network interfaces using rumprun
parameter to instances that should be access to oth-
ers in different bridges. And modify the conﬁguration
ﬁle according to the settings on bridges. Finally, exe-
cuting script with the conﬁguration ﬁle.
Listing 1. An example of json conﬁguration ﬁle
The python script parses the json ﬁle and push static
ﬂows to bridges. Even if this process will be enough
to implement with Open vSwitch CLI, Floodlight REST
API is used to maintain ﬂows with name.
 Open vswitch. http://openvswitch.org/. Accessed:
 Openﬂow. http://archive.openflow.org/. Accessed:
 Rump kernels. http://rumpkernel.org/. Accessed:
 Apache. Apache mesos. http://mesos.apache.org/.
 CentOS. Coreos tectonic. https://tectonic.com. Ac-
 Docker. Docker swarm. https://docs.docker.com/
swarm/. Accessed: 2016-04-02.
 Google. Kubernetes. http://kubernetes.io. Accessed:
 A Big Switch Network. Project ﬂoodlight. http://www.
projectfloodlight.org/. Accessed: 2016-04-02.
 Amazon Web Service. Amazon ec2 container service.
https://aws.amazon.com/ecs/. Accessed: 2016-04-02.