Design of Secure Mobile Cloud with Smart Load Balancing

A Framework and the Design of Secure Mobile Cloud
with Smart Load Balancing
Isaac J. Cushman, Md. Baitul Al Sadi, Lei Chen, Rami J. Haddad
Department of Electrical Engineering, Department of Information Technology
Georgia Southern University
Statesboro, USA
{ic00214, ms12508, lchen, rhaddad}@georgiasouthern.edu
Abstract—The use of mobile devices has exponentially
expanded in recent years. A device which was made with the sole
purpose of making mobile audio phone calls is now the leading
basis for functionality in the social world. The types of applications
widely vary from audio and video calls, internet browsing,
healthcare applications, to mobile games with online connectivity,
among many others. These applications have expanded the
original idea of what a mobile device could be, however there have
been constant drawbacks to these devices, namely short battery
life and limited available storage memory. Another current issue
that exists with mobile devices is the higher data consumption
when on mobile network data. To solve this problem, it is possible
to use cloud computing to mitigate these large applications and use
less data. Integrating in a mobile cloud system to allocate and store
these applications will allow for the mobile devices to conserve
battery and memory by avoiding large computational processes.
Another major concern is security breaches resulting in data theft
and/or invasion of privacy. In this paper, we present a new
framework that will allow for a smart load balancer to efficiently
allocate resources to increase application processing speed for
data and request response of memory stored by mobile devices in
a secure manner.
Keywords—Mobile Cloud Framework, Resource Provisioning,
Smart Load balancer, Mobile Resource Management, Security and
Privacy
I. INT RODUCT ION
Cloud networks offer many benefits to service providers and
users, however there are notable drawbacks that come along
with these benefits. By offering a cloud network, the service
provider can extend to their user resources on demand through
service packages. Cloud computing networks (CCN) are
designed so that many users can be virtually connected into the
same space, as tenants, and rely on the cloud network to store
and/or utilize their data [1]. CCNs are complex networks
consisting of tenants sharing the same space, but with varying
levels of needed security. A proposed approach to assuring
levels of security to different users is address ed later in this
paper. A quickly evolving branch of CCNs is the Mobile Cloud
Computing (MCC). MCC has the potential to overcome the
constraints of the performance of mobile entities, such as
computational power, storage, bandwidth, heterogeneity and
scalability [2]. The recent mobile standard Long-Term
Evolution (LTE) is supporting the cloud augmentation as new
generation mobile applications are needed to overcome the
limitations of computation [2]. Next generation application data
are no longer static as there is much more diversity in mobile
applications [3]. To handle such dynamic data, dynamic
resource management can be used by dynamic resource
allocation technique in a virtual cloud system [3]. This concept
allows users to avoid having to purchase large packages that
may include many other pieces of software or too much
processing power for the required use. The driving force behind
this is known as “as-a-service”, where software, platforms or
infrastructures are offered to the user virtually. A new business
owner will be able to maintain their entire business operation
on a single machine without needing the complete knowledge
of how to configure and operate their operating systems and
servers as all the backend processes and procedures will take
place on the cloud server side. Resource allocation and data
management within mobile clouds have a variety of challenges
that have previously been researched, most critically of which
are: heterogeneity of data, availability to the network,
offloading, and security and privacy [4].
Mobile devices are not only the medium of verbal
interactions but also the intermediate of user accessing,
managing and preserving multimedia data [4]. A mobile device
itself is a source of media-rich application data. From high
definition multimedia to spreadsheet data, from GPS location
information to medical records, from banking information to
regular grocery activates, all are processed in the mobile device.
Much of the data preserved in the mobile device are considered
as private or sensitive data. Hence, mobile data deserve greater
security and privacy. Whether the data is highly important like
banking account information or casual such as daily notes, they
all demand availability, reliability, consistency, redundancy,
integrity and security. Mobile Cloud Computing (MCC) has the
potentiality to meet all such demands and the expectation to
augment the computational limitation of the mobile device. The
computational gain, which is achievable from the mobile cloud
may play an important role to improve the overall performance
of mobile devices [5].
The contributions of this paper investigate current
architecture types of mobile cloud networks and present a
testbed to determine new secure methods of data allocation
2017 5th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering
978-1-5090-6325-3/17 $31.00 © 2017 IEEE
DOI 10.1109/MobileCloud.2017.41
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within the network. The testbed is in current production as
simulation and then physical servers to deploy the smart load
balancer to service multiple virtual machine tenants. The
remainder of this paper is organized as follows: section II
discusses how OpenStack is used in the design of the cloud
platform; section III provides an insight on current technologies
and research on resource management, allocation and provision
in mobile cloud, as well as dynamic resource allocation; section
IV provides the proposed solution in both simulation and
hardware, and section V draws the conclusions.
II. OPEN ST ACK CLOUD DESIGN
In many academic cloud deployments, open source allows
the deployment of cloud networks without the need of expensive
licenses. This project uses OpenStack to build and test the cloud
network. OpenStack is open source and was selected in this
project thanks to its large community of developers in both
industrial and academic cloud deployments. A key strength that
comes with developing a computing network using OpenStack
is that the cloud models can have a variety of configurations to
serve a task with excellent flexibility. Some example uses are
public cloud, high throughput computing, web hosting, and
video processing and content delivery, etc. [6]. The architecture
built for this project requires three components: the controller,
compute and network nodes. The controller node is responsible
for running the virtual machine Identity and Image services,
management portion of compute node and the dashboard. The
compute node is responsible for running the hypervisor that
operates tenant virtual machines or instances, and connects
network plug-ins and firewall services. Lastly, the network node
is responsible for providing switching, routing, Network
Address Translation (NAT) and Dynamic Host Configuration
Protocol (DHCP) [7].
The OpenStack cloud design has several key pieces of
software that enable the server to operate a cloud network. Each
of the required software used in this project and their
descriptions are listed below. It should be noted there is optional
software available, however they are not generally required by
the cloud network. The following OpenStack projects, listed by
their project names, are defined in [8]:
Keystone: Authentication and authorization service that
operates as the identity of the cloud network. It connects all other
OpenStack services.
Glance: Operates as the image service for the cloud network;
this software is responsible for creating, editing and
provisioning virtual machines. Glance will manage any of the
users that need resources while in the cloud.
Neutron: Establishes the internal and external bridge
connections between each of the nodes and the other OpenStack
services; provides an API to define networks and build network
topologies and configure network policies .
Nova: Manages the lifecycle of compute instances in an
OpenStack environment. This includes spawning, scheduling
and decommissioning of virtual machines on demand.
Cinder: This software is the block storage on the cloud network;
it provides persistent block storage on the instances created by
Glance.
Horizon: This software is the web-based systemthat allows for
the cloud provider to quickly access and manages each of the
services in the cloud outside of the command line interface.
Horizon can create, edit and remove virtual machines, users, and
hypervisors inside the network.
The controller node requires Keystone, Glance and Horizon to
be installed on it. To create a network connection between the
other two nodes, networking settings on IPv4 are established
and a portion of Nova on the compute node will call toward the
IP address of the controller. Some nodes will have more
software than others operating on them, and therefore it is
necessary to also include more block and object storage onto
one or more of these nodes. This allows for maximu m
processing capabilities for the entire system.
III. RESOURCE M ANAGEMENT , A LLOCATION AND
PROVISIONING
Resource allocation within mobile cloud computing
networks has been presented in several different ways, typically
generating a cost function per the efficiency of the required
request. The work in [9] presents an adaptation where the
overall cloud network is not localized and requires mobile
social users, cloud brokers and a mobile cloud. When a request
from a social user is presented to the broker, a cost for the
resources is determined and the request is sent to the cloud.
When the cloud broker negotiates higher or lower costs, the
mobile user would then make the decision to connect. Their
work presents a game theoretic method of resource allocation
for better energy efficiency. Another propos al made by the
authors in [9] aims to reduce the overloading on the cloud by
optimizing user traffic through segmenting the data. In this
manner, incoming tasks can be organized in a more dynamic
order to appear as if there is less traffic coming in. While
solutions developed have aimed to solve specific issues, mobile
cloud lacks a common framework that will dynamically
determine the needs of the system based on the user requests.
To determine the ability of a load balancer to efficiently
handle these problems, a measure of quality of service (QoS) is
conducted. QoS can be considered as several different measures
dependent on the system that is being observed. In mobile cloud
computing, the important factors of QoS are the ability to
remain connected to the network and the overall throughput of
the data. Network connectivity and reliability among mobile
carriers has significantly increased, however there are still areas
where dead zones exist. Lack of availability in a system where
major computation and storage for a mobile device takes place
becomes a major concern. Mobile cloud is a technology
supporting online dynamic resource allocation enabled
services. Dynamic Load Balancing (BLD) mechanism can be
used to distribute the resources by maintaining scalable
workload among every node in the network. Features like
resource optimization, diminishing of response time and down
time, maximizing the throughput, avoiding of overload can be
obtained by Dynamic Load Balancing techniques [11].
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A. Dynamic Resource Allocation
In resource allocation, one of the challenging parts is to
categorize the mobile resources per its priority factor. The
priority factor can be assigned per its requirement, time
sensitivity and the size or space of the data. For example, if
there is an application in the mobile device that deals with real-
time voice or gaming data, undoubtedly such data is highly time
sensitive. Similarly, certain applications are required to access
and process the data immediately, depending on the time
sensitivity of the data, such as video broadcasting and
streaming. To explain further, a variety of data that is stored on
a mobile device does not require continuous synchronization or
need to be processed immediately when created and therefore
can be stored in mobile cloud storage. Subsequently, this kind
of data can be considered as less prioritized data. Some
applications, such as High Definition (HD) video capturing,
may generate large amount of data, hence they may consume
large amount of storage in mobile devices and therefore may
affect the overall performance of these devices. In this case,
data from the mobile devices can be sent by sensing the
available space in the mobile devices. If the mobile device does
not have enough space, it should send the data to the cloud
immediately. Otherwise, when there is enough space in the
mobile device, a certain predefined schedule can be set to
transfer the data.
With the context of the origin of the mobile data, data can be
categorized as follows:
 User Generated Data: Such data can be referred to as the
data generated by the user according to the requirement of
the user, such as contact information, text messages,
captured photos and videos, created personal notes.
 Application data: All mobile application driven data can be
classified as application data, such like email applications
data, GPS information, map information, social networking
data, various gaming and application data, etc. Some of the
application data may require frequent access as per user
demand basis or application requirement basis .
 System data: All data associated with the system
information, system files, system configuration belong to
this category.
B. Smart Load Balancer
In cloud computing, load balancing is defined as the abilit y
for the system to take incoming application data from the user,
measure the computational requirements and determine which
of the availability zones it needs to be stored in. It is also
required to handle any incoming data to an application so that
the processing ability of that application is not overloaded [12].
The load balancer will have two main functions, finding the
best location that information should be stored and finding the
best path a request should take to retrieve the information. In
mobile cloud networks, this poses a problem, due to the
heterogeneity of the incoming and outgoing data types and
security. In current load balancing methods, the request fro m
the user is granted based on the current availability in each of
the zones and if the request can be filled without overcoming
the system. The overall basis of how a cloud load balancer is
deployed can be categorized as either in software or in hardware
[13]. From the related work, it is possible to classify sever key
characteristics that are involved when developing a load
balancer for mobile cloud computing. The first to discuss is the
ability to scale up and down in the network. The work in [14]
focuses on the ability for a load balancer to react to the growth
of web applications. When many more machines are added to
the system, the algorithm for load balancing must adapt to this
change. The next characteristic to observe is time based load
balancing. In the work presented in [15], the proposed
algorithms are round-robin, equally-spread current execution
load algorithm, and active VM load balancing.
In the round-robin algorithm, a randomized list of all the
virtual machines is generated and sorted into a list for
processing. The fallback of this method is that certain nodes can
be consistently missed in very large networks. In equally-
spaced current execution, it was noted that the load balancer
was completely in charge of determining the selection of the
VMs. This system works well in terms of overall execution
time, however as addressed in [15], a minor fault in the load
balancer would cause a catastrophic problem to the entire
system. In the active VM load balancing, all the requests made
by each of the VMs would be logged and the least used VM
would be placed at the top of the priority list when resources
are allocated. The drawback to this system alone is that users in
need of using large amounts of data would have less privilege
in acquiring resources compared to users that do not necessarily
need access. Each of the methods stated above can serve as a
foundation candidate for load balancing with appropriate
modifications.
A smart load balancer will be able to intelligently define the
incoming requests by predicting the needs of the request based
on the data type. It will be possible to utilize the discussed
methods in part within the algorithm of the smart load balancer
to effectively maintain large networks. This proposed method
of a smart load balancer will establish a set level of Need before
Greed (NBG) in the system when requests are made. This
parameter is used to determine whether the request from the
user should be granted based on total resource capacity
required, type of data, or the priority level of the user. When
allocating resources, NBG will consider priority users that
absolutely require the system before any others . Examples of
this would be mobile service providers granting a mass
broadcast of emergency information to all users. The process of
how to determine which category and more specifically the
degree of need or greed the tenant is in is determined based on
total system requirement. The total system requirement will
track the weight of each request coming into the SLB and
determine the total processing power requested by the tenant.
Examples of what determines need would be if the request is
for high bandwidth applications such as video broadcasting or
high level signal processing computing; whereas a low-lev el
greed would be webpage browsing.
The metric of NBG is discussed below. A problem is
created on when and how to fairly distribute available resources
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