Autonomous, Seamless and Resilience Carrier Cloud Brokerage Solution for Business Contingencies during Disaster Recovery
Added on 2023-06-09
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Autonomous, Seamless and Resilience Carrier Cloud Brokerage Solution for
Business Contingencies during Disaster Recovery
Sonia Shahzadi∗, George Ubakanma†, Muddesar Iqbal‡, Tasos Dagiuklas§
∗Email: s.shahzadi@swanmesh.com † ‡§Email: {ubakang, m.iqbal, tdagiuklas}@lsbu.ac.uk
∗ Swan Mesh Networks Ltd, Research and Development, London, UK
† ‡§School of Engineering, London South Bank University, UK
Abstract—The challenge of disaster recovery management
for cloud based services is constantly evolving. The costs of
cloud service downtime in the event of disaster striking is
the subject of much international research. The key issue to
resolve is developing suitably resilient and seamless live/real-
time mechanisms for disaster recovery. In this paper, we
have implemented a proof of concept for an autonomous and
fault tolerant carrier cloud brokerage solution with resilient
provisioning of on-the-fly cloud resources. When a disaster
strikes, the proposed solution will trigger the migration of an
entire IaaS from one cloud to another without causing any
disruption to the business. In the event of non-availability
of hosts for the deployment of virtual network functions
for different business processes, an on-the-fly host selection
mechanism is proposed and implemented to locate other active
compute hosts without any disruptions. In order to evaluate the
performance of the proposed solution, we defined several use-
case scenarios for each cloud service. This proposed solution
will not only reduce the capital expenditure but also provides
a reliable and efficient way to access the data during disaster.
Keywords-Cloud Computing, Business Continuity, Infras-
tructure as a Service, Platform as a Service, Software as a
Service
I. INTRODUCTION
Disaster recovery in cloud computing has gained a lot
of attention due to its benefits that facilitate the needs
of business. Currently, many business organizations face
diverse disruptions that could affect organizational assets.
Therefore, a proactive approach is required in order to
counteract these disruptions. Mostly, these organizations
depend on Disaster Recovery (DR) services to prevent
service disruptions, because even short periods of downtime
can cause significant business losses [1]. Generally, these
DR services are expensive increasing the organizations
capital costs. The proposed solution addresses on-demand
and autonomous mechanisms to assure high availability
of services in disaster situations. When we study disaster
recovery in cloud environments different options are
available. Cloud computing depends on cloud models i.e.
private cloud, public cloud and hybrid cloud [2].
The Sendai Framework for Disaster Risk Reduction 2015-
2030 [3], was adopted at the Third UN World Conference
in Sendai, Japan, in March 2015. The Sendai Framework
outlines the need to increase investment efforts in Disaster
Reduction for Resilience. As part of this on-going research
effort we have developed a cost effective, fault tolerant and
resilient carrier cloud architecture that can be deployed at
short notice in disaster management and recovery situations.
The solution builds on the principles of live migration and
disaster recovery [2]. In order to keep the solution flexible
and accessible open source technologies and existing
standards are utilized to maximum effect in delivering a
practical, robust and extensible solution.
Cloud computing provides feasible disaster recovery
solutions due to its dynamic scalable and high availability
structure. To reduce the disaster outcomes, a multi-cloud
disaster recovery model is implemented that mange the
resources from multiple cloud providers. In this paper, we
will argue that cloud computing is an effective platform
for DR services with low costs, and it minimizes recovery
time without data loss. The reason to choose cloud based
DR solution [4]:
• Easy to deploy and manage
• Maximum flexibility
• Agility
• High availability
• Reduce capital cost
• Reduce DR solution cost
• Ease of access for business continuity
The remainder of this paper is organized as follows. Sec-
tion II describes the relevant work and state of the art.
Section III presents use cases relevant to cloud disaster
management. Section IV presents the architecture of our
proposed solution. Section V describes the implementation
of a proposed framework as proof of concept. Section
VI describes the performance evaluation while section VII
describes the conclusion of this paper and future work.
II. RELEVANT WORK
Today, as technology evolves rapidly, dynamic and re-
sponsive applications are required to support users. Develop-
ment flow of Business Continuity (BC) and disaster recovery
are given in [5] where two parts of a disaster recovery
program (i.e. building the program and choosing cloud
solution) using cloud are discussed. According to study
Business Contingencies during Disaster Recovery
Sonia Shahzadi∗, George Ubakanma†, Muddesar Iqbal‡, Tasos Dagiuklas§
∗Email: s.shahzadi@swanmesh.com † ‡§Email: {ubakang, m.iqbal, tdagiuklas}@lsbu.ac.uk
∗ Swan Mesh Networks Ltd, Research and Development, London, UK
† ‡§School of Engineering, London South Bank University, UK
Abstract—The challenge of disaster recovery management
for cloud based services is constantly evolving. The costs of
cloud service downtime in the event of disaster striking is
the subject of much international research. The key issue to
resolve is developing suitably resilient and seamless live/real-
time mechanisms for disaster recovery. In this paper, we
have implemented a proof of concept for an autonomous and
fault tolerant carrier cloud brokerage solution with resilient
provisioning of on-the-fly cloud resources. When a disaster
strikes, the proposed solution will trigger the migration of an
entire IaaS from one cloud to another without causing any
disruption to the business. In the event of non-availability
of hosts for the deployment of virtual network functions
for different business processes, an on-the-fly host selection
mechanism is proposed and implemented to locate other active
compute hosts without any disruptions. In order to evaluate the
performance of the proposed solution, we defined several use-
case scenarios for each cloud service. This proposed solution
will not only reduce the capital expenditure but also provides
a reliable and efficient way to access the data during disaster.
Keywords-Cloud Computing, Business Continuity, Infras-
tructure as a Service, Platform as a Service, Software as a
Service
I. INTRODUCTION
Disaster recovery in cloud computing has gained a lot
of attention due to its benefits that facilitate the needs
of business. Currently, many business organizations face
diverse disruptions that could affect organizational assets.
Therefore, a proactive approach is required in order to
counteract these disruptions. Mostly, these organizations
depend on Disaster Recovery (DR) services to prevent
service disruptions, because even short periods of downtime
can cause significant business losses [1]. Generally, these
DR services are expensive increasing the organizations
capital costs. The proposed solution addresses on-demand
and autonomous mechanisms to assure high availability
of services in disaster situations. When we study disaster
recovery in cloud environments different options are
available. Cloud computing depends on cloud models i.e.
private cloud, public cloud and hybrid cloud [2].
The Sendai Framework for Disaster Risk Reduction 2015-
2030 [3], was adopted at the Third UN World Conference
in Sendai, Japan, in March 2015. The Sendai Framework
outlines the need to increase investment efforts in Disaster
Reduction for Resilience. As part of this on-going research
effort we have developed a cost effective, fault tolerant and
resilient carrier cloud architecture that can be deployed at
short notice in disaster management and recovery situations.
The solution builds on the principles of live migration and
disaster recovery [2]. In order to keep the solution flexible
and accessible open source technologies and existing
standards are utilized to maximum effect in delivering a
practical, robust and extensible solution.
Cloud computing provides feasible disaster recovery
solutions due to its dynamic scalable and high availability
structure. To reduce the disaster outcomes, a multi-cloud
disaster recovery model is implemented that mange the
resources from multiple cloud providers. In this paper, we
will argue that cloud computing is an effective platform
for DR services with low costs, and it minimizes recovery
time without data loss. The reason to choose cloud based
DR solution [4]:
• Easy to deploy and manage
• Maximum flexibility
• Agility
• High availability
• Reduce capital cost
• Reduce DR solution cost
• Ease of access for business continuity
The remainder of this paper is organized as follows. Sec-
tion II describes the relevant work and state of the art.
Section III presents use cases relevant to cloud disaster
management. Section IV presents the architecture of our
proposed solution. Section V describes the implementation
of a proposed framework as proof of concept. Section
VI describes the performance evaluation while section VII
describes the conclusion of this paper and future work.
II. RELEVANT WORK
Today, as technology evolves rapidly, dynamic and re-
sponsive applications are required to support users. Develop-
ment flow of Business Continuity (BC) and disaster recovery
are given in [5] where two parts of a disaster recovery
program (i.e. building the program and choosing cloud
solution) using cloud are discussed. According to study
[4], many organizations are considering migration to cloud
computing for business continuity and a survey revealed
that only 50% of re-respondents have proper DR plans for
the whole organization, while 36% say they only have DR
plans for back-end infrastructure that will only work for data
center, not for remote offices and desktops, 7% don’t have
DR plans, but they can deploy within 6 months, 5% don’t
have DR plans, but they can deploy within 12 months and
2% also don’t have DR plans, but they can deploy within 24
months. Business Continuity and Disaster Recovery (BCDR)
plans for healthcare scenario are discussed in [6] to make
sure fast and secure access of patient data with reasonable
budget. A practical solution is implemented to deal with
disaster recovery and business continuity[7] in Portugal.
Cloud brokerage solutions can provide on-the-fly configu-
ration of existing cloud platforms at the Infrastructure as a
Service (IaaS), Platform as a Service (PaaS) and Software
as a Service (SaaS) levels [8], while supporting portability
and interoperability solutions [9]. Mostly, the cloud porta-
bility and interoperability approaches are put into practice
to overcome certain cloud platform limitations. Although,
portability and interoperability technically complement each
other, both approaches differ in many ways [9]. To reduce
the human effort, design and deployment of cloud appli-
cations is provisioned such that solutions can move from
one cloud to another, this is called portability [9]. Whereas
cloud interoperability supports different cloud services and
applications to work together and the customer is largely
unaware of this [9]. Based on the literature review, the
existing approaches can be categorized into the following
two units [9]:
A. Using existing standards
1) Cloud Data Management Interface (CDMI) specifies
how applications: create, delete, update and retrieve
data on the cloud.
2) Open Cloud Computing Interface (OCCI) supports the
deployment, monitoring and autonomic scaling. Its
API supports compute, network and storage services
[9].
3) Open Virtualization Format (OVF) for import and
export of VMs using OVF standards.
4) Topology and Orchestration Specification for Cloud
Applications (TOSCA) specify a language to a define
service and its components to deal with portability [8,
9].
B. Using open source libraries
1) Jclouds abstract the differences between multiple
cloud providers and also provide application portabil-
ity [10].
2) δ-Cloud is REST based API framework in Ruby
language that abstract the differences between multiple
cloud IaaS platforms [11].
3) Libcloud is a Python library that support extensive
cloud providers APIs [12].
4) Fog provides a high level interface to different clouds
using ruby language [13].
5) Dasein Cloud is a Java based library for compute
service access [14].
6) Simple Cloud is a PHP based library for storage, queue
and infrastructure services [15].
These open source abstraction solutions provide an interme-
diate layer for cloud management [8, 9, 16, 17, 18] while
comparisons of these solutions are given in Table I.
III. CLOUD DISASTER MANAGEMENT: USE CASES
Regardless of whether the disaster is classified as:
Natural e.g. hurricane, tornado, flood or earthquake.
Man-made e.g. infrastructure failure and cyber-attacks etc.
A Disaster Recovery (DR) plan incorporating procedures
and techniques for prevention, mitigation, managing recov-
ery is essential. Within the scope of the practical measures
for executing the DR plan, our research focuses on the
delivery of an instant and scalable approach enabling:
A. Reduced Recovery Waiting Times
For good DR services according to cost, these matrices are
used: Recovery Time Objective (RTO), Recovery Point Ob-
jective (RPO), performance and geographic separation [19].
We are developing a proof of concept utilizing our Multi-
Cloud Broker Orchestrator and Cloud Carrier Architecture
to provision instant (on the fly) deployment/restoration of
essential data center services. This enables deployment of
essential services sooner when a disaster has struck. To
enable this were centrally orchestrating all services instead
of provisioning them separately. Our goal is improving
metrics such as RTO, the maximum time to service recovery
and the RPO the maximum allowable data loss. The values
assigned to metrics will be defined on an individual basis
by problem domain and application [20]. Cloud Replication
backup approach offers short Recovery Time Objectives
(RTOs) with maximum protection for critical applications
[6].
B. Service Traffic Optimization
Our goal is to optimize traffic flows to help keep recovery
time and data losses down, while minimizing costs. Our
concept employs open source Apache libcloud to utilize a
wide range of cloud resources and to optimize in real-time,
both inter-cloud management and load balancing.
C. Backup as a Service
For good DR services according to backup, these matrices
are used: Hot Backup Site, Warm Backup Site and Cold
Backup Site [19]. Multi-Cloud Broker Orchestration and
Cloud Carrier Architectures can play an increasing role in
delivering Backup as a Service. Ensuring that clients can not
computing for business continuity and a survey revealed
that only 50% of re-respondents have proper DR plans for
the whole organization, while 36% say they only have DR
plans for back-end infrastructure that will only work for data
center, not for remote offices and desktops, 7% don’t have
DR plans, but they can deploy within 6 months, 5% don’t
have DR plans, but they can deploy within 12 months and
2% also don’t have DR plans, but they can deploy within 24
months. Business Continuity and Disaster Recovery (BCDR)
plans for healthcare scenario are discussed in [6] to make
sure fast and secure access of patient data with reasonable
budget. A practical solution is implemented to deal with
disaster recovery and business continuity[7] in Portugal.
Cloud brokerage solutions can provide on-the-fly configu-
ration of existing cloud platforms at the Infrastructure as a
Service (IaaS), Platform as a Service (PaaS) and Software
as a Service (SaaS) levels [8], while supporting portability
and interoperability solutions [9]. Mostly, the cloud porta-
bility and interoperability approaches are put into practice
to overcome certain cloud platform limitations. Although,
portability and interoperability technically complement each
other, both approaches differ in many ways [9]. To reduce
the human effort, design and deployment of cloud appli-
cations is provisioned such that solutions can move from
one cloud to another, this is called portability [9]. Whereas
cloud interoperability supports different cloud services and
applications to work together and the customer is largely
unaware of this [9]. Based on the literature review, the
existing approaches can be categorized into the following
two units [9]:
A. Using existing standards
1) Cloud Data Management Interface (CDMI) specifies
how applications: create, delete, update and retrieve
data on the cloud.
2) Open Cloud Computing Interface (OCCI) supports the
deployment, monitoring and autonomic scaling. Its
API supports compute, network and storage services
[9].
3) Open Virtualization Format (OVF) for import and
export of VMs using OVF standards.
4) Topology and Orchestration Specification for Cloud
Applications (TOSCA) specify a language to a define
service and its components to deal with portability [8,
9].
B. Using open source libraries
1) Jclouds abstract the differences between multiple
cloud providers and also provide application portabil-
ity [10].
2) δ-Cloud is REST based API framework in Ruby
language that abstract the differences between multiple
cloud IaaS platforms [11].
3) Libcloud is a Python library that support extensive
cloud providers APIs [12].
4) Fog provides a high level interface to different clouds
using ruby language [13].
5) Dasein Cloud is a Java based library for compute
service access [14].
6) Simple Cloud is a PHP based library for storage, queue
and infrastructure services [15].
These open source abstraction solutions provide an interme-
diate layer for cloud management [8, 9, 16, 17, 18] while
comparisons of these solutions are given in Table I.
III. CLOUD DISASTER MANAGEMENT: USE CASES
Regardless of whether the disaster is classified as:
Natural e.g. hurricane, tornado, flood or earthquake.
Man-made e.g. infrastructure failure and cyber-attacks etc.
A Disaster Recovery (DR) plan incorporating procedures
and techniques for prevention, mitigation, managing recov-
ery is essential. Within the scope of the practical measures
for executing the DR plan, our research focuses on the
delivery of an instant and scalable approach enabling:
A. Reduced Recovery Waiting Times
For good DR services according to cost, these matrices are
used: Recovery Time Objective (RTO), Recovery Point Ob-
jective (RPO), performance and geographic separation [19].
We are developing a proof of concept utilizing our Multi-
Cloud Broker Orchestrator and Cloud Carrier Architecture
to provision instant (on the fly) deployment/restoration of
essential data center services. This enables deployment of
essential services sooner when a disaster has struck. To
enable this were centrally orchestrating all services instead
of provisioning them separately. Our goal is improving
metrics such as RTO, the maximum time to service recovery
and the RPO the maximum allowable data loss. The values
assigned to metrics will be defined on an individual basis
by problem domain and application [20]. Cloud Replication
backup approach offers short Recovery Time Objectives
(RTOs) with maximum protection for critical applications
[6].
B. Service Traffic Optimization
Our goal is to optimize traffic flows to help keep recovery
time and data losses down, while minimizing costs. Our
concept employs open source Apache libcloud to utilize a
wide range of cloud resources and to optimize in real-time,
both inter-cloud management and load balancing.
C. Backup as a Service
For good DR services according to backup, these matrices
are used: Hot Backup Site, Warm Backup Site and Cold
Backup Site [19]. Multi-Cloud Broker Orchestration and
Cloud Carrier Architectures can play an increasing role in
delivering Backup as a Service. Ensuring that clients can not
Table I
COMPARISON OF CLOUD ABSTRACTION SOLUTIONS
Features Jclouds δ-Cloud Libcloud Fog Dasein Cloud Simple Cloud
Type Library Framework Library Library Library Library
Database N N/A N N N/A N/A
Supported IaaS Y Y Y Y Y Y
Supported PaaS Y Y Y N/A N/A N/A
Multi-IaaS Support Y Y Y N/A N/A Y
Multi-Cloud Support Y Y Y N/A Y Y
DNS N N/A Y Y N/A N/A
License Apache License 2.0 Apache License 2.0 Apache License 2.0 MIT License Apache License 2.0 Open BSD License
Documentation Good Good Good N/A Very less to no documentation. Good
Supported CSPs 30 17 60 43 N/A N/A
Compute Y Y Y Y Y N/A
Network N Y Y N/A Y N/A
Storage Y Y Y Y Y Y
Amazon EC2 Support Y Y Y N Y Y
Programming Language Java Ruby Python Ruby Java PHP
Platform Integration Maven Drivers Drivers N/A N/A N/A
EBS Storage Support Y No, but available in road-map. N N/A N/A Y
Container N N/A Y N N/A N/A
CDN N N/A Y Y N/A N/A
Load Balancing Y Y Y N N/A N/A
Y=Yes; N=No; N/A=Not Available;
only recover their cloud infrastructure and services on the fly
when disaster strikes; but also be assured of reliable access
to backup data resources as vital services are re-stored [21].
Based on the use-cases discussed above, to provision cloud
contingency services during emergency and dis-aster situa-
tions, we consider following solutions to deliver the cloud
infrastructure as a service.
During a natural disaster when communication infra-
structure is destroyed, it can also effect regional cloud data-
centers located within that specific zone. This can partially
or fully disrupt business services, resulting in potential losses
of millions. The Cloud is a promising solution to providing
on-demand provisioning services between different regions.
As a cloud manages all these resources through a central
location. Therefore, as shown in Figure 1, if regional a
cloud goes down or collapses for example in Zone B,
then all the user requests will be redirected to Zone A
and vice-versa. This may effect Zone A performance due
to extra load. During disaster recovery situations every
second counts and restoring cloud resources can become a
matter of survival for a particular business. This requires an
autonomous seamless and resilience carrier cloud broker-age
solution as shown in Figure 2, where multiple clouds are
connected to a single cloud brokerage solution which can
provision resources to accommodate different IaaS requests.
Cloud service providers can provide scalable resources to
accommodate the requirements within minutes. The entire
process is required to be initiated dynamically. In order to
achieve this, we have proposed a real-time cloud brokerage
that provides seamless, autonomous (self-service and self-
managed) resource provisioning for a contingency cloud
to replicate the destroyed IaaS during a natural disaster.
The proposed cloud brokerage will automatically trigger the
deployment of contingency cloud resources and redirect the
user request to the alternative cloud services.
Figure 1. Cloud Service Scenario for Disaster Management
IV. PROPOSED ARCHITECTURE
There are multiple solutions of data recovery in cloud
computing that depends on user requirements and IT budget
or you can mix multiple approaches according to your
unique scenario. As, we have merged two approaches, one
is for rapid data recovery while other is for cloud controller
lost. We have proposed a carrier cloud brokerage solution
for federated cloud portability and to provide resilient in-
frastructure services on demand. It provides synchronization
among multiple clouds platforms through a central broker.
The proposed carrier cloud brokerage solution will on-the-
fly identify and select the best available resources in the
region and migrate the load to it. Multiple options for cloud
services available in a particular zone are given to the users
to select resources based on their preferences. If one server
in a specific zone collapses, the user will automatically be
connected to an-other server transparently.
COMPARISON OF CLOUD ABSTRACTION SOLUTIONS
Features Jclouds δ-Cloud Libcloud Fog Dasein Cloud Simple Cloud
Type Library Framework Library Library Library Library
Database N N/A N N N/A N/A
Supported IaaS Y Y Y Y Y Y
Supported PaaS Y Y Y N/A N/A N/A
Multi-IaaS Support Y Y Y N/A N/A Y
Multi-Cloud Support Y Y Y N/A Y Y
DNS N N/A Y Y N/A N/A
License Apache License 2.0 Apache License 2.0 Apache License 2.0 MIT License Apache License 2.0 Open BSD License
Documentation Good Good Good N/A Very less to no documentation. Good
Supported CSPs 30 17 60 43 N/A N/A
Compute Y Y Y Y Y N/A
Network N Y Y N/A Y N/A
Storage Y Y Y Y Y Y
Amazon EC2 Support Y Y Y N Y Y
Programming Language Java Ruby Python Ruby Java PHP
Platform Integration Maven Drivers Drivers N/A N/A N/A
EBS Storage Support Y No, but available in road-map. N N/A N/A Y
Container N N/A Y N N/A N/A
CDN N N/A Y Y N/A N/A
Load Balancing Y Y Y N N/A N/A
Y=Yes; N=No; N/A=Not Available;
only recover their cloud infrastructure and services on the fly
when disaster strikes; but also be assured of reliable access
to backup data resources as vital services are re-stored [21].
Based on the use-cases discussed above, to provision cloud
contingency services during emergency and dis-aster situa-
tions, we consider following solutions to deliver the cloud
infrastructure as a service.
During a natural disaster when communication infra-
structure is destroyed, it can also effect regional cloud data-
centers located within that specific zone. This can partially
or fully disrupt business services, resulting in potential losses
of millions. The Cloud is a promising solution to providing
on-demand provisioning services between different regions.
As a cloud manages all these resources through a central
location. Therefore, as shown in Figure 1, if regional a
cloud goes down or collapses for example in Zone B,
then all the user requests will be redirected to Zone A
and vice-versa. This may effect Zone A performance due
to extra load. During disaster recovery situations every
second counts and restoring cloud resources can become a
matter of survival for a particular business. This requires an
autonomous seamless and resilience carrier cloud broker-age
solution as shown in Figure 2, where multiple clouds are
connected to a single cloud brokerage solution which can
provision resources to accommodate different IaaS requests.
Cloud service providers can provide scalable resources to
accommodate the requirements within minutes. The entire
process is required to be initiated dynamically. In order to
achieve this, we have proposed a real-time cloud brokerage
that provides seamless, autonomous (self-service and self-
managed) resource provisioning for a contingency cloud
to replicate the destroyed IaaS during a natural disaster.
The proposed cloud brokerage will automatically trigger the
deployment of contingency cloud resources and redirect the
user request to the alternative cloud services.
Figure 1. Cloud Service Scenario for Disaster Management
IV. PROPOSED ARCHITECTURE
There are multiple solutions of data recovery in cloud
computing that depends on user requirements and IT budget
or you can mix multiple approaches according to your
unique scenario. As, we have merged two approaches, one
is for rapid data recovery while other is for cloud controller
lost. We have proposed a carrier cloud brokerage solution
for federated cloud portability and to provide resilient in-
frastructure services on demand. It provides synchronization
among multiple clouds platforms through a central broker.
The proposed carrier cloud brokerage solution will on-the-
fly identify and select the best available resources in the
region and migrate the load to it. Multiple options for cloud
services available in a particular zone are given to the users
to select resources based on their preferences. If one server
in a specific zone collapses, the user will automatically be
connected to an-other server transparently.
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