Towards Resilience Metrics for Future Cloud Applications

Marko Novak

, Syed Noorulhassan Shirazi

, Aleksandar Hudic

, Thomas Hecht

, Markus Tauber

1,3

David Hutchison

, Silia Maksuti

1,3

and Ani Bicaku

1,3

Austrian Institute of Technology, Vienna, Austria

InfoLab21, School of Computing and Communications, Lancaster University, Lancaster, U.K.

University of Applied Science Burgenland, Eisenstadt, Austria

Keywords:

Security Metrics, Technology Trend Analysis, Threat Trend Analysis, Cloud Applications, Resilience

Abstract:

An analysis of new technologies can yield insight into the way these technologies will be used. Inevitably,

new technologies and their uses are likely to result in new security issues regarding threats, vulnerabilities and

attack vectors. In this paper, we investigate and analyse technological and security trends and their potential

to become future threats by systematically examining industry reports on existing technologies. Using a cloud

computing use case we identify potential resilience metrics that can shed light on the security properties of the

system.

1 INTRODUCTION

Technology has become a common part of

everyday life. In fact, technology is concerned

with improvements in a variety of human and

organizational endeavours through the design,

development, and use of technologically based

systems and processes that enhance the efﬁciency

and effectiveness of our daily operations. Therefore,

the analysis of technological trends is vital to all

organizations for their future overall effectiveness.

Cloud computing is evolving as an obvious

solution for user requirements, due to their intrinsic

capabilities of elasticity and resource transparency.

Consequently, they are becoming increasingly

mission-critical since they provide always-on

services for many everyday applications (e.g.,

IPTV), critical industrial services (e.g., Air Trafﬁc

Control (ATC) networks), critical manufacturing

services (e.g., utility networks and Industrial Control

Systems) and critical real-time services (e.g.,

surveillance systems). This trend is emphasised in

report (Dekker, 2012) published by ENISA, which

provides speciﬁc guidelines in this area. Despite

the potential beneﬁts of cloud computing, deploying

services increases various concerns because the high

degree of virtualization and resource abstraction

offered by cloud environments comes with a new

set of challenges in terms of security and resilience.

These challenges include malicious behaviour,

cyber-attacks, worms and viruses-compounded

with privacy and legal issues. Compared to

non-cloud-based systems, understanding of the

nature of challenges experienced by cloud providers

is also somewhat opaque, as highlighted in the

literature (Grobauer et al., 2011). Therefore, the

resilience and ability of such cloud environments to

remain operational in the face of challenges becomes

paramount, and it should be thoroughly addressed by

considering the inherent operational characteristics

and investigation of technological trends that bear

the potential to become future threats in cloud

environments.

This paper is based on EU FP7 project SECCRIT

(SEcure Cloud computing for CRitical infrastructure

IT). The project aims to analyse and evaluate cloud

computing technologies with respect to security

risks in sensitive environments, and consequently

to develop methodologies, technologies, and best

practices for creating a secure, trustworthy, and

high assurance cloud computing environment for

critical infrastructures (Simpson et al., 2013). More

precisely, the work presented here is oriented on

cloud assurance activities and resilience management

activities, which includes assurance and resilience

management frameworks supported by monitoring of

Novak, M., Shirazi, S., Hudic, A., Hecht, T., Tauber, M., Hutchison, D., Maksuti, S. and Bicaku, A.

Towards Resilience Metrics for Future Cloud Applications.

In Proceedings of the 6th International Conference on Cloud Computing and Services Science (CLOSER 2016) - Volume 1, pages 295-301

ISBN: 978-989-758-182-3

295

cloud services (Shirazi et al., 2015), (Hudic et al.,

2014), (Scholler et al., 2013).

2 RELATED WORK

Forecasting of the next generation technologies and

their usage faces considerable difﬁculties. Related

work in (Khajeh et al., 2010), (Khajeh et al.,

2011) and (Khajeh et al., 2012) has developed

research results about the cloud adaptation to other

technologies. However, none of these results mention

a resilience as a major issue in cloud operational

context. Various monitoring systems have been

developed, as in (Ballard et al., 2010), (Ibrahim et al.,

2011) and (Payne et al., 2008), but likewise none

of them mention resilience as a monitoring subject.

Using trend analysis, a use case, and by adding a

resilience as a ”big thing” for better cloud operability,

we believe that our work present an approach for

detecting hot spots of the security issues in future

based cloud applications.

3 TECHNOLOGY TREND

ANALYSIS

3.1 Technology Trend Analysis

Approach

The method used for collecting information with

regard to technological trends and threats follow

the pattern of the information collection, analysis

and collation. We considered public information

from research work performed by governments,

organisations, and industries. This approach complies

with the principles of Open-Source Intelligence.

As stated in (Authorization., 2006), ”OSINT

is intelligence that is produced from publicly

available information and is collected, exploited, and

disseminated in a timely manner to an appropriate

audience for the purpose of addressing a speciﬁc

intelligence requirement”, applied to enable a broader

view of the system, i.e., a representation of the

whole system from the perspective of a related set

of concerns. For the analysis of technological trends

and future threats, we have used aggregation approach

in order to harmonize all trends. Harmonisation of

the all trend analysis results provided by the experts

presents correct lines to get precise future trend

analysis. In order to get summary of the results, we

created cross-mapping table with technology trends

and proposed sources. Raw data has been compressed

and we added some simple calculation to get required

results (see Figure 1).

Figure 1: Calculation Model.

Relevant information is cross-mapping block

indexed with ”1”, which presents trend identiﬁed by

a corresponding source. The calculation method is

structured as follows:

• Total percentage for the category is calculated

with the sum of blocks indexed with ”1” within

category divided with the number of the trend and

sources combination, (T 1 =

∑

C1/K).

• Normalization1 is calculated by multiplying of

the total percentage with index ”1” frequency

average, (N1C1 = T 1 × AvrgF1).

• Normalization2, which gets required results is

calculated by dividing of the Normalization 1

value with its sum from the all categories,

(N2C1 = N1C1/

∑

N1).

Technology trends are structured as follows:

• Mobile Technologies - Internet of Things, Mobile

Applications, Digital Payments, Wiﬁ Calling, Soft

SIM Cards and Beacons.

• Cloud Computing - Hybrid Cloud, Cloud/Client

Architecture, Software Deﬁned Networking

(SDN), Docker and other container technologies,

Agile infrastructures, Challengers to AWS, Era of

personal cloud.

• Market and Device Diversity - Mobility

(BYOD), Chinese brands, Telecoms

Consolidation, Smart machines, Third Platform

(mobile computing) and 3rd Smartphone business

model (Xiaomi).

• Network Protocols - VoLTE (Voice over LTE),

NFC (Near Field Communication) and 3GPP.

• Others - Wearable and 3D Printing.

Summary of the technology trend forecast from our

sources is showed in Figure 2. Percentage values

represent expectation index, or more closely output of

the relevant sources chosen for the future technology

trend analysis.

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296

Figure 2: Technology Trends.

3.2 Future Cloud Trends

• Big Data - Big Data spending will grow with

video, audio and image analytic taking on

more importance. Data visualisation, wireless

communications, and cloud infrastructure are

extending the power and reach of information

(McKinsey and Company, 2015). (KPMG, IDC

(BRZ), Consol, ISreport, NetApp, UK Business

Insider).

• Hybrid Cloud - Bringing together personal

clouds and external private cloud services is

an imperative. Hybrid cloud services can be

composed in many ways, varying from relatively

static to very dynamic (Gartner, 2014), (Forbes,

ISreport, NetApp).

• Cloud/Client Architecture - Cloud/client

computing models are shifting. Increasingly

complex demands of mobile users will drive apps

to demand increasing amounts of server-side

computing and storage capacity (McKinsey and

Company, 2015), (Gartner, KPMG, Forbes).

• Software Deﬁned Networking (SDN) - SDN is

a collective term that encapsulates the growing

market momentum for improved standards for

infrastructure programmability and data center

interoperability driven by automation inherent to

cloud computing, DevOps and fast infrastructure

provisioning (Gartner, 2014), (Cisco, Forbes,

Mason).

• Docker and other Container Technology -

As new applications for SaaS or large-scale

enterprise use cases are written using the

scale-out micro services model, Docker

application containers have proven to be

more resource efﬁcient than VMs with a complete

OS (Insider, 2014), (NetApp).

• Agile Infrastructures - Agility will be one of the

most important criteria for the company’s success

and thus one of the trends in future. Agile system

landscapes and infrastructures are required to

insist about disruptive technologies and business

models in a market or to require Big Data analysis

amendments and adjustments (IsReport, 2014).

• Challenges to Amazon Web Services (AWS) -

Companies are learning to test and experiment

using this type of data. Many advanced marketing

organisations are assembling data from real-time

monitoring of blogs, news reports, and tweets to

detect subtle shifts in sentiment that can affect

product and pricing strategy (Gartner, 2014), (UK

Business Insider).

• Era of Personal Cloud - The personal cloud era

will mark a power shift away from devices toward

services. Access to the cloud and the content

stored or shared from the cloud will be managed

and secured, rather than solely focusing on the

device itself (McKinsey and Company, 2015),

(Gartner).

Figure 3 shows in detail a Cloud trend expectation

summarised by sources.

Figure 3: Cloud Computing Trends.

4 THREAT TREND ANALYSIS

Since presentation of future technology trends does

not appear to be the only base of information for

creation of the use case relevant for our research,

we also did threat trend analysis which includes

all potential future threats in technology evaluation

shown in Figure 4. The process of threat trend

analysis has the same approach like in previous

section with sources listed in. Future threat trends

are categorised as follows based on Symantec,

Kaspersky, Sophos, Symantec, and SentinelOne:

• Targeted Attacks - Cyber criminals developed

attacks that only execute on a speciﬁc machine or

set up. Since they behave like a benign application

on the way to the target machine these threats

Towards Resilience Metrics for Future Cloud Applications

297

are able to evade layers of detection mechanisms

(Kaspersky, 2014).

• Internet of Things Attacks - Attacks on Internet

of Things devices will increase rapidly due to

hyper growth in the number of connected objects,

poor security hygiene and the high value of data

on IoT devices (Kaspersky, 2014).

• Distributed Denial of Service (DDoS) Attacks

- The installation of malicious applications and

the visit malicious websites will no longer pose

infection vectors which are valid exclusively

for the mobile telecommunications sector. The

cross-platform exploitation of vulnerabilities, will

be a far greater threat to rest of the technologies

(Kaspersky, 2014).

• Mobile Attacks - Mobile attacks will continue

to grow rapidly as new technologies expand

the attack surface and App. store abuse goes

unchecked (McAfee, 2014).

• Beyond Windows Attacks - The Shellshock

vulnerability will fuel non-Windows malware

attacks that will continue for years. Attackers

will capitalise on Shellshock by exﬁltrating data,

holding systems ransom, and assimilating spam

bots (McAfee, 2014).

• New Cyber War Players - This refers to

prediction that states will continue to use

cyber-attacks as a political retaliation tool. More

speciﬁcally, some states will continue to carry

out brute force cyber-attacks and espionage

campaigns, primarily against the other countries

and human rights activists (WebSense, 2014).

• E-mail Attacks - An email exploit is an exploit

embedded that can be executed on the recipient’s

machine once the user opens or receives the

email. This allows a hacker to bypass ﬁrewalls

and anti-virus products (WebSense, 2014).

Figure 4: Threat Trends.

5 USE CASE

With the summary of the results from trend analysis

we deﬁne a use case scenario which includes expected

trends in the cloud computing environments including

attack scenarios/vectors. A trend hybrid cloud spans

at least one public and one private cloud, with

the workload distributed across the two which also

communicates with the cloud infrastructure provider.

The client may also have a private, on-premises

virtualized environment that is connected to a public

cloud. Stored data on the cloud infrastructure

provider present users data maintained by cloud

service provider. On the other side, communication

between the client and the private/public cloud

service provider will be determined as a cloud/client

architecture trend. The future cloud trend big data

ﬁts perfectly for the created architecture since users

are constantly facing with the lack of resources. Big

data is also one of the main reasons for developing

hybrid technologies to the cloud. SDN is used for

the realization of the networking, means that available

software will maintain the resource and network

usage in the proposed data centres. SDN will be

based on open-source tools, like OpenStack. Other

cloud trends like Docker and container technologies

could be also included but the result of the cloud

trend analysis exclude their relevance for the use case.

The use case presented in Figure 5, is structured

as: Private Cloud - Company Cloud, continuously

monitoring of the user data, high-level protection,

Client - Employer of the company, any employer of

the company which uses any kind of service on the

internet, Cloud Service Provider - Provides services

across the Internet, Cloud Infrastructure Provider -

Keep sensitive data from the users of the services

managed by cloud service provider The threat trends

relevant for our use case are:

• Targeted Attacks - The client can act as a

”bridge” to run targeted attacks to collect sensitive

information in public clouds.

• DDoS Attacks - According to (Kaspersky,

2014), irresponsible behaviour from the client

on the open network can lead to unimaginable

consequences, which would suggest the use of a

private cloud infrastructure.

• Mobile Attacks - Because App stores present

a vulnerable environment, there is a signiﬁcant

possibility that the client could be a victim by

unwittingly downloading malicious applications.

Other attacks are either not related to our use case or

did not get any attention from our sources, so for that

reason we deem them to be less relevant.

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Figure 5: Use Case Scenario, Future Cloud based

Applications.

6 RESILIENCE

To achieve the maximum security for our use case, we

take a resilience perspective as the key concern for the

overall system. In doing so, we aim to derive relevant

metrics for expected hazards, weak points and attack

methods in future cloud based applications.

6.1 Relation to the Use Case

Resilience is a wide-ranging concern, and can

be deﬁned as the ability of a system to provide

an acceptable level of service in light of various

challenges (Sterbenz et al., 2010). Resilience is

supposed to be a fundamental property of Cloud

service provisioning platforms. However, a number

of signiﬁcant outages have occurred that demonstrate

Cloud services are not as resilient as one would

hope, particularly for providing critical infrastructure

services (Neal, 2011). The potential resilience

related issues relevant to our use case include: (i)

Attacks aiming to break conﬁdentiality (e.g., snifﬁng

and scanning) and integrity (e.g., session hijacking),

and (ii) Denial of Service based on techniques,

e.g., ﬂooding attacks resulting in performance

degradation, i.e., its reliability and availability.

6.2 Monitoring Metrics

It is important to deﬁne correctly the metrics that will

be used in order to measure resilience. These could

also be used for creating the service levels that will be

agreed with the cloud provider and in order to monitor

whether there are any violations of these agreements.

In this work, the metrics discussed are related to

security and resilience, but others that could have an

effect directly or indirectly should be added in a real

scenario (assurance, legal etc.). Services offered by

a provider can have one or more attributes, and these

can be represented by one or more metrics. A few

examples of the possible attributes and metrics used

are service availability, mean time between failures,

mean time to repair, mean time to invoke remediation

action, mean time to recovery, scalability etc.

In relation to the use case, we argue that a

systematic approach is required. Conceptually, the

resilience management can be instantiated in cloud

infrastructure provider, addressing management in

both single and cross domain cases as sketched in

use case. Such a resilience manager compose of

multiple components which mainly include collector,

detector, and remediation. The services offered by

a provider in our use case can have one or many

attributes, and they can be represented by one or

more metrics. Firstly, collector collects those metrics

given that cloud provider provision those resources

in detector supervised part of the infrastructure and

as per the resilience requirements of a tenant. In

case of challenge such as Denial of service attacks,

relevant metrics by collector fed into detector which

consume these metrics to compute a likelihood of

anomaly score and subsequently alert the remediation

component. Later will then invoke actions based on

policies and eventually recovering the system from

challenge. These mechanisms are also supported by

IND

UCE framework developed within the scope of

SECCRIT (Jung et al., 2015).

Figure 6 shows some of the relevant metrics for

such attributes at network and service levels.

Avai l ability

Memory

utilization

Network

utilization

Disk

utilization

Mean time to

repair

Mean time to

failure

Reliability

Network

traffic

No. of

flows/sec

Bytes count

Packets

count

Network

topology

No. of nodes Link density

Avg. node

betweenness

Figure 6: Example metrics for resilience.

6.3 Monitoring Solution

Monitoring is an essential component and helps to

control the infrastructure by collecting and analysing

data (e.g., bandwidth, disk and CPU utilisation)

from various components at host and VM level.

Monitoring is becoming increasingly difﬁcult due to

the changing workload patterns and scalability of

cloud environments. It is becoming even harder

with increasing user expectations for resilience and

security. The SECCRIT consortium has proposed

a resilience management framework that introduces

novel mechanisms to support overall resilience to

Towards Resilience Metrics for Future Cloud Applications

299

challenges arising in cloud environments. More

speciﬁcally, it introduced an online anomaly detection

technique based on data density that can be applied

at the cloud infrastructure level. The method

is embodied by a resilience architecture that was

initially deﬁned in (Simpson et al., 2013) and

further explored in (Shirazi et al., 2014) and

(Shirazi et al., 2015). The framework uses

monitoring API Monasca

that leverage high speed

message queues and computational engines. It also

supports authentication of all data associated with an

OpenStack tenant to support multi-tenancy. Metrics

and events are received by the API and published

to a message queue (Kafka)

. The anomaly engine

consumes metrics from the same message queue,

predict metrics and evaluate likelihood and anomaly

score as probabilities in order to provide overall

resilience to challenges.

7 CONCLUSIONS

In this paper, we have analysed technological trends,

and considered their potential to become future

threats. We did this by means of a systematic

examination of industry reports on existing and

emerging technologies. Using a cloud use case we

have identiﬁed potential resilience metrics that can

shed light on the security properties of cloud systems.

The research also gives a basic overview as to what

may be expected in terms of technology futures

with the aid of threat analysis for their adoption in

critical infrastructure environments where there are

stringent security requirements. As cloud computing

is expanding very fast and new threats arising from

security issues are being created, we believe that

our research presents a basis for helping create more

secure cloud systems.

Future work should include other relevant topics,

for example assurance and legal issues, as a next

step towards achieving a high security level in future

cloud applications. Also, the approach should help

to improve network monitoring and management

systems through such a technology evaluation.

ACKNOWLEDGEMENTS

The research presented in this paper has been funded

by the European Union (FP7 Project SECCRIT, grant

agreement no. 312758).

Monasca: www.wiki.openstack.org/wiki/Monasca

Kafka: http://kafka.apache.org/

REFERENCES

Authorization., N. D. (2006). National defense

authoriazation act for ﬁscal year 2006.

Ballard, J. R., Rae, I., and Akella, A. (2010). Extensible and

scalable network monitoring using opensafe. Proc.

INM/WREN, pages 8–8.

Dekker, M. (2012). Critical cloud computing: A ciip

perspective on cloud computing services. Technical

report, European Network and Information Security

Agency (ENISA).

Gartner (2014). Gartner symphosium it xpo, executive

summary report.

Grobauer, B., Walloschek, T., and Stocker, E. (2011).

Understanding cloud computing vulnerabilities. IEEE

Security and Privacy, 9(2):50–57.

Hudic, A., Tauber, M., Lorunser, T., Krotsiani, M.,

Spanoudakis, G., Mauthe, A., and Weippl, E.

(2014). A multi-layer and multitenant cloud assurance

evaluation methodology. In Cloud Computing

Technology and Science (CloudCom), 2014 IEEE 6th

International Conference on, pages 386–393.

Ibrahim, A. S., Hamlyn-Harris, J., Grundy, J., and Almorsy,

M. (2011). Cloudsec: a security monitoring appliance

for virtual machines in the iaas cloud model. Network

and System Security (NSS), 2011 5th International

Conference, pages 113–120.

IDC (2014). Idc predictions 2015: Accelerating innovation

and growth on the 3rd platform.

Insider, U. B. (2014). Billions of dollars are set to ﬂow into

these 7 areas of tech in 2015.

IsReport (2014). Information platform for business

solutions, it forecast for 2015.

Jung, C., Schwarz, R., Rudolf, M., Moucha, C., and Eitel,

A. (2015). Seccrit deliverable d4.4 policy decision and

enforcement tools.

Kaspersky (2014). Next 9 security predictions for 2015.

Khajeh, H. A., Greenwood, D., Smith, J. W., and

Sommerville, I. (2012). The cloud adoption toolkit:

Supporting cloud adoption decisions in the enterprise.

Software Practice and Experience, 42:447–465.

Khajeh, H. A., Greenwood, D., and Sommerville, I.

(2010). Cloud migration: A case study of migrating

an enterprise it system to iaas. Cloud Computing

(CLOUD), pages 450–457.

Khajeh, H. A., Sommerville, I., Bogaerts, J., and

Teregowda, P. (2011). Decision support tools for

cloud migration in the enterprise. Cloud Computing

(CLOUD), pages 541 – 548.

Mason, A. (2015). Global telecoms market: trends and

forecasts 20152020.

McAfee (2014). Information platform for business

solutions, it forecast for 2015.

McKinsey and Company (2015). Ten it enabled business

trends for the decade ahead.

Neal, D. (2011). Amazon web services outages raise serious

cloud questions.

Payne, B. D., Carbone, M., Sharif, M., and Lee, W. (2008).

Lares: An architecture for secure active monitoring

CLOSER 2016 - 6th International Conference on Cloud Computing and Services Science

300

using virtualization. Security and Privacy, 2008. SP

2008. IEEE Symposium, pages 233–247.

Scholler, M., Bless, R., Pallas, F., Horneber, J.,

and Smith, P. (2013). An architectural model

for deploying critical infrastructure services in the

cloud. In Cloud Computing Technology and

Science (CloudCom), 2013 IEEE 5th International

Conference on, volume 1, pages 458–466. IEEE.

Shirazi, N., Simpson, S., Marnerides, A., Watson, M.,

Mauthe, A., and Hutchison, D. (2014). Assessing

the impact of intra-cloud live migration on anomaly

detection. In Cloud Networking (CloudNet), 2014

IEEE 3rd International Conference on, pages 52–57.

Shirazi, N., Simpson, S., Oechsner, S., Mauthe, A., and

Hutchison, D. (2015). A framework for resilience

management in the cloud. e & i Elektrotechnik und

Informationstechnik, 132(2):122–132.

Simpson, S., Shirazi, N., Hutchison, D., and Backhaus, H.

(2013). Seccrit deliverable d4.2 anomaly detection

techniques for cloud computing.

Sterbenz, J. P., Hutchison, D., C¸ etinkaya, E. K.,

Jabbar, A., Rohrer, J. P., Sch

oller, M., and

Smith, P. (2010). Resilience and survivability

in communication networks: Strategies, principles,

and survey of disciplines. Computer Networks,

54(8):1245–1265.

WebSense (2014). Websense security labs 2015.

Towards Resilience Metrics for Future Cloud Applications

301