140
attempts to communicate performance metrics and service measurement
mechanisms similar to those outlined in Sect. 7.3 above, some researchers
have focussed on more holistic evaluations of cloud services and service
providers. As referenced briefly in Chap. 1, feedback systems and reputation management systems are two approaches explored in research to
build trust. For example, Baranwal and Vidyarthi (2014) propose a Service
Measurement Index (SMI) comprising two sets of metrics—applicationdependent metrics and user-dependent metrics. Notably, in the context of
Mundie et al. (2002), they include customer support as an applicationdependent metric. Unlike the SLA-focused measurements discussed earlier, SMI includes reputation metrics based on feedback from users, user
experience and certification of compliance with industry best practice and
regulations. In a similar vein, Machhi and Jethava (2016) present a trust
management framework that measures service provider trustworthiness
based on feedback, aging factor, and other parameters, while eliminating
or otherwise discounting unreliable feedback. Indeed a number of works
have sought to combine SLA metrics with feedback systems as a means of
communicating trust in the service and vendor (see, for example, Nguyen
et al. 2010; Habib et al. 2011; Yau and Yin 2011; Garg et al. 2013; Noor,
et al. 2015; Tang et al. 2017).
While these researchers have sought to explore integrity as a quantifiable attribute of a service, business integrity is typically either conflated as
competence (see for example Chakraborty and Roy 2012), or as a function of information assurance practices and qualitative audits such as certification (Chakraborty et al. 2010).
7.5 concluSIon
This chapter presented a discussion on trustworthy computing from three
perspectives—security and privacy, reliability, and business integrity.
Computer science research has typically sought to focus on trust as an
objective attribute of systems, and on occasion cloud service providers,
that can be ultimately measured, compared and benchmarked. One might
argue that it is a narrow view of trust that misses the more nuanced aspects
of the psychological underpinnings of trust. This may go some way to
explaining why trust remains a significant barrier to cloud computing
adoption. As a starting point, researchers might consider using the taxonomy of trustworthy computing laid out by Microsoft in Mundie et al.
(2002), i.e. goals, means and execution, to identify gaps in the literature
and state of the art, and guide future avenues for research. As we move
O. M. ALOFE AND K. FATEMA
attempts to communicate performance metrics and service measurement
mechanisms similar to those outlined in Sect. 7.3 above, some researchers
have focussed on more holistic evaluations of cloud services and service
providers. As referenced briefly in Chap. 1, feedback systems and reputation management systems are two approaches explored in research to
build trust. For example, Baranwal and Vidyarthi (2014) propose a Service
Measurement Index (SMI) comprising two sets of metrics—applicationdependent metrics and user-dependent metrics. Notably, in the context of
Mundie et al. (2002), they include customer support as an applicationdependent metric. Unlike the SLA-focused measurements discussed earlier, SMI includes reputation metrics based on feedback from users, user
experience and certification of compliance with industry best practice and
regulations. In a similar vein, Machhi and Jethava (2016) present a trust
management framework that measures service provider trustworthiness
based on feedback, aging factor, and other parameters, while eliminating
or otherwise discounting unreliable feedback. Indeed a number of works
have sought to combine SLA metrics with feedback systems as a means of
communicating trust in the service and vendor (see, for example, Nguyen
et al. 2010; Habib et al. 2011; Yau and Yin 2011; Garg et al. 2013; Noor,
et al. 2015; Tang et al. 2017).
While these researchers have sought to explore integrity as a quantifiable attribute of a service, business integrity is typically either conflated as
competence (see for example Chakraborty and Roy 2012), or as a function of information assurance practices and qualitative audits such as certification (Chakraborty et al. 2010).
7.5 concluSIon
This chapter presented a discussion on trustworthy computing from three
perspectives—security and privacy, reliability, and business integrity.
Computer science research has typically sought to focus on trust as an
objective attribute of systems, and on occasion cloud service providers,
that can be ultimately measured, compared and benchmarked. One might
argue that it is a narrow view of trust that misses the more nuanced aspects
of the psychological underpinnings of trust. This may go some way to
explaining why trust remains a significant barrier to cloud computing
adoption. As a starting point, researchers might consider using the taxonomy of trustworthy computing laid out by Microsoft in Mundie et al.
(2002), i.e. goals, means and execution, to identify gaps in the literature
and state of the art, and guide future avenues for research. As we move
O. M. ALOFE AND K. FATEMA