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difficult to measure and is typically based on a qualitative evaluation of
cloud service provider policies and system features (Shaikh and Sasikumar
2015). Once such metrics have been extracted from the system, they can
be shared with consumers to build trust or select cloud service providers.
An example of the former is the cloud trust label mentioned earlier
(Emeakaroha et al. 2016; van der Werff et al. 2018). Regarding the latter,
Garg, et al. (2013) propose a Service Measurement Index Cloud
(SMICloud) framework for assisting consumers to identify the most suitable cloud service provider to contract with. The SMICloud reviews
Quality of Service (QoS) requirements and ranks services based on previous user experiences and performance of services based on KPIs such as
those previously mentioned. As a final note on cloud performance metrics,
the determination of the intervals for this data is an essential and somewhat open challenge. This includes the monitoring intervals between the
collection of low-level metrics and the intervals between the aggregate
KPIs or high-level quality indicators (Sun et al. 2012). A balance between
intrusiveness and utility is required to avoid adverse impacts on system
performance while ensuring the availability of sufficiently time-sensitive
data to assure accurate SLA measurement (Sun et al. 2012).
7.4 buSIneSS IntegrIty
As discussed in Chap. 1, the trust literature views integrity generally as one
party’s perception that another party will adhere to a set of acceptable
principles, act honestly, and fulfil their promises (Mayer et al. 1995;
McKnight et al. 2011). This is consistent with the principles laid out by
Microsoft in Mundie et al. (2002), namely that a vendor, in this case a
cloud service provider, will behave in a responsive and responsible manner.
While Mundie et al. (2002) exemplify this behaviour in terms of responsiveness to problems that may arise, others expand this, in a technological
context, to mean that both the service and vendor behave predictably to
the extent which it is possible to anticipate the system and the service provider’s behaviour accurately (van der Werff et al. 2018). In one sense, it is
no surprise that computer scientists have found it difficult to distinguish
reliability, as an attribute, from integrity.
In computer science literature, integrity is more commonly found as an
attribute of data and underlying systems rather than the service as a whole
or the vendor. This is not to say that computer science researchers have
not explored technological innovations in this regard. In addition to
7 TRUSTWORTHY CLOUD COMPUTING
difficult to measure and is typically based on a qualitative evaluation of
cloud service provider policies and system features (Shaikh and Sasikumar
2015). Once such metrics have been extracted from the system, they can
be shared with consumers to build trust or select cloud service providers.
An example of the former is the cloud trust label mentioned earlier
(Emeakaroha et al. 2016; van der Werff et al. 2018). Regarding the latter,
Garg, et al. (2013) propose a Service Measurement Index Cloud
(SMICloud) framework for assisting consumers to identify the most suitable cloud service provider to contract with. The SMICloud reviews
Quality of Service (QoS) requirements and ranks services based on previous user experiences and performance of services based on KPIs such as
those previously mentioned. As a final note on cloud performance metrics,
the determination of the intervals for this data is an essential and somewhat open challenge. This includes the monitoring intervals between the
collection of low-level metrics and the intervals between the aggregate
KPIs or high-level quality indicators (Sun et al. 2012). A balance between
intrusiveness and utility is required to avoid adverse impacts on system
performance while ensuring the availability of sufficiently time-sensitive
data to assure accurate SLA measurement (Sun et al. 2012).
7.4 buSIneSS IntegrIty
As discussed in Chap. 1, the trust literature views integrity generally as one
party’s perception that another party will adhere to a set of acceptable
principles, act honestly, and fulfil their promises (Mayer et al. 1995;
McKnight et al. 2011). This is consistent with the principles laid out by
Microsoft in Mundie et al. (2002), namely that a vendor, in this case a
cloud service provider, will behave in a responsive and responsible manner.
While Mundie et al. (2002) exemplify this behaviour in terms of responsiveness to problems that may arise, others expand this, in a technological
context, to mean that both the service and vendor behave predictably to
the extent which it is possible to anticipate the system and the service provider’s behaviour accurately (van der Werff et al. 2018). In one sense, it is
no surprise that computer scientists have found it difficult to distinguish
reliability, as an attribute, from integrity.
In computer science literature, integrity is more commonly found as an
attribute of data and underlying systems rather than the service as a whole
or the vendor. This is not to say that computer science researchers have
not explored technological innovations in this regard. In addition to
7 TRUSTWORTHY CLOUD COMPUTING