1. Maintenance—The resources are not under the explicit control of the service provider.

2. Security—The cloud might not be within the enterprise boundaries of the service provider.

3. Service-Level Agreements (SLAs)— Resource allocation is the responsibility of the cloud provider. For example, a service might be unavailable not only due to updates by the service provider, but also due to updates by the cloud provider.

These problems must be addressed as services are moved to clouds and, recently, containers within clouds.

Big data driven service composition. Due to limitations of existing methods, new service selection, composition, and recommendation technologies are key approaches to leveraging state-of-the-art big data research outcomes. One important topic in current big data research is the development of algorithms and models for processing data online. This is fundamentally different from the traditional batch processing approach. Online service composition may provide a promising direction to achieve scalable and adaptive composition solutions to deal with large-scale, highly dynamic, and diverse big data services. Another important consideration is how humans interpret the outcomes of service computing and big data frameworks. As such, it will become increasingly important to relate big data analytic frameworks with the nature of the humans for whom they serve. 21

Social-network-based service compo-
sition. Service selection, recommenda-
tion, and composition in large-scale
social networks should target combin-
ing social network and complex net-
work analysis methods, as well as trust
computing techniques. One promis-
ing direction is to incorporate social
network data that records the interac-
tion of service users with the service
data to detect hidden relationship be-
tween services and generate potential
service compositions. In particular,
user activities exhibited through social
media services, such as posting experi-
ences, questions and feedbacks about
services, could bring novel insight to
better understand and leverage both
traditional and emerging services. For
example, the records of using services
to build service mashups posted on
public programming forums can be
leveraged to recommend interesting
services to other users and build new
service mashups. As another example,
user behavior patterns can be detected
from event logs recorded by social me-
dia or e-business platforms and used
to discover latent knowledge for busi-
ness process mining. Furthermore,
emerging services may bring novel QoS
features that go beyond the traditional
ones, such as reliability, availabil-
ity, and response time. Domain-spe-
cific quality features that reflect users’
interests in choosing and composing
services could be extracted through
social media services that capture user
personal judgment. 10
Crowdsourcing-based reputation.
Future research on crowdsourcing-
based reputation of services should
target the following directions:
Quality of crowdsourcing. Future
studies should focus on how mon-
etary or other interesting factors af-
fect the quality of crowdsourcing data
for choosing services. Social studies
should be carried out to survey the
impact of these interest factors on the
reliability of crowdsourcing and the
scope of the crowdsourcing contribu-
tors. The three factors (ambiguity,
context-dependency, and individual
preference) that influence the quality
of crowdsourcing data should be stud-
ied. For the ambiguity issue, future
research should focus on how trust
assessment questions and scales as
well as human computer interactions
should be designed to precisely cap-
ture users’ trust-related perceptions,
through the collaboration with cog-
nitive science and human-computer
interaction research. The difference
between context-dependency and in-
dividual preference is that the former
relies more on the objective factors,
for example, time, location, and social
relations, and the latter relies more
on the subjective factors, for example,
personal experience. These two kinds
of factors, for example, social rela-
tions and personal preference, may
influence each other simultaneously.
Future research should target how to
model the interrelation between the
two groups of factors and how they can
be integrated to predict their impact
on the quality of crowdsourcing data.
The design of
service systems
should build upon
a formal model
of services that
enables efficient
access to a large
service space
with diverse
functionalities.