Clustering Services Based on Community Detection in Service Networks

Abstract

Service-oriented computing has become a promising way to develop software by composing existing services on the Internet. However, with the increasing number of services on the Internet, how to match requirements and services becomes a difficult problem. Service clustering has been regarded as one of the effective ways to improve service matching. Related work shows that structure-related similarity metrics perform better than semantic-related similarity metrics in clustering services. erefore, it is of great importance to propose much more useful structure-related similarity metrics to improve the performance of service clustering approaches. However, in the existing work, this kind of work is very rare. In this paper, we propose a SCAS (service clustering approach using structural metrics) to group services into different clusters. SCAS proposes a novel metric A2S (atomic service similarity) to characterize the atomic service similarity as a whole, which is a linear combination of C2S (compositesharing similarity) and A3S (atomic-service-sharing similarity). en, SCAS applies a guided community detection algorithm to group atomic services into clusters. Experimental results on a real-world data set show that our SCAS performs better than the existing approaches. Our A2S metric is promising in improving the performance of service clustering approaches.

The New Service Realities

“…Real estate site Trulia uses Twilio for its click-to-call app. The app enables potential buyers to connect with an agent (Zhou & Wang, 2019). EMC uses Twilio to send texts to employees when an IT service goes down (Zhou & Wang, 2019).…”

“…The app enables potential buyers to connect with an agent (Zhou & Wang, 2019). EMC uses Twilio to send texts to employees when an IT service goes down (Zhou & Wang, 2019). Airbnb uses it to automatically text information about potential renters to hosts (Stallkamp, 2019).…”

Leverage Label and Word Embedding for Semantic Sparse Web Service Discovery

“…Ma et al [18] and Pan et al [23][24][25] proposed software metrics by using parameters in complex networks. Zhou and Wang [26] and Pan et al [27,28] proposed an approach to cluster services by using community detection approaches in complex networks.…”

Hierarchical Aggregation for Reputation Feedback of Services Networks

“…For instance, in [66], the authors took advantage of the theory of complex network and existing networked software research works to explore the basic characteristics of services and service networks, such as the service network's "small world," "scale-free" characteristics and service network topology. Zhou and Wang [67] proposed a SCAS (service clustering approach using structural metrics) to group services into different clusters, where a metric A2S (atomic service similarity) was utilized to characterize the atomic service similarity. To explore the needs of support tools and service provisioning environments, [68] introduced the architecture of the opensource SONATA system, a service programming, orchestration, and management framework, where a development toolchain for virtualized network services could be fully integrated with a service platform and orchestration system.…”