Towards a Visualization Framework for Service Selection in Cloud E-Marketplaces

Abstract

Abstract-In spite of the success of many commercial cloud service e-marketplaces the search results from these platforms are usually presented as an unordered list of icons representing the services that best fit users' keyword-based queries. The drawback of such presentation mechanisms is that users are not able to immediately discriminate among the cloud services for easy decision making. A number of cloud service selection frameworks have been proposed; however, some of these frameworks do not enable users make comparisons among services. In this paper, we introduce a visualization framework for cloud service selection. Our framework takes into cognizance the set of cloud services that matches a user's request and based on QoS attributes, users can interact with the results via bubble graph visualization to compare and contrast the search results to ascertain the best alternative. The bubble graph enables the exploration of services in a unified view of the QoS space, exhibiting both high object coherence and correlation. Result from our experiments shows that our framework simplifies decision making as users can identify services that best fit their requirements quicker and easier compared to tabular formats.

QoS-based ranking and selection of SaaS applications using heterogeneous similarity metrics

“…The proliferation of cloud application services in the cloud e-marketplace without a systematic framework to guide the selection of the most relevant ones usually leaves the users with the problem of which service to select, a phenomenon that can be described as service choice overload [5][6][7][8]. Currently, these existing cloud service e-marketplaces elicit keyword-based search queries that do not allow users to indicate their preferences in terms of quality of service (QoS) requirements and present search results as an unordered list of icons that must be explored individually by a user before making a decision [9]. This mode of presentation does not enable the user to discriminate among services in terms of their suitability with respect to user's request, which complicates decision making [10].…”

Context‐Aware Cloud Service Selection Model for Mobile Cloud Computing Environments

“…The most employed approaches include multicriteria decision analysis-based service selection [7,[12][13][14], reputation-aware service selection [15], adaptive learning mechanism-based service selection [8,16], economic theoretical model-based service selection [17,18], service level agreement-based service ranking [6], visualization framework for service selection [19], and trust evaluation middleware for cloud service selection [20]. Though these approaches can efficiently measure service quality, the implementation of some approaches is time-consuming and costly.…”

“…The visualization framework for service selection [19] takes into cognizance the set of cloud services that matches a user's request and based on QoS attributes, users can interact with the results via bubble graph visualization to compare and contrast the search results to ascertain the best alternative. Although the result from the experiments shows that visualization framework simplifies decision-making, the use of bubble graph introduces additional complexity for the user when making a suitable service selection.…”