Cloud computing, a superset of heterogeneous distributed computing, allows sharing of geographically dispersed resources across multiple organizations on a rental basis using virtualization as per demand. In cloud computing, workflow allocation to achieve the optimum schedule has been reported to be NP-hard. This paper proposes a Levelized Multiple Workflow Heterogeneous Earliest Finish Time (LMHEFT) model to optimize makespan in the cloud computing environment. The model has two phases: task prioritization and task allocation. The task prioritization phase begins by dividing workflows into the number of partitions as per the level attribute; after that, upward rank is employed to determine the partition-wise task allocation order. In the allocation phase, the best-suited virtual machine is determined to offer the lowest finish time for each task in partition-wise mapping to minimize the workflow task’s completion time. The model considers the inter-task communication between the cooperative workflow tasks. A comparative performance evaluation of LMHEFT has been conducted with the competitive models from the literature implemented in MATLAB, i.e., heterogeneous earliest finish time (HEFT) and dynamic level scheduling (DLS), on makespan, flowtime, and utilization. The experimental findings indicate that LMHEFT surpasses HEFT and DLS in terms of makespan 15.51% and 85.12% when varying the number of workflows, 41.19% and 86.73% when varying depth levels, and 13.74% and 80.24% when varying virtual machines, respectively. Further statistical analysis has been carried out to confirm the hypothesis developed in the simulation study by using normality tests, homogeneity tests, and the Kruskal–Wallis test.

Web services are very dynamic as they can be added, modified and deleted from the repository on the fly. Discovering web services for service composition have special requirements. In the service composition, service requirement is not only to satisfy its functional specification but it also needs to ensure that the selected composing services are compatible with respect to their input/output structure and its associated data types. Web Service community [1] organized the functionally similar services together in a group. But one important drawback of using web service community in the service composition is that it requires further analysis of the discovered service communities for selecting appropriate service which can be composed based on its input/output structure and data types. This paper proposes data structures, which can be used to create web service family [2] by filtering not only on the basis of functionality but also on the basis of its input/output structure, which are required for composing services with one another. In order to speed up the web service family creation process, the creation of web service family is divided in two phases. The first phase of the proposed approach introduces a novel concept of business family and dynamic business web service (DBWS) tree, which are used to organize all web services in the tree structure on the basis of their business context and input/output structure. In the second phase, the concept of DBWS tree is used further to create the web service families. The DBWS resembles the classification and organization of businesses in NAICS. The DBWS tree can be used very efficiently for creating web service families, which in turn can be used for selecting service efficiently in service composition.

Quality of service (QoS) determines the service usability and utility and both of which influence the service selection process. The QoS varies from one service provider to other. Each web service has its own methodology for evaluating QoS. The lack of transparent QoS evaluation model makes the service selection challenging. Moreover, most QoS evaluation processes do not consider their historical data which not only helps in getting more accurate QoS but also helps for future prediction, recommendation and knowledge discovery. QoS driven service selection demands a model where QoS can be provided as a service to end users. This paper proposes a layered QaaS (quality as a service) model in the same line as PaaS and software as a service, where users can provide QoS attributes as inputs and the model returns services satisfying the user’s QoS expectation. This paper covers all the key aspects in this context, like selection of data sources, its transformation, evaluation, classification and storage of QoS. The paper uses server log as the source for evaluating QoS values, common methodology for its evaluation and big data technologies for its transformation and analysis. This paper also establishes the fact that Spark outperforms the Pig with respect to evaluation of QoS from logs.