A Benchmarking application on Workload and Performance forecasting of micro services

Micro services are increasingly understood as the ideal architectural framework for building large cloud applications within and beyond organizational boundaries. These micro services architectures scale up the application, but are expensive to work on, so pay attention to workflow planning and workflow planning. However, this issue is not very clear. In this work, we are developing independent micro services workflows suitable for modeling and prediction methods and designing three-step game models for based applications. Solved the problem of designing micro services based applications to reduce end-to-end delays under user-specific limitations (MAWS-BC) and recommended micro services routing algorithms. The design process and estimation methods are improved and adequate. The experimental results produced by a well-known micro service bank cover a wide variety of statistical analyzes and the production utility of graphic design is shown by a large comparison copy compared to current algorithms.

COST-OPTIMIZED HEURISTIC ALGORITHM FOR SCHEDULING WORKFLOWS IN THE CLOUD ENVIRONMENT OF IAAS SERVICES

В статье основное внимание уделено решению проблемы планирования рабочего процесса. Представлен эвристический алгоритм для планирования рабочих процессов в облачных вычислениях. The article focuses on solving the problem of workflow planning. A heuristic algorithm for scheduling workflows in cloud computing is presented.

CheckMates, Helping Nurses Plan Ahead in the Neonatal Intensive Care Unit

Workflow in a neonatal intensive care unit (NICU) is relatively unpredictable, which makes it difficult to plan activities. Simple tasks, such as checking device statuses may be forgotten, resulting in disturbing alarms. In this paper, we will present CheckMates, ambient lighting displays, which visualize device statuses to provide nurses with more overview. We performed expert reviews to obtain insights into the different potentials of CheckMates. Additionally, we performed a simulation study to gather user experiences regarding the functioning of CheckMates and their capacity to improve planning in an NICU environment. The results showed a variety of potential benefits for increasing nurses’ overview of device statuses and their opportunities for workflow planning. Furthermore, CheckMates did not appear to be distracting.

Sensitivity Analysis of Workspace Conflicts According to Changing Geometric Conditions

Workspace conflicts and building components can happen in different forms and both permanently and temporarily. These spatial clashes affect the work process and deplete the project process. Geometric clash detection system of 4D simulation tools can identify the number of clashes for construction resources in the worksite to improve workflow planning. In the present research, building components and their corresponding workspaces were simulated, based on the schedule and activities, using a visual simulation tool. First, the total daily volumes of workspace were calculated according to the activities' schedule and compared by the available space in order to determine the critical days for the project. Then, the number of time-based conflicts were examined and analyzed for building components and resources among activities and by different tolerance distances. The main objective of this study was to evaluate the sensitivity analysis of clash numbers based on the geometrical conditions in different statuses (Inflexible, Semi-flexible and flexible) to assist the planner for detecting real conflicts. The results show that the tolerance distance of 0.2 to 1 meter for the clashes of workspace and the building components and 0.2 to 2 meters for the clashes of workspaces with each other to provide realistic results of actual construction operation conflicts. By the help of this methodology, the project planners are able to identify and prioritize the effective conflicts on the work process in comparison to the clashes resulted from iteration or minor design inaccuracy.

Using Meta-mining to Support Data Mining Workflow Planning and Optimization

Knowledge Discovery in Databases is a complex process that involves many different data processing and learning operators. Today's Knowledge Discovery Support Systems can contain several hundred operators. A major challenge is to assist the user in designing workflows which are not only valid but also -- ideally -- optimize some performance measure associated with the user goal. In this paper we present such a system. The system relies on a meta-mining module which analyses past data mining experiments and extracts meta-mining models which associate dataset characteristics with workflow descriptors in view of workflow performance optimization. The meta-mining model is used within a data mining workflow planner, to guide the planner during the workflow planning. We learn the meta-mining models using a similarity learning approach, and extract the workflow descriptors by mining the workflows for generalized relational patterns accounting also for domain knowledge provided by a data mining ontology. We evaluate the quality of the data mining workflows that the system produces on a collection of real world datasets coming from biology and show that it produces workflows that are significantly better than alternative methods that can only do workflow selection and not planning.