Workers on Demand? Effects of Undesirable and Unpredictable Schedules on Absenteeism and Performance

AbstractWe present context-aware benchmarking and performance engineering of a mature TByte-scale air quality database system which was created by the Tropospheric Ozone Assessment Report (TOAR) and contains one of the world’s largest collections of near-surface air quality measurements. A special feature of our data service https://join.fz-juelich.de is on-demand processing of several air quality metrics directly from the TOAR database. As a service that is used by more than 350 users of the international air quality research community, our web service must be easily accessible and functionally flexible, while delivering good performance. The current on-demand calculations of air quality metrics outside the database together with the necessary transfer of large volume raw data are identified as the major performance bottleneck. In this study, we therefore explore and benchmark in-database approaches for the statistical processing, which results in performance enhancements of up to 32%.

Download Full-text

Integration Model between Heterogeneous Data Services in a Cloud

JUCS - Journal of Universal Computer Science ◽

10.3897/jucs.67046 ◽

2021 ◽

Vol 27 (4) ◽

pp. 387-412

Author(s):

Marcelo Aires Vieira ◽

Elivaldo Lozer Fracalossi Ribeiro ◽

Daniela Barreiro Claro ◽

Babacar Mane

Keyword(s):

Heterogeneous Data ◽

Cloud Services ◽

Test Case ◽

Integration Model ◽

Data Services ◽

On Demand ◽

Database As A Service ◽

And Performance ◽

Data As A Service ◽

Integrate Data

With the growth of cloud services, many companies have begun to persist and make their data available through services such as Data as a Service (DaaS) and Database as a Service (DBaaS). The DaaS model provides on-demand data through an Application Programming Inter- face (API), while DBaaS model provides on-demand database management systems. Different data sources require efforts to integrate data from different models. These model types include unstructured, semi-structured, and structured data. Heterogeneity from DaaS and DBaaS makes it challenging to integrate data from different services. In response to this problem, we developed the Data Join (DJ) method to integrate heterogeneous DaaS and DBaaS sources. DJ was described through canonical models and incorporated into a middleware as a proof-of-concept. A test case and three experiments were performed to validate our DJ method: the first experiment tackles data from DaaS and DBaaS in isolation; the second experiment associates data from different DaaS and DBaaS through one join clause; and the third experiment integrates data from three sources (one DaaS and two DBaaS) based on different data type (relational, NoSQL, and NewSQL) through two join clauses. Our experiments evaluated the viability, functionality, integration, and performance of the DJ method. Results demonstrate that DJ method outperforms most of the related work on selecting and integrating data in a cloud environment.

Download Full-text

Scalability and Performance Management of Internet Applications in the Cloud

Advances in Systems Analysis, Software Engineering, and High Performance Computing - Communication Infrastructures for Cloud Computing ◽

10.4018/978-1-4666-4522-6.ch019 ◽

2013 ◽

pp. 434-464

Author(s):

Wesam Dawoud ◽

Ibrahim Takouna ◽

Christoph Meinel

Keyword(s):

Performance Management ◽

Resource Provisioning ◽

Infrastructure As A Service ◽

Internet Applications ◽

Application Performance ◽

Total Cost ◽

On Demand ◽

And Performance ◽

The Impact

Elasticity and on-demand are significant characteristics that attract many customers to host their Internet applications in the cloud. They allow quick reacting to changing application needs by adding or releasing resources responding to the actual rather than to the projected demand. Nevertheless, neglecting the overhead of acquiring resources, which mainly is attributed to networking overhead, can result in periods of under-provisioning, leading to degrading the application performance. In this chapter, the authors study the possibility of mitigating the impact of resource provisioning overhead. They direct the study to an Infrastructure as a Service (IaaS) provisioning model where application scalability is the customer’s responsibility. The research shows that understanding the application utilization models and a proper tuning of the scalability parameters can optimize the total cost and mitigate the impact of the overhead of acquiring resources on-demand.

Download Full-text