Elsevier’s Journal of Information and Software Technology (INSOF) accepted the manuscript ”A Dynamic Evolutionary Multi-Objective Virtual Machine Placement Heuristic for Cloud Infrastructures”.

Authors: Ennio Torre, Juan J. Durillo (Leibniz Supercomputing Center), Vincenzo de Maio (Vienna University of Technology), Prateek Agrawal (University of Klagenfurt), Shajulin Benedict (Indian Institute of Information Technology), Nishant Saurabh (University of Klagenfurt), Radu Prodan (University of Klagenfurt).

Abstract: Minimizing the resource wastage reduces the energy cost of operating a data center, but may also lead to a considerably high resource over-commitment affecting the Quality of Service (QoS) of the running applications. The effective trade-off between resource wastage and over-commitment is a challenging task in virtualized Clouds and depends on the allocation of virtual machines (VMs) to physical resources. We propose in this paper a multi-objective method for dynamic VM placement, which exploits live migration mechanisms to simultaneously optimize the resource wastage, over-commitment ratio and migration energy. Our optimization algorithm uses a novel evolutionary meta-heuristic based on an island population model to approximate the Pareto optimal set of VM placements with good accuracy and diversity. Simulation results using traces collected from a real Google cluster demonstrate that our method outperforms related approaches by reducing the migration energy by up to 57 % with a QoS increase below 6 %.

Acknowledgements:

This work is supported by:

• European Union’s Horizon 2020 research and innovation programme, grant agreement 825134, “Smart Social Media Ecosytstem in a Blockchain Federated Environment (ARTICONF)”;
• Austrian Science Fund (FWF), grant agreement Y 904 START-Programm 2015, “Runtime Control in Multi Clouds (RUCON)“;
• Austrian Agency for International Cooperation in Education and Research (OeAD-GmbH) and Indian Department of Science and Technology (DST), project number, IN 20/2018, “Energy Aware Workflow Compiler for Future Heterogeneous Systems”.

The manuscript “The Workflow Trace Archive: Open-Access Data from Public and Private Computing Infrastructures” has been accepted for publication in the A* ranked IEEE Transactions on Parallel and Distributed Systems (TPDS) journal.

Authors: Laurens Versluis, Roland Mathá, Sacheendra Talluri, Tim Hegeman, Radu Prodan, Ewa Deelman, and Alexandru Iosup

Abstract: Realistic, relevant, and reproducible experiments often need input traces collected from real-world environments. We focus in this work on traces of workflows—common in datacenters, clouds, and HPC infrastructures. We show that the state-of-the-art in using workflow-traces raises important issues: (1) the use of realistic traces is infrequent, and (2) the use of realistic, open-access traces even more so. Alleviating these issues, we introduce the Workflow Trace Archive (WTA), an open-access archive of workflow traces from diverse computing infrastructures and tooling to parse, validate, and analyze traces. The WTA includes >48 million workflows captured from >10 computing infrastructures, representing a broad diversity of trace domains and characteristics. To emphasize the importance of trace diversity, we characterize the WTA contents and analyze in simulation the impact of trace diversity on experiment results. Our results indicate significant differences in characteristics, properties, and workflow structures between workload sources, domains, and fields.

Acknowledgments: This work is supported by the projects Vidi MagnaData, Commit, the European Union’s Horizon 2020 Research and Innovation Programme, grant agreement number 801091 “ASPIDE”, and the National Science Foundation award number 1664162.

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Title of the talk: Mobility-Aware Scheduling of Extreme Data Workflows across the Computing Continuum

Abstract: The appearance of the Fog/Edge computing paradigm, as an emanation of the computing continuum closer to the edge of the network, unravels important opportunities for execution of complex business and scientific workflows near the data sources. The main characteristics of these workflows are (i) their distributed nature, (ii) the vast amount of data (in the order of petabytes) they generate and (iii) the strict latency requirements. Current workflow management approaches rely exclusively on the Cloud Data Centers, which due to their geographical distance in relation to the data sources, could negatively influence the latency and cause violation of workflow requirements. It is therefore essential to research novel concepts for partial offloading of complex workflows closer to where the data is generated, thus reducing the communication latency and the need for frequent data transfers.

In this talk we will explore the  potential  of  the computing continuum  for  scheduling and partial offloading  of  complex  workflows  with  strict  response time requirements and expose the resource provisioning challenges related to the heterogeneity and mobility of the Fog/Edge environment. Consequently, we will discuss a novel mobility-aware Pareto-based approach for task offloading across the continuum, which considers three optimization objectives, namely response time, reliability, and financial cost. Besides, the approach introduces a Markov model to perform a single-step predictive analysis on the mobility of the Fog/Edge devices, thus constraining the task offloading optimization problem to devices that do not frequently move (roam) within the computing continuum. As a conclusion to the talk, we will discuss the efficiency of the presented approach, based on both a simulated and a real-world testbed environment tailored for a set real-world biomedical, meteorological and astronomy workflows.