Intern
    Chair of Computer Science II - Software Engineering

    Paper Presentations at IEEE MASCOTS 2015 and IEEE CLOUD 2015

    08.06.2015

    New results originating from two successful collaborations with the SPEC OSG Power and with VMware, Inc. will be presented at IEEE MASCOTS 2015 and IEEE CLOUD 2015 respectively.

    Collaboration with SPEC OSG Power:

    Our latest results on the energy-efficiency of hierarchical server load distribution strategies will be presented at the IEEE 23rd International Symposium on Modelling, Analysis and Simulation of Computer and Telecommunications Systems (MASCOTS) in Atlanta, USA. 21 out of 87 papers have been accepted as full papers, leading to an acceptance rate of 24%. The paper is one of the results originating from a successful collaboration between SPEC OSG Power and members of the Chair of Software Engineering at the University of Würzburg.

    Jóakim von Kistowski, John Beckett, Klaus-Dieter Lange, Hansfried Block, Jeremy A. Arnold, and Samuel Kounev. Energy Efficiency of Hierarchical Server Load Distribution Strategies. In Proceedings of the 23nd International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS 2015), Atlanta, GA, USA, October 5-7, 2015. IEEE.

    Abstract: Energy efficiency of servers has become a significant issue over the last years. Load distribution plays a crucial role in the improvement of energy efficiency as (un-)balancing strategies can be leveraged to distribute load over one or multiple systems in a way in which resources are utilized at high performance, yet low overall power consumption. This can be achieved on multiple levels, from load distribution on single CPU cores to machine level load balancing on distributed systems. With modern day server architectures providing load balancing opportunities at several layers, answering the question of optimal load distribution has become non-trivial. Work has to be distributed hierarchically in a fashion that enables maximum energy efficiency at each level. Current approaches balance load based on generalized assumptions about the energy efficiency of servers. These assumptions are based either on very machine-specific or highly generalized observations that may or may not hold true over a variety of systems and configurations. In this paper, we use a modified version of the SPEC SERT suite to measure the energy efficiency of a variety of hierarchical load distribution strategies on single and multi-node systems. We introduce a new strategy and evaluate energy efficiency for homogeneous and heterogeneous workloads over different hardware configurations. Our results show that the selection of a load distribution strategy depends heavily on workload, system utilization, as well as hardware. Used in conjunction with existing strategies, our new load distribution strategy can reduce a single system's power consumption by up to 10.7%.

    Collaboration with VMware, Inc.:

    Our latest results on vertical memory scaling of virtualized applications will be presented at the 8th IEEE International Conference on Cloud Computing (CLOUD) in New York, USA. 54 full papers have been accepted in the research track, leading to an acceptance rate of 15%. The paper is one of the results originating from a successful collaboration between VMware, Inc. and members of the Chair of Software Engineering at the University of Würzburg.

    Simon Spinner, Nikolas Herbst, Samuel Kounev, Xiaoyun Zhu, Lei Lu, Mustafa Uysal, and Rean Griffith. Proactive Memory Scaling of Virtualized Applications. In Proceedings of the 2014 IEEE 8th International Conference on Cloud Computing (IEEE CLOUD 2014), New York, NY, USA, July 02, 2015. IEEE. Acceptance Rate: 15%. [ .pdf ]

    Abstract: Enterprise applications in virtualized environments are often subject to time-varying workloads with multiple seasonal patterns and trends. In order to ensure quality of service for such applications while avoiding over-provisioning, resources need to be dynamically adapted to accommodate the current workload demands. Many memory-intensive applications are not suitable for the traditional horizontal scaling approach often used for runtime performance management, as it relies on complex and expensive state replication. On the other hand, vertical scaling of memory often requires a restart of the application. In this paper, we propose a proactive approach to memory scaling for virtualized applications. It uses statistical forecasting to predict the future workload and reconfigure the memory size of the virtual machine of an application automatically. To this end, we propose an extended forecasting technique that leverages meta-knowledge, such as calendar information, to improve the forecast accuracy. In addition, we develop an application controller to adjust settings associated with application memory management during memory reconfiguration. Our evaluation using real-world traces shows that the forecast accuracy quantified with the MASE error metric can be improved by 11-59%. Furthermore, we demonstrate that the proactive approach can reduce the impact of reconfiguration on application availability by over 80% and significantly improve performance relative to a reactive controller.

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