Jan Schaffner

Jan Schaffner

Berlin, Berlin, Deutschland
2382 Follower:innen 500+ Kontakte

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At SAP, I lead the Foundational Plane (FP) unit that is part of SAP Business Technology…

Aktivitäten

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Berufserfahrung

  • SAP Grafik

    SAP

    Berlin, Deutschland

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    Berlin und Umgebung, Deutschland

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    Potsdam und Umgebung, Deutschland

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    Berlin Area, Germany

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    Potsdam Area, Germany

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    Palo Alto, CA

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    Potsdam Area, Germany

Ausbildung

Veröffentlichungen

  • Multi Tenancy for Cloud-Based In-Memory Column Databases: Workload Management and Data Placement

    Springer

    Available at Amazon:
    http://www.amazon.com/Tenancy-Cloud-Based-In-Memory-Column-Databases/dp/3319004964/

    With the proliferation of Software-as-a-Service (SaaS) offerings, it is becoming increasingly important for individual SaaS providers to operate their services at a low cost. This book investigates SaaS from the perspective of the provider and shows how operational costs can be reduced by using “multi tenancy,” a technique for consolidating a large number of customers onto a small…

    Available at Amazon:
    http://www.amazon.com/Tenancy-Cloud-Based-In-Memory-Column-Databases/dp/3319004964/

    With the proliferation of Software-as-a-Service (SaaS) offerings, it is becoming increasingly important for individual SaaS providers to operate their services at a low cost. This book investigates SaaS from the perspective of the provider and shows how operational costs can be reduced by using “multi tenancy,” a technique for consolidating a large number of customers onto a small number of servers.

    Specifically, the book addresses multi tenancy on the database level, focusing on in-memory column databases, which are the backbone of many important new enterprise applications. For efficiently implementing multi tenancy in a farm of databases, two fundamental challenges must be addressed, (i) workload modeling and (ii) data placement. The first involves estimating the (shared) resource consumption for multi tenancy on a single in-memory database server. The second consists in assigning tenants to servers in a way that minimizes the number of required servers (and thus costs) based on the assumed workload model. This step also entails replicating tenants for performance and high availability. This book presents novel solutions to both problems.

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  • Predicting In-Memory Database Performance for Automating Cluster Management Tasks

    ICDE 2011

    In Software-as-a-Service, multiple tenants are typically consolidated into the same database instance to reduce costs. For analytics-as-a-service, in-memory column databases are especially suitable because they offer very short response times. This paper studies the automation of operational tasks in multi-tenant in-memory column database clusters. As a prerequisite, we develop a model for predicting whether the assignment of a particular tenant to a server in the cluster will lead to…

    In Software-as-a-Service, multiple tenants are typically consolidated into the same database instance to reduce costs. For analytics-as-a-service, in-memory column databases are especially suitable because they offer very short response times. This paper studies the automation of operational tasks in multi-tenant in-memory column database clusters. As a prerequisite, we develop a model for predicting whether the assignment of a particular tenant to a server in the cluster will lead to violations of response time goals. This model is then extended to capture drops in capacity incurred by migrating tenants between servers. We present an algorithm for moving tenants around the cluster to ensure that response time goals are met. In so doing, the number of servers in the cluster may be dynamically increased or decreased. The model is also extended to manage multiple copies of a tenant's data for scalability and availability. We validated the model with an implementation of a multi-tenant clustering framework for SAP's in-memory column database TREX.

    Andere Autor:innen
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  • RTP: Robust Tenant Placement for Elastic In-Memory Database Clusters

    SIGMOD 2013

    In the cloud services industry, a key issue for cloud operators is to minimize operational costs. In this paper, we consider algorithms that elastically contract and expand a cluster of in-memory databases depending on tenants' behavior over time while maintaining response time guarantees.

    We evaluate our tenant placement algorithms using traces obtained from one of SAP's production on-demand applications. Our experiments reveal that our approach lowers operating costs for the database…

    In the cloud services industry, a key issue for cloud operators is to minimize operational costs. In this paper, we consider algorithms that elastically contract and expand a cluster of in-memory databases depending on tenants' behavior over time while maintaining response time guarantees.

    We evaluate our tenant placement algorithms using traces obtained from one of SAP's production on-demand applications. Our experiments reveal that our approach lowers operating costs for the database cluster of this application by a factor of 2.2 to 10, measured in Amazon EC2 hourly rates, in comparison to the state of the art. In addition, we carefully study the trade-off between cost savings obtained by continuously migrating tenants and the robustness of servers towards load spikes and failures.

    Andere Autor:innen
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  • The Effects of Virtualization on Main Memory Systems

    DaMoN 2010

    Virtualization is mainly employed for increasing the utilization of a lightly-loaded system by consolidation, but also to ease the administration based on the possibility to rapidly provision or migrate virtual machines. These facilities are crucial for efficiently managing large data centers. At the same time, modern hardware --- such as Intel's Nehalem microarchitecure --- change critical assumptions about performance bottlenecks and software systems explicitly exploiting the underlying…

    Virtualization is mainly employed for increasing the utilization of a lightly-loaded system by consolidation, but also to ease the administration based on the possibility to rapidly provision or migrate virtual machines. These facilities are crucial for efficiently managing large data centers. At the same time, modern hardware --- such as Intel's Nehalem microarchitecure --- change critical assumptions about performance bottlenecks and software systems explicitly exploiting the underlying hardware --- such as main memory databases --- gain increasing momentum.

    In this paper, we address the question of how these specialized software systems perform in a virtualized environment. To do so, we present a set of experiments looking at several different variants of in-memory databases: The MonetDB Calibrator, a fine-grained hybrid row/column in-memory database running an OLTP workload, and an in-memory column store database running a multi-user OLAP workload.

    We examine how memory management in virtual machine monitors affects these three classes of applications. For the multi-user OLAP experiment we also experimentally compare a virtualized Nehalem server to one of its predecessors. We show that saturation of the memory bus is a major limiting factor but is much less impactful on the new architecture.

    Andere Autor:innen
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Patente

  • Relational database for storing business objects and method for operating the same

    Angemeldet am EU EP2369507 A1

    Andere Erfinder:innen

  • ETL-less zero-redundancy system and method for reporting OLTP data

    Angemeldet am EU EP20070117089

    Andere Erfinder:innen

Sprachen

  • Deutsch

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  • Englisch

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  • Französisch

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