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ClouDMan: Cost-based Data Management in Cloud Environments

Titel Englisch ClouDMan: Cost-based Data Management in Cloud Environments
Gesuchsteller/in Schuldt Heiko
Nummer 150061
Förderungsinstrument Projektförderung (Abt. I-III)
Forschungseinrichtung Fachbereich Informatik Departement Mathematik und Informatik Universität Basel
Hochschule Universität Basel – BS
Hauptdisziplin Informatik
Beginn/Ende 01.11.2013 - 30.04.2015
Bewilligter Betrag 92'464.00
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Keywords (4)

Replication Management; Transaction Management; Cloud Data Management; Cost-based Data Management

Lay Summary (Deutsch)

Lead
Im Rahmen des ClouDMan-Projekts werden neuartige Ansätze zur Datenverwaltung in einer Cloud-Umgebung untersucht. Das Ziel ist es, bestehende Protokolle zu optimieren bzw. neue Protokolle zu entwickeln, mit denen Daten in verteilten Cloud-Umgebungen verwaltet werden können und die gleichzeitig Performanz, Datenkonsistenz und die entstehenden Kosten optimieren.
Lay summary

Das Cloud Computing hat sich in den letzten Jahren zu einer sehr attraktiven Alternative zu den traditionellen Rechenzentren, die von Firmen und Organisationen selbst aufgebaut und gewartet werden, entwickelt. Ein Grund für diese Popularität ist das als 'pay-as-you-go' bekannte Geschäftsmodell der Cloud, in dem man nur für die Ressourcen bezahlt, die auch tatsächlich verwendet werden. Das erlaubt es auch die Kosten offen zu legen, die für die verteilte und replizierte Verwaltung grosser Datenmengen in der Cloud entstehen. Die für die verteilte Datenverwaltung verwendeten Protokolle sind traditionell für Performanz und Datenkonsistenz optimiert. Die Kosten für die benötigten IT-Ressourcen sind in der Regel nicht berücksichtigt. Das Preismodell der Cloud erlaubt es nun, Protokolle und Verfahren für die verteilte Datenverwaltung nicht nur aus der Sicht von Performanz und Datenkonsistenz zu analysieren, sondern als drittes, gleichberechtigtes Kriterium auch die dabei anfallenden Kosten zu berücksichtigen. Ziel des ClouDMan-Projekts ist es, bestehende Protokolle zu optimieren bzw. neue Protokolle zu entwickeln, mit denen Daten in verteilten Cloud-Umgebungen verwaltet werden können und die gleichzeitig Performanz, Datenkonsistenz und die entstehenden Kosten optimieren.

Direktlink auf Lay Summary Letzte Aktualisierung: 28.09.2013

Lay Summary (Englisch)

Lead
The ClouDMan project will develop new approaches to data management in a Cloud environment by jointly considering and in particular optimizing i.) performance, ii.) data consistency, and iii.) costs.
Lay summary
Cloud computing has become a very attractive alternative to traditional in-house data centers. The main reason for this is the so-called 'pay-as-you-go' model of the Cloud where users only pay for the IT resources they have consumed. In terms of data management, the Cloud provides a highly scalable and available platform for storing large volumes of data. Traditionally, protocols for (distributed) data management have been optimized for both performance and data consistency. The cost which incurs in the Cloud for the resources needed opens new possibilities for re-considering and optimizing existing approaches to data management by jointly considering and in particular optimizing i.) performance, ii.) data consistency, and iii.) costs.
Direktlink auf Lay Summary Letzte Aktualisierung: 28.09.2013

Verantw. Gesuchsteller/in und weitere Gesuchstellende

Mitarbeitende

Name Institut

Publikationen

Publikation
Analyzing the Performance of Data Replication and Data Partitioning in the Cloud: the Beowulf Approach
Stiemer Alexander, Fetai Ilir, Schuldt Heiko (2016), Analyzing the Performance of Data Replication and Data Partitioning in the Cloud: the Beowulf Approach, in Proceedings of the 4th Workshop on Scalable Cloud Data Management (SCDM 2016), Washington D.C., USAIEEE, New York, NY, USA.
Comparison of Eager and Quorum-based Replication in a Cloud Environment
Stiemer Alexander, Fetai Ilir, Schuldt Heiko (2015), Comparison of Eager and Quorum-based Replication in a Cloud Environment, in Proceedings of the 3rd International Workshop on Scalable Cloud Data Management (SCDM'15), Santa Clara, CA, USAIEEE, Piscataway, NJ, USA.
Workload-Driven Adaptive Data Partitioning and Distribution – The Cumulus Approach
Fetai Ilir, Murezzan Damian, Schuldt Heiko (2015), Workload-Driven Adaptive Data Partitioning and Distribution – The Cumulus Approach, in Proceedings of the 3rd International Workshop on Scalable Cloud Data Management (SCDM'15), Santa Clara, CA, USAIEEE, Piscataway, NJ, USA.
PolarDBMS: Towards a Cost-Effective and Policy-Based Data Management in the Cloud
Fetai Ilir, Brinkmann Filip-Martin, Schuldt Heiko (2014), PolarDBMS: Towards a Cost-Effective and Policy-Based Data Management in the Cloud, in Proceedings of the 6th International Workshop on Cloud Data Management (CloudDB 2014), Chicago, IL, USAIEEE, Piscataway, NJ, USA.
SO-1SR: Towards a self-optimizing One-Copy Serializability Protocol for Data Management in the Cloud
Fetai Ilir, Schuldt Heiko (2013), SO-1SR: Towards a self-optimizing One-Copy Serializability Protocol for Data Management in the Cloud, in Proceedings of the 5th International Workshop on Cloud Data Management (CloudDB 2013), San Francisco, CA, USAACM, New York, NY, USA.

Wissenschaftliche Veranstaltungen

Aktiver Beitrag

Titel Art des Beitrags Titel des Artikels oder Beitrages Datum Ort Beteiligte Personen
Informatik-Kolloquium Universität Fribourg Poster Cost-Effective Data Management in the Cloud 04.02.2015 Fribourg, Schweiz Schuldt Heiko;


Selber organisiert

Titel Datum Ort
DBTA Workshop on Stream Processing 03.12.2014 Sky Lounge, Wankdorf Stadium, Bern, Schweiz
DBTA Workshop on Semantic Data Processing 07.02.2014 Sky Lounge, Wankdorf Stadium, Bern, Schweiz

Kommunikation mit der Öffentlichkeit

Kommunikation Titel Medien Ort Jahr
Referate/Veranstaltungen/Ausstellungen Cloud Computing: Wie und wo speichert die Wolke Informationen? (VHSBB) Deutschschweiz 2015
Referate/Veranstaltungen/Ausstellungen Infotag für Studieninteressierte: Cloud Computing Deutschschweiz 2014

Auszeichnungen

Titel Jahr
Amazon research grant for conducting evaluations using the AWS cloud infrastructure 2014

Verbundene Projekte

Nummer Titel Start Förderungsinstrument
172763 Polypheny-DB: Cost- and Workload-aware Adaptive Data Management 01.05.2017 Projektförderung (Abt. I-III)
132201 GriDMan: Data Management for Scientific Applications in a Grid Environment 01.05.2011 Projektförderung (Abt. I-III)

Abstract

During the last years, Clouds have increasingly become very attractive environments for deploying different types of applications. The main reason for this popularity is the 'pay-as-you-go' cost model of the Cloud, combined with its almost unlimited scalability and high availability. From the perspective of organizations or companies using the Cloud, the pay-as-you go cost model allows to only pay for the resources actually used. Traditional problems of over-provisioning (i.e., when the IT resources -usually complete compute centers- were designed for a much higher expected load than what was actually faced, which led to additional, unnecessary costs for the organization/company) or under-provisioning (i.e., when due to the lack of IT resources customers had to be turned away) is fortunately belonging to the past. Cloud environments are highly elastic which means that they provide a vast amount of resources that can be used by Cloud customers on very short notice, thus guaranteeing that the underlying IT environment adapts and dynamically scales to the actual needs. Elastic behavior, almost unlimited scalability, and in particular high availability has strong consequences for data management in the Cloud. A high degree of availability is provided by geographically replicating data inside a Cloud, i.e., by using resources at different sites of a Cloud provider. This, in turn, necessitates distributed transactions to guarantee data to be consistent.While distributed transaction management and replication management have been subject to intensive research in the past decades, the Cloud comes with a new dimension that necessitates to reconsider and rethink current approaches, algorithms and protocols: the cost dimension. As a consequence of the pay-as-you go cost model of the Cloud, each resource and its usage comes with a price tag, usually at a very fine-grained level. Users of the Cloud have to pay, for instance, for each megabyte of storage used, for each CPU cycle, for incoming and outgoing megabytes of data traffic, and even for each message placed in a queue hosted by a Cloud provider. Even worse, these prices not only differ between Cloud providers, they may also (significantly) differ between different data centers of the same Cloud provider.Hence, the consideration of i.) data consistency, ii.) performance, and iii.) cost opens new areas for research in distributed data management and new possibilities for optimizing existing protocols.The objective of the ClouDMan project is to investigate new approaches to Cost-based Data Partitioning and to Policy-based Data Management. The former aspect, Cost-based Data Partitioning, takes into account that different sites of a Cloud provider come with different pricing schemes. Therefore, optimizing replicated data management with regard to consistency, performance, and cost needs to seamlessly consider data placement, in addition to the number of replicas and the protocol for propagating updates to replicated data. The second aspect, Policy-based Data Management, takes into account that many applications come with dedicated requirements and restrictions on data placement, performance, cost, or consistency such as `data may not be stored outside the country of its origin', `data management has to be provided as cheaply as possible' and/or `1-copy serializability has to be provided'. The goal is to automatically select the best suited protocol for meeting the requirements and constraints for replicated data management in the Cloud, on the basis of the specified policies.
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