6,571 research outputs found
Keeping Research Data Safe 2: Final Report
The first Keeping Research Data Safe study funded by JISC made a major contribution to understanding of long-term preservation costs for research data by developing a cost model and indentifying cost variables for preserving research data in UK universities (Beagrie et al, 2008). However it was completed over a very constrained timescale of four months with little opportunity to follow up other major issues or sources of preservation cost information it identified. It noted that digital preservation costs are notoriously difficult to address in part because of the absence of good case studies and longitudinal information for digital preservation costs or cost variables. In January 2009 JISC issued an ITT for a study on the identification of long-lived digital datasets for the purposes of cost analysis. The aim of this work was to provide a larger body of material and evidence against which existing and future data preservation cost modelling exercises could be tested and validated. The proposal for the KRDS2 study was submitted in response by a consortium consisting of 4 partners involved in the original Keeping Research Data Safe study (Universities of Cambridge and Southampton, Charles Beagrie Ltd, and OCLC Research) and 4 new partners with significant data collections and interests in preservation costs (Archaeology Data Service, University of London Computer Centre, University of Oxford, and the UK Data Archive). A range of supplementary materials in support of this main report have been made available on the KRDS2 project website at http://www.beagrie.com/jisc.php. That website will be maintained and continuously updated with future work as a resource for KRDS users
Cloud-computing strategies for sustainable ICT utilization : a decision-making framework for non-expert Smart Building managers
Virtualization of processing power, storage, and networking applications via cloud-computing allows Smart Buildings to operate heavy demand computing resources off-premises. While this approach reduces in-house costs and energy use, recent case-studies have highlighted complexities in decision-making processes associated with implementing the concept of cloud-computing. This complexity is due to the rapid evolution of these technologies without standardization of approach by those organizations offering cloud-computing provision as a commercial concern. This study defines the term Smart Building as an ICT environment where a degree of system integration is accomplished. Non-expert managers are highlighted as key users of the outcomes from this project given the diverse nature of Smart Buildings’ operational objectives. This research evaluates different ICT management methods to effectively support decisions made by non-expert clients to deploy different models of cloud-computing services in their Smart Buildings ICT environments. The objective of this study is to reduce the need for costly 3rd party ICT consultancy providers, so non-experts can focus more on their Smart Buildings’ core competencies rather than the complex, expensive, and energy consuming processes of ICT management.
The gap identified by this research represents vulnerability for non-expert managers to make effective decisions regarding cloud-computing cost estimation, deployment assessment, associated power consumption, and management flexibility in their Smart Buildings ICT environments. The project analyses cloud-computing decision-making concepts with reference to different Smart Building ICT attributes. In particular, it focuses on a structured programme of data collection which is achieved through semi-structured interviews, cost simulations and risk-analysis surveys. The main output is a theoretical management framework for non-expert decision-makers across variously-operated Smart Buildings. Furthermore, a decision-support tool is designed to enable non-expert managers to identify the extent of virtualization potential by evaluating different implementation options. This is presented to correlate with contract limitations, security challenges, system integration levels, sustainability, and long-term costs. These requirements are explored in contrast to cloud demand changes observed across specified periods. Dependencies were identified to greatly vary depending on numerous organizational aspects such as performance, size, and workload. The study argues that constructing long-term, sustainable, and cost-efficient strategies for any cloud deployment, depends on the thorough identification of required services off and on-premises. It points out that most of today’s heavy-burdened Smart Buildings are outsourcing these services to costly independent suppliers, which causes unnecessary management complexities, additional cost, and system incompatibility. The main conclusions argue that cloud-computing cost can differ depending on the Smart Building attributes and ICT requirements, and although in most cases cloud services are more convenient and cost effective at the early stages of the deployment and migration process, it can become costly in the future if not planned carefully using cost estimation service patterns. The results of the study can be exploited to enhance core competencies within Smart Buildings in order to maximize growth and attract new business opportunities
A Survey on Load Balancing Algorithms for VM Placement in Cloud Computing
The emergence of cloud computing based on virtualization technologies brings
huge opportunities to host virtual resource at low cost without the need of
owning any infrastructure. Virtualization technologies enable users to acquire,
configure and be charged on pay-per-use basis. However, Cloud data centers
mostly comprise heterogeneous commodity servers hosting multiple virtual
machines (VMs) with potential various specifications and fluctuating resource
usages, which may cause imbalanced resource utilization within servers that may
lead to performance degradation and service level agreements (SLAs) violations.
To achieve efficient scheduling, these challenges should be addressed and
solved by using load balancing strategies, which have been proved to be NP-hard
problem. From multiple perspectives, this work identifies the challenges and
analyzes existing algorithms for allocating VMs to PMs in infrastructure
Clouds, especially focuses on load balancing. A detailed classification
targeting load balancing algorithms for VM placement in cloud data centers is
investigated and the surveyed algorithms are classified according to the
classification. The goal of this paper is to provide a comprehensive and
comparative understanding of existing literature and aid researchers by
providing an insight for potential future enhancements.Comment: 22 Pages, 4 Figures, 4 Tables, in pres
Planning and Optimization During the Life-Cycle of Service Level Agreements for Cloud Computing
Ein Service Level Agreement (SLA) ist ein elektronischer Vertrag zwischen dem Kunden
und dem Anbieter eines Services. Die beteiligten Partner kl aren ihre Erwartungen
und Verp
ichtungen in Bezug auf den Dienst und dessen Qualit at. SLAs werden
bereits f ur die Beschreibung von Cloud-Computing-Diensten eingesetzt. Der
Diensteanbieter stellt sicher, dass die Dienstqualit at erf ullt wird und mit den Anforderungen
des Kunden bis zum Ende der vereinbarten Laufzeit ubereinstimmt.
Die Durchf uhrung der SLAs erfordert einen erheblichen Aufwand, um Autonomie,
Wirtschaftlichkeit und E zienz zu erreichen. Der gegenw artige Stand der Technik
im SLA-Management begegnet Herausforderungen wie SLA-Darstellung f ur Cloud-
Dienste, gesch aftsbezogene SLA-Optimierungen, Dienste-Outsourcing und Ressourcenmanagement.
Diese Gebiete scha en zentrale und aktuelle Forschungsthemen. Das
Management von SLAs in unterschiedlichen Phasen w ahrend ihrer Laufzeit erfordert
eine daf ur entwickelte Methodik. Dadurch wird die Realisierung von Cloud SLAManagement
vereinfacht.
Ich pr asentiere ein breit gef achertes Modell im SLA-Laufzeitmanagement, das die
genannten Herausforderungen adressiert. Diese Herangehensweise erm oglicht eine automatische
Dienstemodellierung, sowie Aushandlung, Bereitstellung und Monitoring
von SLAs. W ahrend der Erstellungsphase skizziere ich, wie die Modellierungsstrukturen
verbessert und vereinfacht werden k onnen. Ein weiteres Ziel von meinem Ansatz
ist die Minimierung von Implementierungs- und Outsourcingkosten zugunsten von
Wettbewerbsf ahigkeit. In der SLA-Monitoringphase entwickle ich Strategien f ur die
Auswahl und Zuweisung von virtuellen Cloud Ressourcen in Migrationsphasen. Anschlie
end pr ufe ich mittels Monitoring eine gr o ere Zusammenstellung von SLAs, ob
die vereinbarten Fehlertoleranzen eingehalten werden.
Die vorliegende Arbeit leistet einen Beitrag zu einem Entwurf der GWDG und
deren wissenschaftlichen Communities. Die Forschung, die zu dieser Doktorarbeit
gef uhrt hat, wurde als Teil von dem SLA@SOI EU/FP7 integriertem Projekt durchgef
uhrt (contract No. 216556)
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