3,981 research outputs found
AHP Aided Decision-Making in Virtual Machine Migration for Green Cloud
In this study, an analytical hierarchy process based model is proposed to perform the decision-making for virtual machine migration towards green cloud computing. The virtual machine migration evaluation index system is established based on the process of constructing hierarchies for evaluation of virtual machine migration, and selection of task usage. A comparative judgment of two hierarchies has been conducted. In the experimental study, five-point rating scale has been adopted to map the raw data to the scaled rating score; this rating method is used to analyze the performance of each virtual machine and its task usage data. The results show a significant improvement in the decision-making process for the virtual machine migration. The deduced results are useful for the system administrators to migrate the exact virtual machine, and then switch on the power of physical machine that the migrated virtual machine resides on. Thus the proposed method contributes to the green cloud computing environment
TrIMS: Transparent and Isolated Model Sharing for Low Latency Deep LearningInference in Function as a Service Environments
Deep neural networks (DNNs) have become core computation components within
low latency Function as a Service (FaaS) prediction pipelines: including image
recognition, object detection, natural language processing, speech synthesis,
and personalized recommendation pipelines. Cloud computing, as the de-facto
backbone of modern computing infrastructure for both enterprise and consumer
applications, has to be able to handle user-defined pipelines of diverse DNN
inference workloads while maintaining isolation and latency guarantees, and
minimizing resource waste. The current solution for guaranteeing isolation
within FaaS is suboptimal -- suffering from "cold start" latency. A major cause
of such inefficiency is the need to move large amount of model data within and
across servers. We propose TrIMS as a novel solution to address these issues.
Our proposed solution consists of a persistent model store across the GPU, CPU,
local storage, and cloud storage hierarchy, an efficient resource management
layer that provides isolation, and a succinct set of application APIs and
container technologies for easy and transparent integration with FaaS, Deep
Learning (DL) frameworks, and user code. We demonstrate our solution by
interfacing TrIMS with the Apache MXNet framework and demonstrate up to 24x
speedup in latency for image classification models and up to 210x speedup for
large models. We achieve up to 8x system throughput improvement.Comment: In Proceedings CLOUD 201
Energy-aware Load Balancing Policies for the Cloud Ecosystem
The energy consumption of computer and communication systems does not scale
linearly with the workload. A system uses a significant amount of energy even
when idle or lightly loaded. A widely reported solution to resource management
in large data centers is to concentrate the load on a subset of servers and,
whenever possible, switch the rest of the servers to one of the possible sleep
states. We propose a reformulation of the traditional concept of load balancing
aiming to optimize the energy consumption of a large-scale system: {\it
distribute the workload evenly to the smallest set of servers operating at an
optimal energy level, while observing QoS constraints, such as the response
time.} Our model applies to clustered systems; the model also requires that the
demand for system resources to increase at a bounded rate in each reallocation
interval. In this paper we report the VM migration costs for application
scaling.Comment: 10 Page
Government cloud computing and the policies of data sovereignty
Government cloud services are a new development at the intersection of electronic government and cloud computing which holds the promise of rendering government service delivery more effective and efficient. Cloud services are virtual, dynamic and potentially stateless which has triggered governments' concern about data sovereignty. This paper explores data sovereignty in relation to government cloud services and how national strategies and international policy evolve. It concludes that for countries data sovereignty presents a legal risk which can not be adequately addressed with technology or through contractual arrangements alone. Governments therefore adopt strategies to retain exclusive jurisdiction over government information. --cloud computing,electronic government,data sovereignty,data ownership,information assurance,international data transfers
Software platform virtualization in chemistry research and university teaching
<p>Abstract</p> <p>Background</p> <p>Modern chemistry laboratories operate with a wide range of software applications under different operating systems, such as Windows, LINUX or Mac OS X. Instead of installing software on different computers it is possible to install those applications on a single computer using Virtual Machine software. Software platform virtualization allows a single guest operating system to execute multiple other operating systems on the same computer. We apply and discuss the use of virtual machines in chemistry research and teaching laboratories.</p> <p>Results</p> <p>Virtual machines are commonly used for cheminformatics software development and testing. Benchmarking multiple chemistry software packages we have confirmed that the computational speed penalty for using virtual machines is low and around 5% to 10%. Software virtualization in a teaching environment allows faster deployment and easy use of commercial and open source software in hands-on computer teaching labs.</p> <p>Conclusion</p> <p>Software virtualization in chemistry, mass spectrometry and cheminformatics is needed for software testing and development of software for different operating systems. In order to obtain maximum performance the virtualization software should be multi-core enabled and allow the use of multiprocessor configurations in the virtual machine environment. Server consolidation, by running multiple tasks and operating systems on a single physical machine, can lead to lower maintenance and hardware costs especially in small research labs. The use of virtual machines can prevent software virus infections and security breaches when used as a sandbox system for internet access and software testing. Complex software setups can be created with virtual machines and are easily deployed later to multiple computers for hands-on teaching classes. We discuss the popularity of bioinformatics compared to cheminformatics as well as the missing cheminformatics education at universities worldwide.</p
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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