Cloud computing resource scheduling and a survey of its evolutionary approaches

A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon

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)

Generalized Nash equilibria for SaaS/PaaS Clouds

Cloud computing is an emerging technology that allows to access computing resources on a pay-per-use basis. The main challenges in this area are the efficient performance management and the energy costs minimization. In this paper we model the service provisioning problem of Cloud Platform-as-a-Service systems as a Generalized Nash Equilibrium Problem and show that a potential function for the game exists. Moreover, we prove that the social optimum problem is convex and we derive some properties of social optima from the corresponding Karush-Kuhn-Tucker system. Next, we propose a distributed solution algorithm based on the best response dynamics and we prove its convergence to generalized Nash equilibria. Finally, we numerically evaluate equilibria in terms of their efficiency with respect to the social optimum of the Cloud by varying our algorithm initial solution. Numerical results show that our algorithm is scalable and very efficient and thus can be adopted for the run-time management of very large scale systems

Autonomic Cloud Computing: Open Challenges and Architectural Elements

As Clouds are complex, large-scale, and heterogeneous distributed systems, management of their resources is a challenging task. They need automated and integrated intelligent strategies for provisioning of resources to offer services that are secure, reliable, and cost-efficient. Hence, effective management of services becomes fundamental in software platforms that constitute the fabric of computing Clouds. In this direction, this paper identifies open issues in autonomic resource provisioning and presents innovative management techniques for supporting SaaS applications hosted on Clouds. We present a conceptual architecture and early results evidencing the benefits of autonomic management of Clouds.Comment: 8 pages, 6 figures, conference keynote pape

Dynamic QoS optimization architecture for cloud-based DDDAS

Cloud computing urges the need for novel on-demand approaches, where the Quality of Service (QoS) requirements of cloud-based services can dynamically and adaptively evolve at runtime as Service Level Agreement (SLA) and environment changes. Given the unpredictable, dynamic and on-demand nature of the cloud, it would be unrealistic to assume that optimal QoS can be achieved at design time. As a result, there is an increasing need for dynamic and self- adaptive QoS optimization solutions to respond to dynamic changes in SLA and the environment. In this context, we posit that the challenge of self-adaptive QoS optimization encompasses two dynamics, which are related to QoS sensitivity and conflicting objectives at runtime. We propose novel design of a dynamic data-driven architecture for optimizing QoS influenced by those dynamics. The architecture leverages on DDDAS primitives by employing distributed simulations and symbiotic feedback loops, to dynamically adapt decision making metaheuristics, which optimizes for QoS tradeoffs in cloud-based systems. We use a scenario to exemplify and evaluate the approach