Cloud-SEnergy: A bin-packing based multi-cloud service broker for energy efficient composition and execution of data-intensive applications

© 2018 Elsevier Inc. The over-reliance of today's world on information and communication technologies (ICT) has led to an exponential increase in data production, network traffic, and energy consumption. To mitigate the ecological impact of this increase on the environment, a major challenge that this paper tackles is how to best select the most energy efficient services from cross-continental competing cloud-based datacenters. This selection is addressed by our Cloud-SEnergy, a system that uses a bin-packing technique to generate the most efficient service composition plans. Experiments were conducted to compare Cloud-SEnergy's efficiency with 5 established techniques in multi-cloud environments (All clouds, Base cloud, Smart cloud, COM2, and DC-Cloud). The results gained from the experiments demonstrate a superior performance of Cloud-SEnergy which ranged from an average energy consumption reduction of 4.3% when compared to Based Cloud technique, to an average reduction of 43.3% when compared to All Clouds technique. Furthermore, the percentage reduction in the number of examined services achieved by Cloud-SEnergy ranged from 50% when compared to Smart Cloud and average of 82.4% when compared to Base Cloud. In term of run-time, Cloud-SEnergy resulted in average reduction which ranged from 8.5% when compared to DC-Cloud, to 28.2% run-time reduction when compared to All Clouds

Scheduling mixed tasks with deadlines in grids using bin packing

Abstrac

Análisis de estabilidad de estrategias y optimización exacta de asignación de recursos en empresas de servicios

En este trabajo estudiamos dos problemas que se presentan en empresas que prestan servicios, como por ejemplo soporte técnico. Por un lado, las empresas deben elegir entre dos estrategias desde el punto de vista comercial: brindar sus servicios en una modalidad de cobro de una tarifa fija mensual, o a demanda mediante un cobro por atención, cada una con sus pros y contras. Este problema es tratado en este trabajo mediante Teoría de Juegos Evolutivos. El segundo problema que buscan resolver las empresas es el de asignar de forma óptima sus recursos. Para ello deben decidir ante una diversidad de alternativas en las que deben considerar: la complejidad de los casos, las capacidades de sus técnicos, los costos correspondientes a los distintos niveles de sus técnicos y el cumplimiento de los tiempos de resolución establecidos por contrato. En este trabajo se propone una solución mediante Programación Lineal Entera al problema de asignación de tareas, el que denominamos TAWDP : Task Assignment With Deadlines Problem. Se demuestra que TAWDP es NP-Hard y se realiza una implementación en en AMPL-Cplex del modelo propuesto. Posteriormente se evalúan los tiempos de resolución a medida que el tamaño del problema crece. Los resultados obtenidos muestran que se obtienen soluciones de optimalidad para problemas grandes en tiempos que lo hacen aplicable a empresas nacionales e internacionales.In this work we study two problems that arise in service companies, e.g. technical support services. First, these companies must choose between two commercial strategies: charging a fixed monthly fee or bring their services on demand and then charge for each issue attended. Each strategy has pros and cons. This problem is treated in this work through Evolutionary Game Theory. The second problem that these companies need to solve is optimization of resource allocation. To achieve this goal, they must decide between a lot of alternatives where they must consider: the complexity of cases, the skills of technicians, costs different levels of technicians and compliance with the resolution times established by contract. In this work, a solution is proposed through Integer Linear Programming to the task assignment problem, which we call TAWDP: Task Assignment With Deadlines Problem. We show that TAWDP is NP-Hard and an implementation for the proposed model is made in AMPL-Cplex. Resolution times are evaluated as the size of the problem grows. Obtained results show that optimal solutions are obtained for large problems in times that makes the solution appliable to national and international companies

Requirement-oriented information distribution in connected mobility scenarios

Die Zahl vernetzter Fahrzeuge wächst stetig. Während derzeit die meisten mobilen Onlinedienste in den Bereichen Infotainment und Navigation angesiedelt sind, ist zukünftig mit einer Ausdehnung in die Domäne der Fahrerassistenz zu rechnen. Da Fahrerassistenzsysteme mitunter aktiv in die Fahrzeugführung eingreifen, gelten hohe Anforderungen bezüglich der Qualität und Aktualität der Daten. Zu spät zugestellte bzw. fehlende Daten können zu Fehlfunktionen führen, die ein Sicherheitsrisiko für die Fahrzeuginsassen sowie andere Verkehrsteilnehmer darstellen. Die Vernetzung der Fahrzeuge über das Internet erfolgt derzeit ausschließlich über Mobilfunknetze. Mobilfunknetze zeichnen sich durch eine hohe geografische Abdeckung aus, allerdings unterliegt die Qualität des Datenkanals örtlichen Schwankungen, die sich negativ auf die Funktionsfähigkeit und Zuverlässigkeit der mobilen Dienste auswirken können. Ein Weg zur Steigerung der Funktionsfähigkeit und Zuverlässigkeit ist die Verwendung sogenannter Connectivity-Maps. Connectivity-Maps enthalten georeferenzierte Daten zu Eigenschaften des Mobilfunknetzes, die im Rahmen von Messfahrten gesammelt wurden. Dieses a priori Wissen kann verwendet werden, um die Übertragung von Daten proaktiv zu planen. Die Übertragungszeitpunkte können so gewählt werden, dass eine möglichst hohe Qualität des Mobilfunkkanals vorliegt. In diesem Fall ist die spektrale Effizienz hoch, so dass weniger wertvolle Ressourcen der Luftschnittstelle für die Datenübertragung benötigt werden. In dieser Arbeit wird ein Verfahren vorgestellt, das die Datenverteilung von einem zentralen Server zu den Fahrzeugen optimiert. Ziel der Optimierung ist es, die Sendereihenfolge der Datenobjekte so zu wählen, dass unter Berücksichtigung individueller spätester Zustellzeitpunkte eine möglichst hohe Ausnutzung der Luftschnittstelle erreicht wird. Die Arbeit fokussiert sich auf High Speed Downlink Packet Access (HSDPA) und verwendet als Maß für die Kanalqualität den sogenannten Channel Quality Indicator (CQI). Aus gemessenen CQI-Werten wird zunächst die CQI-Karte erstellt, die den ortsbezogenen Zugriff auf historische CQI-Daten erlaubt. Auf Basis dieser Daten werden Übertragungsdauer und Kanalausnutzung der Datenobjekte prädiziert. Anhand dieser Prädiktion kann eine optimale Sendereihenfolge ermittelt werden. Die umfangreiche Evaluation des Verfahrens erfolgt unter Verwendung einer im Kontext dieser Arbeit entwickelten Simulationsumgebung.The number of connected cars is growing continuously. While currently most mobile services reside in the areas of infotainment and navigation, an extension to the domain of driver assistance systems is to be expected. Systems such as hazard or construction site assists are supplied with new data in real time, map based assistance functions receive updates of the map data on a daily basis. As these systems may actively intervene in the driving process, high requirements concerning the quality and timeliness of the data have to be met. Delayed or missing data may cause malfunctions posing a safety risk to vehicles' passengers and other road users. At the moment cellular networks are used to connect vehicles to the internet. Cellular networks are characterized by a large geographical coverage, but the quality of the wireless data channel suffers from local fluctuations. These fluctuations of channel quality may have a bad impact on the functionality and reliability of the mobile services. A way to improve the functionality and reliability is to use so called connectivity maps. Connectivity maps offer georeferenced characteristics of the cellular network, which have been collected within measurement drives. This a priori knowledge can be used to plan the transmission of data from the internet to the vehicles proactively. The transmission times can be chosen to ensure a maximum channel quality resulting in a high spectral efficiency. With high spectral efficiency, less precious resources of the air interface are required to transmit a data object. This thesis offers a new concept to optimize the data distribution from a central server to vehicles. The objective of the optimization is to find a data objects' order, which maximizes the utilization of the air interface but considers individual deadlines. The work focusses on High Speed Downlink Packet Access (HSDPA) and uses the Channel Quality Indicator (CQI) to measure the channel quality. Initially a CQI-map is created using in field CQI measurements. This map provides location based access to historical CQI values. These values are used to predict transmission duration and channel utilization. Based on the predictions the optimal transmission order is calculated. An extensive evaluation is carried out using a simulation environment, which has been developed within this thesis

Resource-Efficient Wireless Systems for Emerging Wireless Networks

As the wireless medium has become the primary source of communication and Internet connectivity, and as devices and wireless technologies become more sophisticated and capable, there has been a surge in the capacity demands and complexity of applications that run over these wireless devices. To sustain the volume and QoE guarantees of the data generated, the opportunity and need to rethink wireless network design across all the layers of the protocol stack has firmly emerged as a solution to enable the timely and reliable delivery of data, while handling the inherent challenges of a crowded wireless medium, such as congestion, interference, and hidden terminals. The research work presented in this dissertation builds efficient solutions and protocols with a theoretical foundation to address the challenges that arise in rethinking wireless network design. Example challenges include managing the overhead associated with complex systems. My work particularly focuses on the opportunities and challenges of sophisticated technology and systems in emerging wireless networks. I target the main thrusts in the evolution of wireless networks that create significant opportunity to achieve higher theoretical capacity, and have direct implications on our day-to-day wireless interactions: from enabling multifold increase in capacity in wireless physical links, to developing medium access techniques to exploit the high speed links, and making the applications more bandwidth efficient. I build deployable, and resource-aware wireless systems that exploit higher bandwidths by leveraging and advancing diverse research areas such as theory, analysis, protocol design, and wireless networking. Specifically, I identify the erroneous assumptions and fundamental limitations of existing solutions in capturing the true and complex interactions between wireless devices and protocols. I use these insights to guide practical and efficient protocol design, followed by thorough analysis and evaluation in testbed implementations via prototypes and measurements. I show that my proposed solutions achieve significant performance gains, at minimum cost to overhead