Cloud Computing cost and energy optimization through Federated Cloud SoS

2017 Fall.Includes bibliographical references.The two most significant differentiators amongst contemporary Cloud Computing service providers have increased green energy use and datacenter resource utilization. This work addresses these two issues from a system's architectural optimization viewpoint. The proposed approach herein, allows multiple cloud providers to utilize their individual computing resources in three ways by: (1) cutting the number of datacenters needed, (2) scheduling available datacenter grid energy via aggregators to reduce costs and power outages, and lastly by (3) utilizing, where appropriate, more renewable and carbon-free energy sources. Altogether our proposed approach creates an alternative paradigm for a Federated Cloud SoS approach. The proposed paradigm employs a novel control methodology that is tuned to obtain both financial and environmental advantages. It also supports dynamic expansion and contraction of computing capabilities for handling sudden variations in service demand as well as for maximizing usage of time varying green energy supplies. Herein we analyze the core SoS requirements, concept synthesis, and functional architecture with an eye on avoiding inadvertent cascading conditions. We suggest a physical architecture that diminishes unwanted outcomes while encouraging desirable results. Finally, in our approach, the constituent cloud services retain their independent ownership, objectives, funding, and sustainability means. This work analyzes the core SoS requirements, concept synthesis, and functional architecture. It suggests a physical structure that simulates the primary SoS emergent behavior to diminish unwanted outcomes while encouraging desirable results. The report will analyze optimal computing generation methods, optimal energy utilization for computing generation as well as a procedure for building optimal datacenters using a unique hardware computing system design based on the openCompute community as an illustrative collaboration platform. Finally, the research concludes with security features cloud federation requires to support to protect its constituents, its constituents tenants and itself from security risks

Methods to enhance content distribution for very large scale online communities

The Internet has experienced an exponential growth in the last years, and its number of users far from decay keeps on growing. Popular Web 2.0 services such as Facebook, YouTube or Twitter among others sum millions of users and employ vast infrastructures deployed worldwide. The size of these infrastructures is getting huge in order to support such a massive number of users. This increment of the infrastructure size has brought new problems regarding scalability, power consumption, cooling, hardware lifetime, underutilization, investment recovery, etc. Owning this kind of infrastructures is not always affordable nor convenient. This could be a major handicap for starting projects with a humble budget whose success is based on reaching a large audience. However, current technologies might permit to deploy vast infrastructures reducing their cost. We refer to peer-to-peer networks and cloud computing. Peer-to-peer systems permit users to yield their own resources to distributed infrastructures. These systems have demonstrated to be a valuable choice capable of distributing vast amounts of data to large audiences with a minimal starting infrastructure. Nevertheless, aspects such as content availability cannot be controlled in these systems, whereas classic server infrastructures can improve this aspect. In the recent time, the cloud has been revealed as a promising paradigm for hosting horizontally scalable Web systems. The cloud offers elastic capabilities that permit to save costs by adapting the number of resources to the incoming demand. Additionally, the cloud makes accessible a vast amount of resources that may be employed on peak workloads. However, how to determine the amount of resources to use remains a challenge. In this thesis, we describe a hierarchical architecture that combines both: peer-to-peer and elastic server infrastructures in order to enhance content distribution. The peer-topeer infrastructure brings a scalable solution that reduces the workload in the servers, while the server infrastructure assures availability and reduces costs varying its size when necessary. We propose a distributed collaborative caching infrastructure that employs a clusterbased locality-aware self-organizing P2P system. This system, leverages collaborative data classification in order to improve content locality. Our evaluation demonstrates that incrementing data locality permits to improve data search while reducing traffic. We explore the utilization of elastic server infrastructures addressing three issues: system sizing, data grouping and content distribution. We propose novel multi-model techniques for hierarchical workload prediction. These predictions are employed to determine the system size and request distribution policies. Additionally, we propose novel techniques for adaptive control that permit to identify inaccurate models and redefine them. Our evaluation using traces extracted from real systems indicate that the utilization of a hierarchy of multiple models increases prediction accuracy. This hierarchy in conjunction with our adaptive control techniques increments the accuracy during unexpected workload variations. Finally, we demonstrate that locality-aware request distribution policies can take advantage of prediction models to adequate content distribution independently of the system size

Resource management for multimedia traffic over ATM broadband satellite networks

PhDAbstract not availabl

Energy-aware service provisioning in P2P-assisted cloud ecosystems

Cotutela Universitat Politècnica de Catalunya i Instituto Tecnico de LisboaEnergy has been emerged as a first-class computing resource in modern systems. The trend has primarily led to the strong focus on reducing the energy consumption of data centers, coupled with the growing awareness of the adverse impact on the environment due to data centers. This has led to a strong focus on energy management for server class systems. In this work, we intend to address the energy-aware service provisioning in P2P-assisted cloud ecosystems, leveraging economics-inspired mechanisms. Toward this goal, we addressed a number of challenges. To frame an energy aware service provisioning mechanism in the P2P-assisted cloud, first, we need to compare the energy consumption of each individual service in P2P-cloud and data centers. However, in the procedure of decreasing the energy consumption of cloud services, we may be trapped with the performance violation. Therefore, we need to formulate a performance aware energy analysis metric, conceptualized across the service provisioning stack. We leverage this metric to derive energy analysis framework. Then, we sketch a framework to analyze the energy effectiveness in P2P-cloud and data center platforms to choose the right service platform, according to the performance and energy characteristics. This framework maps energy from the hardware oblivious, top level to the particular hardware setting in the bottom layer of the stack. Afterwards, we introduce an economics-inspired mechanism to increase the energy effectiveness in the P2P-assisted cloud platform as well as moving toward a greener ICT for ICT for a greener ecosystem.La energía se ha convertido en un recurso de computación de primera clase en los sistemas modernos. La tendencia ha dado lugar principalmente a un fuerte enfoque hacia la reducción del consumo de energía de los centros de datos, así como una creciente conciencia sobre los efectos ambientales negativos, producidos por los centros de datos. Esto ha llevado a un fuerte enfoque en la gestión de energía de los sistemas de tipo servidor. En este trabajo, se pretende hacer frente a la provisión de servicios de bajo consumo energético en los ecosistemas de la nube asistida por P2P, haciendo uso de mecanismos basados en economía. Con este objetivo, hemos abordado una serie de desafíos. Para instrumentar un mecanismo de servicio de aprovisionamiento de energía consciente en la nube asistida por P2P, en primer lugar, tenemos que comparar el consumo energético de cada servicio en la nube P2P y en los centros de datos. Sin embargo, en el procedimiento de disminuir el consumo de energía de los servicios en la nube, podemos quedar atrapados en el incumplimiento del rendimiento. Por lo tanto, tenemos que formular una métrica, sobre el rendimiento energético, a través de la pila de servicio de aprovisionamiento. Nos aprovechamos de esta métrica para derivar un marco de análisis de energía. Luego, se esboza un marco para analizar la eficacia energética en la nube asistida por P2P y en la plataforma de centros de datos para elegir la plataforma de servicios adecuada, de acuerdo con las características de rendimiento y energía. Este marco mapea la energía desde el alto nivel independiente del hardware a la configuración de hardware particular en la capa inferior de la pila. Posteriormente, se introduce un mecanismo basado en economía para aumentar la eficacia energética en la plataforma en la nube asistida por P2P, así como avanzar hacia unas TIC más verdes, para las TIC en un ecosistema más verde.Postprint (published version

Networking Mechanisms for Delay-Sensitive Applications

The diversity of applications served by the explosively growing Internet is increasing. In particular, applications that are sensitive to end-to-end packet delays become more common and include telephony, video conferencing, and networked games. While the single best-effort service of the current Internet favors throughput-greedy traffic by equipping congested links with large buffers, long queuing at the congested links hurts the delay-sensitive applications. Furthermore, while numerous alternative architectures have been proposed to offer diverse network services, the innovative alternatives failed to gain widespread end-to-end deployment. This dissertation explores different networking mechanisms for supporting low queueing delay required by delay-sensitive applications. In particular, it considers two different approaches. The first one assumes employing congestion control protocols for the traffic generated by the considered class of applications. The second approach relies on the router operation only and does not require support from end hosts

Operating policies for energy efficient large scale computing

PhD ThesisEnergy costs now dominate IT infrastructure total cost of ownership, with datacentre operators predicted to spend more on energy than hardware infrastructure in the next five years. With Western European datacentre power consumption estimated at 56 TWh/year in 2007 and projected to double by 2020, improvements in energy efficiency of IT operations is imperative. The issue is further compounded by social and political factors and strict environmental legislation governing organisations. One such example of large IT systems includes high-throughput cycle stealing distributed systems such as HTCondor and BOINC, which allow organisations to leverage spare capacity on existing infrastructure to undertake valuable computation. As a consequence of increased scrutiny of the energy impact of these systems, aggressive power management policies are often employed to reduce the energy impact of institutional clusters, but in doing so these policies severely restrict the computational resources available for high-throughput systems. These policies are often configured to quickly transition servers and end-user cluster machines into low power states after only short idle periods, further compounding the issue of reliability. In this thesis, we evaluate operating policies for energy efficiency in large-scale computing environments by means of trace-driven discrete event simulation, leveraging real-world workload traces collected within Newcastle University. The major contributions of this thesis are as follows: i) Evaluation of novel energy efficient management policies for a decentralised peer-to-peer (P2P) BitTorrent environment. ii) Introduce a novel simulation environment for the evaluation of energy efficiency of large scale high-throughput computing systems, and propose a generalisable model of energy consumption in high-throughput computing systems. iii iii) Proposal and evaluation of resource allocation strategies for energy consumption in high-throughput computing systems for a real workload. iv) Proposal and evaluation for a realworkload ofmechanisms to reduce wasted task execution within high-throughput computing systems to reduce energy consumption. v) Evaluation of the impact of fault tolerance mechanisms on energy consumption