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On the Performance of Adaptive Bitrate Streaming and Parallel Cloud Applications
As shown in recent studies, video streaming is by far the biggest category of backbone Internet traffic in the US. As a measure to reduce the cost of highly over-provisioned physical infrastructures while remaining the quality of video services, many streaming service providers started to use cloud services, where physical resources can be dynamically allocated based on current demand.
In this dissertation, we seek to evaluate and improve the performance for both Adaptive Bitrate (ABR) video streaming and cloud applications. First, we present a set of measurement studies for ABR streaming applications. Using the data from the application, network, and physical layers in different network environments, we identify the key factors that can impact the quality of video delivering services. Then we develop and evaluate a set of new ABR streaming quality adaptation algorithms to improve the user playback experience.
In addition, we explore the options for better energy efficiency of ABR video transcoding services and parallel cloud applications. We define a set of energy management policies to enable the utilization of renewable energy sources. We show that, for both applications, by effectively utilizing the renewable energy, our policies can significantly reduce the grid energy usage and corresponding energy cost, while ensuring a satisfying application performance
Adaptive Big Data Pipeline
Over the past three decades, data has exponentially evolved from being a simple software by-product to one of the most important companies’ assets used to understand their customers and foresee trends. Deep learning has demonstrated that big volumes of clean data generally provide more flexibility and accuracy when modeling a phenomenon. However, handling ever-increasing data volumes entail new challenges: the lack of expertise to select the appropriate big data tools for the processing pipelines, as well as the speed at which engineers can take such pipelines into production reliably, leveraging the cloud. We introduce a system called Adaptive Big Data Pipelines: a platform to automate data pipelines creation. It provides an interface to capture the data sources, transformations, destinations and execution schedule. The system builds up the cloud infrastructure, schedules and fine-tunes the transformations, and creates the data lineage graph. This system has been tested on data sets of 50 gigabytes, processing them in just a few minutes without user intervention.ITESO, A. C
Investigations into Elasticity in Cloud Computing
The pay-as-you-go model supported by existing cloud infrastructure providers
is appealing to most application service providers to deliver their
applications in the cloud. Within this context, elasticity of applications has
become one of the most important features in cloud computing. This elasticity
enables real-time acquisition/release of compute resources to meet application
performance demands. In this thesis we investigate the problem of delivering
cost-effective elasticity services for cloud applications.
Traditionally, the application level elasticity addresses the question of how
to scale applications up and down to meet their performance requirements, but
does not adequately address issues relating to minimising the costs of using
the service. With this current limitation in mind, we propose a scaling
approach that makes use of cost-aware criteria to detect the bottlenecks within
multi-tier cloud applications, and scale these applications only at bottleneck
tiers to reduce the costs incurred by consuming cloud infrastructure resources.
Our approach is generic for a wide class of multi-tier applications, and we
demonstrate its effectiveness by studying the behaviour of an example
electronic commerce site application.
Furthermore, we consider the characteristics of the algorithm for
implementing the business logic of cloud applications, and investigate the
elasticity at the algorithm level: when dealing with large-scale data under
resource and time constraints, the algorithm's output should be elastic with
respect to the resource consumed. We propose a novel framework to guide the
development of elastic algorithms that adapt to the available budget while
guaranteeing the quality of output result, e.g. prediction accuracy for
classification tasks, improves monotonically with the used budget.Comment: 211 pages, 27 tables, 75 figure
A manifesto for future generation cloud computing: research directions for the next decade
The Cloud computing paradigm has revolutionised the computer science horizon during the past decade and has enabled the emergence of computing as the fifth utility. It has captured significant attention of academia, industries, and government bodies. Now, it has emerged as the backbone of modern economy by offering subscription-based services anytime, anywhere following a pay-as-you-go model. This has instigated (1) shorter establishment times for start-ups, (2) creation of scalable global enterprise applications, (3) better cost-to-value associativity for scientific and high performance computing applications, and (4) different invocation/execution models for pervasive and ubiquitous applications. The recent technological developments and paradigms such as serverless computing, software-defined networking, Internet of Things, and processing at network edge are creating new opportunities for Cloud computing. However, they are also posing several new challenges and creating the need for new approaches and research strategies, as well as the re-evaluation of the models that were developed to address issues such as scalability, elasticity, reliability, security, sustainability, and application models. The proposed manifesto addresses them by identifying the major open challenges in Cloud computing, emerging trends, and impact areas. It then offers research directions for the next decade, thus helping in the realisation of Future Generation Cloud Computing
Model-driven Scheduling for Distributed Stream Processing Systems
Distributed Stream Processing frameworks are being commonly used with the
evolution of Internet of Things(IoT). These frameworks are designed to adapt to
the dynamic input message rate by scaling in/out.Apache Storm, originally
developed by Twitter is a widely used stream processing engine while others
includes Flink, Spark streaming. For running the streaming applications
successfully there is need to know the optimal resource requirement, as
over-estimation of resources adds extra cost.So we need some strategy to come
up with the optimal resource requirement for a given streaming application. In
this article, we propose a model-driven approach for scheduling streaming
applications that effectively utilizes a priori knowledge of the applications
to provide predictable scheduling behavior. Specifically, we use application
performance models to offer reliable estimates of the resource allocation
required. Further, this intuition also drives resource mapping, and helps
narrow the estimated and actual dataflow performance and resource utilization.
Together, this model-driven scheduling approach gives a predictable application
performance and resource utilization behavior for executing a given DSPS
application at a target input stream rate on distributed resources.Comment: 54 page
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