2 research outputs found

    Enabling Distributed Applications Optimization in Cloud Environment

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    The past few years have seen dramatic growth in the popularity of public clouds, such as Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), and Container-as-a-Service (CaaS). In both commercial and scientific fields, quick environment setup and application deployment become a mandatory requirement. As a result, more and more organizations choose cloud environments instead of setting up the environment by themselves from scratch. The cloud computing resources such as server engines, orchestration, and the underlying server resources are served to the users as a service from a cloud provider. Most of the applications that run in public clouds are the distributed applications, also called multi-tier applications, which require a set of servers, a service ensemble, that cooperate and communicate to jointly provide a certain service or accomplish a task. Moreover, a few research efforts are conducting in providing an overall solution for distributed applications optimization in the public cloud. In this dissertation, we present three systems that enable distributed applications optimization: (1) the first part introduces DocMan, a toolset for detecting containerized application’s dependencies in CaaS clouds, (2) the second part introduces a system to deal with hot/cold blocks in distributed applications, (3) the third part introduces a system named FP4S, a novel fragment-based parallel state recovery mechanism that can handle many simultaneous failures for a large number of concurrently running stream applications

    Video Popularity Metrics and Bubble Cache Eviction Algorithm Analysis

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    Video data is the largest type of traffic in the Internet, currently responsible for over 72% of the total traffic, with over 883PB of data per month in 2016. Large scale CDN solutions are available that offer a variety of distributed hosting platforms for the purpose of transmitting video over IP. However, the IP protocol, unlike ICN protocol implementations, does not provide an any-cast architecture from which a CDN would greatly benefit. In this thesis we introduce a novel cache eviction strategy called ``Bubble,'' as well as two variants of Bubble, that can be applied to any-cast protocols to aid in optimising video delivery. Bubble, Bubble-LRU and Bubble-Insert were found to greatly reduce the quantity of video associated traffic observed in cache enabled networks. Additionally, analysis on two British Telecom (BT) provided video popularity distributions leveraging Kullback-Leibler and Pearson Chi-Squared testing methods was performed. This was done to assess which model, Zipf or Zipf-Mandelbrot, is best suited to replicate video popularity distributions and the results of these tests conclude that Zipf-Mandelbrot is the most appropriate model to replicate video popularity distributions. The work concludes that the novel cache eviction algorithms introduced in this thesis provide an efficient caching mechanism for future content delivery networks and that the modelled Zipf-Mandelbrot distribution is a better method for simulating the performance of caching algorithms
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