Performance modelling and optimization for video-analytic algorithms in a cloud-like environment using machine learning

CCTV cameras produce a large amount of video surveillance data per day, and analysing them require the use of significant computing resources that often need to be scalable. The emergence of the Hadoop distributed processing framework has had a significant impact on various data intensive applications as the distributed computed based processing enables an increase of the processing capability of applications it serves. Hadoop is an open source implementation of the MapReduce programming model. It automates the operation of creating tasks for each function, distribute data, parallelize executions and handles machine failures that reliefs users from the complexity of having to manage the underlying processing and only focus on building their application. It is noted that in a practical deployment the challenge of Hadoop based architecture is that it requires several scalable machines for effective processing, which in turn adds hardware investment cost to the infrastructure. Although using a cloud infrastructure offers scalable and elastic utilization of resources where users can scale up or scale down the number of Virtual Machines (VM) upon requirements, a user such as a CCTV system operator intending to use a public cloud would aspire to know what cloud resources (i.e. number of VMs) need to be deployed so that the processing can be done in the fastest (or within a known time constraint) and the most cost effective manner. Often such resources will also have to satisfy practical, procedural and legal requirements. The capability to model a distributed processing architecture where the resource requirements can be effectively and optimally predicted will thus be a useful tool, if available. In literature there is no clear and comprehensive modelling framework that provides proactive resource allocation mechanisms to satisfy a user's target requirements, especially for a processing intensive application such as video analytic. In this thesis, with the hope of closing the above research gap, novel research is first initiated by understanding the current legal practices and requirements of implementing video surveillance system within a distributed processing and data storage environment, since the legal validity of data gathered or processed within such a system is vital for a distributed system's applicability in such domains. Subsequently the thesis presents a comprehensive framework for the performance ii modelling and optimization of resource allocation in deploying a scalable distributed video analytic application in a Hadoop based framework, running on virtualized cluster of machines. The proposed modelling framework investigates the use of several machine learning algorithms such as, decision trees (M5P, RepTree), Linear Regression, Multi Layer Perceptron(MLP) and the Ensemble Classifier Bagging model, to model and predict the execution time of video analytic jobs, based on infrastructure level as well as job level parameters. Further in order to propose a novel framework for the allocate resources under constraints to obtain optimal performance in terms of job execution time, we propose a Genetic Algorithms (GAs) based optimization technique. Experimental results are provided to demonstrate the proposed framework's capability to successfully predict the job execution time of a given video analytic task based on infrastructure and input data related parameters and its ability determine the minimum job execution time, given constraints of these parameters. Given the above, the thesis contributes to the state-of-art in distributed video analytics, design, implementation, performance analysis and optimisation

Cloud Services Brokerage: A Survey and Research Roadmap

A Cloud Services Brokerage (CSB) acts as an intermediary between cloud service providers (e.g., Amazon and Google) and cloud service end users, providing a number of value adding services. CSBs as a research topic are in there infancy. The goal of this paper is to provide a concise survey of existing CSB technologies in a variety of areas and highlight a roadmap, which details five future opportunities for research.Comment: Paper published in the 8th IEEE International Conference on Cloud Computing (CLOUD 2015

ENORM: A Framework For Edge NOde Resource Management

Current computing techniques using the cloud as a centralised server will become untenable as billions of devices get connected to the Internet. This raises the need for fog computing, which leverages computing at the edge of the network on nodes, such as routers, base stations and switches, along with the cloud. However, to realise fog computing the challenge of managing edge nodes will need to be addressed. This paper is motivated to address the resource management challenge. We develop the first framework to manage edge nodes, namely the Edge NOde Resource Management (ENORM) framework. Mechanisms for provisioning and auto-scaling edge node resources are proposed. The feasibility of the framework is demonstrated on a PokeMon Go-like online game use-case. The benefits of using ENORM are observed by reduced application latency between 20% - 80% and reduced data transfer and communication frequency between the edge node and the cloud by up to 95\%. These results highlight the potential of fog computing for improving the quality of service and experience.Comment: 14 pages; accepted to IEEE Transactions on Services Computing on 12 September 201

Parallel genetic algorithms in the cloud

2015 - 2016Genetic Algorithms (GAs) are a metaheuristic search technique belonging to the class of Evolutionary Algorithms (EAs). They have been proven to be effective in addressing several problems in many fields but also suffer from scalability issues that may not let them find a valid application for real world problems. Thus, the aim of providing highly scalable GA-based solutions, together with the reduced costs of parallel architectures, motivate the research on Parallel Genetic Algorithms (PGAs). Cloud computing may be a valid option for parallelisation, since there is no need of owning the physical hardware, which can be purchased from cloud providers, for the desired time, quantity and quality. There are different employable cloud technologies and approaches for this purpose, but they all introduce communication overhead. Thus, one might wonder if, and possibly when, specific approaches, environments and models show better performance than sequential versions in terms of execution time and resource usage. This thesis investigates if and when GAs can scale in the cloud using specific approaches. Firstly, Hadoop MapReduce is exploited designing and developinganopensourceframework,i.e.,elephant56, thatreducestheeffortin developing and speed up GAs using three parallel models. The performance of theframeworkisthenevaluatedthroughanempiricalstudy. Secondly, software containers and message queues are employed to develop, deploy and execute PGAs in the cloud and the devised system is evaluated with an empirical study on a commercial cloud provider. Finally, cloud technologies are also exploredfortheparallelisationofotherEAs,designinganddevelopingcCube,a collaborativemicroservicesarchitectureformachinelearningproblems. [edited by author]I Genetic Algorithms (GAs) sono una metaeuristica di ricerca appartenenti alla classe degli Evolutionary Algorithms (EAs). Si sono dimostrati efficaci nel risolvere tanti problemi in svariati campi. Tuttavia, le difficoltà nello scalare spesso evitano che i GAs possano trovare una collocazione efficace per la risoluzione di problemi del mondo reale. Quindi, l’obiettivo di fornire soluzioni basate altamente scalabili, assieme alla riduzione dei costi di architetture parallele, motivano la ricerca sui Parallel Genetic Algorithms (PGAs). Il cloud computing potrebbe essere una valida opzione per la parallelizzazione, dato che non c’è necessità di possedere hardware fisico che può, invece, essere acquistato dai cloud provider, per il tempo desiderato, quantità e qualità. Esistono differenti tecnologie e approcci cloud impiegabili a tal proposito ma, tutti, introducono overhead di computazione. Quindi, ci si può chiedere se, e possibilmente quando, approcci specifici, ambienti e modelli mostrino migliori performance rispetto alle versioni sequenziali, in termini di tempo di esecuzione e uso di risorse. Questa tesi indaga se, e quando, i GAs possono scalare nel cloud utilizzando approcci specifici. Prima di tutto, Hadoop MapReduce è sfruttato per modellare e sviluppare un framework open source, i.e., elephant56, che riduce l’effort nello sviluppo e velocizza i GAs usando tre diversi modelli paralleli. Le performance del framework sono poi valutate attraverso uno studio empirico. Successivamente, i software container e le message queue sono impiegati per sviluppare, distribuire e eseguire PGAs e il sistema ideato valutato, attraverso uno studio empirico, su un cloud provider commerciale. Infine, le tecnologie cloud sono esplorate per la parallelizzazione di altri EAs, ideando e sviluppando cCube, un’architettura a microservizi collaborativa per risolvere problemi di machine learning. [a cura dell'autore]XV n.s