A Tool Based Edge Server Selection Technique using Spatial Data Structure

Space partitioning is the process of dividing a Euclidean space into a non-overlapping regions. Kdimensional tree is such space-partitioning data structure for partitioning a Euclidean plane like the surface of earth. This paper describes a tool-based logically partitioning technique of earth surface using K-dimensional tree to segregate the edge servers over the earth surface into a nonoverlapping regions for the particular Content Delivery Network. Consequently selecting an edge server based on Least Response Time lo ad balancing algorithm is introduced to improve end-user response time and fault tolerance of the host server

An Efficient Edge Servers Selection in Content Delivery Network Using Voronoi Diagram

Handle on demand network popularity of the Content Delivery Network and solve the flash crowd problem, caching web content at the internet’s edge server has been emerged. To provide faster service of the web users, content come from nearby edge servers. Therefore nearest edge server finding of a particular web user is an open research problem and it ensures a faster response time and download time of the requested content due to reduced latency. Our simulation study and application to a real set of geographical coordinates of the edge servers which are geographically dispersed and a well-known geometric device, Voronoi diagram is used for decomposing the earth surface around each location of a particular edge server and closest edge servers of the requested web user is searched by the nearest neighbor queries using Delaunay triangulation property over the aforesaid decomposed earth surface

Implementação da integração do barramento de serviços da UnB com ferramentas de monitoramento

Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2020.A implementação de serviços e microsserviços para aplicações de sistemas distribuí- dos com a utilização de uma Arquitetura Orientada a Serviços (SOA) permite utilizar padrões de desenvolvimento, facilitar a manutenção, flexibilizar o desenvolvimento de serviços e permitir a interoperabilidade de serviços e sistemas. O Centro de Informática (CPD) da Universidade de Brasília (UnB) trabalha com vários processos de automação de softwares, desde a manutenção de sistemas legados, passando pelo desenvolvimento de novas aplicações até a implantação de softwares adquiridos, com várias frentes tec- nológicas relacionadas à sistemas. Acompanhar e monitorar o funcionamento de serviços, microsserviços e sistemas é imprescindível. Este trabalho tem caráter exploratório e busca investigar sobre soluções e ferramentas para implementação e implantação de monitora- mento de serviços e sistemas distribuídos da Universidade de Brasília (UnB), por meio de um mapeamento sistemático. Com embasamento teórico obteve-se um modelo que foi im- plementado como módulo de monitoramento do barramento de serviços da Universidade de Brasília (UnB). Neste trabalho foram executadas simulações na solução que permi- tiu analisar a integração do barramento de serviços com a ferramenta de monitoramento através da solução proposta.The Implementation services and microservices for distributed system applications us- ing a Service Oriented Architecture (SOA) allows to use development standards facilitate maintenance flexibly develop services and enable interoperability of services and systems. Computer Center (CPD) of the University of Brasilia (UnB) works with several softwares automation processes, from the maintenance of legacy systems, through the development of new applications to the deployment of purchased softwares, with several systems related technological fronts. Mark and monitor the functioning of services, microservices and sys- tems is essential. This work is exploratory and seeks to investigate solutions and tools for the implementation of monitoring of distributed services and systems of the University of Brasilia (UnB), through systematic mapping. With a theoretical basis, a model was obtained, which was implemented as a service bus monitoring module at the University of Brasilia (UnB). In this work, simulations were performed on the solution that allowed to analyze the integration of the service bus with the monitoring tool through the proposed solution

Monitoring and analysis system for performance troubleshooting in data centers

It was not long ago. On Christmas Eve 2012, a war of troubleshooting began in Amazon data centers. It started at 12:24 PM, with an mistaken deletion of the state data of Amazon Elastic Load Balancing Service (ELB for short), which was not realized at that time. The mistake first led to a local issue that a small number of ELB service APIs were affected. In about six minutes, it evolved into a critical one that EC2 customers were significantly affected. One example was that Netflix, which was using hundreds of Amazon ELB services, was experiencing an extensive streaming service outage when many customers could not watch TV shows or movies on Christmas Eve. It took Amazon engineers 5 hours 42 minutes to find the root cause, the mistaken deletion, and another 15 hours and 32 minutes to fully recover the ELB service. The war ended at 8:15 AM the next day and brought the performance troubleshooting in data centers to world’s attention. As shown in this Amazon ELB case.Troubleshooting runtime performance issues is crucial in time-sensitive multi-tier cloud services because of their stringent end-to-end timing requirements, but it is also notoriously difficult and time consuming. To address the troubleshooting challenge, this dissertation proposes VScope, a flexible monitoring and analysis system for online troubleshooting in data centers. VScope provides primitive operations which data center operators can use to troubleshoot various performance issues. Each operation is essentially a series of monitoring and analysis functions executed on an overlay network. We design a novel software architecture for VScope so that the overlay networks can be generated, executed and terminated automatically, on-demand. From the troubleshooting side, we design novel anomaly detection algorithms and implement them in VScope. By running anomaly detection algorithms in VScope, data center operators are notified when performance anomalies happen. We also design a graph-based guidance approach, called VFocus, which tracks the interactions among hardware and software components in data centers. VFocus provides primitive operations by which operators can analyze the interactions to find out which components are relevant to the performance issue. VScope’s capabilities and performance are evaluated on a testbed with over 1000 virtual machines (VMs). Experimental results show that the VScope runtime negligibly perturbs system and application performance, and requires mere seconds to deploy monitoring and analytics functions on over 1000 nodes. This demonstrates VScope’s ability to support fast operation and online queries against a comprehensive set of application to system/platform level metrics, and a variety of representative analytics functions. When supporting algorithms with high computation complexity, VScope serves as a ‘thin layer’ that occupies no more than 5% of their total latency. Further, by using VFocus, VScope can locate problematic VMs that cannot be found via solely application-level monitoring, and in one of the use cases explored in the dissertation, it operates with levels of perturbation of over 400% less than what is seen for brute-force and most sampling-based approaches. We also validate VFocus with real-world data center traces. The experimental results show that VFocus has troubleshooting accuracy of 83% on average.Ph.D