A Time-driven Data Placement Strategy for a Scientific Workflow Combining Edge Computing and Cloud Computing

Compared to traditional distributed computing environments such as grids, cloud computing provides a more cost-effective way to deploy scientific workflows. Each task of a scientific workflow requires several large datasets that are located in different datacenters from the cloud computing environment, resulting in serious data transmission delays. Edge computing reduces the data transmission delays and supports the fixed storing manner for scientific workflow private datasets, but there is a bottleneck in its storage capacity. It is a challenge to combine the advantages of both edge computing and cloud computing to rationalize the data placement of scientific workflow, and optimize the data transmission time across different datacenters. Traditional data placement strategies maintain load balancing with a given number of datacenters, which results in a large data transmission time. In this study, a self-adaptive discrete particle swarm optimization algorithm with genetic algorithm operators (GA-DPSO) was proposed to optimize the data transmission time when placing data for a scientific workflow. This approach considered the characteristics of data placement combining edge computing and cloud computing. In addition, it considered the impact factors impacting transmission delay, such as the band-width between datacenters, the number of edge datacenters, and the storage capacity of edge datacenters. The crossover operator and mutation operator of the genetic algorithm were adopted to avoid the premature convergence of the traditional particle swarm optimization algorithm, which enhanced the diversity of population evolution and effectively reduced the data transmission time. The experimental results show that the data placement strategy based on GA-DPSO can effectively reduce the data transmission time during workflow execution combining edge computing and cloud computing

Secure Cloud-Edge Deployments, with Trust

Assessing the security level of IoT applications to be deployed to heterogeneous Cloud-Edge infrastructures operated by different providers is a non-trivial task. In this article, we present a methodology that permits to express security requirements for IoT applications, as well as infrastructure security capabilities, in a simple and declarative manner, and to automatically obtain an explainable assessment of the security level of the possible application deployments. The methodology also considers the impact of trust relations among different stakeholders using or managing Cloud-Edge infrastructures. A lifelike example is used to showcase the prototyped implementation of the methodology

Joint Energy Efficient and QoS-aware Path Allocation and VNF Placement for Service Function Chaining

Service Function Chaining (SFC) allows the forwarding of a traffic flow along a chain of Virtual Network Functions (VNFs, e.g., IDS, firewall, and NAT). Software Defined Networking (SDN) solutions can be used to support SFC reducing the management complexity and the operational costs. One of the most critical issues for the service and network providers is the reduction of energy consumption, which should be achieved without impact to the quality of services. In this paper, we propose a novel resource (re)allocation architecture which enables energy-aware SFC for SDN-based networks. To this end, we model the problems of VNF placement, allocation of VNFs to flows, and flow routing as optimization problems. Thereafter, heuristic algorithms are proposed for the different optimization problems, in order find near-optimal solutions in acceptable times. The performance of the proposed algorithms are numerically evaluated over a real-world topology and various network traffic patterns. The results confirm that the proposed heuristic algorithms provide near optimal solutions while their execution time is applicable for real-life networks.Comment: Extended version of submitted paper - v7 - July 201

BriskStream: Scaling Data Stream Processing on Shared-Memory Multicore Architectures

We introduce BriskStream, an in-memory data stream processing system (DSPSs) specifically designed for modern shared-memory multicore architectures. BriskStream's key contribution is an execution plan optimization paradigm, namely RLAS, which takes relative-location (i.e., NUMA distance) of each pair of producer-consumer operators into consideration. We propose a branch and bound based approach with three heuristics to resolve the resulting nontrivial optimization problem. The experimental evaluations demonstrate that BriskStream yields much higher throughput and better scalability than existing DSPSs on multi-core architectures when processing different types of workloads.Comment: To appear in SIGMOD'1

Situation-aware Edge Computing

Future wireless networks must cope with an increasing amount of data that needs to be transmitted to or from mobile devices. Furthermore, novel applications, e.g., augmented reality games or autonomous driving, require low latency and high bandwidth at the same time. To address these challenges, the paradigm of edge computing has been proposed. It brings computing closer to the users and takes advantage of the capabilities of telecommunication infrastructures, e.g., cellular base stations or wireless access points, but also of end user devices such as smartphones, wearables, and embedded systems. However, edge computing introduces its own challenges, e.g., economic and business-related questions or device mobility. Being aware of the current situation, i.e., the domain-specific interpretation of environmental information, makes it possible to develop approaches targeting these challenges. In this thesis, the novel concept of situation-aware edge computing is presented. It is divided into three areas: situation-aware infrastructure edge computing, situation-aware device edge computing, and situation-aware embedded edge computing. Therefore, the concepts of situation and situation-awareness are introduced. Furthermore, challenges are identified for each area, and corresponding solutions are presented. In the area of situation-aware infrastructure edge computing, economic and business-related challenges are addressed, since companies offering services and infrastructure edge computing facilities have to find agreements regarding the prices for allowing others to use them. In the area of situation-aware device edge computing, the main challenge is to find suitable nodes that can execute a service and to predict a node’s connection in the near future. Finally, to enable situation-aware embedded edge computing, two novel programming and data analysis approaches are presented that allow programmers to develop situation-aware applications. To show the feasibility, applicability, and importance of situation-aware edge computing, two case studies are presented. The first case study shows how situation-aware edge computing can provide services for emergency response applications, while the second case study presents an approach where network transitions can be implemented in a situation-aware manner

Processamento de eventos complexos como serviço em ambientes multi-nuvem

Orientadores: Luiz Fernando Bittencourt, Miriam Akemi Manabe CapretzTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: O surgimento das tecnologias de dispositivos móveis e da Internet das Coisas, combinada com avanços das tecnologias Web, criou um novo mundo de Big Data em que o volume e a velocidade da geração de dados atingiu uma escala sem precedentes. Por ser uma tecnologia criada para processar fluxos contínuos de dados, o Processamento de Eventos Complexos (CEP, do inglês Complex Event Processing) tem sido frequentemente associado a Big Data e aplicado como uma ferramenta para obter informações em tempo real. Todavia, apesar desta onda de interesse, o mercado de CEP ainda é dominado por soluções proprietárias que requerem grandes investimentos para sua aquisição e não proveem a flexibilidade que os usuários necessitam. Como alternativa, algumas empresas adotam soluções de baixo nível que demandam intenso treinamento técnico e possuem alto custo operacional. A fim de solucionar esses problemas, esta pesquisa propõe a criação de um sistema de CEP que pode ser oferecido como serviço e usado através da Internet. Um sistema de CEP como Serviço (CEPaaS, do inglês CEP as a Service) oferece aos usuários as funcionalidades de CEP aliadas às vantagens do modelo de serviços, tais como redução do investimento inicial e baixo custo de manutenção. No entanto, a criação de tal serviço envolve inúmeros desafios que não são abordados no atual estado da arte de CEP. Em especial, esta pesquisa propõe soluções para três problemas em aberto que existem neste contexto. Em primeiro lugar, para o problema de entender e reusar a enorme variedade de procedimentos para gerência de sistemas CEP, esta pesquisa propõe o formalismo Reescrita de Grafos com Atributos para Gerência de Processamento de Eventos Complexos (AGeCEP, do inglês Attributed Graph Rewriting for Complex Event Processing Management). Este formalismo inclui modelos para consultas CEP e transformações de consultas que são independentes de tecnologia e linguagem. Em segundo lugar, para o problema de avaliar estratégias de gerência e processamento de consultas CEP, esta pesquisa apresenta CEPSim, um simulador de sistemas CEP baseado em nuvem. Por fim, esta pesquisa também descreve um sistema CEPaaS fundamentado em ambientes multi-nuvem, sistemas de gerência de contêineres e um design multiusuário baseado em AGeCEP. Para demonstrar sua viabilidade, o formalismo AGeCEP foi usado para projetar um gerente autônomo e um conjunto de políticas de auto-gerenciamento para sistemas CEP. Além disso, o simulador CEPSim foi minuciosamente avaliado através de experimentos que demonstram sua capacidade de simular sistemas CEP com acurácia e baixo custo adicional de processamento. Por fim, experimentos adicionais validaram o sistema CEPaaS e demonstraram que o objetivo de oferecer funcionalidades CEP como um serviço escalável e tolerante a falhas foi atingido. Em conjunto, esses resultados confirmam que esta pesquisa avança significantemente o estado da arte e também oferece novas ferramentas e metodologias que podem ser aplicadas à pesquisa em CEPAbstract: The rise of mobile technologies and the Internet of Things, combined with advances in Web technologies, have created a new Big Data world in which the volume and velocity of data generation have achieved an unprecedented scale. As a technology created to process continuous streams of data, Complex Event Processing (CEP) has been often related to Big Data and used as a tool to obtain real-time insights. However, despite this recent surge of interest, the CEP market is still dominated by solutions that are costly and inflexible or too low-level and hard to operate. To address these problems, this research proposes the creation of a CEP system that can be offered as a service and used over the Internet. Such a CEP as a Service (CEPaaS) system would give its users CEP functionalities associated with the advantages of the services model, such as no up-front investment and low maintenance cost. Nevertheless, creating such a service involves challenges that are not addressed by current CEP systems. This research proposes solutions for three open problems that exist in this context. First, to address the problem of understanding and reusing existing CEP management procedures, this research introduces the Attributed Graph Rewriting for Complex Event Processing Management (AGeCEP) formalism as a technology- and language-agnostic representation of queries and their reconfigurations. Second, to address the problem of evaluating CEP query management and processing strategies, this research introduces CEPSim, a simulator of cloud-based CEP systems. Finally, this research also introduces a CEPaaS system based on a multi-cloud architecture, container management systems, and an AGeCEP-based multi-tenant design. To demonstrate its feasibility, AGeCEP was used to design an autonomic manager and a selected set of self-management policies. Moreover, CEPSim was thoroughly evaluated by experiments that showed it can simulate existing systems with accuracy and low execution overhead. Finally, additional experiments validated the CEPaaS system and demonstrated it achieves the goal of offering CEP functionalities as a scalable and fault-tolerant service. In tandem, these results confirm this research significantly advances the CEP state of the art and provides novel tools and methodologies that can be applied to CEP researchDoutoradoCiência da ComputaçãoDoutor em Ciência da Computação140920/2012-9CNP