AcoSched : um escalonador para o ambiente de Nuvem Federada ZooNimbus

Monografia (graduação)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2013.Escalonamento de tarefas é difícil no ambiente de nuvem federada, uma vez que existem muitos provedores de nuvens com capacidades distintas que devem ser consideradas. Em Bioinformática, muitas ferramentas e base de dados necessitam de grandes recursos para processamento e armazenamento de enormes quantidades de dados que são fornecidos por instituições separadas _sicamente. Este trabalho trata o problema de escalonamento de tarefas no ZooNimbus, uma estrutura de nuvem federada para aplicações de bioinform ática. Foi proposto um algoritmo de escalonamento baseado no Load Balancing Ant Colony (LBACO), chamado de AcoSched, para realizar uma distribuição e_ciente que encontre o melhor recurso para executar uma tarefa requisitada. Experimentos foram desenvolvidos com dados biológicos reais executados no ZooNimbus, formado por algumas provedores de nuvem da Amazon EC2 e da UnB.Task scheduling is di_cult in federated cloud environments, since there are many cloud providers with distinct capabilities that should be addressed. In bioinformatics, many tools and databases requiring large resources for processing and storing enormous amounts of data are provided by physically separate institutions. This work treats the problem of task scheduling in ZooNimbus, a federated cloud infrastructure for bioinformatics applications. A scheduling algorithm based on Load Balancing Ant Colony (LBACO), called AcoSched, was propose to perform an e_cient distribution for _nding the best resources to execute each required task. Experiments was developed with real biological data executing on ZooNimbus, formed by some cloud providers executing in Amazon EC2 and UnB. The obtained results show that AcoSched makes a signi_cant improvement in the makespan time of bioinformatics applications executing in ZooNimbus, when compared to the DynamicAHP algorithm

A Research Perspective on Data Management Techniques for Federated Cloud Environment

Cloud computing has given a large scope of improvement in processing, storage and retrieval of data that is generated in huge amount from devices and users. Heterogenous devices and users generates the multidisciplinary data that needs to take care for easy and efficient storage and fast retrieval by maintaining quality and service level agreements. By just storing the data in cloud will not full fill the user requirements, the data management techniques has to be applied so that data adaptiveness and proactiveness characteristics are upheld. To manage the effectiveness of entire eco system a middleware must be there in between users and cloud service providers. Middleware has set of events and trigger based policies that will act on generated data to intermediate users and cloud service providers. For cloud service providers to deliver an efficient utilization of resources is one of the major issues and has scope of improvement in the federation of cloud service providers to fulfill user’s dynamic demands. Along with providing adaptiveness of data management in the middleware layer is challenging. In this paper, the policies of middleware for adaptive data management have been reviewed extensively. The main objectives of middleware are also discussed to accomplish high throughput of cloud service providers by means of federation and qualitative data management by means of adaptiveness and proactiveness. The cloud federation techniques have been studied thoroughly along with the pros and cons of it. Also, the strategies to do management of data has been exponentially explored

Budget-aware scheduling algorithm for scientific workflow applications across multiple clouds. A Mathematical Optimization-Based Approach

Scientific workflows have become a prevailing means of achieving significant scientific advances at an ever-increasing rate. Scheduling mechanisms and approaches are vital to automating these large-scale scientific workflows efficiently. On the other hand, with the advent of cloud computing and its easier availability and lower cost of use, more attention has been paid to the execution and scheduling of scientific workflows in this new paradigm environment. For scheduling large-scale workflows, a multi-cloud environment will typically have a more significant advantage in various computing resources than a single cloud provider. Also, the scheduling makespan and cost can be reduced if the computing resources are used optimally in a multi-cloud environment. Accordingly, this thesis addressed the problem of scientific workflow scheduling in the multi-cloud environment under budget constraints to minimize associated makespan. Furthermore, this study tries to minimize costs, including fees for running VMs and data transfer, minimize the data transfer time, and fulfill budget and resource constraints in the multi-clouds scenario. To this end, we proposed Mixed-Integer Linear Programming (MILP) models that can be solved in a reasonable time by available solvers. We divided the workflow tasks into small segments, distributed them among VMs with multi-vCPU, and formulated them in mathematical programming. In the proposed mathematical model, the objective of a problem and real and physical constraints or restrictions are formulated using exact mathematical functions. We analyzed the treatment of optimal makespan under variations in budget, workflow size, and different segment sizes. The evaluation's results signify that our proposed approach has achieved logical and expected results in meeting the set objectives

AN EFFICIENT APPROACH TO IMPLEMENT FEDERATED CLOUDS

Cloud computing is one of the trending technologies that provide boundless virtualized resources to the internet users as an important services through the internet, while providing the privacy and security. By using these cloud services, internet users get many parallel computing resources at low cost. It predicted that till 2016, revenues from the online business management spent $4 billion for data storage. Cloud is an open source platform structure, so it is having more chances to malicious attacks. Privacy, confidentiality, and security of stored data are primary security challenges in cloud computing. In cloud computing, ‘virtualization' is one of the techniques dividing memory into different blocks. In most of the existing systems there is only single authority in the system to provide the encrypted keys. To fill the few security issues, this paper proposed a novel authenticated trust security model for secure virtualization system to encrypt the files. The proposed security model achieves the following functions: 1) allotting the VSM(VM Security Monitor) model for each virtual machine; 2) providing secret keys to encrypt and decrypt information by symmetric encryption.The contribution is a proposed architecture that provides a workable security that a cloud service provider can offer to its consumers. Detailed analysis and architecture design presented to elaborate security model