A network-aware virtual machine placement and migration approach in cloud computing

Cloud computing represents a major step up in computing whereby shared computation resources are provided on demand. In such a scenario, applications and data thereof can be hosted by various networked virtual machines (VMs). As applications, especially data-intensive applications, often need to communicate with data frequently, the network I/O performance would affect the overall application performance significantly. Therefore, placement of virtual machines which host an application and migration of these virtual machines while the unexpected network latency or congestion occurs is critical to achieve and maintain the application performance. To address these issues, this paper proposes a virtual machine placement and migration approach to minimizing the data transfer time consumption. Our simulation studies suggest that the proposed approach is effective in optimizing the data transfer between the virtual machine and data, thus helping optimize the overall application performance

A Network-aware Virtual Machine Placement and Migration Approach in Cloud Computing

Abstract—Cloud computing represents a major step up in computing whereby shared computation resources are provided on demand. In such a scenario, applications and data thereof can be hosted by various networked virtual machines (VMs). As applications, especially data-intensive applications, often need to communicate with data frequently, the network I/O performance would affect the overall application performance significantly. Therefore, placement of virtual machines which host an application and migration of these virtual machines while the unexpected network latency or congestion occurs is critical to achieve and maintain the application performance. To address these issues, this paper proposes a virtual machine placement and migration approach to minimizing the data transfer time consumption. Our simulation studies suggest that the proposed approach is effective in optimizing the data transfer between the virtual machine and data, thus helping optimize the overall application performance. Keywords—cloud computing, virtual machine, placement, migration, network I

Cloud Trust Management – Issues and Developments

Cloud infrastructure is an evolving technology that offers organizations and enterprises the ability to access various elastic and scalable resources. The cloud provider offers application software that can be implemented by multiple users online. Also, the customer is provided with the capability of creating and deploying custom built applications relevant to the needs of the enterprise. In addition, scalable and elastic massive storage and computing resources is available in the different categories of cloud types. The decision for an organisation or enterprise to migrate and outsource applications to the cloud requires trust. Any customer wanting to adopt the cloud wants to be sure that the cloud provider can be trusted to meet agreed requirements. This study was executed by means of review of some literature available on cloud computing and trust management. The results indicated that users are not able to access services on their own terms, clearly eroding trust. In addition, application of encipherment in trust management was not discussed in details. Criteria for identifying quality cloud providers received less than 30% attention. Mechanisms for auditability and transparency which should have been given over 50% consideration, received less than 20%. This results will be beneficial to cloud service providers, cloud users and researchers alik

Edge Offloading in Smart Grid

The energy transition supports the shift towards more sustainable energy alternatives, paving towards decentralized smart grids, where the energy is generated closer to the point of use. The decentralized smart grids foresee novel data-driven low latency applications for improving resilience and responsiveness, such as peer-to-peer energy trading, microgrid control, fault detection, or demand response. However, the traditional cloud-based smart grid architectures are unable to meet the requirements of the new emerging applications such as low latency and high-reliability thus alternative architectures such as edge, fog, or hybrid need to be adopted. Moreover, edge offloading can play a pivotal role for the next-generation smart grid AI applications because it enables the efficient utilization of computing resources and addresses the challenges of increasing data generated by IoT devices, optimizing the response time, energy consumption, and network performance. However, a comprehensive overview of the current state of research is needed to support sound decisions regarding energy-related applications offloading from cloud to fog or edge, focusing on smart grid open challenges and potential impacts. In this paper, we delve into smart grid and computational distribution architec-tures, including edge-fog-cloud models, orchestration architecture, and serverless computing, and analyze the decision-making variables and optimization algorithms to assess the efficiency of edge offloading. Finally, the work contributes to a comprehensive understanding of the edge offloading in smart grid, providing a SWOT analysis to support decision making.Comment: to be submitted to journa

DATA MIGRATION FROM STANDARD SQL TO NoSQL

Currently two major database management systems are in use for dealing with data, the Relational Database Management System (RDBMS) also knows as standard SQL databases and the NoSQL databases. The RDBMS databases deal with structured data and the NoSQL databases with unstructured or semi-structured data. The RDBMS databases have been popular for many years but the NoSQL type is gaining popularity with the introduction of the internet and social media. Data flow from SQL to NoSQL or vice versa is very much possible in the near future due to the growing popularity of the NoSQL databases. The goal of this thesis is to analyze the data structures of the RDBMS and the NoSQL databases and to suggest a Graphical User Interface (GUI) tool that migrates the data from SQL to NoSQL databases. The relational databases have been in use and have dominated the industry for many years. In contrast, the NoSQL databases were introduced with the increased usage of the internet, social media, and cloud computing. The traditional relational databases guarantee data integrity whereas high availability and scalability are the main advantages of the NoSQL databases. This thesis presents a comparison of these two technologies. It compares the data structure and data storing techniques of the two technologies. The SQL databases store data differently as compared to the NoSQL databases due to their specific demands. The data stored in the relational databases is highly structured and normalized in most environments whereas the data in the NoSQL databases are mostly unstructured. This difference of the data structure helps in meeting the specific demands of these two systems. The NoSQL DBs are scalable with high availability due to the simpler data model but does not guarantee data consistency at all times. On the other hand the RDBMS systems are not easily scalable and available at the same time due to the complex data model but guarantees data consistency. This thesis uses CouchDB and MySQL to represent the NoSQL and standard SQL databases respectively. The aim of the iii research in this document is to suggest a methodology for data migration from the RDBMS databases to the document-based NoSQL databases. Data migration between the RDBMS and the NoSQL systems is anticipated because both systems are currently in use by many industry leaders. This thesis presents a Graphical User Interface as a starting point that enables the data migration from the RDBMS to the NoSQL databases. MySQL and CouchDB are used as the test databases for the relational and NoSQL systems respectively. This thesis presents an architecture and methodology to achieve this objective

VM Allocation in Cloud Datacenters Based on the Multi-Agent System. An Investigation into the Design and Response Time Analysis of a Multi-Agent-based Virtual Machine (VM) Allocation/Placement Policy in Cloud Datacenters

Recent years have witnessed a surge in demand for infrastructure and services to cover high demands on processing big chunks of data and applications resulting in a mega Cloud Datacenter. A datacenter is of high complexity with increasing difficulties to identify, allocate efficiently and fast an appropriate host for the requested virtual machine (VM). Establishing a good awareness of all datacenter’s resources enables the allocation “placement” policies to make the best decision in reducing the time that is needed to allocate and create the VM(s) at the appropriate host(s). However, current algorithms and policies of placement “allocation” do not focus efficiently on awareness of the resources of the datacenter, and moreover, they are based on conventional static techniques. Which are adversely impacting on the allocation progress of the policies. This thesis proposes a new Agent-based allocation/placement policy that employs some of the Multi-Agent system features to get a good awareness of Cloud Datacenter resources and also provide an efficient allocation decision for the requested VMs. Specifically, (a) The Multi-Agent concept is used as a part of the placement policy (b) A Contract Net Protocol is devised to establish good awareness and (c) A verification process is developed to fully dimensional VM specifications during allocation. These new results show a reduction in response time of VM allocation and the usage improvement of occupied resources. The proposed Agent-based policy was implemented using the CloudSim toolkit and consequently was compared, based on a series of typical numerical experiments, with the toolkit’s default policy. The comparative study was carried out in terms of the time duration of VM allocation and other aspects such as the number of available VM types and the amount of occupied resources. Moreover, a two-stage comparative study was introduced through this thesis. Firstly, the proposed policy is compared with four state of the art algorithms, namely the Random algorithm and three one-dimensional Bin-Packing algorithms. Secondly, the three Bin-Packing algorithms were enhanced to have a two-dimensional verification structure and were compared against the proposed new algorithm of the Agent-based policy. Following a rigorous comparative study, it was shown that, through the typical numerical experiments of all stages, the proposed new Agent-based policy had superior performance in terms of the allocation times. Finally, avenues arising from this thesis are included.Al al-Bayt University in Jordan

SOA2Cloud: Un marco de trabajo para la migración de aplicaciones SOA a Cloud siguiendo una aproximación dirigida por modelos

[EN] Software applications are currently considered an element essential and indispensable in all business activities, for example, information exchange and social network. Nevertheless, for their construction and deployment to use all the resources that are available in remote and accessible locations on the network, which leads to inefficient operations in development and deployment, and enormous costs in the acquisition of IT equipment. The present master thesis aims to contribute to the improvement of the previous context proposing SOA2Cloud, a framework for migration of applications based on SOA to Cloud environments, making use Model-Driven Software Development approach. SOA2Cloud aims to provide mechanisms for the migration of SOA applications specified through the OMG SoaML standard, incorporating the service level agreements (SLA) to Cloud Computing environments. The framework proposed to makes to use a SOA application model, defined to conform to SoaML metamodel, and a model of service level agreements defined according to SLA generic metamodelo, to generation a model according to Cloud metamodel, through models transformations. This generated model, over again to model transformation, for obtaining the model Azure platform, according to their generic metamodel built for this research work. At the conclusion model transformations, the obtained model over again a model to text transformation to obtain the source code, and thus be tested and deployed in the platform selected for this research Azure work. This proposal is based on a comprehensive study of the state of the art, made by conducting a systematic mapping, about strategies for migrating applications SOA to Cloud Computing environments. The results contributed in a meaningful way in the definition of the process of migration in the framework. Finally, an example of application that shows the feasibility of our approach was developed. This example demonstrates in detail as the framework for migrating applications proposed SOA to Cloud environments. The results show that our proposal may allow improving the strategy mainly used by researchers and professionals in the area to perform migrations of SOA applications into Cloud environments. This will be through our proposed migration framework which exploits the benefits of Model-Driven Software Development.[ES] Las aplicaciones software son consideradas actualmente un elemento esencial e indispensable en toda actividad empresarial, por ejemplo, intercambio de información y motor de redes sociales. Sin embargo, para su construcción y despliegue se utilizan todos los recursos que estén disponibles en ubicaciones remotas y accesibles de la red, lo que conlleva a realizar operaciones ineficientes en el desarrollo y despliegue, y enormes gastos en la adquisición de equipos de TI. La presente tesina de máster pretende contribuir a la mejora del contexto anterior proponiendo SOA2Cloud, un marco de trabajo para la migración de aplicaciones basadas en SOA a entornos Cloud, haciendo uso de la aproximación del Desarrollo de Software Dirigido por Modelos (DSDM). SOA2Cloud tiene la finalidad de proporcionar mecanismos para la migración de aplicaciones SOA especificadas a través del estándar SoaML de la OMG, incorporando los Acuerdos de Nivel de Servicios (SLA) a entornos Cloud Computing. El marco de trabajo propuesto hace uso de un modelo de la aplicación SOA, definido conforme a SoaML, y un modelo de acuerdos de servicios definido conforme a un metamodelo genérico de SLA para la generación de un modelo conforme a un metamodelo para aplicaciones Cloud, a través de transformaciones de modelos. Este modelo generado, es sometido a una nueva transformación de modelos, para la obtención del modelo de la plataforma Azure, conforme a su metamodelo genérico construido para este trabajo de investigación. Una vez concluidas las transformaciones de modelos, el modelo obtenido es sometido a una transformación de modelo a texto para la obtención del código fuente, y de esta forma ser testeado y desplegado en la plataforma seleccionada para este trabajo de investigación Windows Azure. Esta propuesta se apoya en un amplio estudio del estado del arte, realizado mediante la conducción de un mapeo sistemático, acerca de las estrategias de migración de aplicaciones SOA a entornos Cloud Computing. Los resultados obtenidos aportaron de una forma significativa en la definición del proceso de migración en el marco de trabajo. Finalmente, se desarrolló un ejemplo de aplicación que muestra la viabilidad de nuestro enfoque. Este ejemplo muestra en detalle como el marco de trabajo para la migración de aplicaciones SOA a entornos Cloud propuesto. Los resultados muestran que nuestra propuesta permitiría mejorar el enfoque de algunos investigadores y profesionales del área al realizar migraciones de aplicaciones SOA a entornos Cloud, haciéndolas a través de este marco de trabajo que aprovecha los beneficios del Desarrollo de Software Dirigido por Modelos.Botto Tobar, MÁ. (2014). SOA2Cloud: Un marco de trabajo para la migración de aplicaciones SOA a Cloud siguiendo una aproximación dirigida por modelos. http://hdl.handle.net/10251/47834Archivo delegad

Automatic Generation of Distributed Runtime Infrastructure for Internet of Things

Ph. D. ThesisThe Internet of Things (IoT) represents a network of connected devices that are able to cooperate and interact with each other in order to reach a particular goal. To attain this, the devices are equipped with identifying, sensing, networking and processing capabilities. Cloud computing, on the other hand, is the delivering of on-demand computing services – from applications, to storage, to processing power – typically over the internet. Clouds bring a number of advantages to distributed computing because of highly available pool of virtualized computing resource. Due to the large number of connected devices, real-world IoT use cases may generate overwhelmingly large amounts of data. This prompts the use of cloud resources for processing, storage and analysis of the data. Therefore, a typical IoT system comprises of a front-end (devices that collect and transmit data), and back-end – typically distributed Data Stream Management Systems (DSMSs) deployed on the cloud infrastructure, for data processing and analysis. Increasingly, new IoT devices are being manufactured to provide limited execution environment on top of their data sensing and transmitting capabilities. This consequently demands a change in the way data is being processed in a typical IoT-cloud setup. The traditional, centralised cloud-based data processing model – where IoT devices are used only for data collection – does not provide an efficient utilisation of all available resources. In addition, the fundamental requirements of real-time data processing such as short response time may not always be met. This prompts a new processing model which is based on decentralising the data processing tasks. The new decentralised architectural pattern allows some parts of data streaming computation to be executed directly on edge devices – closer to where the data is collected. Extending the processing capabilities to the IoT devices increases the robustness of applications as well as reduces the communication overhead between different components of an IoT system. However, this new pattern poses new challenges in the development, deployment and management of IoT applications. Firstly, there exists a large resource gap between the two parts of a typical IoT system (i.e. clouds and IoT devices); hence, prompting a new approach for IoT applications deployment and management. Secondly, the new decentralised approach necessitates the deployment of DSMS on distributed clusters of heterogeneous nodes resulting in unpredictable runtime performance and complex fault characteristics. Lastly, the environment where DSMSs are deployed is very dynamic due to user or device mobility, workload variation, and resource availability. In this thesis we present solutions to address the aforementioned challenges. We investigate how a high-level description of a data streaming computation can be used to automatically generate a distributed runtime infrastructure for Internet of Things. Subsequently, we develop a deployment and management system capable of distributing different operators of a data streaming computation onto different IoT gateway devices and cloud infrastructure. To address the other challenges, we propose a non-intrusive approach for performance evaluation of DSMSs and present a protocol and a set of algorithms for dynamic migration of stateful data stream operators. To improve our migration approach, we provide an optimisation technique which provides minimal application downtime and improves the accuracy of a data stream computation

A Knowledge Management Based Cloud Computing Adoption Decision Making Framework

Cloud computing represents a paradigm shift in the way that IT services are delivered within enterprises. There are numerous challenges for enterprises planning to migrate to cloud computing environment as cloud computing impacts multiple different aspects of an organisation and cloud computing adoption issues vary between organisations. A literature review identified that a number of models and frameworks have been developed to support cloud adoption. However, existing models and frameworks have been devised for technologically developed environments and there has been very little examination to determine whether the factors that affect cloud adoption in technologically developing countries are different. The primary research carried out for this thesis included an investigation of the factors that influence cloud adoption in Saudi Arabia, which is regarded as a technologically developing country. This thesis presents an holistic Knowledge Management Based Cloud Adoption Decision Making Framework which has been developed to support decision makers at all stages of the cloud adoption decision making process. The theoretical underpinnings for the research come from Knowledge Management, including the literature on decision making, organisational learning and technology adoption and technology diffusion theories. The framework includes supporting models and tools, combining the Analytical Hierarchical Process and Case Based Reasoning to support decision making at Strategic and Tactical levels and the Pugh Decision Matrix at the Operational level. The Framework was developed based on secondary and primary research and was validated with expert users. The Framework is customisable, allowing decision makers to set their own weightings and add or remove decision making criteria. The results of validation show that the framework enhances Cloud Adoption decision making and provides support for decision makers at all levels of the decision making process