Data Replication and Its Alignment with Fault Management in the Cloud Environment

Nowadays, the exponential data growth becomes one of the major challenges all over the world. It may cause a series of negative impacts such as network overloading, high system complexity, and inadequate data security, etc. Cloud computing is developed to construct a novel paradigm to alleviate massive data processing challenges with its on-demand services and distributed architecture. Data replication has been proposed to strategically distribute the data access load to multiple cloud data centres by creating multiple data copies at multiple cloud data centres. A replica-applied cloud environment not only achieves a decrease in response time, an increase in data availability, and more balanced resource load but also protects the cloud environment against the upcoming faults. The reactive fault tolerance strategy is also required to handle the faults when the faults already occurred. As a result, the data replication strategies should be aligned with the reactive fault tolerance strategies to achieve a complete management chain in the cloud environment. In this thesis, a data replication and fault management framework is proposed to establish a decentralised overarching management to the cloud environment. Three data replication strategies are firstly proposed based on this framework. A replica creation strategy is proposed to reduce the total cost by jointly considering the data dependency and the access frequency in the replica creation decision making process. Besides, a cloud map oriented and cost efficiency driven replica creation strategy is proposed to achieve the optimal cost reduction per replica in the cloud environment. The local data relationship and the remote data relationship are further analysed by creating two novel data dependency types, Within-DataCentre Data Dependency and Between-DataCentre Data Dependency, according to the data location. Furthermore, a network performance based replica selection strategy is proposed to avoid potential network overloading problems and to increase the number of concurrent-running instances at the same time

Efficient data reliability management of cloud storage systems for big data applications

Cloud service providers are consistently striving to provide efficient and reliable service, to their client's Big Data storage need. Replication is a simple and flexible method to ensure reliability and availability of data. However, it is not an efficient solution for Big Data since it always scales in terabytes and petabytes. Hence erasure coding is gaining traction despite its shortcomings. Deploying erasure coding in cloud storage confronts several challenges like encoding/decoding complexity, load balancing, exponential resource consumption due to data repair and read latency. This thesis has addressed many challenges among them. Even though data durability and availability should not be compromised for any reason, client's requirements on read performance (access latency) may vary with the nature of data and its access pattern behaviour. Access latency is one of the important metrics and latency acceptance range can be recorded in the client's SLA. Several proactive recovery methods, for erasure codes are proposed in this research, to reduce resource consumption due to recovery. Also, a novel cache based solution is proposed to mitigate the access latency issue of erasure coding

Assessing the Performance of Raaes: Reliability-Assured and Accessibility-Enriched Storage Structure in Cloud Environment

Cloud computing has revolutionized the world of distributed computing in recent decades, offering new dimensions and enticing opportunities. Presently, numerous organizations and individuals are increasingly transitioning to cloud storage services for their business and personal needs. This shift is primarily driven by the appealing concepts of "on-demand" and "Pay per Use," which provide flexible and cost-effective solutions. Perhaps, that’s why it provides even more services to billions of users in every moment from the most popular companies such as Google, Microsoft, and Yahoo. However, the numerous cloud services are offered by a multitude of data centers equipped with power, network infrastructure, and backup systems. These services cater to users' demands for high availability and swift response times, necessitating the mirroring of each service across multiple geographically dispersed data centers. Previous research addressed this issue by focusing on the perspectives of vendors and users, aiming to develop economical and improved cloud storing solutions that fulfill stability and obtainability requirements for overall storage process. Furthermore, there is a suggestion to enhance the RAAES framework, aiming for efficient cloud storage by significantly reducing space and cost while still meeting reliability demands. Therefore, this study evaluated the performance of the RAAES through this paper, improved the availability requirements, ultimately reduced the response time, and actively promoted the development of the cloud with an efficient impact stock. As a result, it fosters the advancement of cloud technology by facilitating efficient storage, thereby generating a positive impact

A review of studies on Badr Shakir Al-Sayyab’s life and poetry

This paper is a review of literature of the researches and readings that are related to my study of the celebrated Iraqi poet Badr Shakir Al-Sayyab (1926-1964) and his attitude towards women. Badr Shakir Al-Sayyab is the most studied poet and a lot of studies, books and article in various languages dealt with his life and poetry. Researchers followed various approaches to examine his social status, his political stands and even his psychological condition. However, only a limited number of researches studied his poetry in terms of language and linguistics. Besides, and as far as it is related to our study, researchers did not state whether Al-Sayyab‟s ideological and political stands influence his attitude toward the women

Improving performance and capacity utilization in cloud storage for content delivery and sharing services

Content delivery and sharing (CDS) is a popular and cost effective cloud-based service for organizations to deliver/share contents to/with end-users, partners and insider users. This type of service improves the data availability and I/O performance by producing and distributing replicas of shared contents. However, such a technique increases the storage/network resources utilization. This paper introduces a threefold methodology to improve the trade-off between I/O performance and capacity utilization of cloud storage for CDS services. This methodology includes: i) Definition of a classification model for identifying types of users and contents by analyzing their consumption/ demand and sharing patterns, ii) Usage of the classification model for defining content availability and load balancing schemes, and iii) Integration of a dynamic availability scheme into a cloud based CDS system. Our method was implemented ¿This work was partially supported by the Spanish Ministry of Economy, Industry and Competitiveness under the grant TIN2016-79637-P ”Towards Unification of HPC and Big Data Paradigms

Efficient replication of large volumes of data and maintaining data consistency by using P2P techniques in Desktop Grid

Desktop Grid is increasing in popularity because of relatively very low cost and good performance in institutions. Data-intensive applications require data management in scientific experiments conducted by researchers and scientists in Desktop Grid-based Distributed Computing Infrastructure (DCI). Some of these data-intensive applications deal with large volumes of data. Several solutions for data-intensive applications have been proposed for Desktop Grid (DG) but they are not efficient in handling large volumes of data. Data management in this environment deals with data access and integration, maintaining basic properties of databases, architecture for querying data, etc. Data in data-intensive applications has to be replicated in multiple nodes for improving data availability and reducing response time. Peer-to-Peer (P2P) is a well established technique for handling large volumes of data and is widely used on the internet. Its environment is similar to the environment of DG. The performance of existing P2P-based solution dealing with generic architecture for replicating large volumes of data is not efficient in DG-based DCI. Therefore, there is a need for a generic architecture for replicating large volumes of data efficiently by using P2P in BOINC based Desktop Grid. Present solutions for data-intensive applications mainly deal with read only data. New type of applications are emerging which deal large volumes of data and Read/Write of data. In emerging scientific experiments, some nodes of DG generate new snapshot of scientific data after regular intervals. This new snapshot of data is generated by updating some of the values of existing data fields. This updated data has to be synchronised in all DG nodes for maintaining data consistency. The performance of data management in DG can be improved by addressing efficient data replication and consistency. Therefore, there is need for algorithms which deal with data Read/Write consistency along with replication for large volumes of data in BOINC based Desktop Grid. The research is to identify efficient solutions for data replication in handling large volumes of data and maintaining Read/Write data consistency using Peer-to-Peer techniques in BOINC based Desktop Grid. This thesis presents the solutions that have been carried out to complete the research

Partitioning workflow applications over federated clouds to meet non-functional requirements

PhD ThesisWith cloud computing, users can acquire computer resources when they need them on a pay-as-you-go business model. Because of this, many applications are now being deployed in the cloud, and there are many di erent cloud providers worldwide. Importantly, all these various infrastructure providers o er services with di erent levels of quality. For example, cloud data centres are governed by the privacy and security policies of the country where the centre is located, while many organisations have created their own internal \private cloud" to meet security needs. With all this varieties and uncertainties, application developers who decide to host their system in the cloud face the issue of which cloud to choose to get the best operational conditions in terms of price, reliability and security. And the decision becomes even more complicated if their application consists of a number of distributed components, each with slightly di erent requirements. Rather than trying to identify the single best cloud for an application, this thesis considers an alternative approach, that is, combining di erent clouds to meet users' non-functional requirements. Cloud federation o ers the ability to distribute a single application across two or more clouds, so that the application can bene t from the advantages of each one of them. The key challenge for this approach is how to nd the distribution (or deployment) of application components, which can yield the greatest bene ts. In this thesis, we tackle this problem and propose a set of algorithms, and a framework, to partition a work ow-based application over federated clouds in order to exploit the strengths of each cloud. The speci c goal is to split a distributed application structured as a work ow such that the security and reliability requirements of each component are met, whilst the overall cost of execution is minimised. To achieve this, we propose and evaluate a cloud broker for partitioning a work ow application over federated clouds. The broker integrates with the e-Science Central cloud platform to automatically deploy a work ow over public and private clouds. We developed a deployment planning algorithm to partition a large work ow appli- - i - cation across federated clouds so as to meet security requirements and minimise the monetary cost. A more generic framework is then proposed to model, quantify and guide the partitioning and deployment of work ows over federated clouds. This framework considers the situation where changes in cloud availability (including cloud failure) arise during work ow execution