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

Replica maintenance strategy for data grid

Data Grid is an infrastructure that manages huge amount of data files, and provides intensive computational resources across geographically distributed collaboration.Increasing the performance of such system can be achieved by improving the overall resource usage, which includes network and storage resources.Improving network resource usage is achieved by good utilization of network bandwidth that is considered as an important factor affecting job execution time.Meanwhile, improving storage resource usage is achieved by good utilization of storage space usage. Data replication is one of the methods used to improve the performance of data access in distributed systems by replicating multiple copies of data files in the distributed sites.Having distributed the replicas to various locations, they need to be monitored.As a result of dynamic changes in the data grid environment, some of the replicas need to be relocated.In this paper we proposed a maintenance replica placement strategy termed as Unwanted Replica Deletion Strategy (URDS) as a part of Replica maintenance service.The main purpose of the proposed strategy is to find the placement of unwanted replicas to be deleted.OptorSim is used to evaluate the performance of the proposed strategy. The simulation results show that URDS requires less execution time and consumes less network usage and has a best utilization of storage space usage compared to existing approaches

Proactive software rejuvenation solution for web enviroments on virtualized platforms

The availability of the Information Technologies for everything, from everywhere, at all times is a growing requirement. We use information Technologies from common and social tasks to critical tasks like managing nuclear power plants or even the International Space Station (ISS). However, the availability of IT infrastructures is still a huge challenge nowadays. In a quick look around news, we can find reports of corporate outage, affecting millions of users and impacting on the revenue and image of the companies. It is well known that, currently, computer system outages are more often due to software faults, than hardware faults. Several studies have reported that one of the causes of unplanned software outages is the software aging phenomenon. This term refers to the accumulation of errors, usually causing resource contention, during long running application executions, like web applications, which normally cause applications/systems to hang or crash. Gradual performance degradation could also accompany software aging phenomena. The software aging phenomena are often related to memory bloating/ leaks, unterminated threads, data corruption, unreleased file-locks or overruns. We can find several examples of software aging in the industry. The work presented in this thesis aims to offer a proactive and predictive software rejuvenation solution for Internet Services against software aging caused by resource exhaustion. To this end, we first present a threshold based proactive rejuvenation to avoid the consequences of software aging. This first approach has some limitations, but the most important of them it is the need to know a priori the resource or resources involved in the crash and the critical condition values. Moreover, we need some expertise to fix the threshold value to trigger the rejuvenation action. Due to these limitations, we have evaluated the use of Machine Learning to overcome the weaknesses of our first approach to obtain a proactive and predictive solution. Finally, the current and increasing tendency to use virtualization technologies to improve the resource utilization has made traditional data centers turn into virtualized data centers or platforms. We have used a Mathematical Programming approach to virtual machine allocation and migration to optimize the resources, accepting as many services as possible on the platform while at the same time, guaranteeing the availability (via our software rejuvenation proposal) of the services deployed against the software aging phenomena. The thesis is supported by an exhaustive experimental evaluation that proves the effectiveness and feasibility of our proposals for current systems

Maintaining Replica Consistency Over Large-Scale Data Grid Using Update Propagation Technique

A Data Grid is an organized collection of nodes in a wide area network which contributes to various computation, storage data, and application. In Data Grid high numbers of users are distributed in a wide area environment which is dynamic and heterogeneous. Data management is one of the current issues where data transparency, consistency, fault-tolerance, automatic management and the performance are the user parameters in grid environment. Data management techniques must scale up while addressing autonomy, dynamicity and heterogeneity of the data resource. Data replication is a well known technique used to reduce accesses latency, improve availability and performance in a distributed computing environment. Replication introduces the problem of maintaining consistency among the replicas when files are allowed to be updated. The update information should be propagated to all replicas to guarantee correct read of the remote replicas. An asynchronous replication is a commonly agreed solution for the problem in consistency of replicas. A few studies have been done to maintain replica consistency in Data Grid. However, the introduced techniques are neither efficient nor scalable. They cannot be used in real Data Grid since the issues of large number of replica sites, large scale distribution, load balancing and site autonomy where the capability of grid site to join and leave the grid community at any time have not been addressed. This thesis proposes a new asynchronous replication protocol called Update Propagation Grid (UPG) to maintain replica consistency over a large scale data grid. In UPG the updates reach all on-line secondary replicas using a propagation technique based on nodes organized into a logical structure network in the form of two-dimensional grid structure. The proposed update propagation technique is a hybrid push-pull and dynamic technique that addresses the issues of site autonomy, efficiency, scalability, load balancing and fairness. A two performance analysis studies have been conducted to study the performance of the proposed technique in comparison with other techniques. First study involves mathematical and simulation analysis. Second study is based on Queuing Network Model. The result of the performance analysis shows that the proposed technique scales well with high number of replica sites and with high request loads. The result also shows the reduction on the average update reach time by 5% to 97%. Moreover the result shows that the proposed technique is capable of reaching load balancing while providing update propagation fairnes

Semantic-Based Storage QoS Management Methodology -- Case Study for Distributed Environments

The distributed computing environments, e.g. clouds, often deal with huge amounts of data, which constantly increase. The global growth of data is caused by ubiquitous personal devices, enterprise and scientific applications, etc. As the size of data grows new challenges are emerging in the context of storage management. Modern data and storage resource management systems need to face wide range of problems -- minimizing energy consumption (green data centers), optimizing resource usage, throughput and capacity, data availability, security and legal issues, scalability. In addition users or their applications can have QoS (Quality of Service) requirements concerning the storage access, which further complicates the management. To cope with this problem a common mass storage system model taking into account the performance aspects of a storage system becomes a necessity. The model described with semantic technologies brings a semantic interoperability between the system components. In this paper we describe our approach at data management with QoS based on the developed models as a case study for distributed environments