Data backup and recovery with a minimum replica plan in a multi-cloud environment

Cloud computing has become a desirable choice to store and share large amounts of data among several users. The two main concerns with cloud storage are data recovery and cost of storage. This article discusses the issue of data recovery in case of a disaster in a multi-cloud environment. This research proposes a preventive approach for data backup and recovery aiming at minimizing the number of replicas and ensuring high data reliability during disasters. This approach named Preventive Disaster Recovery Plan with Minimum Replica (PDRPMR) aims at reducing the number of replicationsin the cloud without compromising the data reliability. PDRPMR means preventive action checking of the availability of replicas and monitoring of denial ofservice attacksto maintain data reliability. Several experiments were conducted to evaluate the effectiveness of PDRPMR and the results demonstrated that the storage space used one-third to two-thirds compared to typical 3-replicasreplication strategies

Towards a Big Data system disaster recovery in a Private Cloud

Disaster recovery (DR) plays a vital role in restoring the organization's data in the case of emergency and hazardous accidents. While many papers in security focus on privacy and security technologies, few address the DR process, particularly for a Big Data system. However, all these studies that have investigated DR methods belong to the “single-basket” approach, which means there is only one destination from which to secure the restored data, and mostly use only one type of technology implementation. We propose a “multi-purpose” approach, which allows data to be restored to multiple sites with multiple methods to ensure the organization recovers a very high percentage of data close to 100%, with all sites in London, Southampton and Leeds data recovered. The traditional TCP/IP baseline, snapshot and replication are used with their system design and development explained. We compare performance between different approaches and multi-purpose approach stands out in the event of emergency. Data at all sites in London, Southampton and Leeds can be restored and updated simultaneously. Results show that optimize command can recover 1 TB of data within 650 s and command for three sites can recover 1 TB of data within 1360 s. All data backup and recovery has failure rate of 1.6% and below. All the data centers should adopt multi-purpose approaches to ensure all the data in the Big Data system can be recovered and retrieved without experiencing a prolong downtime and complex recovery processes. We make recommendations for adopting “multi-purpose” approach for data centers, and demonstrate that 100% of data is fully recovered with low execution time at all sites during a hazardous event as described in the paper

A First Approach in the Assessment of the Complexity of Disaster Recovery Models for SMEs

In an organization, a well devised disaster recovery plan is not only crucial in the information recovery process, but also vital in the quest to sustain daily operations. While prior research has discussed many recovery sites options, assessment of recovery site communication paths and their associated complexity is still limited in regard to the evaluation of disaster recovery (DR) models. Using the scale-free degree distribution formula, the authors present a methodical discussion concerning the network characteristics of various disaster recovery options. This study marks a pioneering effort in the DR field by applying the scale-free degree distribution formula to assess the network complexity index and overall model failure points. In addition, a modified hot model employing host virtualization designed especially for small and medium size businesses is presented. This method is particularly advantageous to small and medium size businesses as it leverages inexpensive commercial PC hardware

Disaster recovery in cloud computing systems: an overview

With the rapid growth of internet technologies, large-scale online services, such as data backup and data recovery are increasingly available. Since these large-scale online services require substantial networking, processing, and storage capacities, it has become a considerable challenge to design equally large-scale computing infrastructures that support these services cost-effectively. In response to this rising demand, cloud computing has been refined during the past decade and turned into a lucrative business for organizations that own large datacenters and offer their computing resources. Undoubtedly cloud computing provides tremendous benefits for data storage backup and data accessibility at a reasonable cost. This paper aims at surveying and analyzing the previous works proposed for disaster recovery in cloud computing. The discussion concentrates on investigating the positive aspects and the limitations of each proposal. Also examined are discussed the current challenges in handling data recovery in the cloud context and the impact of data backup plan on maintaining the data in the event of natural disasters. A summary of the leading research work is provided outlining their weaknesses and limitations in the area of disaster recovery in the cloud computing environment. An in-depth discussion of the current and future trends research in the area of disaster recovery in cloud computing is also offered. Several work research directions that ought to be explored are pointed out as well, which may help researchers to discover and further investigate those problems related to disaster recovery in the cloud environment that have remained unresolved

A systematic review on cloud storage mechanisms concerning e-healthcare systems

As the expenses of medical care administrations rise and medical services experts are becoming rare, it is up to medical services organizations and institutes to consider the implementation of medical Health Information Technology (HIT) innovation frameworks. HIT permits health associations to smooth out their considerable cycles and offer types of assistance in a more productive and financially savvy way. With the rise of Cloud Storage Computing (CSC), an enormous number of associations and undertakings have moved their healthcare data sources to distributed storage. As the information can be mentioned whenever universally, the accessibility of information becomes an urgent need. Nonetheless, outages in cloud storage essentially influence the accessibility level. Like the other basic variables of cloud storage (e.g., reliability quality, performance, security, and protection), availability also directly impacts the data in cloud storage for e-Healthcare systems. In this paper, we systematically review cloud storage mechanisms concerning the healthcare environment. Additionally, in this paper, the state-of-the-art cloud storage mechanisms are critically reviewed for e-Healthcare systems based on their characteristics. In short, this paper summarizes existing literature based on cloud storage and its impact on healthcare, and it likewise helps researchers, medical specialists, and organizations with a solid foundation for future studies in the healthcare environment.Qatar University [IRCC-2020-009]

Data Transfers in Hadoop: A Comparative Study

Hadoop is an open source framework for processing large amounts of data in distributed computing environment. It plays an important role in processing and analyzing the Big Data. This framework is used for storing data on large clusters of commodity hardware. Data input and output to and from Hadoop is an indispensable action for any data processing job. At present, many tools have been evolved for importing and exporting Data in Hadoop. In this article, some commonly used tools for importing and exporting data have been emphasized. Moreover, a state-of-the-art comparative study among the various tools has been made. With this study, it has been decided that where to use one tool over the other with emphasis on the data transfer to and from Hadoop system. This article also discusses about how Hadoop handles backup and disaster recovery along with some open research questions in terms of Big Data transfer when dealing with cloud-based services

LenticularFS: Scalable filesystem for the cloud

The Hadoop platform is the most common solution to handle the explosion of big-data that both companies and research institutions are facing. In order to store such data, the Hadoop platform provides HDFS, a scalable distributed filesystem, which runs on commodity hardware and enables linear scalability by adding new storage nodes. While storage capacity of the system can be increased by adding new storage nodes, the component that handles metadata for the filesystem, the namenode, is a single point of failure and cannot easily replaced or linearly scaled. The Hops projects provides an alternative implementation of the namenode, which increases performance and scalability by storing metadata on an external distributed NewSQL database called MySQL Cluster. With the new architecture, the system is much more scalable and can transparently manage the failover of namenodes, which are now stateless components. HopsFS is, however, still limited to running within a single datacenter, which can cause severe outages in case the entire datacenter becomes unavailable. Cloud native storage systems, such as Amazon’s Simple Storage Service (S3), solve this problem by replicating data across different, geographically distant datacenters, so that the failure of any given zone does not cause data unavailability. The objective of this thesis is to enable HopsFS to work across geographical regions while, as far as possible, maintaining the semantics of a POSIX-style hierarchical filesystem. We leverage asynchronous replication functionality provided by MySQL Cluster to obtain replication of metadata across geographical regions and we present a detailed analysis on how to maintain the consistency properties of HDFS in such an environment. Furthermore, we analyze the issue of split brain scenarios and propose a way for namenodes to detect this condition and continue operating correctly. Finally, we discuss the changes to the codebase which are required to implement the proposed plan

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

Small Business Responses to Reduce Impacts from Natural Disasters

Florida is a hurricane-prone state, and not all small business owners are prepared to survive in the wake of a hurricane or flood event, as only 14% of small business owners prepare for natural disasters compared to 44.9% for large corporations. Small business owners can enhance the survivability of their companies with well prepared disaster plans. The purpose of this qualitative case study was to explore strategies 5 small business owners in northwest Florida implemented to avoid permanent business closure after a natural disaster. The conceptual framework was the theory of planned behavior. Data were collected through interviews with 5 small business owners; company documentation served as a secondary data collection source. Yin\u27s 5-step analysis process was used to analyze the data. Themes from responses were property insurance coverage, business continuity, disaster recovery plans, cloud computing, and remote working. The implications for positive social change include the potential to minimize unemployment, provide economic growth, and add stability to both the local and state economies. A well-planned disaster preparedness plan could reduce the number of days employees of small businesses would be out of work, keeping the local community thriving

迅速な災害管理のための即時的,持続可能，かつ拡張的なエッジコンピューティングの研究

本学位論文は、迅速な災害管理におけるいくつかの問題に取り組んだ。既存のネットワークインフラが災害による直接的なダメージや停電によって使えないことを想定し、本論文では、最新のICTを用いた次世代災害支援システムの構築を目指す。以下のとおり本論文は三部で構成される。第一部は、災害発生後の緊急ネットワーキングである。本論文では、情報指向フォグコンピューティング（Information-Centric Fog Computing）というアーキテクチャを提案し、既存のインフラがダウンした場合に臨時的なネットワーク接続を提供する。本論文では、六次の隔たり理論から着想を得て、緊急時向け名前ベースルーティング（Name-Based Routing）を考慮した。まず、二層の情報指向フォグコンピューティングネットワークモデルを提案した。次に、ソーシャルネットワークを元に、情報指向フォグノード間の関係をモデリングし、名前ベースルーティングプロトコルをデザインする。シミュレーション実験では、既存のソリューションと比較し、提案手法はより高い性能を示し、有用性が証明された。第二部は、ネットワークの通信効率の最適化である。本論文は、第一部で構築されたネットワークの通信効率を最適化し、ネットワークの持続時間を延ばすために、ネットワークのエッジで行われるキャッシングストラテジーを提案した。本論文では、まず、第一部で提案した二層ネットワークモデルをベースにサーバー層も加えて、異種ネットワークストラクチャーを構成した。次に、緊急時向けのエッジキャッシングに必要なTime to Live （TTL）とキャッシュ置換ポリシーを設計する。シミュレーション実験では、エネルギー消費とバックホールレートを性能指標とし、メモリ内キャッシュとディスクキャッシュの性能を比較した。結果では、メモリ内ストレージと処理がエッジキャッシングのエネルギーを節約し、かなりのワークロードを共有できることが示された。第三部は、ネットワークカバレッジの拡大である。本論文は、ドローンの関連技術とリアルタイム視覚認識技術を利用し、被災地のユーザ捜索とドローンの空中ナビゲーションを行う。災害管理におけるドローン制御に関する研究を調査し、現在のドローン技術と無人捜索救助に対する実際のニーズを考慮すると、軽量なソリューションが緊急時に必要であることが判明した。そのため本論文では、転移学習を利用し、ドローンに搭載されたオンボードコンピュータで実行可能な空中ビジョンに基づいたナビゲーションアプローチを開発した。シミュレーション実験では、1/150ミニチュアモデルを用いて、空中ナビゲーションの実行可能性をテストした。結果では、本論文で提案するドローンの軽量ナビゲーションはフィードバックに基づいてリアルタイムに飛行の微調整を実現でき、既存手法と比較して性能において大きな進歩を示した。This dissertation mainly focuses on solving the problems in agile disaster management. To face the situation when the original network infrastructure no longer works because of disaster damage or power outage, I come up with the idea of introducing different emerging technologies in building a next-generation disaster response system. There are three parts of my research. In the first part of emergency networking, I design an information-centric fog computing architecture to fast build a temporary emergency network while the original ones can not be used. I focus on solving name-based routing for disaster relief by applying the idea from six degrees of separation theory. I first put forward a 2-tier information-centric fog network architecture under the scenario of post-disaster. Then I model the relationships among ICN nodes based on delivered files and propose a name-based routing strategy to enable fast networking and emergency communication. I compare with DNRP under the same experimental settings and prove that my strategy can achieve higher work performance. In the second part of efficiency optimization, I introduce the idea of edge caching in prolong the lifetime of the rebuilt network. I focus on how to improve the energy efficiency of edge caching using in-memory storage and processing. Here I build a 3-tier heterogeneous network structure and propose two edge caching methods using different TTL designs & cache replacement policies. I use total energy consumption and backhaul rate as the two metrics to test the performance of the in-memory caching method and compare it with the conventional method based on disk storage. The simulation results show that in-memory storage and processing can help save more energy in edge caching and share a considerable workload in percentage. In the third part of coverage expansion, I apply UAV technology and real-time image recognition in user search and autonomous navigation. I focus on the problem of designing a navigation strategy based on the airborne vision for UAV disaster relief. After the survey of related works on UAV fly control in disaster management, I find that in consideration of the current UAV manufacturing technology and actual demand on unmanned search & rescue, a lightweight solution is in urgent need. As a result, I design a lightweight navigation strategy based on visual recognition using transfer learning. In the simulation, I evaluate my solutions using 1/150 miniature models and test the feasibility of the navigation strategy. The results show that my design on visual recognition has the potential for a breakthrough in performance and the idea of UAV lightweight navigation can realize real-time flight adjustment based on feedback.室蘭工業大学 (Muroran Institute of Technology)博士（工学