Performance Analysis of Big Data and Cloud Computing Techniques: A Survey

Saif, Subia; Wazir, Samar

doi:10.1016/j.procs.2018.05.172

Cited by 25 publications

(21 citation statements)

References 11 publications

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“…If the situation indicates relocation of IT assets, resources are mobilized in the declaration to implement the necessary steps. The recovery team lead and management should make the final decision to declare and the course of action to be taken, and the communication made quickly and clearly (Saif & Wazir, 2018). The IT asset Recovery Team, based on the specific impact and current business event, determines the appropriate course of action at the time of the incident.…”

Section: Step 3: Disruption/ Disaster Declarationmentioning

confidence: 99%

“…Failover is handled by constant replication and automatic failover within an Multiple Zone region(MZR), which is equivalent to Recovery Period Objective (RPO) and Recovery Time Objective (RTO) from the service perspective. Note database backups are available in geo-replicated backup on cloud object storage (Saif & Wazir, 2018). At the same time, the customers can restore Database backup by themselves since backup restoration is a functional requirement in the last 14 days outside the scope of exercise.…”

Section: Dependenciesmentioning

confidence: 99%

“…In the timeframe predetermined, the plan identifies a Recovery Scope for the disaster Determining Disaster Recovery Strategy and performing due diligence on Disaster Recovery. Also, the BPO defines and documents the disaster Recovery Plan, and the Security focal point verifies that the DR plan is regularly tested on an unplanned and planned basis (Saif & Wazir, 2018). The BPO is responsible for declaring a disaster while the security.…”

Section: Dependenciesmentioning

confidence: 99%

“…The recovery and status outlook communication come from CMT members on site. However, not all issues impacting a critical IT asset is addressed by a site CMT first, the recovery team performs assessment determining the impact level and the estimated time for restoration (Saif & Wazir, 2018). The assessment considers the safety of occupants of the building determined by critical function availability like power, internet/intranet, and phone.…”

mentioning

confidence: 99%

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Disaster Recovery for Database on Cloud Enterprise

Ahmed¹,

Ahmed²

2020

IJAR

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Section: Step 3: Disruption/ Disaster Declarationmentioning

confidence: 99%

Section: Dependenciesmentioning

confidence: 99%

Section: Dependenciesmentioning

confidence: 99%

mentioning

confidence: 99%

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Disaster Recovery for Database on Cloud Enterprise

Ahmed¹,

Ahmed²

2020

IJAR

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“…e concept of data pipelining is based on segmenting a complex task into several simpler and interconnected subtasks (also named pipes) where the output of one subtask becomes the input of the next one [10]. Ever since this divide-and-conquer paradigm emerged in the big data domain, multiple cloud-based pipelining frameworks have been developed by important companies to perform data analysis and processing services [11].…”

Section: Introductionmentioning

confidence: 99%

Using Natural Language Preprocessing Architecture (NLPA) for Big Data Text Sources

Novo-Lourés

Pavón

Laza

et al. 2020

Scientific Programming

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During the last years, big data analysis has become a popular means of taking advantage of multiple (initially valueless) sources to find relevant knowledge about real domains. However, a large number of big data sources provide textual unstructured data. A proper analysis requires tools able to adequately combine big data and text-analysing techniques. Keeping this in mind, we combined a pipelining framework (BDP4J (Big Data Pipelining For Java)) with the implementation of a set of text preprocessing techniques in order to create NLPA (Natural Language Preprocessing Architecture), an extendable open-source plugin implementing preprocessing steps that can be easily combined to create a pipeline. Additionally, NLPA incorporates the possibility of generating datasets using either a classical token-based representation of data or newer synset-based datasets that would be further processed using semantic information (i.e., using ontologies). This work presents a case study of NLPA operation covering the transformation of raw heterogeneous big data into different dataset representations (synsets and tokens) and using the Weka application programming interface (API) to launch two well-known classifiers.

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Dynamic data replication and placement strategy in geographically distributed data centers

Bouhouch

Zbakh

Tadonki

2022

Concurrency and Computation

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With the evolution of geographically distributed data centers in the Cloud Computing landscape along with the amount of data being processed in these data centers, which is growing at an exponential rate, processing massive data applications become an important topic. Since a given task may require many datasets for its execution and the datasets are spread over several different data centers, finding an efficient way to manage the datasets storage across nodes of a Cloud system is a difficult problem.In fact, the execution time of a task might be influenced by the cost of data transfers, which mainly depends on two criterias. The first one is the initial placement of the input datasets during the build-time phase, while the second is the replication of the datasets during the runtime phase. The replication is explicitly consider when datasets are being migrated over the data centers in order to make them locally available wherever needed. Data placement and data replication are important challenges in Cloud Computing. Nevertheless, many studies focus on data placement or data replication exclusively. In this paper, a combination of a data placement strategy followed by a dynamic data replication management strategy is proposed, with the purpose of reducing the associated cost of all data transfers between the (distant) data centers. Our proposed data placement approach considers the main characteristics of a data center such as storage capacity and read/write speeds to efficiently store the datasets, while our dynamic data replication management approach considers three parameters: the number of replicas in the system, the dependency between datasets and tasks and the storage capacity of data centers. The decision of when and whether to keep or to delete replicas is determined by the fulfillment of those three parameters. Our approach estimates the total execution time of the tasks as well as the monetary cost, considering the data transfers activity. Our experiments are conducted using Cloudsim simulator. The obtained results show that our proposed strategies produce an efficient data management by reducing the overheads of the data transfers, compared to both a data placement without replication (by 76%) and the selected data replication approach from Kouidri et al. (by 52%), and by improving the financial cost.

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Performance Analysis of Big Data and Cloud Computing Techniques: A Survey

Cited by 25 publications

References 11 publications

Disaster Recovery for Database on Cloud Enterprise

Disaster Recovery for Database on Cloud Enterprise

Using Natural Language Preprocessing Architecture (NLPA) for Big Data Text Sources

Dynamic data replication and placement strategy in geographically distributed data centers

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