‘MaaS’: Fast Retrieval of Data in Cloud Using Metadata as a Service

Abstract

In cloud era as the data stored is enormous, efficient retrieval of data with reduced latency plays a major role. In cloud, owing to the size of the stored data and lack of locality information among the stored files, metadata is a suitable method of keeping track of the storage. This paper describes a novel framework for efficient retrieval of data from the cloud data servers using metadata with less amount of time. Performance of queries due to availability of files for query processing can be greatly improved by the efficient use of metadata and its analysis thereof. Hence this paper proposes a generic approach of using metadata in cloud, named 'MaaS-Metadata as a Service.' The proposed approach has exploited various methodologies in reducing the latency during data retrieval. This paper investigates the issues on creation of metadata, metadata management and analysis of metadata in a cloud environment for fast retrieval of data. Cloud bloom filter, a probabilistic data structure used for efficient retrieval of metadata is stored across various metadata servers dispersed geographically. We have implemented the model in a cloud environment, and the experimental results show that methodology used is efficient in increasing the throughput and also by handling large number of queries efficiently with reduced latency. The efficacy of the approach is tested through experimental studies using KDD Cup 2003 dataset. In the experimental results, proposed 'MaaS' has outperformed other existing methods.

Quality Evaluation Model for Smart City Social Sports Information Cloud Service

“…Among them, the literature method is for summarizing, sorting, and analyzing, to provide a research basis and ideas for quantitative analysis of corresponding indicators by use of existing academic papers, public news reports, and other materials, while the interview method is to obtain the corresponding attitude and understanding of social sports services through the direct communication with audience or corresponding field experts about some questions [ 14 , 15 ]. On-site inspections are to conduct on-site investigations of residents through visits and reading materials to obtain the most direct evaluation based on the distribution of equipment related to social sports [ 16 , 17 ]. The logical approach is to conduct analysis and inverting according to the collected relevant data and finally get the corresponding index evaluation analysis.…”

Hunting the Pertinency of Bloom Filter in Computer Networking and Beyond: A Survey

“…Over a period of time, the Bloom filter becomes exhausted which results in an erroneous output. [22] models an efficient MDS to retrieve data from cloud data servers. It uses a Bloom filter called cloud Bloom filter (CBF) array.…”

“…Interestingly, Bloom filters are highly adaptable. Bloom filters are adapted in diverse field, namely, recommendation engine to prevent duplicate recommendation [18], metadata server to enhance lookup performance [19][20][21][22], network packet filtering to filter unnecessary data flow [23], IP address lookup [24], network security to prevent malicious user [25][26][27], Big Data security analytics [28,29], biometric [30], BigTable [3], P2P [31][32][33], error correction [34,35], wireless sensor network [36], duplicate filtering [37,38], plagiarism checking [39], biomedical engineering [35,[40][41][42], Web search [43], network router [44,45], searchable encryption scheme [46], IoT environment [47,48], spatial Bloom filter [49], in-packet multicasting [50], and databases [51].…”

Integration of Healthcare Ontologies at Schema Level using Customized Metadata

“…The other part of survey describes that the exploration of metadata of the data being processed will be beneficial for achieving better accuracy of data retrievals [9]. Metadata with cloud based systems promise fastest data retrieval rates [10]. From the detailed survey it is understood that the ontologies coupled with good metadata knowledge services could perform well during knowledge extraction automations.…”