Faculty Publications
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Publications by NITK Faculty
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Item Load balancing of MongoDB with tag aware sharding(Walter de Gruyter GmbH info@degruyter.com, 2015) Shegokar, P.; Thomas, M.V.; Chandrasekaran, K.In the era of Big Data, traditional databases face many problems to process data effectively. To solve problems we have NoSQL databases which can solve the problems and are becoming very popular. MongoDB is a document oriented NoSQL database which can process large data sets with the help of sharding mechanism. MongoDB supports range-based sharding, hash-based sharding and tag aware sharding. Problem with range-based method is that one of the shard will have more load. In case of hash-based method, we can distribute the load among shards but related data aren't together. Tag aware sharding method is better than the other two methods. But this method need to be enhanced as tagged data can belong to more shards and balancer have to migrate the data to most appropriate shard, hence we are enhancing the tag aware sharding in MongoDB. Improper distribution of data can't allow us to use all the benefits of sharding. Tag aware sharding is administrator based method in which tags are mentioned by the administrator. According to tags, data is migrated but we need to balance the load. To solve this problem, Weighted Round Robin (WRR) load balancing algorithm is used and it has improved the writing and querying performance.Item Construing microservice architectures: State-of-the-art algorithms and research issues(Springer Verlag service@springer.de, 2019) Nene, A.V.; Joseph, C.T.; Chandrasekaran, K.Cloud Computing is one of the leading paradigms in the IT industry. Earlier, cloud applications used to be built as single monolithic applications, and are now built using the Microservices Architectural Style. Along with several advantages, the microservices architecture also introduce challenges at the infrastructural level. Five such concerns are identified and analysed in this paper. The paper presents the state-of-art in different infrastructural concerns of microservices, namely, load balancing, scheduling, energy efficiency, security and resource management of microservices. The paper also suggests some future trends and research domains in the field of microservices. © Springer Nature Switzerland AG 2019.Item A Machine Learning Approach for Load Balancing in a Multi-cloud Environment(Springer Science and Business Media Deutschland GmbH, 2022) Divakarla, D.; Chandrasekaran, K.A multi-cloud environment makes use of two or more cloud computing services from different cloud vendors. A typical multi-cloud environment can consist of either only private clouds or only public clouds or a combination of both. Load balancing mechanism is essential in such a computing environment to distribute user requests or network load efficiently across multiple servers or virtual machines, ensuring high availability and reliability. Scalability is also achieved by sending requests only to those servers that are healthy and available to take up the computing workload and thus providing the flexibility to scale up and scale down to satisfy QoS requirements as well, in order to save costs. In our proposed model, a time series-based approach as well as predictive load balancing has been experimented and the results are presented. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.