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These computing paradigms use virtualization and a pay-as-you-go strategy to provide IT resources, including CPU, memory, network and storage. Resource management in such a hybrid environment becomes a challenging task. This problem is exacerbated in the IoT environment, as it generates deadline-driven and heterogeneous data demanding real-time processing. This work proposes an efficient two-step scheduling algorithm comprising a Bi-factor classification task phase based on deadline and priority and a scheduling phase using an enhanced artificial Jellyfish Search Optimizer (JS) proposed as an Improved Jellyfish Algorithm (IJFA). The model considers a variety of cloud and fog resource parameters, including speed, capacity, task\u00a0size,\u00a0number of tasks, and number of virtual machines for resource provisioning in a fog integrated cloud environment. The model has been tested for the real-time task scenario with the number of tasks considering both the smaller workload and the relatively higher workload scenario matching the real-time situation. The model addresses the Quality of Service (QoS) parameters of minimizing the batch\u2019s make-span time, lowering the batch execution costs, and increasing the resource utilization. Simulation results prove the effectiveness of the proposed model.<\/jats:p>","DOI":"10.1186\/s13677-022-00376-5","type":"journal-article","created":{"date-parts":[[2022,12,21]],"date-time":"2022-12-21T17:05:03Z","timestamp":1671642303000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Improved Jellyfish Algorithm-based multi-aspect task scheduling model for IoT tasks over fog integrated cloud environment"],"prefix":"10.1186","volume":"11","author":[{"given":"Nupur","family":"Jangu","sequence":"first","affiliation":[]},{"given":"Zahid","family":"Raza","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,12,21]]},"reference":[{"key":"376_CR1","doi-asserted-by":"publisher","first-page":"3891","DOI":"10.1007\/s11269-015-1016-9","volume":"29","author":"A Afshar","year":"2015","unstructured":"Afshar A, Massoumi F, Afshar A, Mari\u00f1o MA (2015) State of the Art Review of Ant Colony Optimization Applications in Water Resource Management. 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