Mobile device users are involved in social networking, gaming, learning, andeven some office work, so the end users expect mobile devices with highresponsecomputing capacities, storage, and high battery power consumption.The data-intensive applications, such as text search, online gaming, and facerecognition usage, have tremendously increased. With such high complexapplications, there are many issues in mobile devices, namely, fast batterydraining, limited power, low storage capacity, and increased energy consumption.The novelty of this work is to strike a balance between time and energyconsumption of mobile devices while using data-intensive applications by findingthe optimal offloading decisions. This paper proposes a novel efficient DataSize-Aware Offloading Model (DSAOM) for data-intensive applications and topredict the appropriate resource provider for dynamic resource allocation inmobile cloud computing. Based on the data size, the tasks are separated andgradually allocated to the appropriate resource providers for execution. Thetask is placed into the appropriate resource provider by considering the availabilityservices in the fog nodes or the cloud. The tasks are split into smallerportions for execution in the neighbor fog nodes. To execute the task in theremote side, the offloading decision is made by using the min-cut algorithm byconsidering the monetary cost of the mobile device. This proposed systemachieves low-latency time 13.2% and low response time 14.1% and minimizes24% of the energy consumption over the existing model. Finally, according toexperimental findings, this framework efficiently lowers energy use andimproves performance for data-intensive demanding application activities, andthe task offloading strategy is effective for intensive offloading requests.
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