Multi-objective task scheduling in heterogeneous fog environments
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Date
2023
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Thesis (Ph.D.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2023.
Abstract
Limitations of the conventional cloud computing have surfaced as the internet evolves towards Future Internet where billions of devices connected to the global network producing enormous volume of data. Fog computing is proposed to overcome the drawbacks of the cloud computing which brings the computation and storage towards the edge of the network. Task scheduling on fog environments surges new challenges compared to scheduling on conventional cloud computing. Although there are a few recent work on task scheduling in fog computing, they are very limited and they do not represent most of the major challenges in fog computing. Various levels of heterogeneity and dynamism cause task scheduling problem to be more challenging for fog computing. In this thesis, we present a multi-objective task scheduling model with total of five objectives; and we propose two multi-objective multi rank scheduling algorithms for fog computing, the MOMRank and the LAMOMRank algorithms. The performance of the proposed strategies is assessed with well- known multi objective metaheuristics (the NSGA-II and the SPEA2 algorithms) and a widely used algorithm from the literature (the MOHEFT algorithm) using three common multi-objective metrics. Furthermore, set of highlighted individual metrics are also measured to address open issues in fog environments. We populated our workloads with the Pegasus workflows with dependent tasks that will produce network traffic and the DeFog applications that will demand real-time requirements. Additionally, we incorporate two task clustering schemes to the algorithms in order to improve data transmissions on interconnection networks. Results of empirical evaluations given in performance profiles over all instances validate significance of our algorithms in terms of multiobjective metrics, diminishing fog cluster network and reducing latency for real time applications.