LSDMA: Levelized security driven deadline constrained multiple workflow allocation model in cloud computing

Security and deadline sensitivity play a crucial role in many cloud applications, requiring a high level of security to ensure confidentiality, integrity, and authentication in cross-platform data transmission. Therefore, we designed a levelized security driven deadline constrained multiple workflow allocation (LSDMA) model in order to optimize the risk probability of satisfying the workflow tasks’ security demands and deadline constraints. To ensure the security in cloud platform, three-level security services, i.e., authentication, integrity, and confidentiality, are employed. However, existing secured workflow allocation literature reports very few models for multiple workflows addressing security and deadline requirements. It leaves scope to develop new models in the domain. Further, the completion time of the workflows is also improved by using level-wise allocation, inserting best-fit successor tasks into idle gaps, and adopting a parallel communication mechanism between levels during execution to reduce overall communication overhead. The prototype simulator for the secured workflow allocator is designed and implemented in MATLAB for performance evaluation with the competitive models from the domain. The meticulous simulation results show that the LSDMA model outperforms at both batch of random workflows and real workflows among the state-of-the-art models on the considered QoS parameters under study. The experimental findings indicate that LSDMA surpasses SDS, LBSIR, SAHEFT, SODA, and HEFT regarding the risk probability, achieving improvements of 21 %–73 %, 15 %–74 %, 12 %–72 %, and 14 %–73 % when varying the batch of random, Montage, CyberShake, and LIGO workflows, respectively.