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Mixed-criticality systems (MCS) have developed as an efficient solution in several industries, where numerous tasks with different criticality levels (safety requirements) are assimilated onto a shared computational platform. Today, increased energy consumption in MCS, especially in critical situations, leads to temperature hotspots, which may disrupt the reliability and correctness of the system. As processors with multiple processing elements are becoming the vital paradigm in MCS, an integrated timeliness and power management is an important issue. This paper proposes a laxity-aware mixed-critical task scheduling (LMTS) algorithm that provides correctness, timeliness, power management, and guaranteed service level in MCS simutaneously. This method minimizes energy consumption of the system considerably through dynamic voltage and frequency scaling (DVFS) method. It collects several workloads concurrently and form clusters with one high-critical workload and a set of low-critical workloads. It determines the laxities and selects the most suitable cluster to exploit the available laxity based on its impact on the energy consumption and hotspot problems of the system. However, changing the core frequency, allocating more suitable cluster for available laxity, and finding out an appropriate core for mapping at runtime are difficult processes and cause deadline desecration which is not suitable for safetycritical tasks. Therefore, we develop an effective scheduling method using DVFS schemes and task migration techniques in online mode to utilize available laxity. We also defined cost functions to select the most apposite cluster to right …