## 2 Related Works
The performance of the proposed algorithm was validated in terms of makespan, total cost, reliability, and cost score reduction. Such methods are helpful in handling concurrent access to shared memory. However, the problem with shared memory buffer solutions is that the worst-case response time can lead to situations where tasks use outdated data, which can affect the performance of real-time algorithms. On the other hand, [PERSON] et al. [19] and [PERSON] et al. [20] explored hardware-specific solutions such as ScratchPad Memories (SPMs) and Direct Memory Access (DMA) for scheduling mechanisms. These mechanisms employ time-based schemes like TDMA to temporally isolate the entire task and data exchange processes. Although these solutions are highly efficient, they are highly dependent on SPMs, which limits their portability. Furthermore, their approach focused on scheduling mechanisms that allocate both task code and data in the SPM, increasing dependency due to varying SPM sizes across different controllers. The study by [PERSON] et al. [21] presented a significant contribution to the field of LET implementation in POSIX (Portable Operating System Interface) systems. This research focused on real-time tasks that utilized topic-based messaging and the producer-consumer paradigm. The system model ran on multicore platforms with global memory and ensured deterministic behavior in control applications. The LET paradigm mandated input and output operations at the beginning and end of a specified period. To achieve this, the researchers designed communication mechanisms and protocols that were integrated into dynamic systems compatible with POSIX. [PERSON] et al. [22] extended this concept to the system level (SL LET) in automotive systems, ensuring predictable and composable times. While the traditional usage of LET was limited to individual components, SL LET expanded this approach to a more systemic level. The methodology translated the AUTOSAR functional model into an implementation model that met the requirements, representing the system as a set of LET tasks. Furthermore, [PERSON] et al. [23] addressed the challenges of programming cyber-physical systems in high-performance architectures by applying the LET paradigm and its extension. These systems, which monitor and control physical processes, face complexities due to hardware heterogeneity. The methodology focused on the need for proper programming for these architectures, which resemble parallel programming in high-performance computing but with time and security requirements. [PERSON]'s research [24] focused on programming deep learning applications on embedded devices with heterogeneous processors. In the same year, [PERSON] [25] proposed a hybrid algorithm for programming scientific workflows in heterogeneous distributed computing systems. [PERSON] et al. [26] proposed optimizing latency in embedded automotive and aviation systems, emphasizing the application of the LET paradigm in characterizing end-to-end latency. Another interesting approach, proposed by [PERSON] et al. [27], lies in the development and application of the hybrid GASA algorithm for workflow scheduling optimization in cloud heterogeneous platforms. This novel and effective solution combines the strengths of different meta-heuristic approaches to enhance the efficiency and performance of scheduling algorithms in distributed computing environments. These research papers show the significance and flexibility of the LET paradigm across different technologies. They also demonstrate its essential role in developing critical systems and real-time applications.