The recent shift in the VLSI industry from conventional MOSFET to FinFET for designing contemporary chip-multiprocessor (CMP) has noticeably improved hardware platforms’ computing capabilities, but at the cost of several thermal issues. Unlike the conventional MOSFET, FinFET devices experience a significant increase in circuit speed at a higher temperature, called temperature effect inversion (TEI), but higher temperature can also curtail the circuit lifetime due to self-heating effects (SHEs). These fundamental thermal properties of FinFET introduced a new challenge for scheduling time-critical tasks on FinFET-based multicores that how to exploit TEI towards improving performance while combating SHEs. In this work,TREAFET, a temperature-aware real-time scheduler, attempts to exploit the TEI feature of FinFET-based multicores in a time-critical computing paradigm. At first, the overall progress of individual tasks is monitored, tasks are allocated to the cores, and finally, a schedule is prepared. By considering the thermal profiles of the individual tasks and the current thermal status of the cores, hot tasks are assigned to the cold cores and vice-versa. Finally, the performance and temperature are balanced on-the-fly by incorporating a prudential voltage scaling towards exploiting TEI while guaranteeing the deadline and thermal safety. Moreover,TREAFETstimulates the average runtime frequency by employing an opportunistic energy-adaptive voltage spiking mechanism, in which energy saving during memory stalls at the cores is traded off during the time slice having the spiked voltage. Simulation results claimTREAFETmaintains a safe and stable thermal status (peak temperature below 80 °C) and improves frequency up to 17% over the assigned value, which ensures legitimate time-critical performance for a variety of workloads while surpassing a state-of-the-art technique. The stimulated frequency inTREAFETalso finishes the tasks early, thus providing opportunities to save energy by power gating the cores, and achieves a 24% energy delay product (EDP) gain on average.

Real-time embedded systems are designed to perform specific functions in real-time, with a microcontroller, memory, and input/output devices. The scheduler is a critical component that manages resource allocation and schedules jobs based on priority and available resources. Multiprocessor platforms improve performance, scalability, redundancy, and flexibility, with different approaches to scheduling, such as global, partitioned, and semi-partitioned. Minimizing dynamic energy consumption and processor temperatures is essential for improving battery life and reliability and meeting power and thermal constraints in applications such as mobile devices, aerospace, and defense systems. There are many energy and temperature management techniques, but their effect on each other has not been studied in detail. Hence, we want to employ a few of those techniques and want to observe their impacts. In this work, we first propose a basic semi-partitioned scheduler for heterogeneous multiprocessor systems which supports the execution of real-time jobs. Then, we apply well-known energy and temperature minimization techniques over the proposed scheduler to study their impact on the system. To conduct our experiments, we use benchmark programs whose characteristics have been extracted using various simulators.

In this work, we propose RESTORE that exploits the unique thermal feature of FinFET based multicore platforms, where processing speed increases with temperature, in the context of time-criticality to meet other design constraints of real-time systems. RESTORE is a temperature aware real-time scheduler for FinFET based multicore system that first derives a task-to-core allocation, and prepares a schedule. Next, it balances the performance and temperature on the fly by incorporating a prudential temperature cognizant voltage/frequency scaling while guaranteeing task deadlines. Simulation results claim, RESTORE is able to maintain a safe and stable thermal status (peak temperature below 80°C), hence the frequency (3.7 GHz on an average), that ensures legitimate time-critical performance for a variety of workloads while surpassing state-of-the-arts.

In real-time systems, schedulers have to perform tasks with a specific degree of urgency; otherwise, it may lead to significant damage. Additionally, modern systems also want to focus on other design criteria like providing high system utilization, executing tasks with a low energy budget etc., making the job of a scheduler more challenging in such systems. EDF is one of the first optimal schedulers designed for single-core real-time systems, and since then, it has remained in the focus of the research fraternity. Unfortunately, it is not suitable for multicore platforms. In this paper, we have designed a semi-partitioned energy-aware scheduler for real-time systems based on EDF. Initially, the proposed scheduler uses the strategy of deadline partitioning to make the algorithm semi-partitioned. Then, it applies EDF to schedule the tasks on available cores. Next, it carefully schedules tasks that require migration across multiple cores. At last, it scales the frequency of individual cores based on the workload assignment of previous steps. Our experimental analysis shows that the proposed scheduler can provide high resource utilization (unlike traditional multicore schedulers) and reduce dynamic energy consumption (up to 8.94% on an average) of the systems.

Nowadays, multi-core processing systems have to perform complex functionalities on densely packed multi-million gate platforms, which makes such systems prone to uncontrolled surges in core temperatures, if not effectively controlled. Increasing temperature above specified limits not only leads to high cost of cooling but also results in higher dissipation of leakage power together with a reduction in performance and lower system life expectancy. In this work, we propose a two-level low-overhead proportional fair resource allocation strategy called TEARS: A temperature-aware real-time scheduler for heterogeneous multi-core systems, for scheduling of periodic tasks with bounded number of migrations and context-switches. The proposed algorithm's first level divides time into distinct windows based on deadlines of tasks, so that exact proportional fairness is maintained at all window boundaries. The second level accomplishes intra-window scheduling with the aim of maximising the use of resources while not breaching a specified thermal threshold. Our experimental analysis shows that the presented strategy not only improves upon the state-of-the-art in terms of resource utilisation (as high as 16.09%) but also reduces average temperatures of cores in the system.

Over the years, the nature of processing platforms is witnessing a significant shift in most of the battery supported real-time systems, which currently underpins a blend of specific multicores to satisfy the needs of present day applications. Devising temperature-aware schedulers has become a critical issue for such kind of systems. Hence, this research presents a heuristic strategy named TA-HRT, for temperature-aware scheduling of a set of real-time periodic tasks on a heterogeneous multicore platform. The presented strategy operates in three stages, namely Deadline Partitioning, Core Clustering and Temperature-Aware Task Scheduling. Our experimental analysis shows that the presented strategy not only improves upon the state-of-the-art [1] in terms of resource utilisation (as high as 10.71%) but also reduces average temperatures of cores in the system.