Evolution-Based Real-Time Job Scheduling for Co-Optimizing Processor and Memory Power Savings

Bahn, Hyokyung; Cho, Kyungwoon

doi:10.1109/access.2020.3017014

Cited by 9 publications

(3 citation statements)

References 41 publications

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“…, v m }, where the task characteristics are defined similarly as those of the fixed task set, i.e., WCET v i , Period v i , Size v i , where WCET v i is the worst-case execution time of v i , Period v i is the period of v i , and Size v i is the memory size of v i . This paper assumes the following for the proposed workload model, which is similar to previous research [13].…”

Section: Workload Model and Resource Planningmentioning

confidence: 98%

Adaptive Resource Planning for AI Workloads with Variable Real-Time Tasks

Nam¹,

Cho²,

Bahn³

2023

Computers, Materials &Amp; Continua

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AI (Artificial Intelligence) workloads are proliferating in modern real-time systems. As the tasks of AI workloads fluctuate over time, resource planning policies used for traditional fixed real-time tasks should be reexamined. In particular, it is difficult to immediately handle changes in real-time tasks without violating the deadline constraints. To cope with this situation, this paper analyzes the task situations of AI workloads and finds the following two observations. First, resource planning for AI workloads is a complicated search problem that requires much time for optimization. Second, although the task set of an AI workload may change over time, the possible combinations of the task sets are known in advance. Based on these observations, this paper proposes a new resource planning scheme for AI workloads that supports the re-planning of resources. Instead of generating resource plans on the fly, the proposed scheme pre-determines resource plans for various combinations of tasks. Thus, in any case, the workload is immediately executed according to the resource plan maintained. Specifically, the proposed scheme maintains an optimized CPU (Central Processing Unit) and memory resource plan using genetic algorithms and applies it as soon as the workload changes. The proposed scheme is implemented in the opensource simulator SimRTS for the validation of its effectiveness. Simulation experiments show that the proposed scheme reduces the energy consumption of CPU and memory by 45.5% on average without deadline misses.

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Section: Workload Model and Resource Planningmentioning

confidence: 98%

Adaptive Resource Planning for AI Workloads with Variable Real-Time Tasks

Nam¹,

Cho²,

Bahn³

2023

Computers, Materials &Amp; Continua

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“…In [32] one can find a technique of the effective utilization of processing elements in the clusters of processors with the shared L1 cache memory based on the optimized synchronization and communication between the system components. Some works demonstrated the application of heuristic methods and AI algorithms in the task scheduling process [33], [34], [35].…”

Section: Related Workmentioning

confidence: 99%

Validation of Task Scheduling Techniques in Multithread Time Predictable Systems

Antolak

Pułka

2023

IEEE Access

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“…To reduce the time overhead of scheduling and maximize the power saving effect, their policy adopts dynamic programming with the constraint of resource utilization. Bahn et al present a new task model for hybrid memory placement in hard real-time systems [15]. Specifically, they re-evaluate the worst-case execution time of a task by considering the memory location of each task in heterogeneous memory environments.…”

Section: Dynamic Voltage Scalingmentioning

confidence: 99%

Supporting Swap in Real-Time Task Scheduling for Unified Power-Saving in CPU and Memory

et al. 2022

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As the size of data grows rapidly in modern IoT (Internet-of-Things) and CPS (Cyber-Physical System) applications, the memory power consumption of real-time embedded systems increases dramatically. Unlike general-purpose systems where memory consumes about 10% of the CPU power consumption, modern real-time systems have the memory power of 20-50% of CPU power. This is because the memory size of a real-time system should be large enough to accommodate the entire task set, and thus DRAM refresh operations become a major source of power consumption. In this article, we present a new swap scheme for real-time systems, which aims at reducing memory power consumption. To support swap with real-time constraints, we adopt high-speed NVM storage and co-optimize power-savings in CPU and memory. Unlike traditional real-time task models that only consider the executions in CPU, we define an extended task model that characterizes memory and storage paths of tasks as well, and tightly evaluate the worst-case execution time by formulating the overlapped latency between CPU and memory. By optimizing the CPU supply voltage and the memory swap ratio of given task set, our scheme reduces the energy consumption of realtime systems by 31.1% on average under various workload conditions.INDEX TERMS Real-time task scheduling; partial swap; genetic algorithm; power saving; voltage scaling; deadline; high-speed storage; NVM.

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Evolution-Based Real-Time Job Scheduling for Co-Optimizing Processor and Memory Power Savings

Cited by 9 publications

References 41 publications

Adaptive Resource Planning for AI Workloads with Variable Real-Time Tasks

Adaptive Resource Planning for AI Workloads with Variable Real-Time Tasks

Validation of Task Scheduling Techniques in Multithread Time Predictable Systems

Supporting Swap in Real-Time Task Scheduling for Unified Power-Saving in CPU and Memory

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