Dynamic partitioned scheduling of real-time tasks on ARM big.LITTLE architectures

Mascitti, Agostino; Cucinotta, Tommaso; Marinoni, Mauro; Abeni, Luca

doi:10.1016/j.jss.2020.110886

Cited by 10 publications

(5 citation statements)

References 26 publications

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“…Due to the dynamic nature of the cloud, we argue that it is essential for RT scheduler i) to consider a wide variety of heterogeneous processors in a multi-node cloud system, ii) to run fast (in polynomial time) to minimize scheduling overhead, and iii) not to migrate tasks among nodes. As far as we know, the most recent real-time scheduler studies assume a single node environment, meaning that they either consider identical CPUs/cores [26], [27] or a limited degree of heterogeneity for the processing entities [29], [30]. These schedulers are not comparable with our partitioned-EDF design since the various task runtimes on multiple different cores/processors cannot be modeled.…”

Section: Discussionmentioning

confidence: 99%

“…Existing papers which study the partitioned scheduling problem of RT tasks typically consider either identical cores or CPUs as [26], [27], [28] or only a limited heterogeneity among of them (e.g., dual-core types of the ARM big.LITTLE architecture) [29], [30], [31]. [32], [33] propose methods running in non-polynomial time to solve the partitioning problem.…”

Section: Related Workmentioning

confidence: 99%

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Real-Time FaaS: Towards a Latency Bounded Serverless Cloud

Szalay

Mátray

Toka

2023

IEEE Trans. Cloud Comput.

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Today, Function-as-a-Service is the most promising concept of serverless cloud computing. It makes possible for developers to focus on application development without any system management effort: FaaS ensures resource allocation, fast response time, schedulability, scalability, resiliency, and upgradability. Applications of 5G, IoT, and Industry 4.0 raise the idea to open cloud-edge computing infrastructures for time-critical applications too, i.e., there is a strong desire to pose real-time requirements for computing systems like FaaS. However, multi-node systems make real-time scheduling significantly complex since guaranteeing real-time task execution and communication is challenging even on one computing node with multi-core processors. In this paper, we present an analytical model and a heuristic partitioning scheduling algorithm suitable for real-time FaaS platforms of multi-node clusters. We show that our task scheduling heuristics could outperform existing algorithms by 55%. Furthermore, we propose three conceptual designs to enable the necessary real-time communications. We present the architecture of the envisioned real-time FaaS platform, emphasize its benefits and the requirements for the underlying network and nodes, and survey the related work that could meet these demands.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Real-Time FaaS: Towards a Latency Bounded Serverless Cloud

Szalay

Mátray

Toka

2023

IEEE Trans. Cloud Comput.

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“…This work also highlighted how the CPU type in use (big vs. LITTLE), its operating frequency, and the type of workload running on it (e.g., CPU vs. memory-bound) are all contributing factors that determine both how the power consumption and the execution time scale when changing the system configuration. This extension has been recently used to evaluate the performance of energy-aware task placement strategies for realtime systems running on ARM big.LITTLE architectures [16,17]. However, the RTSim energy-aware support for just one hardware architecture limits its usefulness.…”

Section: Related Workmentioning

confidence: 99%

Simulating execution time and power consumption of real-time tasks on embedded platforms

Ara

Cucinotta

Mascitti

2022

Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing

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In this paper, we present PARTSim, an open-source power/thermalaware simulator for embedded real-time systems. This tool is a fork of the well-known RTSim simulator, which can simulate the timing behavior of a set of real-time tasks with various characteristics when running on a multi-processor platform in presence of a number of real-time scheduling policies. PARTSim extends the functionality of RTSim by introducing support for power-aware embedded platforms exhibiting frequency scaling and specific architectural patterns like the ARM big.LITTLE and DynamIQ ones. Experimental results that compare simulated data against execution profiles collected on real platforms show a simulation error under 10 % for both execution time and power consumption at 90th percentile when simulating the effects of DVFS. CCS CONCEPTS• Computer systems organization ✙ Embedded systems; Realtime system architecture; • Software and its engineering ✙Scheduling; Power management; • Hardware ✙ Temperature simulation and estimation; Power estimation and optimization;

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“…In the context of platforms with heterogeneous cores, a popular target platform is the big.LITTLE from ARM [42], which is composed of a "little" and power-efficient set of cores, together with a "big" set of cores for high-performance computation. Partitioning heuristics exist for such a specific architecture, e.g., see [49][50][51]73]. In the works mentioned above, schedulability is checked by means of utilizationbased tests.…”

Section: Related Workmentioning

confidence: 99%

Optimized Partitioning and Priority Assignment of Real-Time Applications on Heterogeneous Platforms with Hardware Acceleration

Casini,

Pazzaglia,

Biondi

et al. 2022

Preprint

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Hardware accelerators, such as those based on GPUs and FPGAs, offer an excellent opportunity to efficiently parallelize functionalities. Recently, modern embedded platforms started being equipped with such accelerators, resulting in a compelling choice for emerging, highly computational intensive workloads, like those required by next-generation autonomous driving systems. Alongside the need for computational efficiency, such workloads are commonly characterized by real-time requirements, which need to be satisfied to guarantee the safe and correct behavior of the system. To this end, this paper proposes a holistic framework to help designers partition real-time applications on heterogeneous platforms with hardware accelerators. The proposed model is inspired by a realistic setup of an advanced driving assistance system presented in the WATERS 2019 Challenge by Bosch, further generalized to encompass a broader range of heterogeneous architectures. The resulting analysis is linearized and used to encode an optimization problem that jointly (i) guarantees timing constraints, (ii) finds a suitable task-to-core mapping, (iii) assigns a priority to each task, and (iv) selects which computations to accelerate, seeking for the most convenient trade-off between the smaller worst-case execution time provided by accelerators and synchronization and queuing delays.

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Dynamic partitioned scheduling of real-time tasks on ARM big.LITTLE architectures

Cited by 10 publications

References 26 publications

Real-Time FaaS: Towards a Latency Bounded Serverless Cloud

Real-Time FaaS: Towards a Latency Bounded Serverless Cloud

Simulating execution time and power consumption of real-time tasks on embedded platforms

Optimized Partitioning and Priority Assignment of Real-Time Applications on Heterogeneous Platforms with Hardware Acceleration

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