A Survey and Comparative Study of Hard and Soft Real-Time Dynamic Resource Allocation Strategies for Multi-/Many-Core Systems

Singh, Amit Kumar; Dziurzański, Piotr; Mendis, Hashan Roshantha; Indrusiak, Leandro Soares

doi:10.1145/3057267

Cited by 78 publications

(53 citation statements)

References 190 publications

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“…When it comes to HRT task scheduling, hybrid methods are always employed. The hybrid approaches utilize design-time computations to identify a timing constraint satisfying allocation at runtime [36,37].…”

Section: Multi-robot Task Schedulingmentioning

confidence: 99%

CPS-Based Smart Warehouse for Industry 4.0: A Survey of the Underlying Technologies

et al. 2018

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This paper discusses how the state-of-the-art techniques in cyber-physical systems facilitate building smart warehouses to achieve the promising vision of industry 4.0. We focus on four significant issues when applying CPS techniques in smart warehouses. First, efficient CPS data collection: when limited communication bandwidth meets numerous CPS devices, we need to make more effort to study efficient wireless communication scheduling strategies. Second, accurate and robust localization: localization is the basis for many fundamental operations in smart warehouses, but still needs to be improved from various aspects like accuracy and robustness. Third, human activity recognition: human activity recognition can be applied in human-computer interaction for remote machine operations. Fourth, multi-robot collaboration: smart robots will take the place of humans to accomplish most tasks particularly in a harsh environment, and smart and fully-distributed robot collaborating algorithms should be investigated. Finally, we point out some challenging issues in the future CPS-based smart warehouse, which could open some new research directions.

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Section: Multi-robot Task Schedulingmentioning

confidence: 99%

CPS-Based Smart Warehouse for Industry 4.0: A Survey of the Underlying Technologies

et al. 2018

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“…The approach has some o ine and online steps (falling into the category of hybrid approach [31]) where o ine computed results for various applications are used to identify energy e cient run-time mapping and partitioning of threads of concurrent applications to be executed on the heterogeneous MPSoC. The main steps of the approach are as follows:…”

Section: Proposed Run-time Thread Mapping and Partitioningmentioning

confidence: 99%

Energy-Efficient Run-Time Mapping and Thread Partitioning of Concurrent OpenCL Applications on CPU-GPU MPSoCs

Singh

Prakash

Basireddy

et al. 2017

ACM Trans. Embed. Comput. Syst.

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Heterogeneous Multi-Processor Systems-on-Chips (MPSoCs) containing CPU and GPU cores are typically required to execute applications concurrently. However, as will be shown in this paper, existing approaches are not well suited for concurrent applications as they are developed either by considering only a single application or they do not exploit both CPU and GPU cores at the same time. In this paper, we propose an energy-e cient run-time mapping and thread partitioning approach for executing concurrent OpenCL applications on both GPU and GPU cores while satisfying performance requirements. Depending upon the performance requirements, for each concurrently executing application, the mapping process nds the appropriate number of CPU cores and operating frequencies of CPU and GPU cores, and the partitioning process identi es an e cient partitioning of the applications' threads between CPU and GPU cores. We validate the proposed approach experimentally on the Odroid-XU3 hardware platform with various mixes of applications from the Polybench benchmark suite. Additionally, a case-study is performed with a real-world application SLAMBench. Results show an average energy saving of 32% compared to existing approaches while still satisfying the performance requirements.

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“…Therefore, efficient and scalable RL algorithms are desired for large scale many-core systems with hundreds of cores. In order to address the scalability issues imposed by centralized resource management in many-core systems, distributed or hierarchical management has been employed [29,81]. In distributed management, each core is checked independently to make management decisions, whereas the cores are grouped into multiple clusters in hierarchical management and each cluster is managed by a local manager.…”

Section: Distributed Reinforcement Learningmentioning

confidence: 99%

“…Different types of timing requirements are imposed depending upon the kind of target system, e.g., hard real-time and soft real-time systems [29]. Examples of hard real-time systems are time critical systems such as automotive engine and flight control software.…”

Section: Introductionmentioning

confidence: 99%

Learning-Based Run-Time Power and Energy Management of Multi/Many-Core Systems: Current and Future Trends

Singh¹,

Leech²,

Basireddy³

et al. 2017

Journal of Low Power Electronics

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Abstract-Multi/Many-core systems are prevalent in several application domains targeting different scales of computing such as embedded and cloud computing. These systems are able to fulfil the ever-increasing performance requirements by exploiting their parallel processing capabilities. However, effective power/energy management is required during system operations due to several reasons such as to increase the operational time of battery operated systems, reduce the energy cost of datacenters, and improve thermal efficiency and reliability. This article provides an extensive survey of learning-based run-time power/energy management approaches. The survey includes a taxonomy of the learning-based approaches. These approaches perform design-time and/or run-time power/energy management by employing some learning principles such as reinforcement learning. The survey also highlights the trends followed by the learning-based run-time power management approaches, their upcoming trends and open research challenges.

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A Survey and Comparative Study of Hard and Soft Real-Time Dynamic Resource Allocation Strategies for Multi-/Many-Core Systems

Cited by 78 publications

References 190 publications

CPS-Based Smart Warehouse for Industry 4.0: A Survey of the Underlying Technologies

CPS-Based Smart Warehouse for Industry 4.0: A Survey of the Underlying Technologies

Energy-Efficient Run-Time Mapping and Thread Partitioning of Concurrent OpenCL Applications on CPU-GPU MPSoCs

Learning-Based Run-Time Power and Energy Management of Multi/Many-Core Systems: Current and Future Trends

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