Affinity-Driven Modeling and Scheduling for Makespan Optimization in Heterogeneous Multiprocessor Systems

Cao, Kun; Zhou, Junlong; Cong, Peijin; Li, Liying; Wei, Tongquan; Chen, Mingsong; Hu, Xiaobo Sharon

doi:10.1109/tcad.2018.2846650

Cited by 45 publications

(11 citation statements)

References 35 publications

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“…Figure 2 exhibits the location distributions of 3233 base stations in this base station database, where the number in every circle marked by red color refers to the number of base stations that have already been properly deployed in this zone. For every base station, we set its task arriving rate and data volume on average to the intervals of [4 × 10 6 , 6 × 10 8 ] and [1, 100] Mb, respectively 21,31‐34 . In addition, we construct a set of heterogeneous edge/cloud servers based on five kinds of commercial servers in the real world.…”

Section: Numerical Resultsmentioning

confidence: 99%

Exploring reliable edge‐cloud computing for service latency optimization in sustainable cyber‐physical systems

et al. 2021

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In recent years, the advance in information technology has promoted a wide span of emerging cyber‐physical systems (CPS) applications such as autonomous automobile systems, healthcare monitoring, and process control systems. For these CPS applications, service latency management is extraordinarily important for the sake of providing high quality‐of‐experience to terminal users. Edge‐cloud computing, integrating both edge computing and cloud computing, is regarded as a promising computation paradigm to achieve low service latency for terminal users in CPS. However, existing latency‐aware edge‐cloud computing methods dedicated for CPS fail to jointly consider energy budgets and reliability requirements, which may greatly degrade the sustainability of CPS applications. In this article, we explore the problem of minimizing service latency of edge‐cloud computing coupled CPS under the constraints of energy budgets and reliability requirements. We propose a two‐stage approach composed of static and dynamic service latency optimization. At static stage, Monte‐Carlo simulation with integer‐linear‐programming technique is adopted to find the optimal computation offloading mapping and task backup number. At dynamic stage, a backup‐adaptive dynamic mechanism is developed to avoid redundant data transmissions and executions for achieving additional energy savings and service latency enhancement. Experimental results show that our solution is able to reduce system service latency by up to 18.3% compared with representative baseline solutions.

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Section: Numerical Resultsmentioning

confidence: 99%

Exploring reliable edge‐cloud computing for service latency optimization in sustainable cyber‐physical systems

et al. 2021

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“…The design of reliabilityaware function scheduling policies in serverless edge computing confronts the challenge of dealing with the stochastic characteristic of application executions. Since real-world IoT applications often incorporate conditional commands and operations, distinct inputs to such applications would likely result in dissimilar completion time [20], [21]. Traditional reliability optimization techniques commonly rely on the metric of worstcase execution time (WCET) to pessimistically estimate the actual execution time of application instances.…”

Section: B Technical Challengesmentioning

confidence: 99%

REPFS: Reliability-Ensured Personalized Function Scheduling in Sustainable Serverless Edge Computing

Cao,

Weng

2024

IEEE Trans. Sustain. Comput.

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In recent years, serverless edge computing has been widely employed in the deployments of Internet-of-things (IoT) applications. Despite considerable research efforts in this field, existing works fail to jointly consider essential factors such as energy, reliability, personalized user requirements, and stochastic application executions. This oversight results in an inefficient utilization of computation and communication resources within serverless edge computing networks, subsequently diminishing the profit of service providers and degrading the quality-ofexperience (QoE) of end users. In this paper, we explore the problem of reliability-ensured personalized function scheduling (REPFS) to jointly optimize the profit of service providers and the holistic QoE of end users in sustainable serverless edge computing. A personality-driven user QoE prediction method is first designed to accurately estimate the QoE of individual end users with differentiated personality types. Afterward, a deterministic function scheduling policy is developed on the problem-specific augmented non-dominated sorting genetic algorithm II (PSA-NSGA-II). Given the inherent uncertainty of application executions, a stochastic function scheduling strategy that can be easily parallelized for modern multicore scheduler platforms is also devised to accelerate solution generation for stochastic applications. Experimental results show that our deterministic function scheduling policy achieves 15% performance enhancement compared with representative multiobjective evolutionary algorithms B-NSGA-II, c-DPEA, DLS-MOEA, MOALO, MOEA/D, and MaOEA/IGD. Furthermore, our stochastic function scheduling strategy promotes the service profit by 78% and the holistic user QoE by 118% on average compared with the developed deterministic scheduling policy.

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“…However, no fault tolerance techniques are considered in these above works. In [19], the authors studied the problem of makespan optimization jointly considering reliability, temperature and stochastic characteristics of precedence-constrained tasks. An affinity-driven task allocation and scheduling approach is proposed and a heuristic is designed by assigning a task to the processor with highest affinity.…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Partial-Duplication Task Mapping Under Multiple DVFS Schemes

Cui

Kritikakou

et al. 2022

Int J Parallel Prog

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Affinity-Driven Modeling and Scheduling for Makespan Optimization in Heterogeneous Multiprocessor Systems

Cited by 45 publications

References 35 publications

Exploring reliable edge‐cloud computing for service latency optimization in sustainable cyber‐physical systems

Exploring reliable edge‐cloud computing for service latency optimization in sustainable cyber‐physical systems

REPFS: Reliability-Ensured Personalized Function Scheduling in Sustainable Serverless Edge Computing

Energy-Efficient Partial-Duplication Task Mapping Under Multiple DVFS Schemes

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