Decentralized Low-Latency Task Scheduling for Ad-Hoc Computing

Edinger, Janick; Breitbach, Martin; Gabrisch, Niklas; Schäfer, Dominik; Becker, Christian; Rizk, Amr

doi:10.1109/ipdps49936.2021.00087

Cited by 9 publications

(5 citation statements)

References 42 publications

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“…The system consists of a network of resource providers, providing their computational resources to other participants, and resource consumers, offloading computational tasks to providers for remote calculation. The system can operate using a central broker for task scheduling, or using a decentralized approach [5], allowing consumers to chose a producer for performing the task calculation themselves. Different applications offloading tasks can have differing non-functional requirements such as latency or reliability.…”

Section: Evaluation Of Resultsmentioning

confidence: 99%

Applying Reconfiguration Cost and Control Pattern Modeling to Self-Adaptive Systems

Matthé

2022

2022 IEEE/ACM 44th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)

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Self-adaptive systems have become a popular research topic to overcome challenges of developing highly complex, interconnected, and heterogeneous systems and networks. These systems aim to autonomously adapt to a changing environment by adapting system behavior or composition to improve performance. Many selfadaptive systems are designed in a purely reactive way and without considering costs that may be incurred by performing adaptation. This thesis therefore aims to develop an approach for proactive self-adaptive systems and evaluate the impact of reconfiguration cost and proactive adaptation in an edge computing system. Additionally the autonomous adaptation of different control patterns for centralized or decentralized control will be explored and evaluated. This thesis proposes to extend feature models, as used in dynamic software product lines, with modeling for reconfiguration cost and of uncertainty in the system's environment.

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Section: Evaluation Of Resultsmentioning

confidence: 99%

Applying Reconfiguration Cost and Control Pattern Modeling to Self-Adaptive Systems

Matthé

2022

2022 IEEE/ACM 44th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)

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“…Researchers have also proposed hierarchical scheduling solutions for the edge similar to ours [33]- [35]. Most of these approaches exploit different domain knowledge by distributing the scheduling across the cloud-to-edge hierarchy.…”

Section: Background and Related Workmentioning

confidence: 99%

Oakestra white paper: An Orchestrator for Edge Computing

Bartolomeo¹,

Yosofie²,

Bäurle³

et al. 2022

Preprint

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Edge computing seeks to enable applications with strict latency requirements by utilizing compute resources deployed closer to the users. The diverse, dynamic, and constrained nature of edge infrastructures necessitates a flexible orchestration framework that dynamically supports application QoS requirements. However, existing state-of-the-art orchestration platforms were designed for datacenter environments and make strict assumptions about underlying infrastructures that do not hold for edge computing. This work proposes a novel hierarchical orchestration framework specifically designed for supporting service operation over edge infrastructures. Through its novel federated cluster management, delegated task scheduling, and semantic overlay networking, our system can flexibly consolidate multiple infrastructure operators and absorb dynamic variations at the edge. We comprehensively evaluate our proof-of-concept implementation -Oakestra -against state-of-the-art solutions in both controlled and realistic testbeds and demonstrate the significant benefits of our approach as we achieve ≈ 10× and 30% reduction in CPU and memory consumption, respectively.

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“…In edge computing, workload management must, however, deal with user mobility and higher variance in server and network topologies and capacities, thus making it a distinct research topic. Workload management on the edge can be handled with different strategies, such as the physical placement of edge servers [5,12,36] or reallocating services on the softwareside with different optimization algorithms [18,37,38]. Reallocation can rely on known edge server features, such as capacity, or their current state, such as load or even price [39].…”

Section: Related Workmentioning

confidence: 99%

“…This reflects a scenario where the workload of an ES exceeds a threshold such that the processing time of a task becomes unacceptable. For an analysis of such a model, see, for example, [38].…”

Section: Assumptionsmentioning

confidence: 99%