Task Scheduling Algorithms in Cloud Computing: A Survey

Tom, Linz; Bindu, V. R.

doi:10.1007/978-3-030-33846-6_39

Cited by 6 publications

(3 citation statements)

References 27 publications

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“…At last, the global minimal makespan of the DAG is min p ( f Q+1 ) T (p(f Q+1 )). The optimal embedding of each function can be retrieved from Θ. Update T p(f j ) by (10) 22 end for 23 end for 24 return min p ( f Q+1 ) T (p(f Q+1 )) and Θ…”

Section: Algorithm Designmentioning

confidence: 99%

“…5. For each f ∈ F, the processing power required and output data size are extracted from the corresponding job in the dataset and scaled to [1,10] giga flop and [5,15] × 10 3 kbits, respectively. Heterogenous edge servers.…”

Section: A Experiments Setupmentioning

confidence: 99%

“…Studying the optimal function placement is not new. Since cloud computing paradigm became popular, it has been extensively studied in the literature [9] [10]. When bringing function placement into the paradigm of edge computing, especially for the IoT stream processing, different constraints, such as the response time requirement of latency-critical applications, availability of function instances on the heterogenous edge servers, and the wireless and wired network throughput, etc., should be taken into consideration.…”

Section: Related Workmentioning

confidence: 99%

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Placement is not Enough: Embedding with Proactive Stream Mapping on the Heterogenous Edge

Zhao¹,

Liu²,

Xiang³

et al. 2020

Preprint

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Edge computing is naturally suited to the applications generated by Internet of Things (IoT) nodes. The IoT applications generally take the form of directed acyclic graphs (DAGs), where vertices represent interdependent functions and edges represent data streams. The status quo of minimizing the makespan of the DAG motivates the study on optimal function placement. However, current approaches lose sight of proactively mapping the data streams to the physical links between the heterogenous edge servers, which could affect the makespan of DAGs significantly. To solve this problem, we study both function placement and stream mapping with data splitting simultaneously, and propose the algorithm DPE (Dynamic Programmingbased Embedding). DPE is theoretically verified to achieve the global optimality of the embedding problem. The complexity analysis is also provided. Extensive experiments on Alibaba cluster trace dataset show that DPE significantly outperforms two state-of-the-art joint function placement and task scheduling algorithms in makespan by 43.19% and 40.71%, respectively.

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Section: Algorithm Designmentioning

confidence: 99%