Joint Coflow Optimization for Data Center Networks

Wu, Zhaoxi

doi:10.1109/access.2021.3102067

Cited by 4 publications

(1 citation statement)

References 45 publications

(63 reference statements)

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“…The work in [51] considers the optimization of task placement and routing to minimize the coflow completion time. The work in [52] proposed a solution to jointly optimize the task placement, coflow bandwidth scheduling, and path choice for minimizing the average CCT in the intra-data center. Another work in [53] studied joint cotask aware offloading and scheduling in mobile edge computing systems.…”

Section: Related Workmentioning

confidence: 99%

Multihop Offloading of Multiple DAG Tasks in Collaborative Edge Computing

Sahni

Cao

Yang

et al. 2021

IEEE Internet Things J.

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Collaborative edge computing has become a popular paradigm where edge devices collaborate by sharing resources. Data dissemination is a fundamental problem in CEC to decide what data is transmitted from which device and how. Existing works on data dissemination have not focused on coflow scheduling in CEC, which involves deciding the order of flows within and across coflows at network links. Coflow implies a set of parallel flows with a shared objective. The existing works on coflow scheduling in data centers usually assume a non-blocking switch and do not consider congestion at different links in the multihop path in CEC, leading to increased coflow completion time (CCT). Furthermore, existing works do not consider multiple flow sources that cannot be ignored, as data can have duplicate copies at different edge devices. This work formulates the multisource coflow scheduling problem in CEC, which includes jointly deciding the source and flow ordering for multiple coflows to minimize the sum of CCT. This problem is shown to be NPhard and challenging as each flow can have multiple dependent conflicts at multiple links. We propose a source and coflowaware search and adjust (SCASA) heuristic that first provides an initial solution considering the coflow characteristics. SCASA further improves the initial solution using the source search and adjust heuristic by leveraging the knowledge of both coflows and network congestion at links. Evaluation done using simulation experiments shows that SCASA leads to up to 83% reduction in the sum of CCT compared to benchmarks without a joint solution.

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Section: Related Workmentioning

confidence: 99%