VA-Store: A Virtual Approximate Store Approach to Supporting Repetitive Big Data in Genome Sequence Analyses

Liu, Xianying; Zhu, Qiang; Pramanik, Sakti; Brown, C. Titus; Qian, Gang

doi:10.1109/tkde.2018.2885952

Cited by 4 publications

(1 citation statement)

References 29 publications

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“…e large-scale tasks processing based on edge computing environment for big data [4][5][6] has become one of the research hotspots. In order to realize the parallel processing of large-scale tasks, we can exploit the combination mode of cloud data center [7,8] and multiple edge computing centers to realize data sharing and multicategory tasks processing.…”

Section: Introductionmentioning

confidence: 99%

FLOM: Toward Efficient Task Processing in Big Data with Federated Learning

2022

Security and Communication Networks

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With the diversification and individuation of user requirements as well as the rapid development of computing technology, the large-scale tasks processing for big data in edge computing environment has become a research focus nowadays. Many recent efforts for task processing are designed and implemented based on some traditional protocols and optimization methods. Therefore, it is more difficult to explore the task allocation strategy that maximizes the overall system revenue from the perspective of global load balancing. In order to overcome this problem, a large-scale tasks processing approach called Federated Learning based Optimization Methodology (FLOM) for large-scale tasks processing was presented to achieve accurate task classification and overall load balancing while satisfying task allocation requirements. FLOM performs the data aggregation and establishes the personalized models by federated learning. The deep network model is designed for deep feature learning of task requests and hosts in the substrate network. The experimental results show the capability of FLOM in terms of large-scale task classification as well as allocation.

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