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Optimal Decision Making for Big Data Processing at Edge-Cloud Environment: An SDN Perspective
تصمیم گیری بهینه برای پردازش داده های بزرگ در محیط لبه-ابر: چشم انداز SDN-2018 With the evolution of Internet and extensive usage of smart devices for computing and storage, cloud computing has become popular. It provides seamless services
such as e-commerce, e-health, e-banking, etc., to the end
users. These services are hosted on massive geodistributed
data centers (DCs), which may be managed by different service providers. For faster response time, such a data explosion creates the need to expand DCs. So, to ease the load on DCs, some of the applications may be executed on the edge
devices near to the proximity of the end users. However,
such a multiedge-cloud environment involves huge data
migrations across the underlying network infrastructure,
which may generate long migration delay and cost. Hence,
in this paper, an efficient workload slicing scheme is proposed for handling data-intensive applications in multiedgecloud environment using software-defined networks (SDN).
To handle the inter-DC migrations efficiently, an SDN-based
control scheme is presented, which provides energy-aware
network traffic flow scheduling. Finally, a multileader multifollower Stackelberg game is proposed to provide costeffective inter-DC migrations. The efficacy of the proposed
scheme is evaluated on Google workload traces using various parameters. The results obtained show the effectiveness of the proposed scheme.
Index Terms: Cloud data centers, edge computing, energy efficiency, software-defined networks (SDNs), Stackel berg game |
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