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 multi-edge-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 multiedge-cloud 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 cost-effective 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.
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