A Locality Sensitive Hashing Based Collaborative Service Offloading Method in Cloud-Edge Computing

Benefiting by the big data produced by ever increasing IoT devices, big data services are gaining popular attention in many areas. However, general IoT terminals are unable to execute these services due to the exponentially growing data and the limited computing resources. And a possible solution is to execute the services on remote cloud data centers. However, transferring all data to remote cloud for process brings huge energy consumption and congestion on the backends under high load conditions. The development of edge servers makes it possible to handle some simple tasks on edge servers. Towards this end, it is imperative to design a collaborative service offloading scheme to process data of complex big data services on both edge servers and clouds. In this paper, to protect user’s privacy and quickly decide offloading destination for big data services, we propose a locality sensitive hashing based allocating strategy called Loyal. Loyal relies on E2LSH technique to hash and encrypt the sensitive data information. In addition, Loyal is able to retrieve suitable service that can be offloaded to the ES in a short time. Finally, the performance of Loyal is presented by simulation experiment.

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