If an edge-node orchestrator can partition Big Data tasks of variable computational
complexity between the edge and cloud resources, major reductions in total task completion …

High-computational complexity tasks benefit greatly in time requirements by offloading from
edge nodes to cloud resources. In contrast, low-complexity tasks exhibit little competitive …

Large reductions in completion times can result from transfer of Big Data tasks from edge
nodes to cloud resources, which can reduce the completion times by up to 97% and meet …

While edge computing is gaining traction, organizations operating in geographically
distributed locations are still using cloud computing to collect and post-process data. In this …

With the increasing number of connected devices and the generation of a large amount of
data, efficient methods are required to deal with the complexities of big data processing …

The explosive growth of big data imposes a heavy burden on computation, storage, and
communication resources in today's infrastructure. To efficiently exploit the bulk cloud …

This paper presents an analysis of the scalability and performance of an edge cloud system
designed to support latency-sensitive applications. A system model for geographically …

Recent years have witnessed a rapid growth of deep-network based services and
applications. A practical and critical problem thus has emerged: how to effectively deploy the …

In recent years, with the proliferation of the Internet of Things (IoT) and the wide penetration
of wireless networks, the number of edge devices and the data generated from the edge …

The amount of data collected in various IT systems has grown exponentially in the recent
years. So the challenge rises how we can process those huge datasets with the fulfillment of …