ABSTRACT
Due to the proliferation of Internet of Things (IoT) and application/user demands that challenge communication and computation, edge computing has emerged as the paradigm to bring computing resources closer to users. In this paper, we present Whispering, an analytical model for the migration of services (service offloading) from the cloud to the edge, in order to minimize the completion time of computational tasks offloaded by user devices and improve the utilization of resources. We also empirically investigate the impact of reusing the results of previously executed tasks for the execution of newly received tasks (computation reuse) and propose an adaptive task offloading scheme between edge and cloud. Our evaluation results show that Whispering achieves up to 35% and 97% (when coupled with computation reuse) lower task completion times than cases where tasks are executed exclusively at the edge or the cloud.
ABSTRACT
Named Data Networking (NDN) architectural features, including multicast data delivery, stateful forwarding, and in-network data caching, have shown promise for applications such as video streaming and file sharing. However, collaborative applications, requiring a multi-producer participation introduce new NDN design challenges. In this paper, we highlight these challenges in the context of the Network Time Protocol (NTP) and one of its most widely-used deployments for NTP server discovery, the NTP pool project. We discuss the design requirements for the support of NTP and NTP pool and present general directions for the design of a time synchronization protocol over NDN, coined Named Data Networking Time Protocol (NDNTP).
ABSTRACT
Delay-sensitive applications have been driving the move away from cloud computing, which cannot meet their low-latency requirements. Edge computing and programmable switches have been among the first steps toward pushing computation closer to end-users in order to reduce cost, latency, and overall resource utilization. This article presents the "compute-less" paradigm, which builds on top of the well known edge computing paradigm through a set of communication and computation optimization mechanisms (e.g.,, in-network computing, task clustering and aggregation, computation reuse). The main objective of the compute-less paradigm is to reduce the migration of computation and the usage of network and computing resources, while maintaining high Quality of Experience for end-users. We discuss the new perspectives, challenges, limitations, and opportunities of this compute-less paradigm.
