Abstract-Internet of Things typically involves a significant number of smart sensors sensing information from the environment and sharing it to a cloud service for processing. Various architectural abstractions, such as Fog and Edge computing, have been proposed to localize some of the processing near the sensors and away from the central cloud servers. In this paper, we propose Edge-Fog Cloud which distributes task processing on the participating cloud resources in the network. We develop the Least Processing Cost First (LPCF) method for assigning the processing tasks to nodes which provide the optimal processing time and near optimal networking costs. We evaluate LPCF in a variety of scenarios and demonstrate its effectiveness in finding the processing task assignments.

Edge computing has gained attention from both academia and industry by pursuing two significant challenges: 1) moving latency critical services closer to the users, 2) saving network bandwidth by aggregating large flows before sending them to the cloud. While the rationale appeared sound at its inception almost a decade ago, several current trends are impacting it. Clouds have spread geographically reducing end-user latency, mobile phones' computing capabilities are improving, and network bandwidth at the core keeps increasing. In this paper, we scrutinize edge computing, examining its outlook and future in the context of these trends. We perform extensive client-to-cloud measurements using RIPE Atlas, and show that latency reduction as motivation for edge is not as persuasive as once believed; for most applications the cloud is already "close enough" for majority of the world's population. This implies that edge computing may only be applicable for certain application niches, as opposed to a general-purpose solution.

Multipath TCP (MPTCP) allows applications to transparently use all available network interfaces by creating a TCP subflow per interface. One critical component of MPTCP is the scheduler that decides which subflow to use for each packet. Existing schedulers typically use estimates of end-to-end path properties, such as delay and bandwidth, for making the scheduling decisions. In this paper, we show that these scheduling decisions can be significantly improved by including readily available local information from the device driver queues to the decision-making process. We propose QAware, a novel cross-layer approach for MPTCP scheduling. QAware combines end-to-end delay estimates with local queue buffer occupancy information and allows for a better and faster adaptation to the network conditions. This results in more efficient use of the available resources and considerable gains in aggregate throughput. We present the design of QAware and evaluate its performance through simulations, and also through real experiments, comparing it to existing schedulers. Our results show that QAware performs significantly better than other available approaches for all use-cases and applications.

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