Summary Due to an ever‐increasing number of Internet of Everything (IoE) devices, massive amounts of data are produced daily. Cloud computing offers storage, processing, and analysis services for handling of such large quantities of data. The increased latency and bandwidth consumption is not acceptable to real‐time applications like online gaming, smart health, video surveillance, etc. Fog computing has emerged to overcome the increase in latency and bandwidth consumption in Cloud computing. Fog Computing provides storage, processing, networking, and analytical services at the edge of a network. As Fog Computing is still in its infancy, its significant challenges include resource‐allocation and job‐scheduling. The Fog devices at the edge of the network are resource‐constrained. Therefore, it is important to decide the assignment and scheduling of a job on a Fog node. An efficient job scheduling algorithm can reduce energy consumption and response time of an application request. In this paper, we propose a novel Fog computing scheduler that supports service‐provisioning for Internet of Everything, which optimizes delay and network usage. We present a case study to optimally schedule the requests of Internet of Everything devices on Fog devices and efficiently address their demands on available resources on every Fog device. We consider delay and energy consumption as performance metrics and evaluate the proposed scheduling algorithm using iFogSim in comparison with existing approaches. The results show that the delay and network usage of the proposed scheduler improve by 32% and 16%, respectively, in comparison with FCFS approach.

The Internet of Everything paradigm is being rapidly adopted in developing applications for different domains like smart agriculture, smart city, big data streaming, etc. These IoE applications are leveraging cloud computing resources for execution. Fog computing, which emerged as an extension of cloud computing, supports mobility, heterogeneity, geographical distribution, context awareness, and services like storage, processing, networking, and analytics on nearby fog nodes. The resource-limited, heterogeneous, dynamic, and uncertain fog environment makes task scheduling a great challenge that needs to be investigated. The paper is motivated by this consideration and presents a systematic, comprehensive, and detailed comparative study by discussing the merits and demerits of different scheduling algorithms, focused optimization metrics, and evaluation tools in the fog computing and IoE environment. The goal of this survey paper is fivefold. First, we review the fog computing and IoE paradigms. Second, we delineate the optimization metric engaged with fog computing and IoE environment. Third, we review, classify, and compare existing scheduling algorithms dealing with fog computing and IoE environment paradigms by leveraging some examples. Fourth, we rationalize the scheduling algorithms and point out the lesson learned from the survey. Fifth, we discuss the open issues and future research directions to improve scheduling in fog computing and the IoE environment.

During the last decade, Cloud computing has efficiently exploited the economy of scale by providing low cost computational and storage resources over the Internet, eventually leading to consolidation of computing resources into large data centers. However, the nascent of the highly decentralized Internet of Things (IoT) technologies that cannot effectively utilize the centralized Cloud infrastructures pushes computing towards resource dispersion. Fog computing extends the Cloud paradigm by enabling dispersion of the computational and storage resources at the edge of the network in a close proximity to where the data is generated. In its essence, Fog computing facilitates the operation of the limited compute, storage and networking resources physically located close to the edge devices. However, the shared complexity of the Fog and the influence of the recent IoT trends moving towards deploying and interconnecting extremely large sets of pervasive devices and sensors, requires exploration of adaptive Fog architectural approaches capable of adapting and scaling in response to the unpredictable load patterns of the distributed IoT applications. In this paper we introduce a promising new nature-inspired Fog architecture, named SmartFog, capable of providing low decision making latency and adaptive resource management. By utilizing novel algorithms and techniques from the fields of multi-criteria decision making, graph theory and machine learning we model the Fog as a distributed intelligent processing system, therefore emulating the function of the human brain.

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Peer-to-peer (P2P) overlay networks were developed initially for file sharing such as Napster and Gnutella; but later, they have become popular for content sharing, media streaming, telephony applications, etc. Underlay-unawareness in P2P systems can result in suboptimal peer selection for overlay routing and hence poor performance. In this paper, we present a comprehensive survey of the research work carried out to solve the overlay-underlay mapping problems up till now. The majority of underlay-aware proposals for peer selection focus on finding the shortest overlay routes by selecting nearest nodes according to proximity information. Another class of approaches is based on passive or active probing for provision of underlay information to P2P applications. Some other optimizations propose use of P2P middleware to extract, process, and refine underlay information and provide it to P2P overlay applications.Another class of approaches strive to use ISPs or third parties to provide underlay information to P2P overlay applications according to their requirements. We have made a state-of-the-art review and comparison for addressing the overlay-underlay mismatch in terms of their operation, merits, limitations, and future directions.