Resource Allocation and Task Scheduling in Fog Computing and Internet of Everything Environments: A Taxonomy, Review, and Future Directions

Abstract

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.

Implementation of multi-source and multi-agent and fuzzy logic optimized QOS-based dynamic resource allocation

“…The ETC and MTC matrices provide assistance in mapping the jobs to the appropriate VM, which results in a reduction in the overall response time as well as the waiting time for the tasks. In the article [17], the authors provide a different strategy that takes into account the size, dynamism, and heterogeneity of network facilities found in distributed cloud networks with the goal of making the strategy suitable for online solutions. They were able to enhance the overall goals by doing things like increasing resource usage via local communication, which was based on the data collected from service requests.…”

CaPTS scheduler: A context‐aware priority tuple scheduling for Fog computing paradigm

“…There are several articles in the literature that emphasize on resource allocation and task scheduling in the Fog computing paradigm 19 . Additionally, various studies have been published that demonstrate the usefulness of context‐awareness in scheduling approaches in the Fog computing paradigm for Fob‐based applications.…”

“…There are several articles in the literature that emphasize on resource allocation and task scheduling in the Fog computing paradigm. 19 Additionally, various studies have been published that demonstrate the usefulness of context-awareness in scheduling approaches in the Fog computing paradigm for Fob-based applications. For example, Dsouza et al 13 introduced a policy for managing the resources in Fog computing in order to maintain the reliable interaction and interoperability between dissimilar application requests by the user.…”

“…Based on this contextual information retrieved, priority  t i of tuple T i is calculated and is added in descending sort order in waiting tuple queue Q w (line [15][16]. Upon completion of a tuple, CaPTS scheduler selects the next tuple T high from the head of the Q w that has high priority and allocates VM to it until its execution process is finished (line [19][20][21][22]. After finishing, T high is set to T f , which is then added to the finished tuples queue Q f (line [23][24].…”

D2FO: Distributed Dynamic Offloading Mechanism for Time-Sensitive Tasks in Fog-Cloud IoT-based Systems

“…Computational resources can be utilized locally, remotely, or even hybrid in the Edge-Fog-Cloud computing approaches. Although, centralized cloud computing has some pros, such as easy management, high computing power, high storage, and high network capacities, it suffers from some cons, such as not being distributed, having high network latency, high power consumption, low security, and cost [1], [2]. To overcome the mentioned limitations and also to increase Quality of Experience (QoE) for end users, other promising computational approaches, such as FC, has been integrated into the IoT-cloud systems.…”