Due to increased use of IoT devices and data sensors a huge amount of data is being produced for processing in real-time. Fog computing has evolved as a solution for fast processing of data. To complete the data processing as per requirement of user, it is processed at fog nodes which are near the user. To complete work in a specified time with limited resources, task scheduling is performed. With the increased amount of data to be processed, the completion of task within given time in fog computing is a major challenge. So, scheduling of tasks and resources is a very important issue. A lot of research has been undertaken in recent years. In this survey, authors have reviewed various task scheduling algorithms suggested by researchers to meet the user requirements.The focus of this article is on diverse scheduling techniques being deployed in fog computing. An effort has been made to classify existing approaches, research issues and determine significant issues existing in this field. There are four major scheduling categories that are used by researchers namely, static, dynamic, heuristic, and hybrid. As per this study, 17% researchers deployed static, 23% dynamic, 47% heuristic, and 13% hybrid approaches, respectively.Analysis shows that for QoS (Quality of Service) parameters 19% researchers are focused on response time, 18% on cost and energy consumption, and 16% on makespan. For QoS parameter other factors have much smaller contribution in comparison to the above factors. In the tools used, researchers observed that 40% of the researches have used iFogSim, according to literature. Besides, this research article also discusses open issues and future work in the field of fog computing. This article also underlines various open issues and future directions in the field of fog computing.
Cloud computing is the extensively used technology these days. Due to the usage of smart devices, a huge amount of data is produced. The processing of this data in real time is a big challenge for cloud servers. Fog computing is the solution for this, but fog has its own limitation in form of storage. To overcome, this cloud-fog architecture is preferred. In cloud-fog architecture, workflow scheduling is an open research area but finding an optimal algorithm is a major challenge. Some researchers proposed meta-heuristic algorithms to solve workflow scheduling issues but they are trapped locally and fails to give the global optimal solution. To solve workflow scheduling problems, we propose the PWOA algorithm, a hybrid of Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA). The goal of this algorithm is to minimize the Total Execution Time (TET) and Total Execution Cost (TEC) of dependent tasks in a cloud-fog-mobile computing environment. Because it uses the features of both the standard PSO and WOA algorithms, the proposed algorithm overcomes the trapping problem also. In this article, the simulation results were compared to standard PSO and WOA algorithms using several benchmarks of four different scientific workflows (Cybershake, Epigenomics, Inspiral, Montage, and Sipht) with different numbers of tasks assigned in the proposed algorithm performed better.
Finite State Machines (FSMs) are mathematical abstractions which have a rich use in computer applications. If we build a FSM for software, then it becomes much easier and simpler to understand, debug and modify. The insights developed in FSMs have had great influence on various domains. FSM is as important as any other computer science tool. This paper minimizes the gap which currently exists between software development and the formal method of theoretical computer science. So importance of FSMs has been explored over various such domains in this article. These domains include spoken web technology that enables a user to access a massive network of voice sites through speech. Information transfer protocol for Vehicular Computing is another domain, where users can get on road support services using vehicular sensors and global position system. Adding on to the domain list, we have modeled FSMs for Ankle Monitor and Sensor Dust which are applications of mobile ad-hoc networks. Ankle monitor is primarily used to track movements of an individual through wireless communication and Sensor Dusts are tiny sensors which monitor the environment in which they are deployed. We have also modeled Palm Operating System, which is a mobile OS that runs on Linux kernel. This paper presents the FSM for the booting sequence and the user interface of Palm OS. To conclude with we have taken up the domain of satellite simulation and have presented a FSM for the steps involved in Satellite launching and Image Handling within a satellite. Therefore, by modeling a range of applications using FSM we attempt to add-on to the significance of this concept and at the same time provide a single document comprising numerous FSM models. This concept can be used by software developers for easy modeling of their designs and can further use it to verify and debug the model whenever required.
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