An Energy-Aware High Performance Task Allocation Strategy in Heterogeneous Fog Computing Environments

With the continuous evolution of technology, the prevalence of the Internet of Things (IoT) has grown significantly. However, with this rise comesa number of challenges for integrated Cloud Computing (CC), including security, performance, latency, and network issues. Fortunately, Fog Computing is a solution that addresses these concerns by bringing CC closer to IoT devices. Essentially, fog serves as a data hub that processes and stores information locally on the fog node, rather than sending it to a cloud server. This results in faster response times and higher- quality services compared to those offered by traditional cloud servers. Fog Computing can optimize service delivery for multiple IoT clients by allowing the administrationof services and resource provisioning outside of CC, nearer to devices, or at specific locations for Service Level Agreements (SLAs). It's important to note that Fog Computing is not intended to replace CC, but rather to complement itas a critical component. In this paper, we explore various computing paradigms, examine the features and architecture of Fog Computing, analyze its relationship with IoT, and assess differentalgorithms used in Fog Computing systems. Furthermore, we tackle the distinctive challenges that emerge with Fog Computing as an intermediate layer between IoT sensors/devices and data centers. Keywords: Fog Computing, Internet of Things(IOT), Cloud Computing (CC).

A Review on Fog Computing: Architecture, Fog with IoT, Algorithms and Research Challenges

A walkthrough of the emerging IoT paradigm: Visualizing inside functionalities, key features, and open issues

The use of Internet of Things (IoT) in sports leads to an exponential growth in data generation, It creates a need for efficient processing and analysis mechanisms. Cloud computing works efficiently in such situations but due to its central location there are some issues like latency, Increased workload on cloud and Network congestion due to huge data transfer to cloud. Fog computing is technology that extends the cloud computing services closer to the data source and offers promising solutions for real-time analytics. In sports there is a need to process data in a time-effective manner such as data related to finding strategies of the opponent team or Players health related data which needs to process time effectively. Fog Computing may work effectively. This paper reviews existing literature on the applications of cloud computing and fog computing in sports. The study explores the benefits, challenges, and future directions in use of fog computing in sports Domain. Keywords: Fog Computing, Sports, IoT, Real-time Analytics, Performance Monitoring

This chapter describes how traditionally, Cloud Computing has been used for processing Internet of Things (IoT) data. This works fine for the analytical and batch processing jobs. But most of the IoT applications demand real-time response which cannot be achieved through Cloud Computing mainly because of inherent latency. Fog Computing solves this problem by offering cloud-like services at the edge of the network. The computationally powerful edge devices have enabled realising this idea. Witnessing the exponential rise of IoT applications, Fog Computing deserves an in-depth exploration. This chapter establishes the need for Fog Computing for processing IoT data. Readers will be able to gain a fair comprehension of the various aspects of Fog Computing. The benefits, challenges and applications of Fog Computing with respect to IoT have been mentioned elaboratively. An architecture for IoT data processing is presented. A thorough comparison between Cloud and Fog has been portrayed. Also, a detailed discussion has been depicted on how the IoT, Fog, and Cloud interact among them.

The Internet of Things (IoT) is generating an immense volume of data. With cloud computing, medical sensor and actuator data can be stored and analyzed remotely by distributed servers. The results can then be delivered via the Internet. The number of devices in IoT includes such wireless diabetes devices as blood glucose monitors, continuous glucose monitors, insulin pens, insulin pumps, and closed-loop systems. The cloud model for data storage and analysis is increasingly unable to process the data avalanche, and processing is being pushed out to the edge of the network closer to where the data-generating devices are. Fog computing and edge computing are two architectures for data handling that can offload data from the cloud, process it nearby the patient, and transmit information machine-to-machine or machine-to-human in milliseconds or seconds. Sensor data can be processed near the sensing and actuating devices with fog computing (with local nodes) and with edge computing (within the sensing devices). Compared to cloud computing, fog computing and edge computing offer five advantages: (1) greater data transmission speed, (2) less dependence on limited bandwidths, (3) greater privacy and security, (4) greater control over data generated in foreign countries where laws may limit use or permit unwanted governmental access, and (5) lower costs because more sensor-derived data are used locally and less data are transmitted remotely. Connected diabetes devices almost all use fog computing or edge computing because diabetes patients require a very rapid response to sensor input and cannot tolerate delays for cloud computing.

In 21st century, Internet of Things (IoT) is playing an important role in developing Smart cities. With the growth of IoT, data is growing with acceleration. As the data is growing the need to store data is also increasing. More the data, the latency will be high to store and retrieve data from the cloud. The concept of fog computing was originated to diminish the latency for accessing the data to and from the cloud. Fog computing imparts the storage, computing as well as networking services at the end point of the network. Fog nodes also have limited computational capabilities. Due to some weaknesses, fog computing and cloud computing cannot sustain alone, so both these technologies are integrated to build smart IoT infrastructure for Smart city. Fog computing have a major role and supreme responsibility in development of a Smart city. This paper discusses various applications of fog computing and their implementations in a Smart cities. It also proposes a prototype for Waste management system in a city. Fog computing can help to govern the waste collection of the city in a smart way. On the basis of our review, several open concerns and challenges of fog computing are discussed, and the directions for future researchers have also been discussed.

Exploring and Optimizing the Fog Computing in Different Dimensions

SummaryIn the modern Internet of Things (IoT) era, several applications generate a vast amount of data and that needs to be handled appropriately. The conventional cloud computing system delivers us with enormous resources to manage such voluminous data. Despite that, the growing demands of IoT applications on minimal energy consumption, minimal latency, the privacy of data, data processing based on location, and maximum Quality of Service(QoS) impels the advent of fog computing. As the devices in the fog layer are heterogeneous, distributed, and resource‐constrained, how the fog resources are effectively utilized for executing latency and time‐sensitive applications is a primary challenge. The data generated by the IoT devices are voluminous and produce overhead in network bandwidth during transmission and slow down the response time. This article addresses the task scheduling problem in fog computing to minimize the makespan and energy consumption. The proposed work consists of two phases. In the first phase, the tasks are ordered based on the heuristic method, heterogeneous earliest finish time (HEFT). Then the ordered tasks are scheduled by applying the improved gaining sharing knowledge (IGSK) based algorithm. To reduce the energy consumption dynamic voltage frequency scaling (DVFS) is applied in the proposed scheme. To evaluate the proposed scheduling method the simulations are performed on different workflows with varying sizes. The performance evaluation results exhibit that the proposed work outperforms the task scheduling with similar methods in terms of makespan and energy consumption.

Fog computing is a new computational paradigm proposed by Cisco that subdues the shortcomings of cloud computing by transferring some of the core functions of cloud towards the network edge. Internet of Things (IOT) is a very promising technology, which are used heavily in many areas of life and with such a large and rapid spread produces a lot of challenges in IOT. As reported by Cisco, by the year 2020, the next generation Internet-connected devices would comprise of around 50 billion Internet-connected devices which are located at the edge of the network and require support for mobility, low latency, real-time, and location-aware services. The Cloud computing is used as a main component in IOT architecture to provide processing and storage services for IOT system but the conventional cloud computing architecture is completely centralized in nature, and therefore, fails to provide real-time and low latency services to billions of IOT devices at the edge of the network, simultaneously. The main objective of this research is to combine the capabilities of Fog Computing, Cognitive and Context-Aware IOT to build a cognitive management framework for Fog computing capable of adopting it's behavior in a context-aware fashion to maximize the number of functions that can be carried out in The Fog and to provide a solution for determining which functions should be carried out in the Fog and which should be carried out in the cloud and how the Fog should interact with the Cloud, The same function in some situations can be carried out in the fog while in other situations can be carried out in the cloud according to its context.

Internet of Things (IoT) has been commercially explored as Platforms as a Services (PaaS). The standard solution for this kind of service is to combine the Cloud computing infrastructure with IoT software, services, and protocols also known as CoT (Cloud of Things). However, the use of CoT in latency-sensitive applications has been shown to be unfeasible due to the inherent latency of cloud computing services. One proposal to solve this problem is the use of the computational resources available at the edge of the network, which is called Fog computing. Fog computing solves the problem of latency but adds complexity to the use of these resources due to the dynamism and heterogeneity of the IoT. An even more accentuated form of fog computing is Mist computing, where the use of the computational resources is limited to the close neighborhood of the client device. The decision of what computing infrastructure (Fog, Mist, or Cloud computing) is the best to provide computational resources is not always simple, especially in cases where latency requirements should be met by CoT. This work proposes an algorithm for selecting the best physical infrastructure to use the computational resource (Fog, Mist, or Cloud computing) based on cost, bandwidth, and latency criteria defined by the client device, resource availability, and topology of the network. The article also introduces the concept of feasible Fog that limits the growth of device search time in the neighborhood of the client device. Simulation results suggest the algorithm’s choice adequately attends the client’s device requirements and that the proposed method can be used in IoT environment located on the edge of the network.

With the development in computing technologies, fog computing is developing as public and robust computing, which complements cloud computing to provide services, computation, and storage on the edge network. The future growth and support of 5G access networks additional advance the viability and implementation of fog networks and widen the scope of devices that can participate and serve in IoT communication. However, scaling fog computing, the number of end-users increases. Hence, the workload between fog nodes needs to be distributed efficiently. Otherwise, some of the nodes will be overloaded, and others will be under-loaded. Consequently, one of the critical factors for managing resources in fog computing efficiently and avoiding overloaded or under-loaded is load balancing. Therefore, load balancing between these resources is a challenge in fog computing. There are different techniques to balance the load, such as optimization algorithms or machine learning. This paper proposes a load balancing model in fog computing based on a many-objective particle swarm optimization (PSO) algorithm with support vector regression (SVR). The proposed load balancing model considered four metrics to optimize them while distributing the load: response time, energy consumption, resource utilization, and throughput. Besides, It combines SVR with PSO to improve PSO performance. The proposed model has been simulated and tested to evaluate the performance from different aspects. The experiments show that the proposed model efficiently balances the load with optimizing the four metrics. In addition, it improves the performance of PSO, which is used to balance the load.

: A large part of these day’s global population is absolutely structured upon agriculture enterprise for its supply of livelihood. Smart agriculture is an improvement which emphasizes the use of statistics and conversation era. Fog computing, cloud computing and information pushed are the unique strategies to offer a notable opportunity for smart agriculture to boom within the productiveness with the aid of getting actionable insights to improve farming activities and increase within the performance and yield. Data driven is typically used to refer a manner or pastime this is spurred on by means of information, in place of being pushed by means of mere instinct or depending on the collection or analysis of data. Data pushed is the collection of actual time records associated with soil, environmental factors, planting, intercultural management and harvesting, value and pest management. The existing systems which might be primarily depends on traditional cloud models are insufficient to deal with a lot of information produced by the Internet of Things (IoT) gadgets which is associated. After decreasing latency in assisting the real time selections which is primarily based at the statistics produced. To be the most important issue, the facts processing closer to the supply of its production. This can be passed off with the aid of adopting the fog based mechanism. Fog computing which is dwells among cloud and Internet of Things (IoT), was proposed. In the Fog figuring environment, IoT gadgets are associated with fog gadgets. Smart agriculture based totally on IoT-Fog may be extra in a position in phrases of greatest utilization of bandwidth or latency for real time decision making. In this research paper, we had to provide the facts exploration of Fog Computing (FC) function above Cloud Computing (CC) towards observing smart agriculture.

In Healthcare big data, data is originated from various heterogeneous sources. Numerous novel base particular healthcare applications offered to handling source of data from electronic health record (EHR) to medical images. Imaging, Electronic Health Report, technology in light of sensor and numerous different procedures created an immense measure of Healthcare data. Cloud computing development was an excellent paradigm to substantiate big data which incited find of imperceptible examples. Cloud computing is a developing new registering design intended to answer different contending administrations on the Web. Fog Computing is a design style in which arrange segments amongst devices and the cloud execute application-particular rationale. We in this paper investigate, characterize, and talk about various application of cloud and fog computing. We talk about the impact of cloud computing and fog computing on healthcare big data. Cloud base framework for Homediagnosis Service, Fog computing architecture and the justification of moving from cloud to Fog presented comprehensively in this paper.

Fog computing is a challenging task because it requires all of the available resources, which increases as the number of users working in this environment grows. Most existing resource allocation techniques focus on providing performance for a particular workload, so more users create more demand for resources. The resource management algorithm is an NP-complete problem that has varying time requirements depending on the size of the problem. Optimal management of resources in fog computing can be done through heuristic, meta-heuristic and hybrid approaches. The meta-heuristics approaches can handle large search spaces and discover better solutions for resource management within a reasonable time. This paper presents a detailed review of heuristic and metaheuristic algorithms for resource optimization in fog computing environments. It compares each category of the algorithm based on performance metrics, tools used, advantages, and disadvantages so to make informed decisions about which algorithm will work best for any formulated problem. These algorithms are capable of achieving more than the previous state-of-the-art performance at lower cost, as well as improved utilization of resources and increased energy efficiency.