Prioritized Task Scheduling in Fog Computing

Concurrency and Computation: Practice and Experience, 2019

SummaryDue 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 t...

International Journal of Modern Education and Computer Science, 2018

Cloud computing is an innovative technology which is based on the internet to preserve large applications. It is warehoused as a shared data over one platform. In addition, it offers better services to clients who belong to different organizations. In spite of the maximum utilization of computational resources provided by the cloud computing with lower cost, it suffers from specific restrictions. These restrictions are encountered through the load balancing of data in the cloud data centers. These restrictions are represented in the less bandwidth utilization, resource limitations, fault tolerance and security etc. In order to overcome these limitations, new computing model called Fog Computing is presented. It aims to offer the required service of the sensitive data to end users without delaying. The function of the fog computing is similar to the cloud computing with two preferred advantages. The first one is that it is placed more near to the end users to introduce its service in less time. Secondly, it is more valuable for streaming the real time applications, sensor networks, IOT which need high speed and reliable internet connection. In this paper, a novel load balancing algorithm has been proposed over a novel architectural model in the Fog Computing environment. The proposed model aims to serve the real-time tasks within their deadline. In addition, it serves the different soft tasks without starving. The soft tasks are classified according to the execution time and the priority levels. In addition, they are served according to their waiting time and priority-level. Furthermore, the proposed algorithm is employed to maximize the throughput, the resources and the network utilization and preserving the data consistency with less complexity to accomplish the end users demand.

International Journal of Innovative Research in Engineering and Management (IJIREM), 2024

In the area of fog and cloud computing, efficient work scheduling is critical for optimising resource allocation and raising Quality of Service (QOS) levels. Research in this area has resulted in a variety of ways for dealing with this problem. One line of research focuses on lowering data latency and improving QOS in fog-cloud systems by precise job scheduling. This requires intelligently dividing jobs across fog and cloud resources to reduce data processing and delivery delays, ultimately improving the overall user experience. Furthermore, the investigation encompasses novel methodologies such as hybrid evolutionary algorithms and heuristic strategies. These approaches seek to create a compromise between competing objectives, such as cost and energy efficiency, while also achieving QOS criteria. Researchers aim to develop scheduling systems that optimise resource utilisation and reduce operating costs without sacrificing service quality by applying evolutionary concepts or combining heuristic methods. Furthermore, ongoing discussions focus on improving resource scheduling approaches to achieve optimal QOS results. This entails continual evaluation and adaption of scheduling algorithms to changing environmental conditions, workload fluctuations, and user requests. Furthermore, developing methodologies such as the CODA approach provide intriguing avenues for efficient job scheduling in fog computing settings, with the potential to revolutionise future resource allocation and QOS enhancement efforts.

International Journal of Computer Applications, 2014

This paper presented a proposed model for cloud computing scheduling based on multiple queuing models. This allowed us to improve the quality of service by minimize execution time per jobs, waiting time and the cost of resources to satisfy user's requirements. By taking advantage of some useful proprieties of queuing theory scheduling algorithm is proposed to improve scheduling process. Experimental results indicate that our model increases utilization of global scheduler and reduce waiting time.

International Journal of Grid and Distributed Computing, 2018

The new era of Internet of Everything (IoE) is progressively making items online, but centralized cloud data processing does not scale to desires of the environment. The performance degradation can be seen in the cloud applications which are deadline like health monitoring and which need lesser response time and delay (instigated by transfer of data to cloud and then cloud to application). Fog computing appears as a solution to reduce the network congestion and latency as in this approach cloud is boosted to the area of the network. Fog computing is a model for handling a virtual and extremely distributed environment which delivers network and compute services between cloud data centres and users. In this paper, a Refinement of Resource Management in Fog Computing Aspect of QoS is proposed for efficient management of resources which considers execution time.

2019 International Conference on Information Technology (ICIT), 2019

Due to the transformation and expansion of Internet of Things(IoT), a large number of services are deployed on the edge of the network to provide the services to the end users rather than from the cloud data center since processing the data at the edge can reduce the response time and bandwidth cost while fulfilling the Quality of services(QoS). The fog-cloud computing environment offers promising solution to provision the available resources for IoT based application.Undoubtely Fog computing compliment of cloud computing helps to provide efficient solution to deal with diverse IoT application. However to provide efficient solution in such environment is challenge in different IoT based application such as health care applications, intelligent transportation system and smart cities. Task scheduling and Resource allocation are the NP-hard issues in distributed computing. Each Application consists of several modules that requires resources to execute. However, providing an optimal task scheduling policy in such a heterogeneous system is a NP class problem and has been proposed by different methods like Greedy, meta-heuristic and all nature inspired algorithm for solving an NP-complete problem. The task scheduling problem is a key challenge in the distributed computing system. In this paper, we are trying to map the independent task into the fog layer and our algorithm gives good result if we place the services in fog layer rather than cloud data center. The system resources available may be CPU, RAM, etc by assigning some priority to the task based on its deadline. Also, we have assumed that once a task assigned to a particular node will not leave that until its execution complete. In this paper, we also proposed a three-layer architecture for efficient task scheduling for application such as health care in smart homes.

2018

Cloud computing system offers reliable services to the users based on their service requests. The users should pay for the amount of resources availed by them. The main goal of the cloud computing system is to minimize the capital and maintenance cost by eliminating the process of the owning the resources. It supports the users by providing the equipment and programming resources required for them. The cloud data centers comprise a group of configurable resources including networks, applications and servers. The data center of the cloud computing system should satisfy the demands of the user to gain reputation and profit. A Service Level Agreement (SLA) should be signed between the cloud users and service providers to enter into an agreed upon business environment. The requirements of the users will be clearly specified in the SLA, which must be strictly followed (Ghosh, 2012). The SLA is necessary to attain the Quality of Service (QoS) and to validate whether the QoS is met. The Qo...

2021

Received: 4 January 2021 Accepted: 16 March 2021 The rapid deployment of IoT in different areas generates a massive amount of data transferred to the Cloud. To solve this challenge a new paradigm, called Fog Computing, is located at the edge of the network and close to the connected objects. Its main role is to extend the capacities of Cloud and improve the performance and the QoS required by the applications by the use of different methods and techniques based on scheduling algorithms. In this paper, we review various recent studies available in the literature that are interested in the scheduling methods and algorithms used in Fog computing. The use of fog layer, in solving optimization problem, is faced with serious challenges. Therefore, to help practitioners and researchers, we present an in-depth overview of Fog Computing studying various scheduling methods and algorithms. We analyze, compare and classify these different scheduling approaches according to the nature of the alg...

Journal of Information Systems Engineering and Management, 2020

Application execution required in cloud and fog architectures are generally heterogeneous in terms of device and application contexts. Scaling these requirements on these architectures is an optimization problem with multiple restrictions. Despite countless efforts, task scheduling in these architectures continue to present some enticing challenges that can lead us to the question how tasks are routed between different physical devices, fog nodes and cloud. In fog, due to its density and heterogeneity of devices, the scheduling is very complex and in the literature, there are still few studies that have been conducted. However, scheduling in the cloud has been widely studied. Nonetheless, many surveys address this issue from the perspective of service providers or optimize application quality of service (QoS) levels. Also, they ignore contextual information at the level of the device and end users and their user experiences. In this paper, we conducted a systematic review of the lit...

International Journal of Distributed Sensor Networks, 2017

The rapid development of Internet of Things applications, along with the limitations of cloud computing due mainly to the far distance between Internet of Thing devices and cloud-based platform, has promoted a newly distributed computing platform based on collaboration between cloud computing and fog computing. Fog computing helps to reduce transmission latency and monetary cost for cloud resources, while cloud computing helps to fulfill the increasing demands of large-scale compute-intensive offloading applications. In this article, we study the tradeoff issue between the makespan and cloud cost when scheduling large-scale applications in such a platform. We propose a scheduling algorithm called Cost-Makespan aware Scheduling heuristic whose major objective is to achieve the balance between the performance of application execution and the mandatory cost for the use of cloud resources. Additionally, an efficient task reassignment strategy based on the critical path of the directed a...