Efficient distribution of service requests between fog and cloud nodes considering user mobility and fog nodes’ overload is an important issue of fog computing. This paper proposes a heuristic method for task placement considering the mobility of users, aiming to serve a higher number of requested services and minimize their response time. This method introduces a formula to overload prediction based on the entry-exit ratio of users and the estimated time required to perform current requests that are waiting in the queue of a fog node. Then, it provides a solution to avoid the predicted overloading of fog nodes by sending all delay-tolerant requests in the overloaded fog node’s queue to the cloud to reduce the time required for servicing delay-sensitive requests and to increase their acceptance rate. In addition, to prevent requests from being rejected when the mobile user leaves the coverage area of the current fog node, the requests in the current fog node’s queue will be transferred to the destination fog node. Simulation results indicate that the proposed method is effective in avoiding the overloading of the fog nodes and outperforms the existing methods in terms of response time and acceptance rate.

The study explored the effect of task-based collaborative interactions in a Synchronous Computer-mediated Communication (SCMC) environment on English as a Foreign Language (EFL) learners' development of grammatical knowledge and accuracy of a specified structure, that is, conditional clauses. To this end, at first, two intact EFL-grammar classes from an Iranian university were randomly assigned to control and experimental groups. A grammar test focusing on the above-mentioned structure was used at the pretest and posttest times. The control group learners were exposed to mainstream noncollaborative instruction in which they performed a number of tasks individually within the context of a teacher-fronted classroom. The learners of the experimental group, however, carried out the same planned tasks through peer-peer and teacher-student collaborative interactions in the form of text chats in an SCMC environment (i.e., Skype). Secondly, to trace learners' trajectories of grammatical accuracy, their L2 written outputs produced during the task performance were analyzed, employing the error-free T-unit ratio. The SCMC participants were also interviewed to elicit their attitudes towards employing the approach to grammar instruction. As to grammatical knowledge test, ANCOVA results revealed that the experimental group outperformed the control group in the posttest. In terms of grammatical accuracy, subsequent t-test results indicated significant gains for the SCMC group. Furthermore, learner interviews indicated that most learners had generally positive attitudes toward CMC-oriented grammar instruction. The findings suggested that collaborative interactions in form of text-based exchanges in CMC learning environments can be a useful platform for L2 grammar instruction and learning.

The interest in cloud computing has grown considerably over the recent years, primarily due to the scalable virtualized resources. Thus cloud computing has contributed to the advancement of the real-time applications such as the signal processing, environment surveillance, and weather forecast, where time and energy considerations are critical in order to perform the tasks. In the real-time applications, missing the deadlines for the tasks will cause catastrophic consequences Thus real-time task scheduling in a cloud computing environment is an important and essential issue. Furthermore, energy-saving in the cloud data center, regarding the benefits such as the reduction in the system operating costs and environmental protection is an important concern that has been considered during the recent years, and is reducible with an appropriate task scheduling. In this paper, we present an energy-aware real-time task (EaRT) scheduling approach for the real-time applications. We employ the virtualization and consolidation techniques subject to minimizing the energy consumptions, improve resource utilization, and meeting the deadlines of the tasks. In the consolidation technique, the scale-up and scale-down of the virtualized resources could improve the performance of task execution. The proposed approach comprises four algorithms, namely energy-aware task scheduling in cloud computing (ETC), vertical VM scale-up (V2S), horizontal VM scale-up (HVS), and physical machine scale-down (PSD). We present the formal model of the proposed approach using timed automata in order to prove precisely the schedulability feature and correctness of EaRTs. We will show that our proposed approach is more efficient in terms of the deadline hit ratio, resource utilization, and energy consumption compared to the other energy-aware real-time tasks scheduling algorithms.

The advent of Internet of Things (IoT) technology has led to the concept of the smart city, in which smart devices are recognized as a necessity. The applications installed on these devices generate large volumes of data that often require real-time processing. However, these devices have limited capabilities and are not capable of processing large amounts of data. Moving all this data to cloud data centers results in higher bandwidth usage, latency, cost and energy consumption. Therefore, providing services to delay-sensitive smart city applications in the cloud is a challenging issue, and meeting the requirements of these applications requires the use of a hybrid cloud and fog paradigm. Fog computing as a complement to the cloud allows data to be processed near smart devices. However, the resources in the fog layer are heterogeneous and have different capabilities, hence, appropriate scheduling of these resources is of great importance. In this paper, the problem of task scheduling for the smart city applications in the cloud-fog environment has been addressed. To this purpose, the task scheduling problem has been modeled as a multi-objective optimization problem, which aims to minimize service delay and energy consumption of the system under deadline constraint. Then, in order to solve this problem and achieve an appropriate scheduling strategy, non-dominated sorting genetic algorithm II (NSGA-II) with customized operators has been applied. In addition, in order to improve the diversity of the population and the convergence speed of the proposed algorithm, a combination of chaotic map and opposition-based learning methods have been used to generate the initial population. Also, the approach based on the penalty function has been employed to penalize the solutions that do not meet the deadline constraint. The simulation results reveal that the proposed scheduling algorithm, compared to its best competitor, improves service response delay, waiting time, execution delay and system energy consumption by 1. 49%, 1. 70%, 2. 7% and 1. 86%, respectively. Furthermore, by properly assigning tasks to the computing nodes, compared to the best competitor, the percentage of missed-deadline tasks is reduced by 1. 89%.

Over the recent years, one of the important aspects of cloud computing is the dynamic scheduling of a large number of task requests which are submitted with variable rate by users. Task scheduling plays a key role in cloud computing systems, and this type of scheduling can not be done on a single criterion, but many rules and conditions must be considered as an agreement between users and cloud providers. In fact, this agreement is the quality of the services that users expect from providers. Cloud data centers should not only execute these huge tasks, but also should meet the multiple needs of different users. In this paper, a multi-objective task scheduling strategy is proposed using non-dominated sorting, calculate normal and threshold rates. The aim of the proposed approach is considering some of the most important criteria for quality of service at the time of tasks execution, that means deadline and cost. In addition, the cloud elasticity property is considered. The simulation results show improvement in the conditions of makespan, cost, mean utilization of virtual machines and deadlines violation compared to MultiObjective, FCFS, Min-Min, Priority Schedulig and MOF approaches.

This paper investigates the scheduling problem of independent tasks in market-based grids. The heterogeneity and autonomy of resources in grids highlight the need for more flexible models and approaches to be exploited in these environments. To address this issue, a two-level market model is presented in this paper to schedule tasks to the grid resources. In the proposed model, users submit their own tasks to a centralized resource manager named meta-scheduler.Meta-scheduler knows general information about each of the administrative domains, called sites, existing in the low-level part of the model. Using the information gathered from all of the sites, meta-scheduler selects more suitable sites to execute the tasks with the aim of minimizing the overall cost of tasks execution. In this model, meta-scheduler not only targets the minimization of overall cost of the tasks execution, but also achieves this objective without any presumption about the policies and algorithms implemented in the lower layers of the system which addresses the dynamicity of environment. In addition to the two-level market model, a new task scheduling algorithm called GA-VNS which is an enhanced version of genetic algorithm is presented to be applied in market-based grids. GA-VNS can be used by local schedulers in each site with the policy of cost minimization considering the makespan of the system as a second criterion. The results obtained from performance evaluation of GA-VNS and other well-known algorithms in this context show that GA-VNS outperforms other algorithms in terms of the overall cost of tasks execution.

During the scheduling process, it is important to respect the constraints given by the jobs and the cloud providers. In addition to maintaining a balance between Quality of Service (QoS), fairness, and efficiency of jobs, scheduling is challenging. This paper aims to propose an efficient algorithm for load-balanced task scheduling in the cloud. Our algorithm uses a new meta-heuristic algorithm called COA (Coati Optimization Algorithm) to solve the task scheduling problem. This method is called COTSA (Coati Optimization-based Task Scheduling Algorithm). Its main goal is to reduce execution costs, load balancing, resource consumption, and makepan. Additionally, experimental results indicate that COTSA contributes to reduced energy consumption and enhanced system scalability and fault tolerance under simulated conditions. These improvements suggest potential suitability for dynamic and large-scale cloud infrastructures, though performance may vary depending on workload characteristics and system configurations. It is compared with Walrus Optimizer (WO), Slap Swarm Algorithm (SSA), Whale Optimization Algorithm (WOA), Zebra Optimization Algorithms (ZOA), Grasshopper Optimization Algorithm (GOA), Sooty Tern Optimization Algorithm (STOA), Golden Eagle Optimizer (GEO), Grey Wolf Optimizer (GWO), Subtraction-Average-Based Optimizer (SABO), and Sand Cat Swarm Optimization (SCSO), which are popular meta-heuristics. Experimental results demonstrate that COTSA reduces makespan by approximately 9%, lowers execution cost by up to 40%, improves resource utilization by around 3%, and enhances load balance by up to 30%, energy consumption about 36%, scalability near 17%, and fault tolerance about 16%, making it a robust and scalable solution for efficient cloud task scheduling.

In recent years, cloud computing is becoming eminent in the field of information technology. In a cloud computing environment, there is a potential for faults. There are different methods for dealing with faults, but with regard to the features and characteristics of the cloud computing environment, the use of fault tolerance methods is the best choice for this environment. One of the biggest issues in fault tolerance methods is the efficient use of resources. The optimal use of resources is important for cloud providers and customers. Unfortunately, the optimal use of resources in fault tolerance methods has not been much considered by researchers and cloud service providers. In this paper taking into account the dependence between tasks, an attempt has been made to provide a fault tolerance method on virtual machines, which in addition to being tolerant of fault, achieves optimum use of resources. In this method, by using a priority scheduler, each task is assigned a priority, then tasks are sent by the order of priority to their virtual machines for processing. The results of simulation by the cloudsim simulator show that the proposed method has been able to improve the use of resources more than other methods and with 95% confidence intervals it has achieved (29. 15% and 22. 74%) improvement in the number of processors, (30. 76% and 22. 34%) improvement in memory usage and (29. 71% and 22. 88%) improvement in the use of bandwidth.