The Internet of Things (IoT) technology has become a transformative force in both information and industrial sectors, enabling devices to collect and exchange data. The exponential growth of IoT devices has led to increased big data generation on the one hand and processing demands on the other. Efficient and scalable solutions are needed to handle the complexities associated with IoT systems’ deployments. In this context, scheduling IoT applications that have a special sensitivity to latency pose greater challenges. This research paper focuses on scheduling IoT Bag of Task (BoT) applications in a hybrid fog-cloud environment, with a particular emphasis on hard deadline constraints in latency-sensitive systems (such as IoT healthcare and monitoring systems). The proposed scheduling algorithm dynamically allocates BoTs on the fog platform, forwarding them to the cloud layer if the fog layer cannot meet the application’s deadline. The algorithm considers multiple types of cloud instances to meet processing requirements and minimize execution costs, while ensuring compliance with deadlines. The experimental results reveal that the proposed scheduling algorithm prevents any deadline violations. Additionally, the proposed algorithm maximizes the utilization of processing resources within fog-cloud environments, resulting in minimized execution costs.

The variety of pricing models offered by cloud service providers and the availability of a wide diversity of computing resources has increased the popularity of this paradigm for scientific applications. Such a scalable platform can be an ideal option for the execution of loosely coupled parallel applications, such as scientific workflows. Scientific workflows are regarded as one of the most important elements in different scientific fields in which a complex application may be divided into several dependent tasks. Given that the cost of leasing multicore VMs on the cloud will rise with an increase in the number of processing cores, an efficient scheduling algorithm focusing on utilizing multicore resources can significantly reduce execution costs. As an extension of its authors' previous research, the current paper proposes a heuristic scheduling algorithm, the Cluster Dividing Algorithm that concentrates on expanding the utilization of multicore resources to reduce execution costs while also meeting the user-defined deadline. To increase resource utilization, the proposed scheduling employs different techniques, such as task clustering, directed graph leveling, and task duplicating. The experimental results reveal that the presented algorithm leads to lower execution costs while complying with the deadline.

•Increasing advances in data storage have led to an increase in the volume of medical data.•The imbalanced data is the main problem in medical data mining.•We present a new approach for success predicting of dental implants for imbalanced data.•The imbalanced data is clustered, and then each cluster is balanced by SMOTE algorithm.•The balanced data are classified by ensemble of four well-known classifiers.

With the rapid growth of different massive applications and parallel flow requests in Data Center Networks (DCNs), today's providers are confronting challenges in flow forwarding decisions. Since Software Defined Networking (SDN) provides fine granular control, it can be intelligently programmed to distinguish between flow requirements. The present article proposes a knapsack model in which the link bandwidth and incoming flows are modeled as a knapsack capacity and items, respectively. Furthermore, each flow consists of two size and value aspects, acquired through flow size extraction and the type of service value assigned by the SDN controller decision. Indeed, the current work splits the incoming flow size range into Type of Service (ToS) decimal value numbers. The lower the flow size category, the higher the value dedicated to the flow. Particle Swarm Optimization (PSO) optimizes the knapsack problem and first forwards the selected-flows by KP-PSO, and the non-selected-flows second. To address the shortcomings of these methods in the event of dense parallel flow detection, the present study puts the link under the threshold of a 70% load by simultaneous requests. Experimental results indicate that the proposed method outperforms Sonum, Hedera, and ECMP in terms of flow completion time, packet loss rate, and goodput regarding flow size requirements.

Nowadays, prediction and decision making are two inseparable principles in the management and two distinct roles of managers. The organizations spend a large part of their budgets on predictions from past data. They will lose their money if they are neglected. On the other hand, decision-making is the most critical step in problem-solving. Moreover, it is considered the main task of a manager as a problem solver. Making decision becomes more complicated when we are faced with multi-criteria decision-making issues. Combining prediction and decision-making approaches helps researchers to make a better choice utilizing prior knowledge. One of the most essential and comprehensive systems designed for multi-criteria decision-making is Analytical Hierarchy Process (AHP) process. Deep learning as a valuable extension of artificial neural networks has been the focus of many researchers. In this paper, AHP is used to classify, compare, and determine the weights of a deep learning approach. In order to evaluate the efficiency of the proposed method, the prediction of vehicle price application is chosen, and the results are compared with neural networks. The data set is related to the sale of Hyundai and Kia Motors cars in the United States and Canada. It is emphasized that the data are used only to evaluate the proposed method and can be generalized to solve all similar issues. The sales forecasting data of two car companies showed that the proposed method is superior to other regression methods. To extend the proposed methos as our future work, the aim will be to develop a comprehensive decision-making and forecasting system by combining these two approaches.

This research introduces a new probabilistic and meta-heuristic optimization approach inspired by the Corona virus pandemic. Corona is an infection that originates from an unknown animal virus, which is of three known types and COVID-19 has been rapidly spreading since late 2019. Based on the SIR model, the virus can easily transmit from one person to several, causing an epidemic over time. Considering the characteristics and behavior of this virus, the current paper presents an optimization algorithm called Corona virus optimization (CVO) which is feasible, effective, and applicable. A set of benchmark functions evaluates the performance of this algorithm for discrete and continuous problems by comparing the results with those of other well-known optimization algorithms. The CVO algorithm aims to find suitable solutions to application problems by solving several continuous mathematical functions as well as three continuous and discrete applications. Experimental results denote that the proposed optimization method has a credible, reasonable, and acceptable performance.

Despite the popularity and widespread usage of cloud computing, the cost of resources is one of the most important issues in Infrastructure as a Service (IaaS) clouds. Therefore, dynamic pricing models presented by some IaaS service providers, offers considerable price savings on spot instances or low-priority virtual machines. This significant discount has increased the popularity of using such resources among users. However, some of the most important Quality of Service (QoS) metrics such as reliability and availability are eliminated by obtaining this discount. For instance, the availability criterion is influenced by issues such as the user’s bid, supply, and demand rate of that specific instance, etc. In this paper, an extensive survey has been conducted on the issue of cloud preemptible instances, and the challenges in this context are studied. Furthermore, we point out the challenges that have not yet been investigated and define future directions in this research area.

Nowadays, Internet of Things (IoT) applications have expanded to include smart cities, agriculture, e-health, industry, smart transport, etc. This large number of sensors and widespread applications will generate huge amounts of data that requires processing for analysis and decision-making. Therefore, considering the cloud computing model for the processing of IoT tasks with loose deadlines that do not require hard-real time processing can be an option. Generally, offloading tasks to the cloud will entail costs that must be optimized and reduced by intelligent mechanisms. Consequently, considering cloud computing instances with dynamic pricing referred to as spot instances can significantly reduce the processing costs. Although, these instances offer a considerable price saving compared to on-demand instances, they can be evicted by the cloud providers which poses a scheduling challenge. In this paper, we propose a dynamic scheduling method for IoT task offloading on Amazon EC2 spot instances. The proposed method considers the task's predicted execution time and deadline to specify tasks that can be mapped on spot instances. The experimental results denote that the proposed method leads to a considerable reduction in the execution costs while increasing the number of successful tasks executed before the deadline and decreasing task turnaround time.

Educational certificates serve as proof of qualification for their recepients. These certificates are subject to forgery and manipulation. The traditional way to verify the authenticity and integrity of certificates has not shown much efficiency in preventing fraud. In this paper, a reliabe and tamper-proof certificate verification system is proposed. Unlike the traditional verifcation methods, the proposed system is decentralized by design using the blockchain technology. Decentralization enhances the security and robustness features of the system by avoiding single points of failure and removing the need to put trust in any single party. The proposed design leverages blockchain-based, cryptographically protected smart contracts to automate the verification process and provide transparency and security required for building a fast, relaible and cost-effective verification system.

Workflows are adopted as a powerful modeling technique to represent diverse applications in different scientific fields as a number of loosely coupled tasks. Given the unique features of cloud technology, the issue of cloud workflow scheduling is a critical research topic. Users can utilize services on the cloud in a pay-as-you-go manner and meet their quality of service (QoS) requirements. In the context of the commercial cloud, execution time and especially execution expenses are considered as two of the most important QoS requirements. On the other hand, the remarkable growth of multicore processor technology has led to the use of these processors by Infrastructure as a Service cloud service providers. Therefore, considering the multicore processing resources on the cloud, in addition to time and cost constraints, makes cloud workflow scheduling even more challenging. In this research, a heuristic workflow scheduling algorithm is proposed that attempts to minimize the execution cost considering a user-defined deadline constraint. The proposed algorithm divides the workflow into a number of clusters and then an extendable and flexible scoring approach chooses the best cluster combinations to achieve the algorithm’s goals. Experimental results demonstrate a great reduction in resource leasing costs while the workflow deadline is met.

Scientific workflows can be considered a useful modeling method to model different scientific applications. Service-oriented computing is an attractive platform for most users to execute these applications in a pay-as-you-go manner. Therefore, scheduling workflows on the cloud as the latest trend in service-oriented computing and meeting the required users' Quality of Service requirements is an important problem to be tackled. Furthermore, the scheduling algorithms must consider the available multicore processing resources on the commercial Infrastructure as a Service cloud. Hence, considering multicore resources in addition to Quality of Service constraints makes the workflow scheduling problem more challenging to be solved. In this research, a static workflow scheduling algorithm is proposed which considers the available multicore resources on the cloud and attempts to minimize the leasing costs of the processing resources while considering not violating a user-defined deadline. The proposed algorithm uses a clustering technique to divide the workflow into a number of clusters and attempts to combine the clusters in such a way to achieve the algorithms' main goals. A flexible and extendable scoring approach chooses the best combination available in each step. Extensive simulations reveal a great reduction in the leasing costs of the workflow execution while meeting the user-defined deadline.

One of the main features of High Throughput Computing systems is the availability of high power processing resources. Cloud Computing systems can offer these features through concepts like Pay-Per-Use and Quality of Service (QoS) over the Internet. Many applications in Cloud computing are represented by workflows. Quality of Service is one of the most important challenges in the context of scheduling scientific workflows. On the other hand, the remarkable growth of the multicore processor technology has led to the use of these processors by service providers as building blocks of their infrastructure. Therefore, scheduling scientific workflows on the Cloud requires especial attention to multicore processor infrastructure which adds more challenges to the problem. On the other hand, in addition to these challenges users' QoS constraints like execution time and cost should be regarded. The main objective of this research is scheduling workflows on the Cloud, considering a multicore based infrastructure. A new algorithm is proposed which finds clusters of the workflow that can be executed in parallel while having large data communications. These kinds of clusters could be appropriate candidates to be executed on a multicore processor. In contrast, there are other clusters which should be executed in serial. This algorithm investigates whether serial execution of these clusters is possible or not. The experimental results show that the algorithm has a positive effect on execution time and cost of the workflow execution.

It’s too years that computational model of cellular automata has been proposed for studying natural phenomena of world; including communication, computation, growing and development, reproducing, contesting and evolution. Vehicular travel which demands on the concurrent operations and parallel activities is increasing throughout the world, particularly in large urban areas. In this paper, to control urban traffic, we study the simulation and optimization of traffic light controllers in a city and present an adaptive fuzzy algorithm based on cellular automata properties. We have used CA for simulating transition function of density of vehicles. In the models that have been proposed till now, environmental factors like priority of intersection streets, width and length of streets and so on, have been assumed equal and therefore they have no role in making decision for changing the status of traffic light, whereas parameters like time during the entire day, density of the vehicles of the street, number of shopping centers, offices, malls,… that have plenty of returnees, have determinant effects on amount of traffic of streets. Considering mentioned notes, we have proposed a novel system that outperforms other available models. Our system has three levels; at the first level, priority of each street is computed momently, based on fuzzy rules and regarding to environmental factors. At the second level, real velocity of vehicles of every street is calculated at specific moment and eventually at the third level, by taking into account two parameters, priority of the street and amount of density behind the traffic light, decision for changing status of traffic light is done. Simulation results of our method have been shown and compared with best algorithms of two most famous available traffic light control approaches -Global and adaptive strategies-.