Abstract Edge computing environment provides processing capability and computing at the network edge and close to users. Edge equipment includes small data centers that locally perform process and content delivery. Therefore, edge equipment management has received much attention due to the rapid growth of information and resources limitations. The content caching and proactive caching techniques are management methods of edge equipment resources. We recently witnessed the development of proactive caching mechanisms that have a crucial enabler in improving heavy traffic, energy, and bandwidth. Also, it has huge potential to increase the quick response time to users' requests that today, these services are demanded by many users and applications. This article prepares a systematic literature review content caching approach in the edge computing environment. The purpose of this study is to survey the research done on the proactive caching strategies in the edge computing environment to identify subjects that must be emphasized more in current and future research paths. This research has studied 71 articles divided into three classes: model‐based, machine‐learning‐based, and heuristic‐based. Next, we discuss content caching approaches based on critical factors such as performance metrics, case studies, utilized techniques, assessment tools, advantages, and disadvantages. Finally, open issues and challenges are presented, and the survey is concluded.

Abstract Internet of Things (IoT) represents a new generation of information and communication technology for anyone, anytime and anywhere. Cloud service‐based IoT applications significantly increase latency and network utilization. The fog environment is closer to the user to perform computing, communication, and storage tasks on network edge devices. Therefore, it can greatly reduce the latency of real‐time applications. It is an essential feature of fog computing and its most important advantage compared to cloud computing. This study proposed a new approach to service placement generated by running applications on IoT devices in the fog computing. IoT devices send applications to the fog environment that each application contains a set of services. The purpose of solving the IoT services placement problem is to efficiently deploy these services on fog cells. For this purpose, it is assumed that the received services from the IoT applications are received as a directed acyclic graph that depicts the communication between the cells within the graph that shows the communication between the services. Then, the imperialist competitive algorithm is used to place and select the destination for IoT services. The simulation results of the iFogSim simulator in different experiments showed that the imperialist competitive algorithm with the proposed graph partitioning approach has improved service placement on the fog infrastructure compared to the genetic algorithm and best‐fit algorithm.

Abstract Over the past few years, Internet of Things (IoT) applications have grown rapidly. The data‐intensive IoT applications that take advantage of cloud servers for computations and data storage will result in higher latency and other network traffic in the Internet core. IoT applications are characterized by their sensitivity to latency. As an example, delays will result in irreparable damage in the medical and healthcare industries. Cloud servers are no longer necessary because cloud computing utilizes fog nodes that are closer to users. Nodes with different hardware capabilities pose a significant challenge since they differ significantly in latency and traffic reduction. This article presented a metaheuristic‐based method using the non‐dominated sorting genetic algorithm II for data‐intensive IoT applications in fog infrastructure. Besides, we provide a new automatic method for managing data replica transmissions, including deploying them in a fog cloud environment. The proposed solution was evaluated in the iFogSim simulator and compared with two other data replica placement methods in different scenarios. The results showed a decrease in latency and cost for data access and an increase in data availability.

With the recent advancements in distributed computing technologies, the fog computing model has emerged to provide resource capabilities at the edge of the network for executing IoT applications. However, due to the rapid growth of IoT applications and variability their workload over time, achieving an efficient resource provisioning solution to deal with time-varying workloads as one of the challenging tasks in resource management scope to be considered. In this work, we propose a learning-based resource provisioning approach for managing time-varying workloads of IoT applications in the fog network. Our proposed approach utilises the nonlinear autoregressive (NAR) neural network as prediction method and hidden Markov model (HMM) as a decision-maker to identify scaling decisions to provision the fog resources for serving of workloads of IoT applications. The effectiveness of our proposed solution is evaluated using extension experiments under real-world datasets, and the obtained results from iFogSim toolkit demonstrated that it yields a reduction of the delay and cost and improves resource energy consumption compared with existing baseline mechanisms.

Abstract Divers and the huge amount of data produced by the Internet of Things (IoT) applications on the one hand, and inherent limitations of local equipment to handle these data, on the other hand, leads to present emerging closer technologies to the end‐users such as fog computing environment. Nevertheless, despite the numerous advantages of such an environment, it still needs state‐of‐the‐art approaches to cope with some inherent limitations. In the literature, resource placement strategies are generally proposed to address such problems, in which the IoT applications are mapped to fog nodes. However, despite its importance, different approaches attempt to enhance the overall system's performance and users' expectations: none of such approaches is satisfactory. In this article, to deploy IoT applications on fog nodes, an autonomic IoT service placement approach based on the gray wolf optimization scheme is proposed, enhancing the system's performance while considering execution costs. Besides, the autonomic concepts help make an appropriate automanagement system that fits better the fog environment's dynamic behavior. Simulation results demonstrate that the proposed approach outperforms the other approaches and converges to the solution in near‐optimal application deployment on fog nodes in respect of the performance of performing services that are 93.7%, the performance of the average waiting time for performed services that are 100%, the remaining services sent to an extra provisioned period that is zero.

Summary In recent years, the usage of smart mobile applications to facilitate day‐to‐day activities in various domains for enhancing the quality of human life has increased widely. With rapid developments of smart mobile applications, the edge computing paradigm has emerged as a distributed computing solution to support serving these applications closer to mobile devices. Since the submitted workloads to the smart mobile applications changes over the time, decision making about offloading and edge server provisioning to handle the dynamic workloads of mobile applications is one of the challenging issues into the resource management scope. In this work, we utilized learning automata as a decision‐maker to offload the incoming dynamic workloads into the edge or cloud servers. In addition, we propose an edge server provisioning approach using long short‐term memory model to estimate the future workload and reinforcement learning technique to make an appropriate scaling decision. The simulation results obtained under real and synthetic workloads demonstrate that the proposed solution increases the CPU utilization and reduces the execution time and energy consumption, compared with the other algorithms.

Summary In recent years, novel mobile applications such as augmented reality, virtual reality, and three‐dimensional gaming, running on handy mobile devices have been pervasively popular. With rapid developments of such mobile applications, decentralized mobile edge computing (MEC) as an emerging distributed computing paradigm is developed for serving them near the smart devices, usually in one hop, to meet their computation, and delay requirements. In the literature, offloading mechanisms are designed to execute such mobile applications in the MEC environments through transferring resource‐intensive tasks to the MEC servers. On the other hand, due to the resource limitations, resource heterogeneity, dynamic nature, and unpredictable behavior of MEC environments, it is necessary to consider the computation offloading issues as the challenging problem in the MEC environment. However, to the best of our knowledge, despite its importance, there is not any systematic, comprehensive, and detailed survey in game theory (GT)‐based computation offloading mechanisms in the MEC environment. In this article, we provide a systematic literature review on the GT‐based computation offloading approaches in the MEC environment in the form of a classical taxonomy to recognize the state‐of‐the‐art mechanisms on this important topic and to provide open issues as well. The proposed taxonomy is classified into four main fields: classical game mechanisms, auction theory, evolutionary game mechanisms, and hybrid‐base game mechanisms. Next, these classes are compared with each other according to the important factors such as performance metrics, case studies, utilized techniques, and evaluation tools, and their advantages and disadvantages are discussed, as well. Finally, open issues and future uncovered or weakly covered research challenges are discussed and the survey is concluded.

Summary The development of a communication infrastructure has made possible the expansion of the popular massively multiplayer online games. In these games, players all over the world can interact with one another in a virtual environment. The arrival rate of new players to the game environment causes fluctuations and players always expect services to be available and offer an acceptable service‐level agreement (SLA), especially in terms of response time and cost. Cloud computing emerged in the recent years as a scalable alternative to respond to the dynamic changes of the workload. In massively multiplayer online games applications, players are allowed to lease resources from a cloud provider in an on‐demand basis model. Proactive management of cloud resources in the face of workload fluctuations and dynamism upon the arrival of players are challenging issues. This paper presents a self‐learning fuzzy approach for proactive resource provisioning in cloud environment, where key is to predict parameters of the probability distribution of the incoming players in each period. In addition, we propose a self‐learning fuzzy autoscaling decision‐maker algorithm to compute the proper number of resources to be allocated to each tier in the massively multiplayer online games by applying the predicted workload and user SLA. We evaluate the effectiveness of the proposed approach under real and synthetic workloads. The experimental results indicate that the proposed approach is able to allocate resources more efficiently than other approaches.

Summary In recent years, users are becoming increasingly accustomed to using the Internet to gain software resources in the form of web services provided by information technology organizations. Cloud computing is a service delivery paradigm that shares services and resources to access the web services to the end users over the Internet. In the cloud environment, based on the user's needs, various types of services with similar functionalities but different quality‐of‐service (QoS) criteria can be delivered, which often must be combined to meet the users' requests. The optimal selection and composition of these services are realized as an interesting issue. In this paper, we propose a moth‐flame optimization (MFO) algorithm, which is a novel nature‐inspired metaheuristic paradigm for the web service composition (WSC) problem called “MFO‐WSC,” to improve the QoS criteria in the distributed cloud environment. Also, formal modeling is presented for the QoS‐aware MFO‐WSC algorithm with the model checking approach that receives the particular benefits to collaborate the correctness of the proposed algorithm. The correctness of the proposed behavior model is examined using some logical problems such as deadlock‐free, fairness, and reachability conditions in the new symbolic model verifier model checker. The experimental results indicate the effectiveness of the proposed algorithm in comparison with similar related works.

Summary Recent advancements in web‐based application, especially in cloud computing environment, allows cloud service providers to deploy and provide web‐based services in the form of multitier applications. One of the most suitable infrastructure for the running of multitier applications is a cloud computing infrastructure. Since the arrival rate of users to the multitier applications varies over the time, deciding about the right amount of resources required to handle the each tier of multitier applications is not trivial, and it depends on the current workload of its each tier. Therefore, it is necessary to automatically provision resources to deal with fluctuating demands of the multitier applications. In this paper, we propose a hybrid resource provisioning approach for multitier applications based on a combination of the concept of the autonomic computing and the fuzzy analytical hierarchy process approach. Moreover, we present a framework based on MAPE‐k control loop for autonomous resource provisioning of multitier applications in cloud computing environments. The effectiveness of the proposed approach under real and synthetic workloads was evaluated. The experimental results indicate that the proposed solution outperforms in terms of allocated virtual machines, response time, and cost compared with the other approaches.

Summary Cloud resource management requires complex policies and decisions to ensure the suitable use of computing resources due to fluctuations in the demanding workload. Deciding the right amount of resource usage for performing user requests in cloud environments is not trivial. Therefore, an efficient resource prediction model can play important roles in cloud resource management to estimate the needed resources properly. In this paper, we propose an ensemble CPU load prediction model using a Bayesian information criterion to choose the best constituent model in each time slot based on the cloud resource usage history. Further, we apply a couple of smooth filters in order to decrease the negative impacts of outliers in the observed data points. We also present a framework for cloud resource management including a prediction module to estimate the resource usage more accurately. The experimental results on the data set of the CoMon project indicate that the proposed approach achieves higher accuracy compared with the other ensemble prediction algorithms.

The ever increasing demand for the cloud services requires more data centres. The power consumption in the data centres is a challenging problem for cloud computing, which has not been considered properly by the data centre developer companies. Especially, large data centres struggle with the power cost and the Greenhouse gases production. Hence, employing the power efficient mechanisms are necessary to optimise the mentioned effects. Moreover, virtual machine (VM) placement can be used as an effective method to reduce the power consumption in data centres. In this paper by grouping both virtual and physical machines, and taking into account the maximum absolute deviation during the VM placement, the power consumption as well as the service level agreement (SLA) deviation in data centres are reduced. To this end, the best-fit decreasing algorithm is utilised in the simulation to reduce the power consumption by about 5% compared to the modified best-fit decreasing algorithm, and at the same time, the SLA violation is improved by 6%. Finally, the learning automata are used to a trade-off between power consumption reduction from one side, and SLA violation percentage from the other side.