An energy‐saving algorithm for cloud resource management using a Kalman filter

Cloud computing has emerged as a promising technique to provide storage and computing component on‐demand services over a network. In this paper, we present an energy‐saving algorithm using the Kalman filter for cloud resource management to predict the workload and to further achieve high resource availability with low service level agreement. Using the proposed algorithm, one can estimate the potential future workload trend then predict the computing component workload utilizations and further retrench energy consumption and achieve load balancing in a cloud system. Experimental results show that the proposed algorithm achieves more than 92.22% accuracy in the computing component workload prediction, improves 55.11% energy in energy consumption, and has 3.71% in power prediction error rate, respectively. Copyright © 2013 John Wiley & Sons, Ltd.     

Essentiality of managing the resource information in the coordinated fog‐to‐cloud paradigm

Fog‐to‐cloud (F2C) computing is an emerging computational platform. By combing the cloud, fog, and IoT, it provides an excellent framework for managing and coordinating the resources in any smart computing domain. Efficient management of these kinds of diverse resources is one of the critical tasks in the F2C system. Also, it must be considered that different types of services are offered by any smart system. So, before managing these resources and enabling the various types of services, it is essential to have some comprehensive informational catalogue of resources and services. Hence, after identifying the resource and service‐task taxonomy, our main aim in this paper is finding out a solution for properly organizing this information over the F2C system. For that purpose, we are proposing a modified F2C framework where all the information is distributively stored near to the edge of the network. Finally, by presenting some experimental results, we evaluate and validate the performance of our proposing framework.     

A new approach for service migration in cloud‐based e‐commerce using an optimization algorithm

Service‐oriented architecture (SOA) has a crucial role in backing productive cloud services. Also, the vast spread of the theoretical notion of diverse businesses (like e‐commerce) into the actual use has been recently applied by cloud computing. The service functionality could be affected by overfilling of the network traffic because of the broadly dispersed nature of e‐commerce in clouds—a key challenge for immediate jobs. Throughout the last decade, a vast range of applications or large‐scale operators has increasingly attracted to migrate the services in clouds. An effective method for accessing the applications throughout standard business hours is continually moving virtual machine containers from one data center to another. Now, with the commonness of cloud computing, many applications have been moved to the cloud fully/partly. It can be handled through the migration of cloud services to diverse platforms in a way that minimizes the communication cost of e‐commerce. As this issue has an NP‐hard nature, in the present article, we present an automatic smart service migration outline through the ant colony optimization (ACO) algorithm on cloud‐oriented e‐commerce. In the presented model, we use the ACO algorithm to take the finest (near‐optimal) service migration decisions. Based on the obtained results, the proposed technique has the optimal number of migrations compared to the existing models.     

Load‐balanced data gathering in Internet of Things using an energy‐aware cuckoo‐search algorithm

This paper deals with the lifetime problem in the Internet of Things. We first propose an efficient cluster‐based scheme named “Cuckoo‐search Clustering with Two‐hop Routing Tree (CC‐TRT)” to develop a two‐hop load‐balanced data aggregation routing tree in the network. CC‐TRT uses a modified energy‐aware cuckoo‐search algorithm to fairly select the best cluster head (CH) for each cluster. The applied cuckoo‐search algorithm makes the CH role to rotate between different sensors round by round. Subsequently, we extend the CC‐TRT scheme to present two methods for constructing multi‐hop data aggregation routing trees, named “Cuckoo‐search Clustering with Multi‐Hop Routing Tree (CC‐MRT)” and “Cuckoo‐search Clustering with Weighted Multi‐hop Routing Tree (CC‐WMRT).” Both CC‐MRT and CC‐WMRT rely on a two‐level structure; they not only use an energy‐aware cuckoo‐search algorithm to fairly select the best CHs but also adopt a load‐balanced high‐level routing tree to route the aggregated data of CHs to the sink node. However, CC‐WMRT slightly has a better performance thanks to its low‐level routing strategy. As an advantage, the proposed schemes balance the energy consumption among different sensors. Numerical results show the efficiency of the CC‐TRT, CC‐MRT, and CC‐WMRT algorithms in terms of the number of transmissions, remaining energy, energy consumption variance, and network lifetime.     

Analysis,design and simulation of Internet of Things routing algorithm based on ant colony optimization

In this paper, a routing algorithm to optimize the selection of the best path for the transmitted data within the Internet of Things (IoT) system is proposed. The algorithm controls the use of ant colony ideas in the IoT system to obtain the best routing benefit. It divides the IoT environment into categorized areas depending on network types. Then, it applies the most suitable ant colony algorithm to the concerned network within each area. Furthermore, the algorithm considers routing problem in intersected areas that may arise in case of IoT system. Finally, Network Simulator 2 is used to evaluate the proposed algorithm performance. Simulation results demonstrate the effectiveness of the proposed routing algorithm in terms of end‐to‐end delay, packet loss ratio, bandwidth consumption, throughput, overhead of control bits, and energy consumption ratio. Copyright © 2016 John Wiley & Sons, Ltd.     

Energy aware cloud-edge service placement approaches in the Internet of Things communications

In recent years, applying Internet of Things (IoT) applications has significantly increased to facilitate and improve quality of human life activities in various fields such as healthcare, education, industry, economics, etc. The energy aware cloud-edge computing paradigm has developed as a hybrid computing solution to provide IoT applications using available cloud service providers and fog nodes for the smart devices and mobile applications. Since the IoT applications are developed in the form of several IoT services with various quality of service (QoS) metrics which can deploy on the cloud-edge providers with different resource capabilities, finding an efficient placement solution as one of challenging topics to be measured for IoT applications. The service placement issue arranges IoT applications on the cloud-edge providers with various capabilities of atomic services though sufficient different QoS factors to support service level agreement (SLA) contracts. This paper presents a technical analysis on the cloud-edge service placement approaches in IoT systems. The key point of this technical analysis is to identify substantial studies in the service placement approaches which need additional consideration to progress more efficient and effective placement strategies in IoT environments. In addition, a side-by-side taxonomy is proposed to categorize the relevant studies on cloud-edge service placement approaches and algorithms. A statistical and technical analysis of reviewed existing approaches is provided, and evaluation factors and attributes are discussed. Finally, open issues and forthcoming challenges of service placement approaches are presented.     

基于蚁群算法的P2P网络资源发现算法

Gossip协议是P2P网络的一种流行的资源发现算法,但它没有考虑寻找最低成本的资源。论文提出了基于蚁群算法的新的资源发现算法,在查找资源的同时,综合考虑路径载荷、延时等因素,找到综合费用最低的路径。仿真实验表明,该算法比Dijkstra算法解集的平均综合费用低10%左右,从而能更有效地利用网络资源。     

A web service‐based internet of things framework for mobile resource augmentation

Mobile devices are the primary communication tool in day to day life of the people. Nowadays, the enhancement of the mobile applications namely IoTApps and their exploitation in various domains like healthcare monitoring, home automation, smart farming, smart grid, and smart city are crucial. Though mobile devices are providing seamless user experience anywhere, anytime, and anyplace, their restricted resources such as limited battery capacity, constrained processor speed, inadequate storage, and memory are hindering the development of resource‐intensive mobile applications and internet of things (IoT)‐based mobile applications. To solve this resource constraint problem, a web service‐based IoT framework is proposed by exploiting fuzzy logic methodologies. This framework augments the resources of mobile devices by offloading the resource‐intensive subtasks from mobile devices to the service providing entities like Arduino, Raspberry PI controller, edge cloud, and distant cloud. Based on the recommended framework, an online Repository of Instructional Talk (RIoTalk) is successfully implemented to store and analyze the classroom lectures given by faculty in our study site. Simulation results show that there is a significant reduction in energy consumption, execution time, bandwidth utilization, and latency. The proposed research work significantly increases the resources of mobile devices by offloading the resource‐intensive subtasks from the mobile device to the service provider computing entities thereby providing Quality of Service (QoS) and Quality of Experience (QoE) to mobile users.     

基于蚁群算法的电力基建工程物联网全数据管理

针对现有技术中电力基建工程全生命周期数据管理技术落后,计算效率低下的问题,提出了新型的智能化管理系统,该管理系统融合了大数据、云计算、物联网、移动互联等新型技术,通过使用包括计算机网络系统、Web端主站平台、手持终端APP、移动终端采集系统及专用支撑网络的数据管理平台,实现了电力基建工程设备全生命周期管理信息从数据采集、数据传递、数据计算到应用的全生命周期管理,提高了电网设备的自动化、智能高效管理。通过利用数据融合算法和蚁群最优搜索算法,实现目标数据的最优搜索。试验表明,该研究方法准确率高达93%。     

An optimal energy‐efficient clustering method in wireless sensor networks using multi‐objective genetic algorithm

In this study, an optimal method of clustering homogeneous wireless sensor networks using a multi‐objective two‐nested genetic algorithm is presented. The top level algorithm is a multi‐objective genetic algorithm (GA) whose goal is to obtain clustering schemes in which the network lifetime is optimized for different delay values. The low level GA is used in each cluster in order to get the most efficient topology for data transmission from sensor nodes to the cluster head. The presented clustering method is not restrictive, whereas existing intelligent clustering methods impose certain conditions such as performing two‐tiered clustering. A random deployed model is used to demonstrate the efficiency of the proposed algorithm. In addition, a comparison is made between the presented algorithm other GA‐based clustering methods and the Low Energy Adaptive Clustering Hierarchy protocol. The results obtained indicate that using the proposed method, the network's lifetime would be extended much more than it would be when using the other methods. Copyright © 2011 John Wiley & Sons, Ltd.     

A metaheuristic‐based method for replica selection in the Internet of Things

With the advent of the Internet of Things (IoT), the count of gadgets connected to the Internet has been increased. IoT, as a modern paradigm, has been used to describe the future in which physical things like RFID tags, sensors, actuators, and cellphones can intermingle for achieving shared purposes. Also, we can employ cloud computing for storing the things' information in the IoT. However, this information has been replicated through the network for increasing availability. In this paper, due to the NP‐hard nature of the replica selection problem, an improved version of ant colony optimization (IACO) has been applied. The impact of pheromone on the chosen path is converted by ants to invert the underlying logic of ACO. Due to the existence of different IoT centers, the IACO has been employed for selecting the replicated data in the IoT where the load balancing among IoT centers has been considered. In this method, an ant chooses the ideal point for its movement; then others may not pass the track that the preceding ants have been passed. The obtained outcomes have shown that the method has outperformed the ACO, HQFR, and RTRM approaches regarding the waiting time and load balancing.     

Congestion‐aware multiaccess edge computing collaboration model for 5G

In 5G cloud computing, the most notable and considered design issues are the energy efficiency and delay. The majority of the recent studies were dedicated to optimizing the delay issue by leveraging the edge computing concept, while other studies directed its efforts towards realizing a green cloud by minimizing the energy consumption in the cloud. Active queue management‐based green cloud model (AGCM) as one of the recent green cloud models reduced the delay and energy consumption while maintaining a reliable throughput. Multiaccess edge computing (MEC) was established as a model for the edge computing concept and achieved remarkable enhancement to the delay issue. In this paper, we present a handoff scenario between the two cloud models, AGCM and MEC, to acquire the potential gain of such collaboration and investigate its impact on the cloud fundamental constraints; energy consumption, delay, and throughput. We examined our proposed model with simulation showing great enhancement for the delay, energy consumption, and throughput over either model when employed separately.     

DACASN: Dynamic ant routing‐based channel accessing cognitive sensor network in Internet of Things

The Internet of Things (IoT) is a structure with sensors, base station, gateway, and network servers. Users can access the information contained in it and use it as they need. These sensors are embedded in several data collection locations in a uniform or random manner to collect data. The users in many parts of the world obtain such data that fit their requirements. For example, sensors are used in remote areas to monitor various areas such as forests, wildlife sanctuaries, fields, industrial needs, human condition, and changes in the ocean. The changes like these that take place in the open spaces require the selection of the best routing method and seamless delay reduction in data collection helps to immediately reach those involved in it. In the proposed protocol, dynamic ant routing‐based channel accessing cognitive sensor network (DACASN) in the IoT, cognitive sensors are used. It is made with the knowledge of choosing a channel for communication as well as selecting the parameters for path selection and setting the most accurate path with the channel knowledge and communicating with the internet server. The sensors set the paths for this route selection using the centrifugal method and the anticolonial method. As a result, it can be seen in the results and discussion section that the quality of the routing information is high because it is delivered to the destination in the shortest possible time.     

Quality of service‐aware approaches in fog computing

In recent years, fog computing, a novel paradigm, has emerged for location and latency‐sensitive applications. It is a powerful complement for cloud computing that enables provisioning services and resources outside the cloud near the end devices. In a fog system, the existence of several nonhomogenous devices, which are potentially mobile, led to quality of service (QoS) worries. QoS‐aware approaches are presented in various parts of the fog system, and several different QoS factors are taken into account. In spite of the importance of QoS in fog computing, no comprehensive study on QoS‐aware approaches exists in fog computing. Hence, this paper reviews the current research used to guarantee QoS in fog computing. This paper investigates the QoS‐ensuring techniques that fall into three categories: service/resource management, communication management, and application management (published between 2013 and October 2018). Regarding the selected approaches, this paper represents merits, demerits, tools, evaluation types, and QoS factors. Finally, on the basis of the reviewed studies, we suggest some open issues and challenges which are worth further studying and researching in QoS‐aware approaches in fog computing.     

RAD‐EI: A routing attack detection scheme for edge‐based Internet of Things environment

Internet of Things (IoT) offers various types of application services in different domains, such as “smart infrastructure, health‐care, critical infrastructure, and intelligent transportation system.” The name edge computing signifies a corner or edge in a network at which traffic enters or exits from the network. In edge computing, the data analysis task happens very close to the IoT smart sensors and devices. Edge computing can also speed up the analysis process, which allows decision makers to take action within a short duration of time. However, edge‐based IoT environment has several security and privacy issues similar to those for the cloud‐based IoT environment. Various types of attacks, such as “replay, man‐in‐the middle, impersonation, password guessing, routing attack, and other denial of service attacks” may be possible in edge‐based IoT environment. The routing attacker nodes have the capability to deviate and disrupt the normal flow of traffic. These malicious nodes do not send packets (messages) to the edge node and only send packets to its neighbor collaborator attacker nodes. Therefore, in the presence of such kind of routing attack, edge node does not get the information or sometimes it gets the partial information. This further affects the overall performance of communication of edge‐based IoT environment. In the presence of such an attack, the “throughput of the network” decreases, “end‐to‐end delay” increases, “packet delivery ratio” decreases, and other parameters also get affected. Consequently, it is important to provide solution for such kind of attack. In this paper, we design an intrusion detection scheme for the detection of routing attack in edge‐based IoT environment called as RAD‐EI. We simulate RAD‐EI using the widely used “NS2 simulator” to measure different network parameters. Furthermore, we provide the security analysis of RAD‐EI to prove its resilience against routing attacks. RAD‐EI accomplishes around 95.0% “detection rate” and 1.23% “false positive rate” that are notably better than other related existing schemes. In addition, RAD‐EI is efficient in terms of computation and communication costs. As a result, RAD‐EI is a good match for some critical and sensitive applications, such as smart security and surveillance system.     

Improving MANET performance by a hop‐aware and energy‐based buffer management scheme

A hop‐aware and energy‐based buffer management scheme (HEB) is proposed in this paper. HEB can provide better quality of service to packets with real‐time requirements and improve MANET power efficiency. In our algorithm, the buffer is divided into real‐time and non‐real‐time partitions. We consider the number of hops passed, the power levels of the transmitting node, the predicted number of remaining hops, and waiting time in the buffer to determine packet transmission priority. In addition, specialized queue management and a probabilistic scheduling algorithm are proposed to decrease retransmissions caused by packet losses. Mathematical derivations of loss rates and end‐to‐end delays are also proposed. Coincidence between mathematical and simulation results is also shown. Finally, the HEB is compared with first in first out, random early detection, and hop‐aware buffering scheme. Simulation results show that the proposed algorithm reduces loss rates, power consumption, and end‐to‐end delays for real‐time traffic, considerably improving the efficiency of queue management in MANET. Copyright © 2012 John Wiley & Sons, Ltd.     

Elucidating the challenges for the praxis of fog computing: An aspect‐based study

The evolutionary advancements in the field of technology have led to the instigation of cloud computing. The Internet of Things paradigm stimulated the extensive use of sensors distributed across the network edges. The cloud datacenters are assigned the responsibility for processing the collected sensor data. Recently, fog computing was conceptuated as a solution for the overwhelmed narrow bandwidth. The fog acts as a complementary layer that interplays with the cloud and edge computing layers, for processing the data streams. The fog paradigm, as any distributed paradigm, has its set of inherent challenges. The fog environment necessitates the development of management platforms that effectuates the orchestration of fog entities. Owing to the plenitude of research efforts directed toward these issues in a relatively young field, there is a need to organize the different research works. In this study, we provide a compendious review of the research approaches in the domain, with special emphasis on the approaches for orchestration and propose a multilevel taxonomy to classify the existing research. The study also highlights the application realms of fog computing and delineates the open research challenges in the domain.     

An energy‐aware transmission mechanism for WiFi‐based mobile devices handling upload TCP traffic

This paper presents an energy‐aware transmission mechanism that improves the throughput and reduces the energy consumption of mobile devices in wired‐cum‐wireless TCP networks. The proposed mechanism places an agent at the base station, which identifies the cause of packet losses in the underlying network. When the mobile device acts as a TCP source, it adjusts the size of the congestion window adaptively according to the cause of packet losses with the aids of the agent in order to improve the transmission performance. In addition, the proposed mechanism lets the communication device to stay in sleep mode after completing the transmission in order to reduce the energy consumption. As a result, the cooperation between the mobile device and the agent improves the transmission performance as well as the energy efficiency greatly. To evaluate the performance of the proposed mechanism, we analyzed the effect of TCP on the communication device for mobile devices and present a power model. With extensive simulations based on the power model, we demonstrate that the proposed mechanism significantly improves the transmission performance, and reduces the energy consumption over a wide range of both wired and wireless packet losses. Copyright © 2008 John Wiley & Sons, Ltd.     

A new fuzzy‐based method for load balancing in the cloud‐based Internet of things using a grey wolf optimization algorithm

Cloud computing provides high accessibility, scalability, and flexibility in the era of computing for different practical applications. Internet of things (IoT) is a new technology that connects the devices and things to provide user required services. Due to data and information upsurge on IoT, cloud computing is usually used for managing these data, which is known as cloud‐based IoT. Due to the high volume of requirements, service diversity is one of the critical challenges in cloud‐based IoT. Since the load balancing issue is one of the NP‐hard problems in heterogeneous environments, this article provides a new method for response time reduction using a well‐known grey wolf optimization algorithm. In this paper, we supposed that the response time is the same as the execution time of all the tasks that this parameter must be minimized. The way is determining the status of virtual machines based on the current load. Then the tasks will be removed from the machine with the additional load depending on the condition of the virtual machine and will be transferred to the appropriate virtual machine, which is the criterion for assigning the task to the virtual machine based on the least distance. The results of the CloudSim simulation environment showed that the response time is developed in compared to the HBB‐LB and EBCA‐LB algorithm. Also, the load imbalancing degree is improved in comparison to TSLBACO and HJSA.     

Resource scheduling in mobile edge computing using improved ant colony algorithm for space information network

With the development of space information network (SIN), new network applications are emerging. Satellites are not only used for storage and transmission but also gradually used for calculation and analysis, so the demand for resources is increasing. But satellite resources are still limited. Mobile edge computing (MEC) is considered an effective technique to reduce the pressure on satellite resources. To solve the problem of task execution delay caused by limited satellite resources, we designed Space Mobile Edge Computing Network (SMECN) architecture. According to this architecture, we propose a resource scheduling method. First, we decompose the user tasks in SMECN, so that the tasks can be assigned to different servers. An improved ant colony resource scheduling algorithm for SMECN is proposed. The heuristic factors and pheromones of the ant colony algorithm are improved through time and resource constraints, and the roulette algorithm is applied to route selection to avoid falling into the local optimum. We propose a dynamic scheduling algorithm to improve the contract network protocol to cope with the dynamic changes of the SIN and dynamically adjust the task execution to improve the service capability of the SIN. The simulation results show that when the number of tasks reaches 200, the algorithm proposed in this paper takes 17.52% less execution time than the Min-Min algorithm, uses 9.58% less resources than the PSO algorithm, and achieves a resource allocation rate of 91.65%. Finally, introducing dynamic scheduling algorithms can effectively reduce task execution time and improve task availability.