Lion Optimization Algorithm

Virtual Machine (VM) consolidation is an optimization approach for VM placement in cloud infrastructure, which is one of the effective ways to efficiently utilize cloud resources in order to optimize number of VM migrations, Service Level Agreement (SLA) violations and energy consumption problems. Static VM consolidation strategies have not been effective in handling variation of workloads in the cloud systems. Dynamic VM strategies have been proposed in the literatures. However, in the dynamic VM consolidation process, most existing algorithms consolidate active servers mainly based on the current resource requirements and forgo the demands of resources in the future during VM allocation. Thus, they result in needless VM migrations and cause high rate of SLA violations in data centers. This research proposed a prediction-based VM consolidation approach. The proposed method utilized a multi-resource utilization to predict the current and future CPU and memory utilization of active s...

Mobile cloud computing is a rapidly evolving technology these days and it faces major problems of load imbalance due to the high demand for mobile applications. There are many techniques to solve the problem, but the user can improve load by using a more optimized solution. This research deals with the VM allocation and migration by means of location awareness. The research paper also presents a user authentication and server load management system to reduce the overload of the server. A captcha based authentication mechanism is also presented for user verification. The concept of Feedback is also introduced for the mobile servers. This concept makes the selection of mobile server for the job list. ANN (Artificial neural network) is used for location awareness judgment. ANN is a machine learning approach, which is used to minimize the human effort and also minimize the processing time to allocate job to an accurate server with minimum SLA violation. MBFD (Mobile best fit decreasing) algorithm is used for the VM allocation and selection policy. This research has considered SLA (Service level agreement) violation and energy consumption to compute the performance of the work with an aim of reducing energy consumption with maximized resource efficiency. The proposed work model is also compared with the work presented by Xiong in the same area.

The horse herd algorithm deploys virtual computers in a data center (SLAs) to reduce the danger of overload and adhere to Service Level Agreements. The algorithm is based on the observation that a herd of horses will naturally spread out to cover a larger area than a single horse. In the context of data centers, virtual machines can be placed in a way that minimizes the risk of overload and meets SLAs. The Abstract section is a blog about the Horse Herd Algorithm and how it can be used to ensure energy and SLA awareness in the virtual machine placement for SDN-managed cloud. The horse herd algorithm creates potential solutions, selects the best solution from the set, and returns the best solution. The horse herd algorithm is greedy, meaning it will always choose the solution that appears to be the best at the time without considering future consequences. The horse herd algorithm is not guaranteed to find the optimal solution, but it is often fast and can find reasonable solutions. This technique is a supervised learning technique that is used for the classification of data. The technique is based on the principle of least squares and is used to classify linearly separable data. The method is employed to train data sets that may be linearly separated. Data sets that cannot be separated linearly are trained using this method. Data sets that cannot be separated linearly are trained using this method. Data sets that cannot be separated linearly are trained using this method. The method is employed to train data sets that may be linearly separated. Data sets that cannot be separated linearly are trained using this method. The research paper then discusses the quantized spiking network. This network is used for crime detection. The network is based on the principle of artificial neural networks. The network is used for the classification of data. The categorization of data is done through the network. The network is employed to train data sets. The categorization of data is done through the network. The network is employed to train data sets. The research paper then discusses the results of the study. The study shows that the Landweber iterative supervised classification technique is more accurate than the quantized spiking network. The study also shows that the Landweber iterative supervised classification technique is more efficient than the quantized spiking network.

Software‐defined networking (SDN) is one of the most used network architecture that divides the forwarding plane and control plane. The SDN centrally observes and regulates the network through a software control in the control plane, called as controller. Multiple controllers are needed to manage the software‐defined WAN (SD‐WAN) for handling the scalability and reliability issues of the network as one controller is not enough. Deploying numerous controllers efficiently to improve the performance of the network is known as controller placement problem (CPP). This paper proposes a Varna‐based optimization (VBO) for a reliable CPP that minimizes the total average latency of SDN. To the best of our knowledge, the proposed work is a novel approach, which is compared with particle swarm optimization, teacher learning‐based optimization, and Jaya algorithms to solve reliable CPP. The experimental results show that VBO gives better performance than teacher learning‐based optimization (TLBO...

There has been an explosion of cloud services as organizations take advantage of their continuity, predictability, as well as quality of service and it raises the concern about latency, energy-efficiency, and security. This increase in demand requires new configurations of networks, products, and service operators. For this purpose, the software-defined network is an efficient technology that enables to support the future network functions along with the intelligent applications and packet optimization. This work analyzes the offline cloud scenario in which machines are efficiently deployed and scheduled for user processing requests. Performance is evaluated in terms of reducing bandwidth, task execution times and latencies, and increasing throughput. A minimum execution time algorithm is used to compute the completion time of all the available resources which are allocated to the virtual machine and lion optimization algorithm is applied to packets in a cloud environment. The proposed work is shown to improve the throughput and latency rate.

The problem of optimization has always existed in all scientific and industrial fields. This issue has been raised in mathematics and computer science as finding the best answer among different answers. This problem has become an essential area for research in the last decades. Not only analytical methods but also meta-heuristic methods have been developed to solve this problem. The Lion Optimizing Algorithm (LOA) is a meta-heuristic algorithm for optimization problems. The LOA was inspired by various aspects of lions' lives, such as hunting, mating, roaming, defense, and migration. So far, this algorithm has been used in several fields such as classification, clustering, image processing, cloud computing, and task scheduling. The LOA has proved promising results in these areas. Multiple algorithms have flourished to tackle various constraints and challenges of the LOA. In this paper, the LOA, its variants, and applications are reviewed from the beginning until now.

Green cloud computing, an emerging computing paradigm in IT industry, is about providing efficient utilization of cloud's resources with minimal impact on the environment. The virtual machine placement has a considerable impact on a data center's energy and resource utilisation. The assignment of Virtual Machines (VMs) to Physical Machines (PMs) is an NP-hard problem to be solved using approximate algorithms at a reasonable computing time. In this paper, an energy-aware based algorithm is proposed for virtual machine placement. Our aim is to reduce the energy consumption and CO2 emission under a set of constraints. The performance and the efficiency of the proposed approach is tested on a cloud computing simulation toolkit.

The efficient and safe management of air conditioner (AC), Piped Natural Gas (PNG) and water pipelines in large buildings is a major challenge for the safety of these buildings. In recent years, Linear Wireless Sensor Networks (LWSN) are being used extensively for monitoring of long natural gas, water, and oil pipelines. LWSNs can also be used for efficient and safe management of AC, PNG and water pipelines in large buildings. In this paper, a scheme for optimal placement of sensors and base stations in a linear fashion to monitor the various pipelines used in large buildings has been proposed. The proposed scheme utilizes the Lion Optimization Algorithm (LOA) and has been compared with three strategies, namely Ant Colony Optimization (ACO), Genetic Algorithm (GA) and Greedy Approach with respect to throughput, lifetime and end-to-end delay. The simulation results show that the proposed scheme exhibits better performance in comparison to the other three considered techniques for all...

Natural Computing is an efficient computing field that intends to develop search, optimization and machine learning biologically or naturally inspired search and optimization algorithms have been proposed in the literature. This paper proposes a novel solution search algorithm cal. The natural inspiration behind the proposed algorithm is interpretation of such social behavior to algorithmic perspective helps in searching out highly optimal solutions from a huge solution space. The algorithm solves both single variable and multi-variable cost function problems through the generation of binary structured and integer structured lion, respectively. The algorithm is implemented and tested using De-and the results are compared against the evolutionary programming. The test results show the algorithm performance under varying sizes of solution spaces.

Virtual Machine (VM) consolidation is an optimization approach for VM placement in cloud infrastructure, which is one of the effective ways to efficiently utilize cloud resources in order to optimize number of VM migrations, Service Level Agreement (SLA) violations and energy consumption problems. Static VM consolidation strategies have not been effective in handling variation of workloads in the cloud systems. Dynamic VM strategies have been proposed in the literatures. However, in the dynamic VM consolidation process, most existing algorithms consolidate active servers mainly based on the current resource requirements and forgo the demands of resources in the future during VM allocation. Thus, they result in needless VM migrations and cause high rate of SLA violations in data centers. This research proposed a prediction-based VM consolidation approach. The proposed method utilized a multi-resource utilization to predict the current and future CPU and memory utilization of active s...

The Orienteering Problem with Time Windows (OPTW) is a variant of the Orienteering Problem (OP). Given a set of nodes including their scores, service times and time windows, the goal is to maximize the total of scores collected by a particular route considering a predefined time window during which the service has to start. We propose an Iterated Local Search (ILS) algorithm to solve the OPTW, which is based on several LocalSearch operations, such as swap, 2-opt, insert and replace. We also implement the combination between AcceptanceCriterion and Perturbation mechanisms to control the balance between diversification and intensification of the search. In Perturbation, Shake strategy is introduced. The computational results obtained by our proposed algorithm are compared against optimal solutions or best known solution values obtained by state-of-the-art algorithms. We show experimentally that our proposed algorithm is effective on well-known benchmark instances available in the literature. It is also able to improve the best known solution of some benchmark instances.

Software-defined networking (SDN) plays a critical role in transforming networking from traditional to intelligent networking. The increasing demand for services from cloud users has increased the load on the network. An efficient system must handle various loads and increasing needs representing the relationships and dependence of businesses on automated measurement systems and guarantee the quality of service (QoS). The multiple paths from source to destination give a scope to select an optimal path by maintaining an equilibrium of load using some best algorithms. Moreover, the requests need to be transferred to reliable network elements. To address SDN's current and future challenges, there is a need to know how artificial intelligence (AI) optimization techniques can efficiently balance the load. This study aims to explore two artificial intelligence optimization techniques, namely Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO), used for load balancing in SDN. Further, we identified that a modification to the existing optimization technique could improve the performance by using a reliable link and node to form the path to reach the target node and improve load balancing. Finally, we propose a conceptual framework for SDN futurology by evaluating node and link reliability, which can balance the load efficiently and improve QoS in SDN.

The ever-increasing heterogeneous connections and the demands of the users pose many new challenges to the network service providers to sustain by providing improved quality of service (QoS). Software-defined networking (SDN) is a game changer in networking by allowing user customization to enhance performance. With the advent of 5G and the increasing user requests, a massive volume of heterogeneous traffic is generated in the network, increasing load. Currently, the existing load balancing techniques lack efficiency in handling the load under unicontroller deployment. In addition, the network paths selected must also be reliable and optimal. We proposed the self-socio adaptive, reliable particle swarm optimization (SSAR-PSO) load balancing technique to address the issue of load balancing in the unicontroller deployment of SDN. In the proposed technique, the performance of the node itself, known as direct information, and the performance of the neighbouring nodes, known as indirect information, were considered to identify the reliable node to form an optimal path. Simulation results showed that the proposed technique outperforms the existing state of art techniques under both TCP and UDP load in the following network performance metrics: latency, packet loss ratio, throughput, average round trip time, and bandwidth utilization ratio. INDEX TERMS Load balancing, particle swarm optimization, quality of service, reliability, softwaredefined networking.

Green cloud computing, an emerging computing paradigm in IT industry, is about providing efficient utilization of cloud's resources with minimal impact on the environment. The virtual machine placement has a considerable impact on a data center's energy and resource utilisation. The assignment of Virtual Machines (VMs) to Physical Machines (PMs) is an NP-hard problem to be solved using approximate algorithms at a reasonable computing time. In this paper, an energy-aware based algorithm is proposed for virtual machine placement. Our aim is to reduce the energy consumption and CO2 emission under a set of constraints. The performance and the efficiency of the proposed approach is tested on a cloud computing simulation toolkit.

The efficient and safe management of air conditioner (AC), Piped Natural Gas (PNG) and water pipelines in large buildings is a major challenge for the safety of these buildings. In recent years, Linear Wireless Sensor Networks (LWSN) are being used extensively for monitoring of long natural gas, water, and oil pipelines. LWSNs can also be used for efficient and safe management of AC, PNG and water pipelines in large buildings. In this paper, a scheme for optimal placement of sensors and base stations in a linear fashion to monitor the various pipelines used in large buildings has been proposed. The proposed scheme utilizes the Lion Optimization Algorithm (LOA) and has been compared with three strategies, namely Ant Colony Optimization (ACO), Genetic Algorithm (GA) and Greedy Approach with respect to throughput, lifetime and end-to-end delay. The simulation results show that the proposed scheme exhibits better performance in comparison to the other three considered techniques for all...

Smart Home Architecture is suitable for progressive and symmetric urbanization. Data being generated in smart home appliances using internet of things should be stored in cloud where computing resources can analyze the data and generate the decisive pattern within no time. This additional requirement of storage, majorly, comprising of unfiltered data escalates requirement of host machines which carries with itself extra overhead of energy consumption; thus, extra cost has to be beard by service providers. Various static algorithms are already proposed to improve energy management of cloud data centers by reducing number of active bins. These algorithms are not able to cater to the needs of present heterogeneous requests generated in cloud machines by people of diversified work environment with adhering to the requirements of quality parameters. Therefore, the paper has proposed and implemented dynamic bin-packing approaches for smart architecture that can significantly reduce energy...

Software Defined Network (SDN) deals with huge data processing units which possess network management. However, due to centralization behavior ensuring security in SDN is the major concern. In this work to ensure security, a security server has been at its aid to check the vulnerability of the networks and to keep an eye on the packet according to the screening policies. A Secure Shell Connection (SSH) is established by the security server which does a frequent inspection of the network's logs. Malware detection and the Intrusion Detection System policies are also incorporated in the server for the effective scanning of the packets. In response to a suspicious log or the packets in the SDN network there is a change in the security norms. Hence the proposed work updates the security policies in accordance with the attacker mentality.

This paper discusses a heuristic approach for Team Orienteering Problems with Time Windows. The method we propose takes advantage of a solution model based on a hierarchic generalization of the original problem, which is combined with an Ant Colony System algorithm. Computational results on benchmark instances previously adopted in the literature suggest that the algorithm we propose is effective in practice.

For promising outcome in industry 4.0, scheduling place a vital role for effective utilization of jobs allocate to machine. Job's and machine's are two attributes need to schedule for minimize makespan time, for each job we need to schedule the machine. Each job in a machine has its own process time, manipulation of all process time said to be makespan time that should minimized. Job shop scheduling is an effective tool incorporate with NP hard problems to achieve minimized makespan time. To achieve minimized makespan time optimization involve in this process those are namely Genetic Algorithm (GA), Opposition based Genetic Algorithm with Cauchy Mutation (OGA-CM), Opposition based Particle Swarm Optimization with Cauchy Mutation (OPSO-CM), Particle Swarm Optimization (PSO), Teaching Learning Based Optimization (TLBO) and Evolution of Cub to Predator (ECP). While applying these aforementioned optimization techniques, they reveal minimized makespan time compare with benchmark problems. Amid, ECP revels minimized makespan time for all six-bench mark problems compare with other competitive algorithm.

The widespread distribution of different services on demand and business applications requires a designing and implementation the highly-productivity networking solutions. Traditional multiservice network solutions often not allow to provide services with guaranty QoS characteristics. First of all, such situation connected with a high overloading of network environment – the data forwarding equipment today provide a lot of addition management and monitoring functions that influent to the efficient of service provision greatly. The implementation of Software-Defined Networking (SDN) paradigm and different virtualization technology, i.e. cloud computing is became very popular today. The main principles of SDN paradigm and cloud computing are provide the availability of different type of services which are allocated at different parts of the distributed multiservice network to users who wants to access those resources from their work place in the form as a service through an optimized ...

Cloud data centers always are facing challenges related to Virtual Machine (VM) placement operation, such as high violations in Service Level Agreement (SLA), high power consumption, and unfair load distribution among the physical servers. In this paper, in order to face these challenges, we first presented a multi-objective model for VM placement problem in the cloud data center, and then we formulate the VM placement problem as a multi-objective optimization problem. Finally, we proposed an improved VM placement algorithm, optimized by Particle Swarm Optimization (PSO). We have presented the specific design and implementation of the proposed algorithm such as the design of encoding scheme, fitness values, and mutations. The goal of the proposed algorithm to find the best solution for allocating a set of VMs to a set of physical servers without exceeding the capacity of the servers and considering a variety of objectives such as power consumption, violation of SLA and the degree of imbalance. The proposed algorithm is compared with different VM placement algorithms, such as First Fit Decreasing (FFD), Best Fit Decreasing (BFD) and Modified Best Fit Decreasing (MBFD). Simulation results show that the proposed algorithm leads to better results. The power consumption of the proposed algorithm achieves 36%, 24% decreases less than FDD and BFD respectively. Furthermore, the violation of SLA of the proposed algorithm achieves 31%, 21%, 15% decreasing compared with FDD, BFD and MBFD respectively.

Software-defined networking (SDN) plays a critical role in transforming networking from traditional to intelligent networking. The increasing demand for services from cloud users has increased the load on the network. An efficient system must handle various loads and increasing needs representing the relationships and dependence of businesses on automated measurement systems and guarantee the quality of service (QoS). The multiple paths from source to destination give a scope to select an optimal path by maintaining an equilibrium of load using some best algorithms. Moreover, the requests need to be transferred to reliable network elements. To address SDN's current and future challenges, there is a need to know how artificial intelligence (AI) optimization techniques can efficiently balance the load. This study aims to explore two artificial intelligence optimization techniques, namely Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO), used for load balancing in SDN. Further, we identified that a modification to the existing optimization technique could improve the performance by using a reliable link and node to form the path to reach the target node and improve load balancing. Finally, we propose a conceptual framework for SDN futurology by evaluating node and link reliability, which can balance the load efficiently and improve QoS in SDN.

Cloud Computing is expanding gradually as the number of educational applications rapidly increases. Therefore, Internet connectivity is essential to get the Educational Cloud services, and Cloud Environment uses one of the critical technologies to manage the Physical servers, i.e., Virtualization Technology. In Cloud Computing, the data centers host numerous Virtual Machines (VMs) on top of the Servers. Due to the rapid growth of Educational platforms, the workload of the VM is computationally getting increased. IT resources are provisioned over the network in the Cloud Educational platforms to execute the jobs. Since the data generated from the client-side is dynamic, it is not easy to allocate the computational resources efficiently. So to enhance the energy efficiency and to provide the resources in an optimized way, a VM Scheduling mechanism with Hybrid Genetic Algorithm and Modified Particle Swarm Optimization (GA-MPSO) is proposed in this work to achieve QoS parameters like reduced Energy consumption, SLA violation, and cost reduction over the heterogeneous environments. The Hybrid GA-MPSO predicts the optimal range and improves the best range of scheduling the Virtual resources to VMs from Physical Machines (PMs). The proposed approach, when compared to other VM scheduling algorithms, it intensifies the energy consumption to 105KWH, SLA violation rate of 0.08%, reduces the migrations count to 2122, and consumes the overall cost of 2567.68$. The different scheduling methods for VMs are evaluated against the results, which shows that the Hybrid GA-MPSO method is far better than the existing algorithms.

The Users can access Cloud services anytime and from any location, depending on their needs. In a cloud platform, data of a vast amount is transferred from the user to the server and vice-versa. Whenever the VM Scheduling takes longer than expected, or the selected VM does not exist in the datacenter may utilize more Energy consumption and SLA (Service Level Agreement) violations with more VM Migrations. Because the VM is the primary element in the Cloud Environment, the VM's assignment must be done correctly; resources must be utilized effectively, and no violations must occur with less VM Migrations. Two approaches are implemented for the comparison i.e., Modified Particle Swarm Optimization (MPSO), and Genetic Algorithm (GA). The MPSO resulted better than GA by 6.08%, LR-MMT by 32.2%, and GA at 27.81% compared to Local Regression-Minimum Migration Time (LR-MMT) in energy consumption. The MPSO resulted better than GA by 48.39%, LR-MMT by 91.6%, and GA by 83.73% compared to LR-MMT in VM migrations. The MPSO resulted better than GA by 5%, Local Regression-Random Selection (LR-RS) by 71.21%, and GA resulted in 67.21% compared to Local Regression-Maximum Correlation (LR-MC) in SLA Violation. Therefore, the acquired results indicated that the suggested approach converges to optimal solutions with higher quality than existing algorithms compared to the QoS parameters.

Energy efficiency has become an important measurement of scheduling algorithm for private cloud. The challenge is trade-off between minimizing of energy consumption and satisfying Quality of Service (QoS) (e.g. performance or resource availability on time for reservation request). We consider resource needs in context of a private cloud system to provide resources for applications in teaching and researching. In which users request computing resources for laboratory classes at start times and non-interrupted duration in some hours in prior. Many previous works are based on migrating techniques to move online virtual machines (VMs) from low utilization hosts and turn these hosts off to reduce energy consumption. However, the techniques for migration of VMs could not use in our case. In this paper, a genetic algorithm for poweraware in scheduling of resource allocation (GAPA) has been proposed to solve the static virtual machine allocation problem (SVMAP). Due to limited resources (i.e. memory) for executing simulation, we created a workload that contains a sample of one-day timetable of lab hours in our university. We evaluate the GAPA and a baseline scheduling algorithm (BFD), which sorts list of virtual machines in start time (i.e. earliest start time first) and using best-fit decreasing (i.e. least increased power consumption) algorithm, for solving the same SVMAP. As a result, the GAPA algorithm obtains total energy consumption is lower than the baseline algorithm on simulated experimentation.

Software-defined networking is a new network model proposed to solve the complexity of traditional network problems and facilitate dynamic network operation and management. The separation of the control plane from the data plane is the main idea of software-defined networks. Controllers are the operating system of software-defined networks and are responsible for managing the entire network. It is essential to locate controllers appropriately to have a balanced topology while guaranteeing low latency. In this work, a metaheuristic algorithm is used for controller placement. First, the problem is formulated and the network is partitioned by a clustering algorithm. Then, the seagull optimization algorithm is used to determine a suitable place for the controller in each network partition dynamically. Simulations are performed on the standard network topology from the internet topology zoo dataset to evaluate the proposed method. Simulation results reveal that the proposed method performs well in case of delay and load balancing compared with the state-of-the-art optimization algorithms.

With the speedy progress of mobile devices, a lot of commercial enterprises have exploited crowdsourcing as a useful approach to gather information to develop their services. Thus, spatial crowdsourcing has appeared as a new platform in e-commerce and which implies procedures of requesters and workers. A requester submits spatial tasks request to the workers who choose and achieve them during a limited time. Thereafter, the requester pays only the worker for the well accomplished the task. In spatial crowdsourcing, each worker is required to physically move to the place to accomplish the spatial task and each task is linked with location and time. The objective of this article is to find an optimal route to the worker through maximizing her rewards with respecting some constraint, using an approach based on GRASP with Tabu. The proposed algorithm is used in the literature for benchmark instances. Computational results indicate that the proposed and the developed algorithm is competi...

Smart Home Architecture is suitable for progressive and symmetric urbanization. Data being generated in smart home appliances using internet of things should be stored in cloud where computing resources can analyze the data and generate the decisive pattern within no time. This additional requirement of storage, majorly, comprising of unfiltered data escalates requirement of host machines which carries with itself extra overhead of energy consumption; thus, extra cost has to be beard by service providers. Various static algorithms are already proposed to improve energy management of cloud data centers by reducing number of active bins. These algorithms are not able to cater to the needs of present heterogeneous requests generated in cloud machines by people of diversified work environment with adhering to the requirements of quality parameters. Therefore, the paper has proposed and implemented dynamic bin-packing approaches for smart architecture that can significantly reduce energy...

Software-defined networking (SDN) is one of the most used network architecture that divides the forwarding plane and control plane. The SDN centrally observes and regulates the network through a software control in the control plane, called as controller. Multiple controllers are needed to manage the software-defined WAN (SD-WAN) for handling the scalability and reliability issues of the network as one controller is not enough. Deploying numerous controllers efficiently to improve the performance of the network is known as controller placement problem (CPP). This paper proposes a Varna-based optimization (VBO) for a reliable CPP that minimizes the total average latency of SDN. To the best of our knowledge, the proposed work is a novel approach, which is compared with particle swarm optimization, teacher learning-based optimization, and Jaya algorithms to solve reliable CPP. The experimental results show that VBO gives better performance than teacher learning-based optimization (TLBO), PSO, and Jaya algorithms for the popular topologies that are publicly available.

Software Defined Networking (SDN) is increasingly being used in data centers as well as enterprise networks. In an environment that has strict compliance requirements, such as HIPAA compliance, a critical role for an SDN controller is to route all data packets while considering data privacypreservation and compliance-preservation. In this paper, we address this problem by proposing a routing protocol for SDN which is an efficient risk-based swarm routing protocol. The programmable capability of controllers is exploited in order to minimize privacy and compliance risks in data transmission. The proposed routing protocol is based on the Ant Colony Optimization technique and machine learning, while the data for learning is obtained from OVSDB and the OpenvSwitch Database management protocol. We collect a history of packet transfers for training purposes and learn from the training data to efficiently and intelligently route sensitive data packets while it preserves the target compliance. This routing is obtained by intelligent eviction of rules that are downloaded to the switches. We have implemented the proposed schemes based on an RYU controller.

The increasing complexity of real-world problems motivated computer scientists and researchers to seek more efficient problem-solving strategies. Because of their ability to adapt to a wide range of conditions, Natural Inspired, Bio Inspired, Metaheuristics based on evolutionary computation, and Swarm Intelligence algorithms have been widely used for solving complex, real-world optimization problems. This paper presents a swarm based algorithm that is based on the cooperative behaviors between Loin's, it is called Lion Algorithm (LA) algorithm. This paper provides a review of these algorithms, with a particular emphasis on the Lion algorithm. Lion Algorithm is based on lions' unique social behavior, which makes them the world's strongest animal. Loin Algorithm, like Genetic Algorithm, includes generation, mutation, crossover, and so on. The lion's territorial defense and territorial takeover behavior distinguishes this algorithm from others.

The highly motivated Software-Defined Networking (SDN), the network programmability architecture made network maintenance easy. Multiple controllers are used to provide better scalability and reliability to the network in SDN. However, to enhance the network’s performance in networks at a large scale, the placement of controllers needs to be optimized. The Wisdom of Artificial Crowds (WOAC) genetic algorithm is used to minimize propagation latency by using the fewest possible controllers. To evaluate the performance of proposed technique the Internet2 OS3E topology is considered. The proposed controller placement technique reduces latency and improves reliability by comparing the existing solutions namely Modified Density Peak Clustering (MDPC) and optimized K-means approaches. The proposed method out performs existing techniques by 10% improves in latency and maximizes the reliability.

In cloud computing, high energy consumption and service-level agreements (SLAs) violation are the challenging issues considering that the demand for computational power is growing rapidly, thereby requiring large-scale cloud data centers. Although, there are many existing energy-aware approaches focusing on minimizing energy consumption while ignoring the SLA violation at the time of a virtual machine (VM) selection from overloaded hosts. Also, they do not consider that the current network traffic causes performance degradation and thus may not really reduce SLA violation under a variety of workloads. In this context, this paper proposes three adaptive models, namely, gradient descent-based regression (Gdr), maximize correlation percentage (MCP), and bandwidth-aware selection policy (Bw), that can significantly minimize energy consumption and SLA violation. Energy-aware methods for overloaded host detection and VM selection from an overloaded host are necessary to improve the energy efficiency and SLA violation of a cloud data center after migrating all VM from underloaded host turn to idle host, which switch to energysaving mode is also beneficial. Gdr and MCP are adaptive energy-aware algorithms based on the robust regression model, for overloaded host detection. A Bw dynamic VM selection policy selects VM according to the network traffic from the overloaded host under SLAs. Experimental results on the real workload traces show that the proposed algorithms reduce energy consumption while maintaining the required performance levels in a cloud data center using a CloudSim simulator to validate the proposed algorithms. INDEX TERMS Cloud computing, cloud data center, energy-efficiency, green computing, host overloaded detection, regression method, service level agreements.

Recently, software-defined networking (SDN) is the most promising solutions for future network. In SDN, networking architecture combines central management along with network programmability. It separates network management from the underlying network infrastructure, allowing administrators to dynamically adjust network-wide traffic flow to meet the changing needs. Due to these unique features, SDN easily manages network, gives better performance than traditional network, and provides higher flexibility. In this paper, we have proposed an ant colony optimization-based routing protocol targeting to achieve significant value of various QoS parameters. The proposed methodology is based on the classification of various types of traffic. To classify the traffic, we introduce to the bandwidth requirement for multimedia traffic like audio, video, and text. Here, our method is compared with particle swarm optimization-software-defined network (PSO-SDN), The results obtained in terms of various QoS parameters show that ant colony optimization outperforms to PSO-SDN. Finally, we concluded the paper with few suggested open research challenges.

Over the last decade, many nature-inspired algorithms have been received considerable attention among practitioners and researchers to handle several optimization problems. Lion optimization algorithm (LA) is inspired by a distinctive lifestyle of lions and their collective behavior in their social groups. LA has been presented as a powerful optimization algorithm to solve various optimization problems. In this paper, the LA is proposed to investigate its performance in solving one of the most popular and widespread real-life optimization problems called team orienteering problem with time windows (TOPTW). However, as any population-based metaheuristic, the LA is very efficient in exploring the search space, but inefficient in exploiting it. So, this paper proposes enhancing LA to tackle the TOPTW by utilizing its strong ability to explore the search space and improving its exploitation ability. This enhancement is achieved via improving a process of territorial defense to generate ...

This paper discusses the management of traffic between cloud-based cellular networks. Welldesigned traffic management will allow network operators to draw maximum value from availablecapacity where cloud-based traffic management is called the UE (User Equipment). Runningtraffic management on the side of the UE allows decision making in the UE. Users can reach theQoS level to increase the speed of data received and communication latency or reduce theirpower consumption. Each UE maximizes their utility function, which is modelled based on theQoS parameters, selfishly, at the network side. So, the attempt is to maximize fairness among alluses’ flows by designing new improved model of traffic management cloud based of pricinginternet involving the energy consumption. Also in this paper parameters β as UE parameter valueand α as range value parameter are used. The results show that the improved model is betterthan the original one proposed by previous research in terms of maximum value r...

Software-defined networking (SDN) plays a critical role in transforming networking from traditional to intelligent networking. The increasing demand for services from cloud users has increased the load on the network. An efficient system must handle various loads and increasing needs representing the relationships and dependence of businesses on automated measurement systems and guarantee the quality of service (QoS). The multiple paths from source to destination give a scope to select an optimal path by maintaining an equilibrium of load using some best algorithms. Moreover, the requests need to be transferred to reliable network elements. To address SDN's current and future challenges, there is a need to know how artificial intelligence (AI) optimization techniques can efficiently balance the load. This study aims to explore two artificial intelligence optimization techniques, namely Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO), used for load balancing in SDN. Further, we identified that a modification to the existing optimization technique could improve the performance by using a reliable link and node to form the path to reach the target node and improve load balancing. Finally, we propose a conceptual framework for SDN futurology by evaluating node and link reliability, which can balance the load efficiently and improve QoS in SDN.

The practitioners and researchers have received considerable attention solving complex optimization problems with meta-heuristic algorithms during the past decade. Many of these algorithms are inspired by various phenomena of nature. One of the promising solutions for secure and continuous power flow in the transmission line is rescheduling-based congestion management approach, but the base problem is rescheduling cost. To solve the congestion with minimized rescheduling cost, a new population-based algorithm, the Lion Algorithm (LA), is introduced in this paper. The basic motivation for development of this optimization algorithm is based on special lifestyle of lions and their cooperation characteristics. Based on some benchmark, Lion Algorithm (LA) is compared with the existing conventional algorithms such as particle swarm optimization (PSO), genetic algorithm (GA), artificial bee colony (ABC) and firefly (FF) by analysing the convergence, cost and congestion. In IEEE 30 bus system, experimental investigation is carried out and the obtained results by the proposed algorithm Lion Algorithm (LA) in comparison with the other algorithms used in this paper.

In the design and planning of next-generation Internet of Things (IoT), telecommunication, and satellite communication systems, controller placement is crucial in software-defined networking (SDN). The programmability of the SDN controller is sophisticated for the centralized control system of the entire network. Nevertheless, it creates a significant loophole for the manifestation of a distributed denial of service (DDoS) attack straightforwardly. Furthermore, recently a Distributed Reflected Denial of Service (DRDoS) attack, an unusual DDoS attack, has been detected. However, minimal deliberation has given to this forthcoming single point of SDN infrastructure failure problem. Moreover, recently the high frequencies of DDoS attacks have increased dramatically. In this paper, a smart algorithm for planning SDN smart backup controllers under DDoS attack scenarios has proposed. Our proposed smart algorithm can recommend single or multiple smart backup controllers in the event of DDoS occurrence. The obtained simulated results demonstrate that the validation of the proposed algorithm and the performance analysis achieved 99.99% accuracy in placing the smart backup controller under DDoS attacks within 0.125 to 46508.7 s in SDN.

Software Defined Network (SDN) deals with huge data processing units which possess network management. However, due to centralization behavior ensuring security in SDN is the major concern. In this work to ensure security, a security server has been at its aid to check the vulnerability of the networks and to keep an eye on the packet according to the screening policies. A Secure Shell Connection (SSH) is established by the security server which does a frequent inspection of the network's logs. Malware detection and the Intrusion Detection System policies are also incorporated in the server for the effective scanning of the packets. In response to a suspicious log or the packets in the SDN network there is a change in the security norms. Hence the proposed work updates the security policies in accordance with the attacker mentality.

Over the last decade, many nature-inspired algorithms have been received considerable attention among practitioners and researchers to handle several optimization problems. Lion optimization algorithm (LA) is inspired by a distinctive lifestyle of lions and their collective behavior in their social groups. LA has been presented as a powerful optimization algorithm to solve various optimization problems. In this paper, the LA is proposed to investigate its performance in solving one of the most popular and widespread real-life optimization problems called team orienteering problem with time windows (TOPTW). However, as any population-based metaheuristic, the LA is very efficient in exploring the search space, but inefficient in exploiting it. So, this paper proposes enhancing LA to tackle the TOPTW by utilizing its strong ability to explore the search space and improving its exploitation ability. This enhancement is achieved via improving a process of territorial defense to generate ...

Along with an explosive growth in mobile applications and the emergence of the concept of cloud computing, in mobile cloud computing (MCC) has been familiarized as a potential technology for mobile users. Employing MCC to enable mobile users to realize the benefits of cloud computing in an environment friendly way is an effective strategy to meet today's industrial demands. With the ever-increasing demand for MCC technology, energy efficiency has become extremely relevant in mobile cloud computing infrastructure. The concept of mobile cloud computing offers low cost and high availability to the mobile cloud users on pay-per-use basis. However, the challenges such as resource management and energy consumption are still faced by mobile cloud providers. If the allocation of the resources is not managed in a secure manner, the false allocation will lead to more energy consumption. This article demonstrates the importance of energy-saving mechanisms in cloud data centers and elaborates the importance of the "energy efficiency" relationship to promote the adoption of these mechanisms in practical scenarios. The utilization of resources are being maximized by minimizing the energy consumption. To achieve this, an integrated approach using Cuckoo Search (CS) with Artificial Neural network (ANN) is presented here. Initially, the Virtual Machines (VMs) are sorted as per their CPU utilization using Modified Best Fit Decreasing Approach (MBFD). This suffers from the increase in Service Level Agreement (SLA) violation along with many VM migrations. If the migration is not done at an appropriate host, which can hold the VM for long, Service Level Agreement Violation (SLAV) will be high.

We address the Home Health Care Delivery Problem (HHCDP), which is concerned with staff scheduling in the home health care industry. The goal is to schedule health care providers to serve patients at their homes that maximizes the total collected preference scores from visited patients subject to several constraints, such as workload of the health care providers, time budget for each provider and so on. The complexity lies in the possibility of cancellation of patient bookings dynamically, and the generated schedule should attempt to patients’ preferred time windows. To cater to these requirements, we model the preference score as a step function which depends on the arrival time of the visit and the resulting problem as the Team Orienteering Problem (TOP) with soft Time Windows and Variable Profits. We propose a fast algorithm, Iterated Local Search (ILS), which has been widely used to solve other variants of the Orienteering Problem (OP). We first solve the modified benchmark Team...

The Team Orienteering Problem with Time Windows (TOPTW) is the extension of the Orienteering Problem (OP) where each node is limited by a predefined time window during which the service has to start. The objective of the TOPTW is to maximize the total collected score by visiting a set of nodes with a limited number of paths. We propose two algorithms, Iterated Local Search and a hybridization of Simulated Annealing and Iterated Local Search (SAILS), to solve the TOPTW. As indicated in multiple research works on algorithms for the OP and its variants, determining appropriate parameter values in a statistical way remains a challenge. We apply Design of Experiments, namely factorial experimental design, to screen and rank all the parameters thereby allowing us to focus on the parameter search space of the important parameters. The proposed algorithms are tested on benchmark TOPTW instances. We demonstrate that well-tuned ILS and SAILS lead to improvements in terms of the quality of the solutions. More precisely, we are able to improve 50 best known solution values on the available benchmark instances.

Over the last decade, many nature-inspired algorithms have been received considerable attention among practitioners and researchers to handle several optimization problems. Lion optimization algorithm (LA) is inspired by a distinctive lifestyle of lions and their collective behavior in their social groups. LA has been presented as a powerful optimization algorithm to solve various optimization problems. In this paper, the LA is proposed to investigate its performance in solving one of the most popular and widespread real-life optimization problems called team orienteering problem with time windows (TOPTW). However, as any population-based metaheuristic, the LA is very efficient in exploring the search space, but inefficient in exploiting it. So, this paper proposes enhancing LA to tackle the TOPTW by utilizing its strong ability to explore the search space and improving its exploitation ability. This enhancement is achieved via improving a process of territorial defense to generate ...

We address the Home Health Care Delivery Problem (HHCDP), which is concerned with staff scheduling in the home health care industry. The goal is to schedule health care providers to serve patients at their homes that maximizes the total collected preference scores from visited patients subject to several constraints, such as workload of the health care providers, time budget for each provider and so on. The complexity lies in the possibility of cancellation of patient bookings dynamically, and the generated schedule should attempt to patients’ preferred time windows. To cater to these requirements, we model the preference score as a step function which depends on the arrival time of the visit and the resulting problem as the Team Orienteering Problem (TOP) with soft Time Windows and Variable Profits. We propose a fast algorithm, Iterated Local Search (ILS), which has been widely used to solve other variants of the Orienteering Problem (OP). We first solve the modified benchmark Team...