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Cloud data centers face various challenges, such as high energy consumption, environmental impact, and quality of service (QoS) requirements. Dynamic virtual machine (VM) consolidation is an effective approach to address these challenges, but it is a complex optimization problem that involves trade-offs between energy efficiency and QoS satisfaction. Moreover, the workload patterns in cloud data centers are often non-stationary and unpredictable, which makes it difficult to model them. In this paper, we propose a new method for dynamic VM consolidation that optimizes both energy efficiency and QoS objectives. Our approach is based on Markov chains and the artificial feeding birds (AFB) algorithm. Markov chains are used to model the resource utilization of each individual VM and PM based on the changes that happen in workload data. AFB algorithm is a metaheuristic optimization technique that mimics the behavior of birds in nature. We modify the AFB algorithm to suit the characteristics of the VM placement problem and to provide QoS-aware and energy-efficient solutions. Our approach also employs an online step detection method to capture variations in workload patterns. Furthermore, we introduce a new policy for VM selection from overloaded hosts, which considers the abrupt changes in the utilization processes of the VMs. The proposed algorithms are evaluated extensively using the CloudSim Toolkit with real workload data. The proposed system outperforms evaluation policies in multiple metrics, including energy consumption, SLA violations, and other essential metrics. [ABSTRACT FROM AUTHOR]

With due span of time, Cloud computing has grown phenomenally as it is a best approach for using the scalable shared pool of resources at economical cost. Though it seems easy but distribution of the jobs assigned to various Virtual Machines (VMs) is a cumbersome task for the Data Centre (DC) Controller. Various techniques have been proposed from time-to-time for balancing the load on distinct VMs available in the DCs. The primarily used load balancing techniques are Round Robin, Equal Spread Current Execution and Throttled. The major thrust for using the aforementioned techniques is to improve the performance parameters such as Overall Response Time (ORT), Data Center Processing Time (DCPT) and Cost. In this manuscript, a new algorithm named Pragmatic Load Balancing (PLB) has been designed and developed for optimization of afore-said parameters and various simulation scenarios have also been created for the evaluation of these parameters. In the given Scenarios created by using Cloud Analyst Simulator, different number of VMs like 20, 40, 60, 80 and 100 have been allotted with distinct number of DCs from 1 to 5 to the application at each Cloud Configuration and the divulged that the value of the performance parameters have been improved by using the PLB Algorithm in comparison to other available Load Balancing Algorithms. [ABSTRACT FROM AUTHOR]

Numerous data centers have adopted heterogeneous computing server to accelerate the processing speed with various applications. However, as the growth of efficiency, the issue of high power consumption is increasingly becoming crucial. Traditional researchers focus on the developing strategies for power efficiency that neglects the importance of power prediction that substantially impacts on energy planning on data center. Different to the previous work, this paper utilizes the advantage of Artificial Neural Network (ANN) to design and implement an improved algorithm of power consumption prediction on heterogeneous computing server with various types of jobs. First, the impact of multi-type of jobs has been taken into account, i.e., CPU-intensive, memory-intensive, I/O-intensive and GPU-intensive. We collect the data trace from these jobs by a set of benchmarks. Then, based on the collected data trace, power prediction algorithm has been established, so that developing an improved ANN-based power consumption prediction algorithm with CNN-BiLSTM neural network and attention mechanism. Specifically, one dimensional CNN is utilized to perform local feature extraction and dimension reduction on the input, the BiLSTM network is adopted to extract high-level features from the above results. The miscellaneous features extracted are filtered out by using attention mechanism. Last, the experiment has been conducted, and the peculiarity for four types of jobs has been discussed. Compared with the latest proposed ANN-based prediction power algorithms, our proposed algorithm has better performance in prediction accuracy on power consumption. [ABSTRACT FROM AUTHOR]

Cloud computing is the most widely adapted computing model to process scientific workloads in remote servers accessed through the internet. In the IaaS cloud, the virtual machine (VM) is the execution unit that processes the user workloads. Virtualization enables the execution of multiple virtual machines (VMs) on a single physical machine (PM). Virtual machine placement (VMP) strategically assigns VMs to suitable physical devices within a data center. From the cloud provider's perspective, the virtual machine must be placed optimally to reduce resource wastage to aid economic revenue and develop green data centres. Cloud providers need an efficient methodology to minimize resource wastage, power consumption, and network transmission delay. This paper uses NSGA-III, a multi-objective evolutionary algorithm, to simultaneously reduce the mentioned objectives to obtain a non-dominated solution. The performance metrics (Overall Nondominated Vector Generation and Spacing) of the proposed NSGA-III algorithm is compared with other multi-objective algorithms, namely VEGA, MOGA, SPEA, and NSGA-II. It is observed that the proposed algorithm performs 7% better that the existing algorithm in terms of ONVG and 12% better results in terms of spacing. ANOVA and DMRT statistical tests are used to cross-validate the results. [ABSTRACT FROM AUTHOR]

The evolution of cloud computing results in the development of numerous huge data-centers. These data-centers use large quantities of electrical energy, which in turn results in high operating cost and high emissions of CO 2 . The grounds for this excessive usage of energy is the inefficient usage of data-center resources (hosts). The resources in the data-center can be in either peak or idle or sleep state. It has been experimentally proven that hosts in the idle state consumes more energy than hosts in sleep state. Through an efficient and optimal VM placement strategy, an energy efficient resource utilization can be achieved, thereby reducing the number of idle hosts in the data-center. This paper introduces two energy efficient VM placement algorithms based on bin packing heuristics considering the physical machine's energy efficiency, Energy Efficient VM Placement (EEVMP) and Modified Energy Efficient VM Placement (MEEVMP), that can reduce the overall energy usage in the data-center. The EEVMP algorithm when compared with the default VM placement algorithm Power-Aware Best-Fit Decreasing algorithm (PABFD) of CloudSim, it reduces the energy consumption by 53%, average SLA violation by 3.5% and number of VM migrations by 64.47%. Further, we have performed MEEVMP algorithm where we achieve a reduction in energy consumption by 54.24%, average SLA violation by 4.39% and number of VM migrations by 67.713 % as compared to PABFD. [ABSTRACT FROM AUTHOR]

The purpose of designing embedded Internet of things and studying the ad hoc technology of gateway is to meet the monitoring requirements brought by the continuous expansion of data center. Among them, in the aspect of dynamic data monitoring, this paper designs a dynamic acquisition algorithm, that is, by analyzing the characteristics of data center resources and the number of information acquisition methods, so as to adjust the data acquisition cycle and to reduce the monitoring workload and improve the monitoring flexibility. At the same time, this paper also studies the automatic psychological evaluation system of students. It is found that with the intensification and efficient operation of social competition, some special groups of college students are facing increasing pressure, and events caused by psychological problems emerge one after another. Even though there are many psychological evaluation systems on the network, a large number of psychological knowledge and mental health management on the Internet cannot effectively manage and improve the psychological status of college students. Therefore, the university needs to build a psychological evaluation system for college students in time and combine the automatic psychological evaluation system for college students with embedded Internet of things and cloud service technology to improve the accuracy of psychological evaluation. The application of the system, college students can independently evaluate their own mental state, if there are psychological problems can be timely counseling, not only can improve the level of mental health education in colleges and universities, but also can promote the development of physical and mental health. [ABSTRACT FROM AUTHOR]

Cloud Computing is playing a huge role in future technology. Further, with the explosive growth of the Internet and cloud computing, several service providers, such as Amazon, Microsoft, IBM, and Google, have expanded their data centers and rapidly deployed data centers in different places around the world to deliver various cloud computing services. However, several challenges are raised with the wide spread use of cloud environment such as power consumption, load balance, reliability, scalability, and security. This paper tackles the power consumption problem and presents an efficient algorithm, called Task Consolidation based Power Minimization (TCPM), to efficiently schedule tasks onto available resources of the cloud environment so as to minimize power consumption. In proposed TCPM algorithm, several benefits of the existing algorithms are enhanced and incorporated into the TCPM algorithm, where the best-fit procedure is used to achieve the best possible resource utilization and avoid wasting energy. The results of the proposed TCPM algorithm are compared with other recent algorithms such as FCFS, WWO, and MCT algorithms using the CloudSim toolkit. [ABSTRACT FROM AUTHOR]

As cloud services expand, the need to improve the performance of data center infrastructure becomes more important. High-performance computing, advanced networking solutions, and resource optimization strategies can help data centers maintain the speed and efficiency necessary to provide high-quality cloud services. Running containerized applications is one such optimization strategy, offering benefits such as improved portability, enhanced security, better resource utilization, faster deployment and scaling, and improved integration and interoperability. These benefits can help organizations improve their application deployment and management, enabling them to respond more quickly and effectively to dynamic business needs. Kubernetes is a container orchestration system designed to automate the deployment, scaling, and management of containerized applications. One of its key features is the ability to schedule the deployment and execution of containers across a cluster of nodes using a scheduling algorithm. This algorithm determines the best placement of containers on the available nodes in the cluster. In this paper, we provide a comprehensive review of various scheduling algorithms in the context of Kubernetes. We characterize and group them into four sub-categories: generic scheduling, multi-objective optimization-based scheduling, AI-focused scheduling, and autoscaling enabled scheduling, and identify gaps and issues that require further research. [ABSTRACT FROM AUTHOR]

Resource management is an important issue in the cloud computing paradigm. The maximum Utilization of resources leads the service providers to get maximum profit. The suitable resources in the cloud data centre are needed to satisfy the user needs. The selection of the right resource is very essential in the data centre. Metaheuristic algorithms are used to perform the task allocation in the cloud computing environment. In this paper, a novel task allocation algorithm is proposed using Cat Swarm Optimization and BAT algorithm. BAT algorithm helps CSO algorithm to escape from a pre convergence issue. The proposed HGCSBAT algorithm results are evaluated and compared with the popular algorithms CSO and BAT algorithms. The HGCSBAT results are better than Genetic algorithm, Cat Swarm Optimization, and BAT algorithms in terms of availability and throughput. [ABSTRACT FROM AUTHOR]

Multi-center heterogeneous data are a hot topic in federated learning. The data of clients and centers do not follow a normal distribution, posing significant challenges to learning. Based on the assumption that the client data have a multivariate skewed normal distribution, we improve the DP-Fed-mv-PPCA model. We use a Bayesian framework to construct prior distributions of local parameters and use expectation maximization and pseudo-Newton algorithms to obtain robust parameter estimates. Then, the clipping algorithm and differential privacy algorithm are used to solve the problem in which the model parameters do not have a display solution and achieve privacy guarantee. Furthermore, we verified the effectiveness of our model using synthetic and actual data from the Internet of vehicles. [ABSTRACT FROM AUTHOR]

The emerging technologies such as high-performance computing, cloud computing and large data centre networks demand very high injection rate, high scalability and very low switching latency (SL) that can manage high network traffic, and buffering and also be cost-effective, so that they can be a viable solution for future manufacturing industry. To overcome the biggest challenge of optical buffering and big quantity of data switching, the Data Vortex (DV) all optical switch innovations and its various enhancements such as Augmented Data Vortex (ADV) seem to be the possible solution and can bring the revolution in the industrial world. In this paper, a new generalized algorithm has been used to simulate the equivalent planar virtual model of 3-D ADV and is named as equivalent planar algorithm (EPA). The algorithm is more versatile and provides more flexible design for multiple level networks. To verify the proposed EPA of ADV (or EPA ADV), the simulations have been performed for various input traffic loads and switch size keeping active angle 'A' constant. Also the ADV switch is not studied under asymmetric mode (AM) of operation till now which has been done here. Lastly the paper also provides the performance comparison study of original DV and EPA ADV under symmetric and asymmetric I/O mode. The results obtained from simulation have shown that under modest degree of asymmetric I/O operation of the switch EPA ADV shows performance improvement in injection rate (throughput) and average switch latency (SL) under different traffic pattern as compared to symmetric mode (SM) of operation in ADV. [ABSTRACT FROM AUTHOR]

Cloud Computing is a widely adopted computing model that offloads the in-house processing workloads to remote servers. In recent years, the adoption of cloud computing and related models have increased multifold. The cloud data center consumes an enormous amount of electricity and becomes a major issue for emitting greenhouse gases. The most important power conservation strategy used in IaaS cloud is scheduling the virtual machine appropriately into the physical servers to minimize the number active servers. As the number of active servers decreases, the power consumption of a data center will also decrease. The fundamental aim of the proposed work is to schedule the virtual machine as dense as possible in a minimal number of servers using the proposed modified discrete firefly algorithm for power consumption. The proposed algorithm will effectively explore the large search space to find a placement that uses minimal power consumption in the data centers. The proposed algorithm is executed to place virtual machines of various configurations in IaaS cloud and the results are compared with Genetic Algorithm and Particle Swarm Optimization shows its superiority. [ABSTRACT FROM AUTHOR]

How to improve resource utilization of cloud data centers (CDCs) and ensure users' quality of service (QoS) through efficient virtual machine (VM) scheduling is an urgent problem. Especially when service reliability is taken into consideration, the problem becomes more challenging. However, existing related researches mostly ignore the influence of reliability factors, such as failures and recoveries of computing nodes (CNs), which cannot reflect the realistic situations of real-life CDCs. Therefore, this paper investigates the problem of fault tolerance-aware VM scheduling and formulates it as a multi-objective optimization model with multiple QoS constraints. The proposed model tries to minimize users' total expenditure and, at the same time, maximize the successful execution rate of their businesses. To solve the proposed optimization model, a greedy-based best fit decreasing (GBFD) algorithm is then developed. The GBFD algorithm adopts a cost efficiency factor whose definition is according to the characteristics of CNs, to select a suitable CN for each VM request. Finally, extensive experiments are conducted to verify the feasibility of the proposed models and algorithm based on both the real-world CDC cluster data sets and the simulation ones. The results show that, first, as expected, fault tolerance significantly influences the performance criteria of VM scheduling and second, in most cases, the developed algorithm can decrease users' expenditure, increase success rate for executing their business and improve their overall satisfactions. Specifically, under real-world CDC cluster scenario, GBFD algorithm can increase the overall satisfaction of all cloud users by 38.3%, 20.9% and 14.6%, respectively, compared with the other three ones. Thus, the developed algorithm can perform better under fault tolerance-aware cloud environments. [ABSTRACT FROM AUTHOR]

Recently, cloud computing infrastructure (CCI) models have received much attention for their exceptional scalability, dependability, Data Information Sharing (DIS), and low cost rate. There are many hardware and software elements that are accessed over the internet by cloud data centers. Modern data centers utilize Virtualization Techniques (VT) to offer a dispersed CI that employs Virtual Machines (VM) based on Physical Hosts (PH). With the increasing number of centers, optimizing energy consumption has become vital to saving costs due to DCC's high energy consumption. In our CPS algorithm, we combine the Cuckoo algorithm and the particle swarm optimization (PSO). It is determined which virtual machine can be assigned to each host, thus choosing the best virtual machine. As a result, if the selected host is overloaded, it is determined which virtual machines are generating high loads and migrated to another host, which is determined based on the cuckoo algorithm and PSO. In testing each algorithm separately, the combination method proved to consume less energy and execute faster than the other methods in the CloudSim simulation environment. Fault tolerance for our network and evaluation of VMs have also been emphasized in vSphereTM. [ABSTRACT FROM AUTHOR]

In the private cloud data center, security participated a fundamental position amid the storage of a voluminous amount of information that is intended to share among various nodes. On the other hand, the challenges in moving object detection and movement-based sub-sequences are significant segments of numerous PC apparition functions that incorporate acknowledgment of objects, assessment of interchange, and manufacturing mechanization. In this paper, we propose to actualize hearty moving object detection and following calculation that can recognize quick-paced moving objects in an assortment of testing constant quick-moving applications like traffic reconnaissance, etc. For the detection of moving objects, we utilize a Gaussian Mixture Design Background Subtraction Methodology. To remove noise, morphological processes are concerned with the resultant forefront pretence. Kalman Filter is utilized for movement-based monitoring and the detected object functions carry out movement segmentation using a foreground detector. Ultimately, blob analysis recognizes clusters of associated picture elements that are known to be moving objects, and the values are stored in a private cloud data center. [ABSTRACT FROM AUTHOR]

The rapid growth of the technologies, high bandwidth applications, and cloud data centers consume heavy internet service. So, the consumer of the internet expects a high capacity medium for communication. The Elastic Optical Network (EON) provides a flexible and reliable transmission service for consumers. The spectrum fragmentation is a key challenge in EON. In simple terms, unaligned Frequency Slots (FSs) in the network are referred to as fragmented spectrum, while in defragmentation, the available FSs need to be rearranged to create room for the new connection requests. The problem in defragmentation occurs due to the lack of a continuous spectrum and it leads to depreciation in spectrum usage and simultaneously increasing the Blocking Probability (BP) which disrupts the majority of the existing connections in the network. Several techniques and approaches were suggested to reduce the possibility of disruption and reconfiguration in the network while defragmenting the frequency slots. This paper proposes a new algorithm to overcome the drawbacks and improvement in the quality of service of the network. The proposed algorithm holds the approach of proactive and reactive along with the meta-heuristic nature-inspired optimization technique called Jellyfish Search Optimization (JSO). The proposed combination, PR-DF-JFSO outperforms well in terms of spectrum utilization, network efficiency, and quality of service offered when compared to the state-of-the-art spectrum defragmentation algorithms according to the results of experiments done using standard quality metrics. The simulation results show the better utilization of spectrum, reduce the spectrum fragmentation complexity, better bandwidth fragmentation ratio, and overall connection blocking reduced. [ABSTRACT FROM AUTHOR]

IoT applications have become a pillar for enhancing the quality of life. However, the increasing amount of data generated by IoT devices places pressure on the resources of traditional cloud data centers. This prevents cloud data centers from fulfilling the requirements of IoT applications, particularly delay-sensitive applications. Fog computing is a relatively recent computing paradigm that extends cloud resources to the edge of the network. However, task scheduling in this computing paradigm is still a challenge. In this study, a semidynamic real-time task scheduling algorithm is proposed for bag-of-tasks applications in the cloud–fog environment. The proposed scheduling algorithm formulates task scheduling as a permutation-based optimization problem. A modified version of the genetic algorithm is used to provide different permutations for arrived tasks at each scheduling round. Then, the tasks are assigned, in the order defined by the best permutation, to a virtual machine, which has sufficient resources and achieves the minimum expected execution time. A conducted optimality study reveals that the proposed algorithm has a comparative performance with respect to the optimal solution. Additionally, the proposed algorithm is compared with first fit, best fit, the genetic algorithm, and the bees life algorithm in terms of makespan, total execution time, failure rate, average delay time, and elapsed run time. The experimental results show the superiority of the proposed algorithm over the other algorithms. Moreover, the proposed algorithm achieves a good balance between the makespan and the total execution cost and minimizes the task failure rate compared to the other algorithms. [ABSTRACT FROM AUTHOR]

A burning challenge is balancing the demand for servers in computer data centers. In the modern era, the bulk of computer systems are used for big data and cloud computing. The Internet of Things (IoT) is becoming a hyper-world of physical, cyber and social worlds with big data as a connection. In the latest systems architectures, managing the load in cloud applications is a major challenge. While several techniques and algorithms have been framed to achieve optimality in the load balancing definition, they are limited to the current problems of that time. However, due to a massive increase in data to be handled for the computing activities that are running on servers, exponential petabytes of data that are stored, the complexity of the problem increases day by day. By efficiently balancing the load and high utilization of the available resources from the computing pool, the solution to such problems can be achieved. When these are performed professionally, optimum values can be accomplished with the prominent use of available resources. Genetic Algorithms, Bacteria Foraging, optimizes the algorithm, Artificial Bee Colony, Optimized ABC are current trending algorithms used in load balancing concepts. All these trending algorithms, while strong, lack certain factors. To overcome them, we proposed an EBGO algorithm that has overcome the disadvantages of the trending load balancing algorithms in terms of certain significant parameters such as response time, energy consumption, weighted total cost, procession time, and many. This model aims to achieve overall optimal parameter values, thus efficiently balancing the demand on servers in data centers, resulting in optimal resource use. This model is often appropriate for health care systems, where large number of different types of systems and data are shared between them. [ABSTRACT FROM AUTHOR]

Task Scheduling is one of the important aspects in Cloud Computing. The Primary Objective of the task scheduling is to effectively map tasks on to the corresponding VMs by minimizing makespan and maximizing utilization of resources. Energy consumption is one of the important parameter which needs to be addressed in Cloud Computing. It needs to be minimized as datacenters of Cloud releases huge amount Carbon dioxide which effects the environment. Many of the researchers proposed various scheduling algorithms in Cloud computing to address makespan and resource utilization. This paper proposes a task scheduling algorithm which schedules the tasks on to appropriate VMS based on the calculation of Task and VM priorities and is modeled by using Whale Optimization algorithm while minimizing energy consumption and power cost at datacenters. Initially we have calculated priorities of Task and VMs to effectively maps tasks onto the VMs and thereby evaluating the multi-objective fitness function which addresses energy consumption and power cost at datacenters. Simulation carried out on CloudSim Simulator and analyzed performance of the proposed algorithm with existing PSO and CS algorithms. Simulation results revealed that proposed algorithm minimizes energy consumption and power cost over the existing algorithms. [ABSTRACT FROM AUTHOR]

Due to the development and growth of Internet platforms and web services as communication resources, the competition for the network and its limited resources is increasing day by day. Distribution and use of distributed services is one of the effective solutions in this field. Therefore, one of the most effective ways to implement distributed services is to use content distribution networks. Content Distribution Network (CDN) An extensive network of distributed servers located in different data centers in different geographical locations. One of the most important issues in content distribution networks is the algorithm for selecting a suitable alternative server to serve the user's request. A common solution is to refer the user request to the nearest alternative server. The most important concern when choosing an alternative server is that the nearest alternative server is not always the best choice, but a set of parameters such as server-user distance, server speed, available bandwidth, server processing load and service price. Etc. should be considered in the selection. In this paper, the aim is to provide an algorithm for selecting the most suitable alternative server using the qualitative and quantitative characteristics of different sources. In this regard, the issue is defined as a multi-feature decision problem and our decision method we use PROMETHEE as one of the most widely used methods to solve this type of problem. The simulation results show that the proposed method in terms of response time averaged 63.2051 and 68.756% and in terms of service price with 59.8303 and 58.4607% respectively had better performance than the method. RR and MCT. [ABSTRACT FROM AUTHOR]

PFC (Priority-based Flow Control), which is used to give full play to the performance advantages of RDMA, can cause problems such as head-of-line (HoL) blocking. PFC-based method cannot eliminate the congestion fundamentally. At the same time, another approach to deal with congestion in the data center networks, commonly known as "injection throttling" mechanism, may result in slower effective response due to the delay between congestion detection and feedback to the sources. In this article, we have optimized the two types of algorithms (DCI and NACC) and used them in combination (DCI-NACC). We propose a novel scheduling strategy of combining PFC-based algorithm and "injection throttling"-based algorithm using programmable data plane device for lossless data center networks. Specifically, we design a dynamic congestion isolation (DCI) method to optimize PFC. DCI ensures lossless transmission in the data center networks and eliminates the HoL blocking problem caused by PFC. We also propose a network-assisted congestion control (NACC) algorithm in which the feedback information is directly sent from the switch where congestion occurs in the network. NACC increases the efficiency of network congestion perception and reduces the delay that feedback take effect. Experimental results show that, compared with PFC and ECN algorithm, DCI-NACC algorithm has significant improvement in network performance (normalized throughput, network latency, and flow completion time). Especially, simulation experiments show that NACC can greatly reduce the number of times that PFC PAUSE is triggered. The simulation experiments confirm that DCI-NACC algorithm can effectively improve the performance of the data center networks on the basis of lossless transmission. [ABSTRACT FROM AUTHOR]

Since its inception, cloud computing has greatly transformed our lives by connecting the entire world through shared computational resources over the internet. The COVID-19 pandemic has also disrupted the traditional learning and businesses and led us towards an era of cloud-based activities. Virtual machine is one of the main elements of virtualization in cloud computing that represents physical server into the virtual machine. The utilizations of these VM's are important to achieved effective task scheduling mechanism in cloud environment. This paper focuses on improvment of the task distribution system in VM for cloud computing using load balancing technique. For that reason modification took place at Bat algorithm fitness function value this section used in load balancer section. When algorithm iteration are complete then time to distribute the task among different VM therefore in this section of algorithm was modified. The second modification took place at the search process of Bat at dimension section. The proposed algorithm is known as modified Bat algorithm. Four parameter are used to check the performance of the system which are throughput, makespan, degree of imbalance and processing time. The proposed algorithm provides efficient result as compaire to other standard technique. Hence the proposed algorithm improved cloud data center accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Clustering is an unsupervised data analytic technique that can determine the similarity between data objects and put the similar data objects into one cluster. The similarity among data objects is determined through some distance function. It is observed that clustering technique gains wide popularity due to its unsupervised and can be used in diverse research filed such as image segmentation, data analytics, outlier detection, and so on. This work focuses on the data clustering problems and proposes a new clustering algorithm based on the behavior of micro-bats. The proposed bat algorithm to determine the optimal cluster center for data clustering problems. It is also observed that several shortcomings are associated with bat algorithm such as slow convergence rate, local optima, and trade-off among search mechanisms. The slow convergence issue is addressed through an elitist mechanism. While an enhanced cooperative method is introduced for handling population initialization issues. In this work, a Q-learning based neighbourhood search mechanism is also developed to effectively overcome the local optima issue. Several benchmark non-healthcare and healthcare datasets are selected for evaluating the performance of the proposed bat algorithm. The simulation results are evaluated using intracluster distance, standard deviation, accuracy, and rand index parameters and compared with nineteen existing meta-heuristic algorithms. It is observed that the proposed bat algorithm obtains significant results with these datasets. [ABSTRACT FROM AUTHOR]

Cloud computing technology provides shared computing which can be accessed over the Internet. When cloud data centers are flooded by end-users, how to efficiently manage virtual machines to balance both economical cost and ensure QoS becomes a mandatory work to service providers. Virtual machine migration feature brings a plenty of benefits to stakeholders such as cost, energy, performance, stability, availability. However, stakeholders' objectives are usually conflict with each other. Furthermore, the optimal resource allocation problem in cloud infrastructure is usually NP-Hard or NP-Complete class. In this paper, the virtual migration problem is formulated by applying the game theory to ensure both load balance and resource utilization. The virtual machine migration algorithm, named V2PQL, is proposed based on Markov decision process and Q-learning algorithm. The results of the simulation demonstrate the efficiency of our proposal which are divided into training phase and extraction phase. The proposed V2PQL algorithm has been benchmarked to the Round-Robin, inverse Ant System, Max–Min Ant System, and Ant System algorithms in order to highlight its strength and feasibility in extraction phase. [ABSTRACT FROM AUTHOR]

Recently, the concept of cloud computing has been extended towards the network edge. Devices near the network edge, called fog nodes, offer computing capabilities with low latency to nearby end devices. In the resulting fog computing paradigm (also called edge computing), application components can be deployed to a distributed infrastructure, comprising both cloud data centers and fog nodes. The decision which infrastructure nodes should host which application components has a large impact on important system parameters like performance and energy consumption. Several algorithms have been proposed to find a good placement of applications on a fog infrastructure. In most cases, the proposed algorithms were evaluated experimentally by the respective authors. In the absence of a theoretical analysis, a thorough and systematic empirical evaluation is of key importance for being able to make sound conclusions about the suitability of the algorithms. The aim of this paper is to survey how application placement algorithms for fog computing are evaluated in the literature. In particular, we identify good and bad practices that should be utilized respectively avoided when evaluating such algorithms. [ABSTRACT FROM AUTHOR]

The current data dynamic migration algorithm ignores the attribute characteristics of data in the process of data layout, which leads to more iterations of data in perceptual virtual network, longer downtime of dynamic migration and lower migration efficiency. To solve this problem, a dynamic migration algorithm of perceptual data in virtual network based on machine learning is proposed. Through machine learning algorithm to mine the attribute characteristics of virtual network perception data, Moran's I index is obtained to analyze the correlation index of perception data. By calculating the spatial location of data perception, the data center with less workload in virtual network is selected, and the data center of each data center is calculated. By determining the target node, selecting the migration sensing data and setting the migration factor as the limiting condition, the dynamic migration of sensing data is realized. Experimental results show that the proposed algorithm can effectively reduce the number of iterative replication rounds, shorten the downtime of dynamic migration, and improve the efficiency of virtual network migration in the environment of high dirty page rate and low dirty page rate. [ABSTRACT FROM AUTHOR]

Energy efficiency is an important issue for reducing environmental dissipation. Energy efficient resource provisioning in cloud environments is a challenging problem because of its dynamic nature and varied application workload characteristics. In the literature, live migration of virtual machines (VMs) among servers is commonly proposed to reduce energy consumption and to optimize resource usage, although it comes with essential drawbacks, such as migration cost and performance degradation. Energy efficient provisioning is addressed at the data center level in this research. A novel efficient resource management algorithm for virtualized data centers that optimizes the number of servers to meet the requirements of dynamic workloads without migration is proposed in this paper. The proposed approach, named Look-ahead Energy Efficient VM Allocation (LAA), contains a Holt Winters-based prediction module. Energy efficiency and performance are inversely proportional. The energy-performance trade-off relies on periodic comparisons of the predicted and active numbers of servers. To evaluate the proposed algorithm, experiments are conducted with real-world workload traces from Google Cluster. LAA is compared with the best approach provided by CloudSim based on VM migration called Local Regression-Minimum Migration Time (LR-MMT). The experimental results show that the proposed algorithm leads to a consumption reduction of up to 45% to complete one workload compared with the LR-MMT. [ABSTRACT FROM AUTHOR]

High energy consumption and serious reduction in the number of virtual machine (VM) migrations in cloud data centres have become increasingly urgent challenges. Finding an efficient VM mapping method is vital in dealing with these challenges. Server consolidation is a well-known NP-hard problem. Moreover, efficient resource mapping and VM migration should consider multiple factors synthetically, including quality of service, energy consumption, resource utilisation, and migration overheads, which are multi-objective optimisation problems. This letter aims to address these issues using a novel bio-inspired mapping algorithm. Also, this letter revisits the existing locust-inspired resource scheduling algorithm employed in cloud data centres with a real workload as well as an analogy and model and presents a novel algorithm. Critical analysis of the locust approach has shown that it opens new opportunities for future research, suggestions for which have been offered. Such analysis ensures the hardware reliability of an algorithm and the algorithm's quality of performance. The results show that the proposed algorithm outperforms state-of-the-art bio-inspired algorithms. We compared our algorithm with heuristic and meta-heuristic algorithms. The experimental results show that compared with these algorithms, our algorithm efficiently reduces performance degradation due to migration (PDM), energy consumption, and the number of migrations along with improving server utilisation. [ABSTRACT FROM AUTHOR]

In cloud computing (CC), load balancing tasks remain a critical problem in distributing resources from a data center. Ensure that every virtual machine (VM) has a balanced load to maximize capacity utilization. In the CC world, load balancing is a Non-Polynomial (NP) problem resolved with metaheuristic algorithms. A new Quasi-Oppositional Dragonfly Algorithm for Load Balancing (QODA-LB) has been developed to obtain optimum resource scheduling in a CC configuration. The proposed QODA-LB algorithm uses three variables to calculate an objective function: execution time, execution cost, and charge. The QODA-LB algorithm assigns tasks to VM according to its potential and the resulting objective function. Also, the QODA-LB algorithm employs the Quasi-Oppositional Based Learning principle to increase the standard convergence rate of the Dragonfly (DA) algorithm. A complete series of experiments were conducted, and the results were analyzed in various ways to ensure the increased efficiency of the QODA-LB algorithm. Simulation results demonstrated an optimal load balancing efficiency and outperformed key approaches. [ABSTRACT FROM AUTHOR]

In the past decade, the cloud environment has emerged as a resource-enriched technology for computational modeling. Most cloud data centers are deployed far from the device layer, which may cause higher service delivery time. Therefore, Fog servers are implemented to retain the commitment of minimum service delivery time. The computational resources (processing unit, memory, storage) and network bandwidth are to be appropriately scheduled to make an efficient system. For delay and resource-constrained devices, the essential issues include minimum service latency and improved system performance by offloading non-sensitive devices to the cloud layer. In this study, an adaptive bandwidth adjustment technique is proposed considering the sensitivity of the applications and load on the multiple queues at the access point based on which, a compelling dynamic bandwidth ratio is calculated. Further, blocking probability of task and task dropping probability is used as task offloading probability to overcome the dropping ratio of type-II applications. The random early detection (RED) algorithm is implemented in queueing management to maximize the system performance. Extensive simulation results highlighted that the proposed model significantly improves the system performance in terms of network delay, queue delay, and overall response time over traditional algorithms. [ABSTRACT FROM AUTHOR]

A huge cloud data center makes it possible to offer computing as a utility to customers. However, the main challenge is to fulfill the customer's dynamic workload requirement seamlessly. Additionally, cloud data center consumes an enormous amount of power due to improper scheduling of virtual machines over the physical machines, which lead to inefficient usage of heterogeneous computing resources. So, to minimize energy consumption in the cloud data center, virtual machines should be scheduled in an energy-efficient way. In this paper, an Artificial Immune System based Virtual Machine Scheduling using Modified Clonal Selection Algorithm (VMS-MCSA) is proposed to schedule virtual machines energy efficiently. The classical Clonal Selection Algorithm(CSA) operators are modified such that they can be applied to the discrete optimization dynamic virtual machine scheduling problem. The randomized mutation operator is proposed, which reschedule VMs at each scheduling interval to handle the dynamicity of workload with minimum virtual machine migrations. Additionally, the VM-consolidation model was proposed for constraint-based virtual machine migration. The proposed VMS-MCSA algorithm is implemented on a cloudsim simulator, and the results show that the VM scheduling using VMS-MCSA is energy-efficient compared to other recent approaches. [ABSTRACT FROM AUTHOR]

With the development of big data technologies, green cloud data centers have become a key factor in academia and industry. An energy-efficient cloud data center can save costs for cloud computing users. However, the problem of virtual machine mapping has always been a core problem. In the most existing research, the energy consumption generated by cloud data centers has become an important bottleneck restricting the technology of cloud computing. This paper establishes a consumption model of cloud data center energy and a virtual machine mapping rule. Based on this, a multi-dimensional double descending maximum padding priority (MD3MP2) virtual machine mapping algorithm is proposed. The algorithm can not only solve the one-dimensional virtual machine mapping problem of homogeneous data centers, but also successfully solve the multi-dimensional virtual machine mapping problem of homogeneous data centers. Finally, the algorithm is compared with four other algorithms. The experimental results show that the MD3MP2 algorithm is better than the compared algorithms. [ABSTRACT FROM AUTHOR]

The advent of virtualization technology has created a huge potential application for cloud computing. In virtualization, a large hardware resource is often broken down into smaller virtual units. These small units are then provisioned to different clients. However, these services need to be provided in such a way that resources are properly utilized. To achieve this, many of the scheduling, allocation, and provisioning issues of data centers are formulated as optimization problems. The virtual machine placement problem (VMPP) is a typical provisioning problem of data centers. In VMPP, several virtual machine requests are to be hosted on physical machines such that a minimum number of physical machines are used. This work proposes a cuckoo search (CS) inspired algorithm for solving the VMPP. To improve the algorithm's performance, new cost and perturbation functions are developed. The proposed method was tested on two well-known benchmark datasets. It outperformed the reordered grouping genetic algorithm, best-fit decreasing, first-fit decreasing, and an earlier CS method called multiCSA. [ABSTRACT FROM AUTHOR]

In order to satisfy the rapidly increasing demand for data volume, large data center networks (DCNs) have been proposed. In 2019, Zhang et al. proposed a new highly scalable DCN architecture named HSDC (Highly Scalable Data Center Network), which can achieve greater incremental scalability. In this paper, we give the definition of the logical graph of HSDC, named H n , which can be treated as a compound graph of hypercube and complete graph of the same dimension. First, we prove that the connectivity and tightly super connectivity of H n are both n. Then, we give an O(n) shortest unicast algorithm to find a shortest path between any two distinct nodes in H n , and prove the correctness of this algorithm. In fact, we also prove that the length of the shortest path constructed by this algorithm is no more than 2 d + 1 if d < n and at most 2d if d = n , where d is the Hamming distance between the start and end nodes, and the diameter of H n is 2n. [ABSTRACT FROM AUTHOR]

Fog computing as a complementary paradigm to cloud computing is a heuristic shift in service delivery that promises a leap in efficiency and flexibility for cloud-based Internet of Things applications. The performance characteristics of cloud/fog computing attract significant attention from researchers lately. One of the critical challenges in this field is controlling and reducing the massive amount of energy consumption in the cloudlets while still maintaining the Service Level Agreement's performance requirements. Many virtual machine (VM) allocation and consolidation strategies are investigated to address the challenges mentioned earlier. However, many of the solutions save energy at the cost of performance degradation. This paper proposes a novel multi-step VM allocation algorithm called enhanced performance-to-power ratio for workflow applications "E-PRWA" in cloud/fog environment. The proposed heuristic algorithm strives to achieve a trade-off between node performance and power consumption. Operating machine hosts at the highest performance-to-power ratio can save a tremendous amount of energy without degrading system performance. The proposed model consists of four stages: (a) detecting overutilized or underutilized nodes based on the preferred utilization (PU); (b) VM selection for migration from the overutilized nodes to underutilized nodes; (c) switching off selected underutilized nodes; (d) deploying the migration VMs based on the modified best-fit decreasing algorithm with PPR, latency overhead, and computational cost consideration. Extensive simulation results illustrate that compared with three baseline energy-efficient VM allocation and selection algorithms, E-PRWA can achieve an average of up to 65.41% of energy-saving with fewer migration number in fog computing. [ABSTRACT FROM AUTHOR]

The growing demand for cloud computing adoption presents more challenges for researchers to make cloud computing more efficient and affordable for infrastructure providers and end users. The management of cloud computing involves investing in IT infrastructure in the first phase and investing in energy, maintenance and space costs in the second phase. However, energy costs account for a large portion of cloud management costs, and saving energy consumption can significantly reduce overall cloud management costs. Server consolidation is a strategy to improve data center energy efficiency and resource utilization. Virtual machine (VM) placement is considered one of the main problems with VM consolidation. The VM placement problem aims to reduce the number of active physical machines in data center to reduce data center power consumption and maintenance costs. However, the waste of data center resources has a significant impact on the energy efficiency of the data center, so it should be considered in the VM placement strategy. This paper proposes a new method based on the Monarch Butterfly Optimization algorithm (MBO) called MBO-VM for new virtual machine placement, designed to maximize packaging efficiency and reduce the number of active physical servers. CloudSim toolkit is used to test the efficiency of the proposed MBO-VM approach under real cloud workloads as well as synthetic workloads. Simulation results show that MBO-VM produces significantly better results compared with known VM placement techniques. The proposed MBO-VM can reduce the number of active servers more effectively and maximize the packaging efficiency. [ABSTRACT FROM AUTHOR]

Nowadays, cloud computing is known as an internet-based modern area among emerging technologies that brings up an environment, in which computing resources such as hardware, software, storage, etc. can be rented by cloud users based on a pay per use model. Since the size of cloud computing is widely expanding and the number of cloud users is also increasing day by day, high energy consumption becomes a serious concern in the operation of complex cloud data centers. In this regards, Virtual Machine (VM) consolidation plays a vital role in utilizing cloud resources in an efficient manner. It migrates the running VMs from overloaded Physical Machines (PMs) to other PMs considering multiple factors, such as migration overhead, energy consumption, resource utilization, and migration time. Since the VM consolidation issue is known as an NP-hard problem, various nature‐inspired meta-heuristic algorithms aiming to solve this problem have been utilized in recent years. However, a lack of systematic and detailed survey study in this field is obvious. Therefore, this gap motivated us to provide the current paper aiming to highlight the role of nature-inspired meta-heuristic algorithms in the VM consolidation problem, review the existing approaches, offer a detailed comparison of approaches based on important factors, and finally, outline the future directions. [ABSTRACT FROM AUTHOR]

Traditional data centers are shifted toward the cloud computing paradigm. These data centers support the increasing demand for computational and data storage that consumes a massive amount of energy at a huge cost to the cloud service provider and the environment. Considerable energy is wasted to constantly operate idle virtual machines (VMs) on hosts during periods of low load. Dynamic consolidation of VMs from overloaded or underloaded hosts is an effective strategy for improving energy consumption and resource utilization in cloud data centers. The dynamic consolidation of VM from an overloaded host directly influences the service level agreements (SLAs), utilization of resources, and quality of service (QoS) delivered by the system. We proposed an algorithm, namely, GradCent, based on the Stochastic Gradient Descent technique. This algorithm is used to develop an upper CPU utilization threshold for detecting overloaded hosts by using a real CPU workload. Moreover, we proposed a dynamic VM selection algorithm called Minimum Size Utilization (MSU) for selecting the VMs from an overloaded host for VM consolidation. GradCent and MSU maintain the trade-off between energy consumption minimization and QoS maximization under specified SLA goal. We used the CloudSim simulations with real-world workload traces from more than a thousand PlanetLab VMs. The proposed algorithms minimized energy consumption and SLA violation by 23% and 27.5% on average, compared with baseline schemes, respectively. [ABSTRACT FROM AUTHOR]

Virtualization technology allows service providers to operate data centers in a cost-effective and scalable manner. The data center network (substrate network) and the applications executed in the data center (virtual networks) are often modeled as graphs. The nodes of the graphs represent (physical or virtual) servers and switches, and the edges represent communication links. Nodes and links are annotated with the provided and required resources (e.g., memory and bandwidth). The NP-hard virtual network embedding (VNE) problem deals with the embedding of a set of virtual networks to the substrate network such that (i) all (resource) constraints of the substrate network are fulfilled, and (ii) an objective is optimized (e.g., minimizing the communication costs). The existing two-step highly customizable model-driven virtual network embedding (MdVNE) approach combines model transformation (MT) and integer linear programming (ILP) techniques to solve the VNE problem based on a declarative specification. MdVNE generates element mapping candidates from an MT specification and identifies an optimal and correct embeddings using an ILP solver. In the past, developers created the MT and ILP specifications manually and needed to ensure carefully that both are compatible and respect the problem description. In this article, we present a novel construction methodology for synthesizing the MT and ILP specification from a given declarative model-based VNE problem description. This problem description consists of a metamodel for substrate and virtual networks, additional OCL constraints, and an objective function that assigns costs to a given model. This methodology ensures that the derived embeddings are correct w.r.t. the metamodel and the OCL constraints, and optimal w.r.t. the optimization goal. The novel model-based VNE specification is applicable to different network domains, environments, and constraints. Thus, the construction methodology allows to automate most of the steps to realize a correct and optimal VNE algorithm compared to a hand-crafted VNE implementation. Furthermore, the simulative evaluation confirms that using MT techniques reduces the time for solving the VNE problem considerably in comparison with a purely ILP-based approach. [ABSTRACT FROM AUTHOR]

The cloud services can be received at anytime from anywhere based on the need of the customers. According to the necessity of the customers, the virtualization of technologies is applied to deliver the cloud services accurately. A large amount of data transfers from user to host and hosts to the user, in cloud environment. To pin the virtual machine on an appropriate host and transferring the data is a challenging task. This paper explains DAVmS: Distance Aware Virtual Machine Scheduling Algorithm, which is applied to arrange virtual machines according to its capability, and pin the VMs to the nearest physical host for accessing the cloud services from the adjacent data center of the customer. This introduced virtual machine scheduling algorithm is used to reduce propagation time and enhance the execution process to reduce the response time. The main purpose of the introduced DAVmS: Distance Aware Virtual Machine Scheduling Algorithm is to deliver an enhanced virtual machine provision policy to physical hosts and access the services from the adjacent data center. [ABSTRACT FROM AUTHOR]

Efficiency in cloud servers' power consumption is of paramount importance. Power efficiency makes the reduction in greenhouse gases establishing the concept of green computing. One of the beneficial ways is to apply power-aware methods to decide where to allocate virtual machines (VMs) in data center physical resources. Virtualization is utilized as a promising technology for power-aware VM allocation methods. Since the VM allocation is an NP-complete problem, we use of evolutionary algorithms to solve it. This paper presents an effective micro-genetic algorithm in order to choose suitable destinations between physical hosts for VMs. Our evaluations in simulation environment show that micro-genetic approach provides invaluable improvements in terms of power consumption compared with other methods. [ABSTRACT FROM AUTHOR]

Cloud computing provides infinite resources and a suitable environment for the execution of large scale computing applications. However, it is also susceptible to frequent failures which can affect users as well as service providers adversely. Therefore, fault tolerance techniques are necessary for the reliable execution of applications in the cloud. This work presents checkpointing based fault tolerance protocols for two types of distributed applications. The first kind of applications is the Bags of Tasks (BoT) applications where an application comprises of a set of independent tasks that do not communicate with each other during execution. Hence, an uncoordinated checkpointing algorithm is proposed for fault tolerance of BoT applications. Subsequently, we consider large scale distributed applications composed of multiple tasks dependent on each other due to inter-task message passing. An uncoordinated checkpointing and message logging protocol is presented for this type of applications. The proposed protocols utilize storage at edge switches in a data center to reduce the bandwidth consumption for saving checkpoints and message logs. Simulation results have demonstrated that the proposed protocols provide an increased rate of successful recoveries from failures and cause lower resource overhead than other contemporary and related schemes. [ABSTRACT FROM AUTHOR]

The fuzzy c-means algorithm (FCM) is aimed at computing the membership degree of each data point to its corresponding cluster center. This computation needs to calculate the distance matrix between the cluster center and the data point. The main bottleneck of the FCM algorithm is the computing of the membership matrix for all data points. This work presents a new clustering method, the bdrFCM (boundary data reduction fuzzy c-means). Our algorithm is based on the original FCM proposal, adapted to detect and remove the boundary regions of clusters. Our implementation efforts are directed in two aspects: processing large datasets in less time and reducing the data volume, maintaining the quality of the clusters. A significant volume of real data application (> 106 records) was used, and we identified that bdrFCM implementation has good scalability to handle datasets with millions of data points. [ABSTRACT FROM AUTHOR]

Cloud computing has developed as a high-performance computing environment with a huge set of virtualized, abstracted, and flexible resource. It provides service to the user with high-performance. In a large-scale cloud computing environment, the cloud data centers and users are distributed physically across the globe. In a distributed environment, the arrangement of scientific workflow is considered as a popular NP-complete problem and they prevails to be intractable. An extra-ordinary issue in the distributed environment is scientific workflow scheduling and it is difficult to track the exact solution. It becomes even more challenging in the cloud computing platform due to its dynamic and heterogeneous nature. The biggest challenge for cloud data centers is how to handle and service the millions of requests that are arriving very frequently from end users efficiently and correctly. The aim of this study is to obtain an efficient load-balancing in the large-scale platform of cloud computing based on the proposed Meta-heuristic based multi objective optimisation. The main contributions of this paper are related to the scheduling of tasks to the resource groups using multi-objective memetic algorithm (MOMA), it uses a local search technique to reduce the likelihood of the premature convergence. To reschedule the failed workload to achieve fault tolerance an adaptive plant intelligent behavior optimization (APIBO) is proposed. The experiments using different scientific workflow applications highlight the effectiveness, usefulness, and better performance of the proposed approach and the Performances are evaluated in terms of resource contention, response time, execution time, throughput, and resource utilization. [ABSTRACT FROM AUTHOR]

Virtualization provides the flexibility to distribute the workload among physical servers to reduce overall electrical power consumption, through the consolidation of Virtual Machines (VMs). Many research projects have been done on VM migration to reduce energy consumption in data centers while ensuring a high level of adherence to the Service Level Agreements (SLA). Service levels of running applications are likely to be negatively affected during a live VM migration. For this reason, in this paper, we propose a new intelligent VM migration approach, called CLANFIC, which utilizes modified Cellular Learning Automata based Evolutionary Computing (CLA-EC) and neuro-fuzzy to minimize the number of VM migrations and improve energy consumption. This goal is achieved by utilizing an optimized placement method and delaying migration time based on future resource demand prediction. This algorithm reduces the number of migrations in two steps (i) finding the optimal virtual machine placement and replacement on physical servers by using modified CLA-EC (ii) predicting future resource usage in each host by a neuro-fuzzy algorithm to prevent unnecessary migrations. The experimental results on the real workload traces from PlanetLab show that the proposed method reduces the mean migration number, energy consumption, and SLA violation of the data center by 59.05%, 8.5%, and 70.76%, respectively. [ABSTRACT FROM AUTHOR]

Cloud computing is characterized by efficient provision of services and access to shared resources through its pay-per-use model. The service brokers in this architecture mediate among cloud users and cloud service providers to redirect requests to appropriate data centers while aiming at minimization of response time and monetary cost for cloud users. For selection of data centers, the optimization techniques incur large overhead during execution of applications. Consequently, the recent brokering approaches resort to heuristics which do not guarantee optimal solutions in terms of response time and cost that are pivotal for executing compute intensive applications in a cloud environment. In this paper, we propose multi-objective service brokering with availability-based load balancing (MOSB_ALB) approach that minimizes response time and monetary cost for efficient low-cost provision of services in a cloud environment. The MOSB_ALB approach performs static and dynamic selection of data centers while using availability-based load balancing for distributing load among virtual machines. The static computation of data center index incorporates MOEA/D algorithm and uses z-score values corresponding to indexes in optimal solutions. The dynamic computation of data center indexes uses criteria based on weights and allocation counts. The experimentation performed through a large number of configurations shows that the MOSB_ALB approach outperforms existing well-known cloud service brokering approaches by improving cumulative response time with a speedup factor of 2.19, along with a significant reduction of monetary cost. [ABSTRACT FROM AUTHOR]

Currently, data centers are considered as the most important parts of internet services such as electronic commerce, social networks and mobile networks. The increasing number of mobile users on the one hand, and the use of data traffic on the other, have led LTE operators to find new methods for more optimized and faster data centers localization. The role of data centers in LTE networks is very important because they have the task of maintaining the mobile network functions, which play a key role in processing requests and information and responding to user requests. According to the population density criterion, inaccurate location of data centers will increase the cost and time to respond to user requests. Because failing to put the data center in its proper place increases the number of requests that users send to that data center, which increases response time. Therefore, the introduction of new methods and models in this field can be of great importance. In this article, in addition to proposing a new mathematical model for data centers positioning via applying a meta-heuristic algorithm, we will search for optimized data centers positioning in a faster and more scalable way. Based on the results, the model presented in this paper has been able to show significant results in terms of scalability in the number of data centers and the number of requests as well as the response time of the users' requests. In addition, the implementation of this model using TABU Search algorithm has optimized the results. [ABSTRACT FROM AUTHOR]

Cloud computing delivers practical solutions for long-term image archiving systems. Cloud data centers consume enormous amounts of electrical energy that increases their operational costs. This shows the importance of investing on energy consumption techniques. Dynamic placement of virtual machines to appropriate physical nodes using metaheuristic algorithms is among the methods of reducing energy consumption. In metaheuristic algorithms, there should be a balance between both exploration and exploitation aspects so that they can find better solutions in a search space. Exploration means looking for a solution in a wider area, while exploitation is producing new solutions from existence ones. Artificial bee colony optimization, which is a biological metaheuristic algorithm, is a sign-oriented approach. It has a strong exploration ability, but a relatively weaker exploitation power. On the other hand, tabu search is a popular algorithm that shows better exploitation in comparison with ABC. In this study, cloud computing environments are detailed with an allocation protocol for efficient energy and resource management. The technique of energy-aware allocation splits data centers (DCs) resources among client applications end routes to enhance energy efficacy of DCs and also achieves anticipated quality of service (QoS) for everyone. Heuristic protocols are exercised for optimizing the distribution of resources to upgrade the efficiency of DC. In the current paper, energy-aware resources allotment technique is employed and optimized in clouds via a new approach called Tabu Job Master (JM). Tabu JM claims the benefits of some variables and also rapid convergence speeds. Results are duly achieved for energy consumption—the count of virtual machines (VMs) migration and also makespan. The results shown by Tabu JM are benchmarked by using genetic algorithm (GA), artificial bee colony (ABC), ABC with crossover and technique of mutation, the basic tabu search techniques, and Tabu Job Master. [ABSTRACT FROM AUTHOR]

The rapid development of Cloud Computing and Content Delivery Networks (CDNs) brings a significant increase in data transfers that leads to new optimization challenges in inter-data center networks. In this article, we focus on the cross-stratum optimization of an inter-data center Elastic Optical Network (EON). We develop an optimization approach that employs machine learning Monte Carlo Tree Search (MCTS) algorithm for the simulation of future traffic to improve the performance of the network regarding the request blocking and the operational cost. The key novelty of our approach is using various selection strategies applied to the phase of building a search tree under different network scenarios. We evaluate the performance of these selection strategies using representative topologies and real-data provided by Amazon Web Services. The main conclusion is that the approach based on the policy of Last-Good-Reply with Forgetting enables more efficient cloud resource allocation, which results in lower request blocking, thus, reduces the operational cost of the network. [ABSTRACT FROM AUTHOR]

The ease and affordability offered by the cloud computing has attracted large number of customers towards it. Cloud service providers offer its services, to the cloud customers, usually in form of Virtual Machines (VMs). With the growth in the number of customers, cloud data centers encounter overwhelming number of VM requests. These requests need to be mapped on the real cloud hardware and therefore, VM placement has been an important research area in the cloud research community. Virtual machine placement, being an NP hard problem, is modelled as an optimization problem with the objective to optimize resource wastage. Dragonfly Algorithm (DA), a nature inspired technique, originates from static and dynamic swarming behavior of dragonfly and is well suited to solve VM placement problem. Therefore, in the proposed work, a modified dragonfly algorithm is applied for VM placement for better resource utilization at cloud data centers. The performance of the proposed model is analyzed through simulation and comparative study. Observations, obtained from the experiments, exhibit the superiority of the proposed model in solving VM placement problem. [ABSTRACT FROM AUTHOR]