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Cloud computing has become a popular approach for processing data and running computationally expensive services on a pay-as-you-go basis. Due to the ever-increasing requirement for cloud-based apps, appropriately allocating resources according to user requests while meeting service-level agreements between customers and service providers has become increasingly complex. An efficient and versatile resource allocation method is required to properly deploy these assets and meet user needs. The technique of distributing resources has become more arduous as user demand has increased. One of the key areas of research experts is how to design optimal solutions for this approach. In this paper, a literature review on proposed dynamic resource allocation approaches is introduced. [ABSTRACT FROM AUTHOR]

Multiple Cloud Compute providers can be managed together as a Federated Cloud. Such an agglomeration introduces challenges for effective resource allocation and load balancing. To address such issues, multiple papers have been written describing various algorithms for resource allocation. This paper thus compares and contrasts various algorithms proposed. A Federated Cloud environment is usually organized as a many providers having multiple datacenters (DCs), each with multiple hosts, and each host having multiple VMs. We compare the various algorithms on the basis of execution time (time-till-response), latency (time between task scheduling and VM allocation), and load (resource utilization). [ABSTRACT FROM AUTHOR]

This paper focus on the requirements of load balancing algorithms for essential and efficient operations in the cloud computing technologies. The load balancing approaches is to enhance the performance of the system and fulfil the client expectation for better services and better results. Our purpose is to provide the potential information about the requirement of load balancing in the cloud computing environments, moreover analysis and identification of the challenges of load balancing algorithms and to provide an approachable information for future enhancements. [ABSTRACT FROM AUTHOR]

Cloud computing is an evolving concept which has now come out with a radical shift in storage and hosting services. The huge requirement for cloud computing services desired various technologies and different approaches for the effective and timely delivery of consistent quality to the users. With the increasing growth in the network and arising with the latest technologies including cloud and big data, to control and manage the traditional networks is quite tedious. Therefore, it's mandatory to transform conventional network architecture. To resolve this issue, Software Defined Networking (SDN) came into existence which makes the management of the network more conformable. To satisfy the fulfilments of Quality of Service (QoS) and with the limited network availability, one of the keynotes that have been taken into consideration is the load balancing issue. It is quite a tedious job to handle the massive amount of load for a single server. This paper proposed the minimal loaded load balancing approach and compare it with the different load balancing techniques based on the response time. [ABSTRACT FROM AUTHOR]

In today's world Cloud computing is perhaps the most utilized technology. Distributed computing offers web administrations, information stockpiling and computing assets over the web effortlessly. Cloud computing permits clients to get to the IT assets anyplace and whenever on a compensation for every utilization premise. As the interest for distributed computing develops quickly, the traffic additionally increases. There are two answers for this issue, one is to upgrade a single server to a high-performance server but upgraded server may also overload soon and second is multi server (gathering of workers). Multi server arrangement is adaptable and practical. While making a group of servers, the issue is Load adjusting. Load balancing is one of the critical issues in cloud computing. Load balancing is an interaction of parting the unique responsibility and dispersing the responsibility among every one of the hubs with the end goal that no hub in the cloud climate is overloaded/under loaded or inactive. Load balancing in Cloud computing is utilized to improve the proficiency and utilizing resources effectively. This journal paper objective is on investigation on cloud load balancing, Load balancing methods, Load balancing algorithms. [ABSTRACT FROM AUTHOR]

To minimize makespan for scheduling independent tasks in cloud computing, an improved estimation of distribution algorithm (IEDA) is proposed to tackle the investigated problem in this paper. Considering that the problem is concerned with multi-dimensional discrete problems, an improved population-based incremental learning (PBIL) algorithm is applied, which the parameter for each component is independent with other components in PBIL. In order to improve the performance of PBIL, on the one hand, the integer encoding scheme is used and the method of probability calculation of PBIL is improved by using the task average processing time; on the other hand, an effective adaptive learning rate function that related to the number of iterations is constructed to trade off the exploration and exploitation of IEDA. In addition, both enhanced Max-Min and Min-Min algorithms are properly introduced to form two initial individuals. In the proposed IEDA, an improved genetic algorithm (IGA) is applied to generate partial initial population by evolving two initial individuals and the rest of initial individuals are generated at random. Finally, the sampling process is divided into two parts including sampling by probabilistic model and IGA respectively. The experiment results show that the proposed IEDA not only gets better solution, but also has faster convergence speed. [ABSTRACT FROM AUTHOR]

Cloud infrastructure is the "backbone" technology of the internet era. Ever-growing scale of cloud network concerns the sustainability of centralized cloud. Fog computing aims to decentralizes cloud computing distributing computational processes across network devices, offers a potential solution. If application are distributed effectively in available nodes, network resource usage can be reduce while reducing latency by existing closer to data sources and end points. However, the lack of standardization of fog networks, researcher must list its evaluation setups to contextualize placement algorithm's potential abilities. Thus, selecting an evaluation environment is crucial in researching fog application placement problem. There are several reviews about evaluation environment, but there is a lack of information regarding important aspect placement problem such as algorithm type, placement objective and performance metric available in each evaluation environment. In this paper we discuss the capabilities of several evaluation environments such as iFogSim, YAFS, MATLAB, Python, real world testbed, solver, and other evaluation environments. We surveyed usage trend per year, algorithm type usage, and performance metric that can be produced by each evaluation environment. This survey aims to help future researchers on fog application placement problems to identify evaluation environment based on selected algorithm type and objective metrics. [ABSTRACT FROM AUTHOR]

Cloud computing is among the technologies that have become popular in recent times, due to the increasing demand for their use by individuals, companies and organizations. It offers its resources as an online service upon demand by its users. In order to enhance the trust between the cloud and its users, attention must be paid to addressing the issues that the cloud suffers from, the most outstanding of those are problems relating to the safety and privacy of users' data on the cloud. Encryption is considered one of the successful and efficient ways to raise the security of data kept on the cloud by converting it into data that is incomprehensible to unauthorized persons. In our research paper, we will present a method that provides security and privacy for data stored on the cloud, and this method consists of a combination of two lightweight algorithms (mCrypton and salsa20) to form a new lightweight hybrid algorithm based on the mCrypton algorithm, where stages were added to it and other modifications. Additionally, chaos has been added to generate encryption keys. [ABSTRACT FROM AUTHOR]

The real world has been changed into a virtual one as a result of the pandemic scenario. Cloud computing plays a major role for storing and computing the data using remote computing infrastructure for solving the day to day activities. The LB Techniques are used in the cloud computing process. LB techniques distributing the system's load among its various nodes in order to maximise resource efficiency and job response time. The major issues in load balancing are Geographical Distributed system, Single point of failure (SPoF), live Migration of instance, Heterogeneous device, Storage device Management, Scale-out/scale-in and Complexity of Algorithm. Load balancing ensures that each node in the network shortens reaction times, reduces expenses, and boosts performance. This paper address the various cloud computing balancing algorithm survey. [ABSTRACT FROM AUTHOR]

In the last decade, a scientific workflow has become the dominant trend to enable specialists to accelerate scientific developments in various areas. The scientific workflow requires high computing, therefore, cloud computing is exploited, as it provides the computing required for scientific workflow with a good cost. The frequent use of scientific workflows by researchers in cloud computing stimulates the need to find mechanisms to manage scientific workflow, providing resources, and scheduling in a way that achieves fairness for all users and meets the required Quality of Service (QoS). In a cloud computing environment, scheduling scientific workflows is an NP-hard problem. A lot of work has been done to resolve the decision problem of scheduling workflows, provision of resources, or both. In this survey, many papers related to scientific workflow algorithms of provisioning and scheduling in a cloud computing system are reviewed. We investigate and classify numerous related articles in the field. The objectives of each study, the application domains, and tools that were used are highlighted. [ABSTRACT FROM AUTHOR]

Cloud computing has always been an area of interest for the researchers and the industry oriented professionals. The cloud computing has its primary concern to deliver better performance with low energy consumption and least SLA violations. To rescue the rising challenges in the cloud computing, virtualization is an important process when the Physical Machines (PMs) are expected to work at a higher speed. This paper represents two co-operative algorithms: Cosine Elastic Clustered Scheduling Algorithm (CESCA) and Threshold-Modified Best Fit Algorithm (T-MBFD) for the VM selection and reallocation. CESCA clusters the VMs to be migrated based on three attributes namely CPU-Utilization (CPU), Associated RAM (AsRAM) and Bandwidth Utilization (BU) and the co-relation between them using cosine similarity. CESCA also prioritize the created clusters for the allocation. T-MBFD uses a logarithmic scale to neutralize the priority and the density of the created cluster to finally set the VM to its best suitable PM. The evaluation of the proposed algorithm is done using power consumption and total number of migrations. [ABSTRACT FROM AUTHOR]

With the evolution of cloud computing, the dominant virtualization technology for data centers has shifted from virtual machine to container. Despite its success, Cloud Computing falls short of several Internet of Things requirements (IoT). Edge Computing appears to be a complement to the Cloud, filling gaps in the IoT scene. By 2025, it is expected that 75 percent of the generated data will be processed in locations other than cloud servers or datacenters. As a result, the number of edge computing nodes is expected to grow rapidly. Among others, Kubernetes was created with the goal of automatically deploying and managing cloud applications created using container runtime techniques such as Docker. Although Kubernetes is designed for cloud computing environments, it does not have adequate system support for edge computing. To use Kubernetes in edge computing environments, edge computing nodes must be managed in a more efficient manner. The Hybrid Round-Robin multi-level queue scheduling algorithm (HRRMLQ) is proposed and implemented in this paper to schedule containers to a single board computer (SBC) cluster based on their priorities. HRRMLQ uses a hybridization of round robin and multilevel queue scheduling algorithms to categorize IoT apps as high or low priority. HRRMLQ classifies IoT apps as either high or low priority. To alleviate the starvation problem that traditional multilevel queues suffer from, HRRMLQ assigned 4 quantum to the high priority queue and 2 quantum to the low priority queue. Experiments show that HRRMLQ can schedule high priority pods first while also reducing the starvation problem that multilevel queues may experience. [ABSTRACT FROM AUTHOR]

Cloud users and service providers are the leading players in the cloud computing environment. This environment comprises data centers, hosts, agents and virtual machines. The cloud user's application of varied loads is leased on the provider's resources. Scientific applications are large-scale complex workflow problems that demand more computing power. The cloud fulfills the workflow requirements of huge availability and increased computational power. One of the most crucial issues of cloud computing is scheduling tasks for the system's effective functioning. This paper reviews several existing task-scheduling techniques based on diverse metrics. This work will help the investigators to gain a better understanding of task scheduling techniques. In order to boost an algorithm's performance, a few strategies are offered. [ABSTRACT FROM AUTHOR]

Mobile cloud technology is currently playing a vital role in the field of mobile communication. Frequently new updates are coming in the field of image processing, online games, Natural language processing and other application using mobile and cloud. On mobile phones, the execution of sophisticated software and Apps might result in poor performance in terms of battery consumption and response time. Therefore a new method is required to utilize the benefits of mobile cloud by using computation offloading technique. In this paper we proposed EDOMAC method for execution of Picasa and Mathdroid application using cloudlet environment. We have computed response time and energy consumption for above mentioned applications based on mobile and cloud. At the end compared results with POMAC and M-POMAC. [ABSTRACT FROM AUTHOR]

Cloud computing promises computing surroundings wherein distinct sources are distributed as a carrier to the consumer or numerous stakeholders over the internet network. Task allocation is a crucial and maximum crucial part of the environment of cloud computing. The scheduling of tasks specifically focuses to decorate the optimized usage of sources and as a result discountsthe mission of the entire time. Scheduling of a task is to disseminate a particular task to precise assets at a selected time. Exceptional strategies were proposed to clear up the hurdles of assignment scheduling. Project scheduling enhances the green optimization of useful resources/assets and obtained a reduced response. This paper discusses diverse cloud computing scheduling algorithms. [ABSTRACT FROM AUTHOR]

In order to optimize the task scheduling strategy in cloud environment, we propose a cloud computing task scheduling algorithm based on ant colony algorithm. The main goal of this algorithm is to minimize the makespan and the total cost of the tasks, while making the system load more balanced. In this paper, we establish the objective function of the makespan and costs of the tasks, define the load balance function. Meanwhile, we also improve the initialization of the pheromone, the heuristic function and the pheromone update method in the ant colony algorithm. Then, some experiments were carried out on the Cloudsim platform, and the results were compared with algorithms of ACO and Min-Min. The results shows that the algorithm is more efficient than the other two algorithms in makespan, costs and system load balancing. [ABSTRACT FROM AUTHOR]

Cloud computing is one of the renowned technologies that providing services like data storage, data processing and providing virtualized servers over the Internet. In cloud computing introduce a Container virtualization is a new tool that improve the overall application performance by allocate Operating System level virtualization. The operating system improves its performance for providing more containers based on number of resources availability in that operating system. Optimization of the load balancing is very important concept of equally share all incoming tasks to all available servers in cloud computing environment. The objective of this paper is to fulfill the task of distribution among all servers equally in centralized cloud computing to reduce Makespan using Grey Wolf Optimization (GWO) algorithm. While comparing simulation results, GWO showing better results than that of Ant Colony Optimization (ACO), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms for minimize load balancing, minimize time and maximize efficiency to perform various tasks in cloud. [ABSTRACT FROM AUTHOR]

Business organizations nowadays functioning with more than one cloud provider. By spreading cloud deployment across multiple service providers, it creates space for competitive prices that minimize the burden on enterprises spending budget. To assess the software reliability of multi cloud application layered software reliability assessment paradigm is considered with three levels of abstractions application layer, virtualization layer, and server layer. The reliability of each layer is assessed separately and is combined to get the reliability of multi-cloud computing application. In this paper, we focused on how to assess the reliability of server layer with required algorithms and explore the steps in the assessment of server reliability. [ABSTRACT FROM AUTHOR]

Informed search algorithms in Artificial intelligence are applied to execute the arrangement in cloud. The Best First Search (BFS) is used to allocate the job to Practical Machine (PM) and Virtual Vehicle (VV). In the BFS the best one as first priority assortment of job is used which may relegate the concert of cloud for minimization of Operational Completion Time (OCT). In the cloud computing environment most noteworthy problem is task scheduling to augment the concert and effectiveness of cloud it is an important move The best job selection is enhanced with job assortment, PM selection and VV algorithms. The performance of BFS compared with other task scheduling algorithms. [ABSTRACT FROM AUTHOR]

The best job selection is enhanced with job assortment, PM selection and VV algorithms. The performance of Machine Utilization Algorithm (MUA) compared with other task scheduling algorithms In the cloud computing environment most noteworthy problem is task scheduling to augment the concert and effectiveness of cloud it is an important move. Informed search algorithms in Artificial intelligence are applied to execute the arrangement in cloud. The MUA is used to allocate the job to Practical Machine (PM) and Virtual Vehicle (VV). In the MUA the best one as first priority assortment of job is used which may relegate the concert of cloud for minimization of mean flow time with given process time and travel in the example problem [ABSTRACT FROM AUTHOR]

In order to optimize the cloud computing task scheduling scheme, an improved differential evolution algorithm for cloud computing task scheduling is proposed. Firstly, the cloud computing task scheduling model, according to the model of the fitness function, and then used improved optimization calculation of the fitness function of the evolutionary algorithm, according to the evolution of generation of dynamic selection strategy through dynamic mutation strategy to ensure the global and local search ability. The performance test experiment was carried out in the CloudSim simulation platform, the experimental results show that the improved differential evolution algorithm can reduce the cloud computing task execution time and user cost saving, good implementation of the optimal scheduling of cloud computing tasks. [ABSTRACT FROM AUTHOR]

The brightness and coherence of modern light sources is pushing the limits of X-ray beamline design. The open source Synchrotron Radiation Workshop (SRW) provides physical optics based algorithms for correctly simulating such beamlines. We present new SRW capabilities to calculate source brightness and related quantities for undulator radiation. The Sirepo cloud computing framework includes a browser-based GUI for SRW. In addition to high-accuracy wavefront simulations, the Sirepo interface now supports analytical calculations for flux, photon beam size, divergence and photon brightness. We have included the effects of detuning from resonance and electron beam energy spread, which can be important in realistic operational conditions. We compare our results to features previously available in the Igor Pro interface to SRW, to analytical formulae available in the literature, and also to the results of simulated wavefront propagation. Differences between the various approaches are explained in detail, so that all the assumptions, conventions and ranges of validity can be better understood. [ABSTRACT FROM AUTHOR]

Traffic management is turning out to be an important component in establishing and sustaining smarter cities across the globe. With the vehicle population is consistently on the climb, the traffic management is becoming hugely complicated affair while maintaining the connectivity infrastructure utilization. IT solution and service organizations have come forth with a number of automated traffic management solutions and the primary problem with them is they are unfortunately reactive and hence is an inefficient solution for the increasingly connected cities. Therefore unveiling real-time, adaptive, precision-centric and predictive traffic monitoring, measurement, management and enhancement solutions are being insisted as an indispensable requirement towards smart and sustainable cities. This domain of study and research has been acquiring a lot of momentum these days and considering its growing significance for the human society, we have proposed an innovative approach and framework for smarter traffic management. This is being made possible with the faster maturity and stability of multiple technologies such as the IoT, fog/edge computing, big data analytics, software-defined cloud environments, 5G communications, and AI (machine and deep learning methods). [ABSTRACT FROM AUTHOR]