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Cloud computing has transformed data storage, management, and processing by offering scalable and flexible resources via the internet. A key component of this technology is the efficient allocation and management of resources, particularly through task scheduling at the level of virtual machines (VMs). Task scheduling is critical for maximizing resource utilization and system performance in cloud environments. However, it presents significant challenges due to the dynamic and distributed nature of these environments. Effective task scheduling algorithms are necessary to balance load, minimize response time, and optimize resource usage, making it a crucial area for ongoing research and development in cloud computing. This paper addresses the challenge of task scheduling in cloud computing by employing an analytical approach based on queuing theory. We model the system using a generalized processor sharing (GPS) queue and evaluate its performance through heavy traffic approximation. This method allows us to derive performance metrics for queuing systems prone to congestion, considering general interarrival and service time distributions, thus providing a comprehensive analysis of scheduling efficiency. [ABSTRACT FROM AUTHOR]

Quality of Service is one of the most important research topics in cloud computing, both from the customer's point of view and the cloud service provider's point of view, due to the increasing number of cloud services and applications, along with the significant increase in users and workloads. Task scheduling has been a topic of discussion in many researches, some of which have proposed new ways to improve the Quality of Service in cloud systems. Recently, metaheuristic algorithms have been employed to improve job execution efficiency in such systems, which has proven effective in finding optimal task scheduling solutions. During this research, the latest metaheuristic algorithm Whale Optimization Algorithm (WOA) will be applied for optimizing task scheduling in cloud systems. Additionally, a multi-objective model for optimization will be used to achieve a balance between user satisfaction with these systems, and the requirements needed by service providers. Research results indicate WOA superiority in minimizing the execution time cost, price cost, and total cost parameters as contrasted with the current metaheuristic algorithms, which ultimately result in higher service quality. [ABSTRACT FROM AUTHOR]

The rapid growth of cloud computing has led to the widespread adoption of heterogeneous virtualized environments, offering scalable and flexible resources to meet diverse user demands. However, the increasing complexity and variability in workload characteristics pose significant challenges in optimizing energy consumption. Many scheduling algorithms have been suggested to address this. Therefore, a self-attention-based progressive generative adversarial network optimized with Dwarf Mongoose algorithm adopted Energy and Deadline Aware Scheduling in heterogeneous virtualized cloud computing (SAPGAN-DMA-DAS-HVCC) is proposed in this paper. Here, a self-attention based progressive generative adversarial network (SAPGAN) is proposed to schedule activities in a cloud environment with an objective function of makespan and energy consumption. Then Dwarf Mongoose algorithm is proposed to optimize the weight parameters of SAPGAN. Outcome of proposed approach SAPGAN-DMA-DAS-HVCC contains 32.77%, 34.83% and 35.76% higher right skewed makespan, 31.52%, 33.28% and 29.14% lower cost when analysed to the existing models, like task scheduling in heterogeneous cloud environment utilizing mean grey wolf optimization approach, energy and performance-efficient task scheduling in heterogeneous virtualized Energy and Performance Efficient Task Scheduling Algorithm, energy and make span aware scheduling of deadline sensitive tasks on the cloud environment, respectively. [ABSTRACT FROM AUTHOR]

In a cloud computing environment, tasks are divided among virtual machines (VMs) with different start times, duration and execution periods. Thus, distributing these loads among the virtual machines is crucial, in order to maximize resource utilization and enhance system performance, load balancing must be implemented that ensures balance across all virtual machines (VMs). In the proposed framework, a credit-based resource-aware load balancing scheduling algorithm (HO-CB-RALB-SA) was created using a hybrid Walrus Optimization Algorithm (WOA) and Lyrebird Optimization Algorithm (LOA) for cloud computing. The proposed model is developed by jointly performing both load balancing and task scheduling. This article improves the credit-based load-balancing ideas by integrating a resource-aware strategy and scheduling algorithm. It maintains a balanced system load by evaluating the load as well as processing capacity of every VM through the use of a resource-aware load balancing algorithm. This method functions primarily on two stages which include scheduling dependent on the VM’s processing power. By employing supply and demand criteria to determine which VM has the least amount of load to map jobs or redistribute jobs from overloaded to underloaded VM. For efficient resource management and equitable task distribution among VM, the load balancing method makes use of a resource-aware optimization algorithm. After that, the credit-based scheduling algorithm weights the tasks and applies intelligent resource mapping that considers the computational capacity and demand of each resource. The FILL and SPILL functions in Resource Aware and Load utilize the hybrid Optimization Algorithm to facilitate this mapping. The user tasks are scheduled in a queued based on the length of the task using the FILL and SPILL scheduler algorithm. This algorithm functions with the assistance of the PEFT approach. The optimal threshold values for each VM are selected by evaluating the task based on the fitness function of minimising makespan and cost function using the hybrid Walrus Optimization Algorithm (WOA) and Lyrebird Optimization Algorithm (LOA).The application has been simulated and the QOS parameter, which includes Turn Around Time (TAT), resource utilization, Average Response Time (ART), Makespan Time (MST), Total Execution Time (TET), Total Processing Cost (TPC), and Total Processing Time (TPT) for the 400, 800, 1200, 1600, and 2000 cloudlets, has been determined by utilizing the cloudsim tool. The performance parameters for the proposed HO-CB-RALB-SA and the existing models are evaluated and compared. For the proposed HO-CB-RALB-SA model with 2000 cloudlets, the following parameter values are found: 526.023 ms of MST, 12741.79 ms of TPT, 33422.87$ of TPC, 23770.45 ms of TET, 172.32 ms of ART, 9593 MB of network utilization, 28.1 of energy consumption, 79.9 Mbps of throughput, 5 ms of TAT, 18.6 ms for total waiting time and 17.5% of resource utilization. Based on several performance parameters, the simulation results demonstrate that the HO-CB-RALB-SA strategy is superior to the other two existing models in the cloud environment for efficient resource utilization. [ABSTRACT FROM AUTHOR]

A distributed archetype, the concept of fog computing relocates the storage, computation, and services closer to the network's edge, where the data is generated. Despite these advantages, the users expect proper load management in the fog environment. This has expanded the Internet of Things (IoT) field, increasing user requests for the fog computing layer. Given the growth, Virtual Machines (VMs) in the fog layer become overburdened due to user demands. In the fog layer, it is essential to evenly and fairly distribute the workload among the segment's current VMs. Numerous load-management strategies for fog environments have been implemented up to this point. This study aims to create a hybridized and optimized approach for load management (HOGWO), in which the population set is generated using the Invasive Weed Optimisation (IWO) algorithm. The rest of the functional part is done with the help of the Grey Wolf Optimization (GWO) algorithm. This process ensures cost optimization, increased performance, scalability, and adaptability to any domain, such as healthcare, vehicular traffic management, etc. Also, the efficiency of the enhanced approach is analyzed in various scenarios to provide a more optimal solution set. The proposed approach is well illustrated and outperforms the existing algorithms, such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), etc., in terms of cost and load management. It was found that more than 97% jobs were completed on time, according to the testing data, and the hybrid technique outperformed all other approaches in terms of fluctuation of load and makespan. [ABSTRACT FROM AUTHOR]

Virtualization and server consolidation are the technologies that govern today’s data centers, allowing both efficient management at the functionality level as well as at the energy and performance levels. There are two main ways to virtualize either using virtual machines or containers. Both have a series of characteristics and applications, sometimes being not compatible with each other. Not to lose the advantages of each of them, there is a trend to load data centers by nesting containers in virtual machines. Although there are good experiences at a functional level, the performance and energy consumption trade-off of these solutions is not completely clear. Therefore, it is necessary to study how this new trend affects both energy consumption and performance. In this work, we present an experimental study aimed to investigate the behavior of nesting containers in virtual machines while executing CPU-intensive workloads. Our objective is to understand what performance and energy nesting configurations are equivalent or not. In this way, administrators will be able to manage their data centers more efficiently. [ABSTRACT FROM AUTHOR]

Web server development is often hindered by the cost and resources required, as developing a web server typically requires a bare-metal server. Container technology, which allows for the development of multiple web servers on a single bare-metal server, has become popular. One of the most widely used containers is Docker. Docker reduces the need for costs and resources. Beyond the issues of cost and resource requirements, the performance of web servers also needs to be considered. The performance of web servers with and without Docker needs to be verified. This research aims to test the performance of two web servers, one using Docker and one not using Docker, utilizing the native hypervisor VMware ESXi. The web server performance test items in this study include CPU and RAM resource usage. The method for developing infrastructure systems uses SIDLC (System Infrastructure Development Life Cycle). Performance testing (Load Test) was conducted using Apache JMeter as a tool, with the manipulation of the number of threads predetermined. Resource usage information was monitored using Prometheus and Grafana. The research results show that with the same resources for each virtual machine, the CPU resource usage of Virtual Machine 2 (Undockerized) is less than that of Virtual Machine 1 (Dockerized). Meanwhile, RAM resource usage is not affected by the number of users on both virtual machines. Virtual Machine 2 (Undockerized) is better at handling HTTP requests. Virtual Machine 1 (Dockerized) can handle only 2,790 users, while Virtual Machine 2 (Undockerized) can handle more than 2,790 users without errors.

Task scheduling in the edge computing environment poses significant challenges due to its inherent NP-hard nature. Several researchers concentrated on minimizing simple makespan, disregarding the reduction of the mean time to complete all tasks, resulting in uneven distributions of mean completion times. To address this issue, this study proposes a novel mean makespan task scheduling strategy (MMTSS) to minimize simple and mean makespan. MMTSS optimizes the utilization of virtual machine capacity and uses the mean makespan optimization to minimize the processing time of tasks. In addition, it reduces imbalance by evenly distributing tasks among virtual machines, which makes it easier to schedule batches subsequently. Using genetic algorithm optimization, MMTSS effectively lowers processing time and mean makespan, offering a viable approach for effective task scheduling in the edge computing environment. The simulation results, obtained using cloudlets ranging from 500 to 2000, explicitly demonstrate the improved performance of our approach in terms of both simple and mean makespan metrics. [ABSTRACT FROM AUTHOR]

Cloud computing provide services dynamically according to the contract between service providers and users. However, Inappropriateness of scheduling task on VMs can lead huge resource waste and load unbalance, which becomes a seriously challenging problem. Current Swarm intelligence algorithms like genetic algorithm (GA), particle swarm optimization (PSO) are combination of random initialization and local search algorithm. It avoids inconsistent results for different problem instances. However, existing Swarm intelligence works sometimes search the optima without analysing task scheduling situations comprehensively, global search efficiency is low and convergence is too early. In this paper, we propose SNSK‐IPSO algorithm, which develops as a two‐phases algorithm: enumerating all distributed solutions between VMs and tasks, finding the optimal solution through IPSO. It not only minimizes the execution time, but also improves resource utilization and load balance. Several experiments demonstrate that our novel algorithm outperforms others in terms of achieving load balance, higher resource utilization and lower execution times. [ABSTRACT FROM AUTHOR]

Task scheduling in a process where the cloud has attained substantial attention because of an enhanced demand for computing resources and services. Various load balancing approaches are developed for allocating the tasks and Virtual Machines (VMs) according to the task's priority and execution. However, there are some problems faced in the identification of the best tasks for allocation and to classify the relevant tasks based on the load. In this research, Fuzzy Logic (FL) and Cosine Similarity based Golden Jackal Optimization (CSGJO) algorithm is proposed for solving the problem of load balancing and task scheduling in CC. The FL optimally distributes the tasks based on their fuzzy rules, whereas the CSGJO improves fuzzy rule sets and identifies the optimal resource allocation, minimizing the response time even when handling large number of tasks. The Longest Job to Fastest Processor (LJFP) and Minimum Completion Time (MCT) approaches are the heuristic approaches used to initialize CSGJO. The multiobjective functions of Makespan, Degree of Imbalance (DOI), Execution Time, Energy Consumption and Resource Utilization for validating the proposed method's effectiveness. The CSGJO consumes a minimum makespan of 1.9s, as opposed to Particle Swarm Optimization (PSO) and Enhanced Sunflower Optimization (ESFO). [ABSTRACT FROM AUTHOR]

The management of various multimedia assets, such as photos, videos, audio files, and other rich media content, within a cloud computing environment is referred to as managing multimedia resources in the cloud. To suit the needs of applications and users, this entails the effective storage, retrieval, processing, and distribution of multimedia resources. Given the significance of work planning and managing resources in the cloud computing environment, we present a unique hybrid algorithm in this research. Many cloud-based computing systems have made extensive use of traditional scheduling techniques like ant colony optimization (ACO), first come first serve, etc. The cloud gets client tasks at a high rate, so it is important to handle resource allocation for these tasks carefully. Using the improved pelican optimization algorithm, we efficiently distribute the tasks to the virtual machines in this proposed work. The proposed hybrid algorithm (Improved POA + Improved GJO) is then used to distribute and manage the resources (Memory and CPU) as needed by the tasks. According to experimental findings, the accuracy of the proposed technique increases by 1.12%, 2.11%, and 14.2%, respectively. It shows that the proposed method has good accuracy compared with the existing HUNTER, FT-ERM, and RU-VMM approaches. [ABSTRACT FROM AUTHOR]

This paper explores the practical implementation and performance analysis of distributed learning (DL) frameworks on various client platforms, responding to the dynamic landscape of 6G technology and the pressing need for a fully connected distributed intelligence network for Internet of Things (IoT) devices. The heterogeneous nature of clients and data presents challenges for effective federated learning (FL) techniques, prompting our exploration of federated transfer learning (FTL) on Raspberry Pi, Odroid, and virtual machine platforms. Our study provides a detailed examination of the design, implementation, and evaluation of the FTL framework, specifically adapted to the unique constraints of various IoT platforms. By measuring the accuracy of FTL across diverse clients, we reveal its superior performance over traditional FL, particularly in terms of faster training and higher accuracy, due to the use of transfer learning (TL). Real-world measurements further demonstrate improved resource efficiency with lower average load, memory usage, temperature, power, and energy consumption when FTL is implemented compared to FL. Our experiments also showcase FTL's robustness in scenarios where users leave the server's communication coverage, resulting in fewer clients and less data for training. This adaptability underscores the effectiveness of FTL in environments with limited data, clients, and resources, contributing valuable information to the intersection of edge computing and DL for the 6G IoT. [ABSTRACT FROM AUTHOR]

Summary: Recently, there has been a growing emphasis on reducingenergy consumption in cloud networks and achieving green computing practices toaddress environmental concerns and optimize resource utilization. In thiscontext, efficient task scheduling minimizes energy usage and enhances overallsystem performance. To tackle the challenge ofenergy‐efficient task allocation, we propose a novel approach that harnessesthe power of Artificial Neural Networks (ANN). Our Artificial neural network Dynamic Balancing (ANNDB) method is designed toachieve green computing in cloud environments. ANNDB leverages the feed‐forwardnetwork architecture and a multi‐layer perceptron, effectively allocatingrequests to higher‐power and higher‐quality virtual machines, resulting inoptimized energy utilization. Through extensive simulations, wedemonstrate the superiority of ANNDB over existing methods, including WPEG,IRMBBC, and BEMEC, in terms of energy and power efficiency. Specifically, ourproposed ANNDB method exhibits substantial improvements of 13.81%, 8.62%, and9.74% in the Energy criterion compared to WPEG, IRMBBC, and BEMEC,respectively. Additionally, in the Power criterion, the method achievesperformance enhancements of 3.93%, 4.84%, and 4.19% over the mentioned methods.The findings from this research hold significant promise for organizations seekingto optimize their cloud computing environments while reducing energyconsumption and promoting sustainable computing practices. By adopting theANNDB approach for efficient task scheduling, businesses and institutions cancontribute to green computing efforts, reduce operational costs, and make moreenvironmentally friendly choices without compromising task allocationperformance. [ABSTRACT FROM AUTHOR]

Containerization has become a widely popular virtualization mechanism alongside Virtual Machines (VMs) to deploy applications and services in the cloud. Containers form the backbone of the modern architectures around microservices and provide a lightweight virtualization mechanism for IoT and Edge systems. Elasticity is one of the key requirements of modern applications with various constraints ranging from Service Level Agreements (SLA) to optimization of resource utilization, cost management, etc. Auto Scaling is a technique used to attain elasticity by scaling the number of containers or resources. This work introduces a novel mechanism for auto-scaling containers in cloud environments, addressing the key elasticity requirement in modern applications. The proposed mechanism combines predictive analysis using the Auto-Regressive Integrated Moving Average (ARIMA) model and control theory utilizing the Proportional-Integral-Derivative (PID) controller. The major contributions of this work include the development of the ARIMA-PID algorithm for forecasting resource utilization and maintaining desired levels, comparing ARIMA-PID with existing threshold mechanisms, and demonstrating its superior performance in terms of CPU utilization and average response times. Experimental results showcase improvements of approximately 10% in CPU utilization and 30% [ABSTRACT FROM AUTHOR]

In any service-based computing environment, performance pertains to the effectiveness of a system or application in managing user tasks. The key performance assessment metrics include makespan, responsiveness, speed, throughput, resource utilization, etc. In any distributed landscape, like cloud computing, optimal performance relies on resource management techniques such as scheduling, load balancing, etc. Cloud environments often exhibit varying levels of heterogeneity arising from the diverse characteristics of cloudlets and virtual machines. This research paper focuses on the impact of this heterogeneity and proposes two scheduling algorithms to address it effectively: the Variance Managed Heuristic Scheduler (VMHS) and the Adaptive Heterogeneity Index Cloudlet Scheduler (AHICS). AHICS aims to minimize makespan, virtual machine underutilization, the degree of load imbalance, and the deviation of completion time among virtual machines. AHICS functions as the main scheduler, whereas VMHS and MaxMin act as sub-schedulers in this proposed work. AHICS is designed to be flexible and adjust its scheduling strategy based on the level of heterogeneity within the cloud environment. AHICS utilizes the VMHS scheduler in scenarios with a low heterogeneity index or the MaxMin scheduler when the cloudlets and the virtual machines characteristics are highly diverse or heterogeneous. This multi-objective AHICS scheduling algorithm harnesses the strengths of both schedulers as a hybrid algorithm. Implemented using the CloudSim 3.0.3 simulator, experimental results demonstrate that AHICS outperforms other heuristic scheduling algorithms, including MinMin, TASA, HAMM, PTFR, and RSSM, in terms of makespan, virtual machine utilization ratio, the degree of load imbalance, and the deviation of completion time among the virtual machines in both low and high heterogeneity levels

Cloud computing relies heavily on load balancing to distribute workloads evenly among servers, network connections, and drives. The cloud system has been assigned some load which can be underloaded, overloaded, or balanced depending on the cloud architecture and user requests. An important component of task scheduling in clouds is the load balancing of workloads that may be dependent or independent of virtual machines (VMs). To overcome these drawbacks, a novel Load Balancing of Virtual Machine (LBVM) in Cloud Computing has been proposed in this paper. The input tasks from multiple users were collected in a single task collector and sent towards the load balancer, which contains the deep learning network called the Bi-LSTM technique. When the load is unbalanced, the VM migration will begin by sending the task details to the load balancer. The Bi-LSTM is optimized by a Genetic Expression Programming (GEP) optimizer and finally, it balances the input loads in VMs. The efficiency of the proposed LBVM has been determined using the existing techniques such as MVM, PLBVM, and VMIS in terms of evaluation metrics such as configuration latency, detection rate, accuracy etc. Experimental results shows that the proposed method reduces the Migration Time of 49%, 41.7%, and 17.8% than MVM, PLBVM, VMIS existing techniques respectively.

In recent years, cloud computing has become an essential distributed computing platform and has achieved enormous popularity. Within cloud computing, the Cloud service broker creates an abstraction layer between provider and consumer so that customers notice the cloud service providers’ offered services’ solitary view. The brokers of cloud service help connect the cloud’s substantial resources and select the data centers of the cloud that meet the user’s requirement while maximizing the entire response time and reducing cost. The landscape of autonomic cloud brokers has been reviewed in this systematic literature review study, while the PRISMA approach is used to analyze the literature. This comprehensive review of cloud brokerage mechanisms is tailored towards the autonomic distribution of services. To emphasize autonomic computing and cloud-access security brokers, the evolving paradigms of cloud service selection are detailed and critically analyzed to enhance service distribution efficiency. Further, the role of cloud brokers in load-balancing services is also highlighted in this study. A new taxonomy for the structured framework of cloud brokerage mechanisms is introduced based on functionalities, deployment models, and architecture for the autonomic service distribution. Finally, the study offers valuable insights for future research challenges and best practices in cloud security.

Cloud computing has grown significantly in recent years and is now widely used by both individuals and businesses. Cloud service providers need to be prepared for failures to ensure that they can respond to them appropriately by minimizing downtime and achieving business continuity. This can be accomplished through fault tolerance mechanisms which aim to predict, prevent, and recover from failures. One of the common causes of task failures in the cloud is the deterioration of physical machines. If deterioration is detected in the physical machine, the virtual machines (VMs) being hosted must be moved to healthy physical machines. This paper proposes an approach to VM allocation in the event of host deterioration or failure. Namely, an integer linear programming optimization model is proposed for the allocation of the failing VMs while minimizing VM migration time. In addition, hybrid genetic and simulated annealing (HGA and HSA) algorithms are proposed for providing efficient solutions for the optimization model. The HGA is hybridized by a procedure that penalizes infeasible solutions to reduce their chances of being selected in the offspring populations and a hill-climbing procedure that modifies feasible solutions to produce better (or more fit) ones. The HSA starts from a feasible solution and also uses elitism as the HGA in its search to ensure that good candidate solutions are preserved. To validate the quality of the obtained solutions, we compare our approach to the commercial optimization solver CPLEX. In comparison to the CPLEX solver, the results show that for small size data center problems, the proposed HGA and HSA achieve near-optimal solutions with 70.80% and 81.43% increase in speed with an average solution quality trade-off by 20.57% and 78.52%, respectively. Notably, for medium, large and very large size problems the HGA and HSA obtained solutions where the CPLEX solver could not provide solutions within acceptable time. For medium size problems HGA attains solutions with better quality (75.82%) yet with higher execution time (57.27%). For large and very large problems HGA significantly outperforms HSA in terms of solution quality (better by 84.88% and 86.46%) yet with significantly higher execution time (74.22% and 76.55 %), respectively. Thus, the designer may choose which algorithm to use based on whether the focus is on quality or execution time.

Recent developments in cloud technology enable one to dynamically deploy heterogeneous resources as and when needed. This dynamic nature of the incoming workload causes fluctuations in the cloud environment, which is currently addressed using traditional reactive scaling techniques. Simple reactive approaches affect elastic system performance either by over-provisioning resources which significantly increases the cost, or by under-provisioning, which leads to starvation. Hence automated resource provisioning becomes an effective method to deal with such workload fluctuations. The aforementioned problems can also be resolved by using intelligent resource provisioning techniques by dynamically assigning required resources while adapting to the environment. In this paper, a reinforcement learning-based proactive resource allocation framework (RLPRAF) is proposed. This framework simultaneously learns the environment and distributes the resources. The proposed work presents a paradigm for the optimal allocation of resources by merging the notions of automatic computation, linear regression, and reinforcement learning. When tested with real-time workloads, the proposed RLPRAF method surpasses previous auto-scaling algorithms considering CPU usage, response time, and throughput. Finally, a set of tests demonstrate that the suggested strategy lowers overall expense by 30% and SLA violation by 77.7%. Furthermore, it converges at an optimum timing and demonstrates that it is feasible for a wide range of real-world service-based cloud applications.

Resource allocation is the utmost challenging and common problem, particularly in the cloud service model in Infrastructure as a Service (IaaS). The issue of load balancing is so harmful that irregular load balancing may result in a structure smash. Adopting a suitable access plan and allowing the system to spread work among all existing resources leads to utilizing Virtual Machines (VMs) appropriately. To get enhanced results from the Artificial Bee Colony Algorithm (ABC), a reinforcement learning technique Q-learning is combined using multifaceted job scheduling optimization based on ABC(QMFOABC) has been proposed. The proposed approach improves resource utilization and scheduling created on resource, cost, and makespan. The efficiency of the suggested strategy was evaluated in Datasets Synthetic Workload, Google Cloud Jobs (GoCJ), and Random by using CloudSim to the remaining scheduling strategies for load matching like Max-min, Multifaceted Cuckoo Search (MFCS), Multifaceted Particle Swarm Optimization (MFPSO), Q-learning, Heuristic job scheduling with Artificial Bee Colony approach with Largest Job First algorithm (HABC LJF), First come first serve (FCFS). According to the findings of the experiments, the algorithm that employed the QMFOABC method has better results in resource utilization, throughput, cost, and makespan. Compared to Max-Min (82.31%), MOPSO (35.62%), HABC LJF (21.65%), Q-Learning (11.72%), VTO-QABC FCFS(5.87%), and VTO-ABC LJF (5.86%) shorter time than MOCS, a considerable improvement is found. [ABSTRACT FROM AUTHOR]

Security of multimedia content on the Cloud has emerged as the major research area in today's era of the internet. Most exchange of multimedia content across the globe is done over the Cloud. Such communication is done openly in a cloud environment where anyone can quickly access the data. Therefore, it should provide reliable security mechanisms and standards for the shared multimedia data for users. This study highlights better protection of multimedia data over the Cloud than the existing approaches, where the information cannot be unwrapped reasonably. The encryption process is speedup using the multiple VMs over the Cloud for parallel processing of the chunks of the data. The proposed algorithm is well-designed and suited for efficient computation with limited resources,as most wireless devices come with limited bandwidth and computational power. The qualitative and quantitative evaluation is performed to compare the performance of the proposed S-Method model with the state-of-the-art models. A computing time shows that the proposed approach can meet the requirement of real-time applications. [ABSTRACT FROM AUTHOR]

The adoption of virtualization in edge computing promotes the use of numerous applications and operating systems on a single physical server. The technological environment is increasingly efficient, economical, and hassle-free by virtue of this technology. This study explores the potential of virtualization in edge computing environments by creating virtual versions of physical resources and services. The primary objective of this study is to reduce hardware and operational expenses by consolidating multiple systems into one physical system. Various deployment strategies for virtual machines on edge nodes, including containers, hypervisors, and virtual machines, are discussed. The article also delves into a virtualization-based framework to address challenges in edge computing. Additionally, through historical research on virtualization techniques, this article provides insights into the benefits and limitations of virtualization in edge computing. Moreover, the integration of virtualization and edge computing is examined, highlighting open challenges that need to be addressed for optimal utilization of virtualization in edge computing environments. The findings contribute to a deeper understanding of the possibilities and challenges associated with virtualization in edge computing environments, laying the foundation for further research in this field. [ABSTRACT FROM AUTHOR]

Because of the remote computing and heterogeneous nature of the infrastructure, load balancing and task scheduling are the most significant difficulties in cloud computing. Many strategies can be used to assign tasks to the appropriate virtual machines (VMs), but balancing the load is the main challenge that arises as a result of changing loads and varying VM characteristics. As a result, resources are used in an imbalanced manner, and system performance suffers. We developed a new Levy flight updated pelican optimization (LFUPO) technique to address these issues. We evaluate load balancing characteristics such as response time, turnaround time, server load, migration cost, and job rejection rate using this method. Based on the SLA constraints, the task rejection rate is also estimated. We are determining the multi-objective function based on these estimated values. Accordingly, the LFUPO approach is used to balance the load with the consideration of tasks ranging in 100, 150, 175, and 200, and VM ranges from 20 to 40. When compared to the prior methodology, this proposed algorithm performs better and distributes the load optimally and effectively. [ABSTRACT FROM AUTHOR]

Blockchain technology has garnered interest in several scientific and engineering fields. To improve blockchain services, its execution challenges must be addressed. Container-based virtualization enables running isolated apps on a shared OS where blockchain technology can leverage this technology to run numerous nodes, smart contracts, and decentralized apps in distinct containers allowing resource isolation and allocation, faster deployment and scalability, and improved security through limited host OS and other container access. This article covers container-based virtualization for Blockchain technology, including current methodologies, prospects, and future perspectives. Initially, this study explains blockchain and containerization as well as the reason for their integration. Then reviews container virtualization services to address blockchain complexity, size, scalability, and security. Conversely, Container technology uses blockchain to protect data and enhance resource management. Next, analyzes the latest containerization and blockchain integration studies. Finally, difficulties and future directions are considered to advance this promising research. [JJCIT 2023; 9(3.000): 235-248]

This paper analyzes the performance of the open-source OpenDroneMap image processing software (ODM) across multiple platforms. We tested desktop and laptop computers as well as high-performance cloud computing and supercomputers. Multiple machine configurations (CPU cores and memory) were used. We used eBee S.O.D.A. drone image datasets from Namibia and northern Finland. For testing, we used the OpenDroneMap command line tool with default settings and the fast orthophoto option, which produced a good quality orthomosaic. We also used the “rerun-all option” to ensure that all jobs started from the same point. Our results show that ODM processing time is dependent upon the number of images, a high number of which can lead to high memory demands, with low memory leading to an excessively long processing time. Adding additional CPU cores is beneficial to ODM up to a certain limit. A 20-core machine seems optimal for a dataset of about 1000 images, although 10 cores will result only in slightly longer processing times. We did not find any indication of improvement when processing larger datasets using 40-core machines. For 1000 images, 64 GB memory seems to be sufficient, but for larger datasets of about 8000 images, higher memory of up to 256 GB is required for efficient processing. ODM can use GPU acceleration, at least in some processing stages, reducing processing time. In comparison to commercial software, ODM seems to be slower, but the created orthomosaics are of equal quality.