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Low-resource utilization and high-energy consumption have become progressively protuberant issues in cloud data centers. Virtual Machine (VM) migration is the key objective to resolve this issue. Moreover, extreme VM migration might empower Service-Level Agreement (SLA) violations. Few works are considered for optimizing throughput and energy consumption. An efficient VM migration must consider different parameters like network communication overhead, migration overhead, resource utilization, energy consumption and quality of service which is a multi-objective issue. Hence, in this paper, a Modified Pelican Optimization-based Variable Load Mean Function (MPO-VLMF)-based host overload detection is presented and security enhancement is developed. The main motive of this study is to achieve host overload detection and security enhancement. To obtain host overload detection, the variable load mean function is developed. In this mean function, the weight parameter is selected by considering Modified Pelican Optimization (MPO). The Levy flight (LF) is considered for enhancing the updating process of Pelican Optimization (PO). To enhance the security of the system, the Digital Signature-based Encryption (DSE) is developed. Based on this proposed approach, security and host overload detection are obtained. The proposed technique is implemented and evaluated by performance measures. It is compared with the conventional approaches to justify the performance of the system. [ABSTRACT FROM AUTHOR]

Service-based systems (SBSs) are a unique category of software systems that dynamically combine various third-party services at runtime to deliver complex and adaptive functionality. This dynamic composition introduces a high level of unpredictability and uncertainty, creating potential anomalies and exerting significant pressure on system maintenance. To tackle this challenge, the conventional approach involves employing prediction-based proactive self-adaptation. Despite the prevalence of existing approaches emphasizing prediction accuracy, the critical aspect of "earliness" in predictions is often overlooked. Striking a balance between early and accurate predictions is paramount in practice. In response, we propose Proactive Self-Adaptation based on Ensemble Prediction (PSA-EP) to effectively balance the trade-off between the prediction earliness and accuracy of in SBSs. At the heart of PSA-EP lies an ensemble prediction model built upon a deep neural network and an enhanced long short-term memory (DNN-ELSTM) architecture. PSA-EP is crafted to empower SBSs to adapt to the inherent unpredictability and instability, facilitating the achievement of their adaptation goals. This adaptation mechanism not only enables effective prediction and analysis of adaptation goal violations but also addresses the reliability and performance of service level agreements (SLAs) governing quality of service (QoS). We evaluated the performance of PSA-EP in a decentralized tele-assistance system using four key metrics, and the experimental results, examined from various perspectives, underscore its exceptional performance. [ABSTRACT FROM AUTHOR]

An efficient task scheduling is mandatory in cloud computing for providing virtual resources used to carry out the tasks. An effective allocation of VM with the presence of diverse resource requirements, inaccurate information and uncertainties existing in the system is difficult. In this research, an effective task scheduling is done by using the fuzzy logic (FL) with best-fit-decreasing (BFD) in a cloud computing environment. The developed FL–BFD is optimized using resource usage, power, cost and time. Accordingly, the FL–BFD reallocates virtual machine (VM) in the cloud, based on the user demands. Therefore, the adaptability of FL is leveraged to handle uncertainties and imprecise information, which is helpful for an appropriate allocation of VM using BFD according to user requirements. The developed FL–BFD is analyzed using makespan, execution time, degree of imbalance, energy consumption and service level agreements (SLA) violations. The existing approaches named minimum completion time (MCT), particle swarm optimization (PSO), improved wild horse optimization with levy flight algorithm for task scheduling in cloud computing (IWHOLF-TSC), inverted ant colony optimisation (IACO), fuzzy system and modified particle swarm optimization (FMPSO), and task-scheduling using whale optimization (TSWO) are used for comparison. The makespan of FL–BFD with 1000 tasks is 9.2 ms, which is higher when compared to the IWHOLF-TSC and MCT-PSO. [ABSTRACT FROM AUTHOR]

Deploying virtual machines poses a significant challenge for cloud data centers, requiring careful consideration of various objectives such as minimizing energy consumption, resource wastage, ensuring load balancing, and meeting service level agreements. While researchers have explored multi-objective methods to tackle virtual machine placement, evaluating potential solutions remains complex in such scenarios. In this paper, we introduce two novel multi-objective algorithms tailored to address this challenge. The VMPMFuzzyORL method employs reinforcement learning for virtual machine placement, with candidate solutions assessed using a fuzzy system. While practical, incorporating fuzzy systems introduces notable runtime overhead. To mitigate this, we propose MRRL, an alternative approach involving initial virtual machine clustering using the k-means algorithm, followed by optimized placement utilizing a customized reinforcement learning strategy with multiple reward signals. Extensive simulations highlight the significant advantages of these approaches over existing techniques, particularly energy efficiency, resource utilization, load balancing, and overall execution time. [ABSTRACT FROM AUTHOR]

Cloud computing (CC) is rising quickly as a successful model presenting an on-demand structure. In the CC, the present investigation shows that loadbalancing methods established on meta-heuristics offer better solutions for appropriate scheduling and allotment of resources. Conversely, several traditional approaches believe in only some quality of service (QoS) metrics and reject several significant components. Network load balancing and data categorization (NBDC) is proposed. This approach aims to enhance load balancing in the cloud field. This approach consists of two phases: the support vector machine (SVM) algorithm-based data categorization and the ant colony optimization (ACO) algorithm for distributing the network load on the virtual machine (VM). The SVM algorithm performs several data formats, such as text, image, audio, and video, resultant data class that offers high categorization accuracy in the cloud. The ACO algorithm reaches an efficient load balancing based on the time of execution (TE), time of throughput (TT), time of overhead (TO), time of optimization, and migration count (MC). Simulation results related to the baseline approach demonstrate an enhanced system function in terms of service level agreement violation, throughput, execution time, energy utilization, and execution time. [ABSTRACT FROM AUTHOR]

In distributed systems and parallel computing, optimal load balancing is difficult. These abstract addresses load balancing in distributed situations, highlighting current solutions' flaws and emphasizing the need for new ones. Load balancing research includes centralized and distributed algorithms, heuristics, and predictive models. Despite various successful methods, workload adaptability, overhead reduction, and scaling to large systems remain unresolved. This study proposes a particle swarm optimization (PSO) load balancing method that considers global and local stability considerations. The proposed method uses PSO principles to balance exploration and exploitation and allocate resources among distributed nodes. Predictive components improve preventative load management by predicting workload changes. Global and local load balancing stability criteria distinguish this study. The recommended method considers global systemwide performance indicators, local node-level characteristics, and microlevel stability to maximize system efficiency. A dual-focus technique distinguishes the proposed load balancing strategy from others, solving dynamic distributed system challenges. The study examines load balancing system advances and suggests improvements and further research. More accurate prediction modeling, stability measures, and application-specific enhancements may be studied in the future. Experimental validation and real-world implementation of the recommended approach are necessary to determine its practicality and ability to handle modern distributed computing systems. [ABSTRACT FROM AUTHOR]

Device-to-device (D2D) is a pivotal technology in the next generation of communication, allowing for direct task offloading between mobile devices (MDs) to improve the efficient utilization of idle resources. This paper proposes a novel algorithm for dynamic task offloading between the active MDs and the idle MDs in a D2D–MEC (mobile edge computing) system by deploying multi-agent deep reinforcement learning (DRL) to minimize the long-term average delay of delay-sensitive tasks under deadline constraints. Our core innovation is a dynamic partitioning scheme for idle and active devices in the D2D–MEC system, accounting for stochastic task arrivals and multi-time-slot task execution, which has been insufficiently explored in the existing literature. We adopt a queue-based system to formulate a dynamic task offloading optimization problem. To address the challenges of large action space and the coupling of actions across time slots, we model the problem as a Markov decision process (MDP) and perform multi-agent DRL through multi-agent proximal policy optimization (MAPPO). We employ a centralized training with decentralized execution (CTDE) framework to enable each MD to make offloading decisions solely based on its local system state. Extensive simulations demonstrate the efficiency and fast convergence of our algorithm. In comparison to the existing sub-optimal results deploying single-agent DRL, our algorithm reduces the average task completion delay by 11.0% and the ratio of dropped tasks by 17.0%. Our proposed algorithm is particularly pertinent to sensor networks, where mobile devices equipped with sensors generate a substantial volume of data that requires timely processing to ensure quality of experience (QoE) and meet the service-level agreements (SLAs) of delay-sensitive applications. [ABSTRACT FROM AUTHOR]

To mitigate severe physical resource consumption in cloud data centers, we propose an Improved Sparrow Search Algorithm-Based Virtual Machine Placement (ISSA-VMP) method. Incorporating Chebyshev chaotic mapping and Levy flight disturbance enhances resource allocation diversity in the search space. The mapping encoding scheme transforms virtual machine placement solutions into continuous positional information. ISSA-VMP establishes a cloud data center resource consumption model to maximize physical host resource utilization efficiency. The simulation results demonstrate the excellent performance of ISSA-VMP in virtual machine migration and physical resource utilization, significantly reducing task completion time. Compared to the best performing algorithm, the execution rate has increased by 5.57–18.11%. ISSA-VMP achieves high and stable physical resource utilization rates, ensuring efficient utilization, with a stable Service Level Agreement (SLA) violation rate. In summary, ISSA-VMP is a promising, efficient solution for optimizing resource allocation in cloud data centers. [ABSTRACT FROM AUTHOR]

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Summary By controlling the network, the Internet of Things (IoT)‐connected software‐defined network (SDN) limits the scalability of IoT devices. Since SDN depends on a centralized controller that attackers can easily affect, it is incredibly susceptible to attacks. Secure access control to the SDN controller was the focus of the prior methods for controller scalability and restricted trust management. A framework called Safeguard Authentication Dynamic Access Control (SANDMAC) is suggested to safeguard and offer useful services to enterprises. Authentication confirms legitimacy after all users and applications have been registered. To improve network security, policies let users grant access to account attributes, legal activities, and temporal components. The administrator lessens conflicts between the methods by validating and saving the policies in the database. The services are provided to dependable customers using the forensic‐based investigation algorithm, depending on the quality of service and software level agreements requirements, decreasing reaction times and maximizing resource usage. Performance comparisons between the new and previous efforts are validated using a variety of parameters, and the proposed work is validated using the iFogSim application. According to the findings, SANDMAC significantly raises key performance indicators. SANDMAC specifically keeps false positives at 3.5% and accomplishes a low response time of 60 ms for roughly 800 authorized accesses. SANDMAC is a better option because of these enhancements, which result in longer network lifetimes and more dependable data transmission. [ABSTRACT FROM AUTHOR]

Summary The rapid evolution of user equipment (UE) and 5G networks drives significant transformations, bringing technology closer to end‐users. Managing resources in densely crowded areas such as airports, train stations, and bus terminals poses challenges due to diverse user demands. Integrating mobile edge computing (MEC) and network function virtualization (NFV) becomes vital when the service provider's (SP) primary goal is maximizing profitability while maintaining service level agreement (SLA). Considering these challenges, our study addresses an online resource allocation problem in an MEC network where computing resources are limited, and the SP aims to boost profit by securely admitting more UE requests at each time slot. Each UE request arrival rate is unknown, and the requirement is specific resources with minimum cost and delay. The optimization problem objective is achieved by allocating resources to requests at the MEC network in appropriate cloudlets, utilizing abandoned instances, reutilizing idle and soft slice instances to shorten delay and reduce costs, and immediately scaling inappropriate instances, thus minimizing the instantiation of new instances. This paper proposes a deep reinforcement learning (DRL) method for request prediction and resource allocation to mitigate unnecessary resource waste. Simulation results demonstrate that the proposed approach effectively accepts network slice requests to maximize profit by leveraging resource availability, reutilizing instantiated resources, and upholding goodwill and SLA. Through extensive simulations, we show that our proposed DRL‐based approach outperforms other state‐of‐the‐art techniques, namely, MaxSR, DQN, and DDPG, by 76%, 33%, and 23%, respectively. [ABSTRACT FROM AUTHOR]

Resources are offered to customers on demand in the modern era of computing, communication, and technology. User demand for the resources depends on the service provider and consumer. The optimal assignment of the cloud resources depends on fitness function and resource management technique. In this manuscript, the key focus is to propose a model based on a meta-heuristic evaluation technique. The meta-heuristic evaluation technique provides optimal placement of the virtual machines to the user requests across the globe. The presented framework, elephant heard optimization with neural network (EHO-ANN) outperforms the existing static, dynamic, and nature-inspired techniques. The EHO-ANN is evaluated and analyzed against the Harmony Search Approach, Elephant Heard Optimizer, BAT, and GA cost-aware approach. The evaluation and analysis include the performance metrics, average Execution Time (ms), average Start Time (ms), average utilization, and average Finish Time (ms). The presented model EHO-ANN is validated using two configuration scenarios with 10 virtual machines and 5 virtual machines. The results are generated by fifteen times repeated experimentation which assures the accuracy of the model. [ABSTRACT FROM AUTHOR]

The demand for cloud-based computation is increasing exponentially in the age of information technology virtualization, which is increasing data centre energy consumption and service level agreement (SLA) violations. This contributes to global warming and excessive load on existing infrastructure. As a result, it is critical to improve the resource utilisation of cloud data centres (CDCs). Virtual machine (VM) consolidation is the most effective method for optimising resource utilisation in CDCs. In this context, this research proposes the global optimal search-based meta-heuristic algorithm black widow adaptive VM placement (BWAVP) approach-based VM consolidation that combines energy conservation, resource utilisation and required quality of services with accurate VM-PM mapping. The investigation of the BWAVP approach shows that it reduces energy consumption by 18% on average when compared to other VM placement approaches, and reduces SLA violations, and VM migrations by more than 80%. Further, The research proposed a localized adaptive over and underutilisation host detection technique that further reduces energy consumption by 10% while maintaining the quality of services (QoSs) at the same level. [ABSTRACT FROM AUTHOR]

The fifth generation (5G) of mobile networks is emerging as a key enabler of modern factory automation (FA) applications that ensure timely and reliable data exchange between network components. Network slicing (NS), which shares an underlying infrastructure with different applications and ensures application isolation, is the key 5G technology to support the diverse quality of service requirements of modern FA applications. In this article, an end-to-end (E2E) NS solution is proposed for FA applications in a 5G network. Regression approaches are used to construct a performance model for each slice to map the service level agreement to the network attributes. Interference coordination approaches for switched beam systems are proposed to optimize radio access network (RAN) performance models. A case study of a non-public network is used to show the proposed NS solution. Simulation result shows that for services with different QoS requirements, different IC approaches should be used as optimization methods. Design prediction using regression approach has been evaluated and shows that the prediction successful rate increases when more existing data are used. [ABSTRACT FROM AUTHOR]

With the increasing demand for resources from cloud computing users, enhancing the energy efficiency of data centers and ensuring the Quality of Service have become noteworthy research topics. Most Previous studies on Virtual Machine (VM) consolidation often neglect the delay and energy consumption associated with Power Mode Transitions (PMTs), leading to potential deviations in experimental results from real-world scenarios. In this paper, we propose a Two-Level Heuristics Virtual Machine Consolidation (TLHVMC) method that takes into account the overheads of PMTs. This method employs a Greedy Strategy-based hybrid heuristic Placement (GSP) algorithm for resource allocation. After incorporating underloaded host detection for host classification, GSP applies distinct greedy strategies on various host lists, which balances data center workload and minimizes active hosts. Moreover, Time Window-based Centralized power mode Control policies are implemented to centrally manage host power modes. These policies improve energy-saving by suspending idle hosts and reduce the Service Level Agreement violations by adaptively resuming hosts based on the data center's state within a time window. Experimental results on real workload traces show that TLHVMC significantly improves the energy efficiency of the data center and maintains the required cloud system performance compared to some other VM consolidation algorithms. [ABSTRACT FROM AUTHOR]

Host load prediction is essential in computing to improve resource utilization and for achieving service level agreements. However, due to variations in load and the inefficiency of feature extraction, prediction of load on hosts in fog computing an immense challenge. A predictive model with variable load patterns can better estimate future resource needs, which is crucial for capacity planning, service-level goals and energy efficiency. To improve workload prediction accuracy, proposed framework proposes a time-series-based multivariate ensemble model using anomaly detection techniques. In the proposed work, virtual machines are deployed on different platforms and numerous parameters such as CPU utilization, number of cores, RAM, allocated memory, available memory, disk I/O and network I/O are extracted. There may exist inconsistencies in load prediction due to the enormous volume of data. To reduce redundancy in data, various anomaly detection techniques are utilized. The performance of the proposed ensemble model is compared with various time series models using Mean Absolute Error, Mean Squared Error, Root Mean Squared Error and Mean Absolute Percentage Error (MAPE) and Accuracy. Moreover, the effectiveness of the proposed ensemble model is demonstrated on the generated dataset and performance is measured based on the performance evaluation metrics. The ensemble model exhibits higher accuracy in workload prediction as compared to the current state-of-the-art models, it achieves the lowest MAPE and provides an accuracy of about 88%. [ABSTRACT FROM AUTHOR]

Cloud computing has several benefits over traditional systems, such as scalability and high availability. However, these benefits, to be eventuated, require efforts in the area of resource provisioning and scaling, to match resources to current and future demand, and this is not always trivial to achieve. Since workload may fluctuate substantially in cloud environments, over-provisioning is a common practice to avoid abrupt quality of service (QoS) drops that may result in service level agreement (SLA) violations, but at the price of increased provisioning costs and energy consumption. Workload prediction is one of the strategies by which efficiency and operational cost of a clouds can be improved. Therefore, in this paper, we show the potential benefits of a proactive resource provisioning scheme augmented by three of the most promising machine learning prediction techniques in this context, namely ARIMA, MLP, and GRU, that are known to be able to cope with the dynamic behavior of our target applications. We analyze the trade-off between resource consumption and quality of service using SLA violations in web workloads, considering real case provisioning requirements and constraints, extensively simulating and analyzing the impact of prediction and scaling intervals, and publishing all used tools and datasets to allow reproducibility. Simulation experiments with a proactive approach are executed using real traces from NASA and Wikipedia workloads and achieved a reduction of 40% in SLA violations on average when compared to a reactive approach, while reducing the provisioned resources by almost 3%. [ABSTRACT FROM AUTHOR]

Cloud data centers present a challenge to environmental sustainability because of their significant energy consumption. Additionally, performance degradation resulting from energy management solutions, such as virtual machine (VM) consolidation, impacts service level agreements (SLAs) between cloud service providers and users. Thus, to achieve a balance between efficient energy consumption and avoiding SLA violations, we propose a novel VM consolidation algorithm. Conventional algorithms result in unnecessary migrations when improperly selecting VMs. Therefore, our proposed E2SVM algorithm addresses this issue by selecting VMs with high load fluctuations and minimal resource usage from overloaded servers. These selected VMs are then placed on normally loaded servers, considering their stability index. Moreover, our approach prevents server underutilization by either applying all or no VM migrations. Simulation results demonstrate a 12.9% decrease in maximum energy consumption compared with the minimum migration time VM selection policy. In addition, a 47% reduction in SLA violations was observed when using the medium absolute deviation as the overload detection policy. Therefore, this approach holds promise for real-world data centers because it minimizes energy waste and maintains low SLA violations. [ABSTRACT FROM AUTHOR]

Ensuring optimal load balancing is imperative for maintaining reliability and upholding quality of service as specified in service-level agreements (SLAs) for cloud computing providers. This research addresses the most common shortcomings of existing state-of-the-art methods, which often lack responsiveness and struggle to adapt to exponentially increasing demand, especially in the era of the internet of things (IoT). The proposed hybrid approach surpasses current literature approaches in performance metrics such as makespan, response time, number of cloudlet migrations, and SLA violations. It operates on two levels, initially employing a k-means clustering algorithm to group servers within each datacenter based on similar utilization rates. Subsequently, a round-robin method allocates task groups sequentially to non-overloaded clusters, and within each cluster, a genetic algorithm optimally assigns tasks to servers. This multilayered approach facilitates hot-deployment and scalability in operational cloud environments while promoting strong interoperability and decoupling of core mechanisms missions. Simulation experiments conducted on CloudSim Plus validate the superiority of our method, positioning it as a robust solution for enhancing load balancing and tasks scheduling in cloud environments, especially in the face of rapidly increasing IoT-related demands. [ABSTRACT FROM AUTHOR]

Background: literature on last mile logistic electrification has primarily focused either on the stakeholder interactions defining urban rules and policies for urban freight or on the technical aspects of the logistic EVs. Methods: the article incorporates energy sourcing, vehicles, logistics operation, and digital cloud environment, aiming at economic and functional viability. Using a combination of engineering and business modeling combined with the unique opportunity of the actual insights from Europe's largest tender in the automotive aftermarket electrification. Results: the Last Mile Logistics (LML) electrification is possible and profitable without jeopardizing the high-tempo deliveries. Critical asset identification for a viable transition to EVs leads to open new lines of research for future logistic dynamics rendered possible by the digital dimensions of the logistic ecosystem. Conclusions: beyond the unquestionable benefits for the environment, the electrification of the LML constitutes an opportunity to enhance revenue and diversify income. [ABSTRACT FROM AUTHOR]

This research explores the optimization of firewall systems within private cloud environments, specifically focusing on a 30-day evaluation of the Omni-Secure Firewall. Employing a multi-metric approach, the study introduces an innovative effectiveness metric (E) that amalgamates precision, recall, and redundancy considerations. The evaluation spans various machine learning models, including random forest, support vector machines, neural networks, k-nearest neighbors, decision tree, stochastic gradient descent, naive Bayes, logistic regression, gradient boosting, and AdaBoost. Benchmarking against service level agreement (SLA) metrics showcases the Omni-Secure Firewall's commendable performance in meeting predefined targets. Noteworthy metrics include acceptable availability, target response time, efficient incident resolution, robust event detection, a low false-positive rate, and zero data-loss incidents, enhancing the system's reliability and security, as well as user satisfaction. Performance metrics such as prediction latency, CPU usage, and memory consumption further highlight the system's functionality, efficiency, and scalability within private cloud environments. The introduction of the effectiveness metric (E) provides a holistic assessment based on organizational priorities, considering precision, recall, F1 score, throughput, mitigation time, rule latency, and redundancy. Evaluation across machine learning models reveals variations, with random forest and support vector machines exhibiting notably high accuracy and balanced precision and recall. In conclusion, while the Omni-Secure Firewall System demonstrates potential, inconsistencies across machine learning models underscore the need for optimization. The dynamic nature of private cloud environments necessitates continuous monitoring and adjustment of security systems to fully realize benefits while safeguarding sensitive data and applications. The significance of this study lies in providing insights into optimizing firewall systems for private cloud environments, offering a framework for holistic security assessment and emphasizing the need for robust, reliable firewall systems in the dynamic landscape of private clouds. Study limitations, including the need for real-world validation and exploration of advanced machine learning models, set the stage for future research directions. [ABSTRACT FROM AUTHOR]

Containers in cloud computing provide a logical packaging technique for applications to be isolated from the computing environment in which they actually execute, allowing for efficient sharing of memory, processor, storage, and network resources at the Operating System (OS) level. Since they are so compact, container‐based clouds have recently gained significant popularity. In order to maximize resource usage and minimize energy consumption, the container consolidation technique is widely employed in the cloud environment. This work introduces container consolidation in cloud computing that exploits Fractional Pelican Hawks Optimization (FPHO). In Container as a Service (CaaS) model, containers are placed in the Virtual Machines (VMs), and virtual machines are hosted in Physical Machines (PMs) or servers. The proposed method for container consolidation consists of two modules, namely, the host status module and the consolidation module. In the host status module, the PM's load is predicted using Long Short Term Memory (LSTM) and checked whether the PM is overloaded or underloaded using a threshold. If it is overloaded, the container selection algorithm is performed, and the migration list is also generated. In the consolidation module, the created migration list which is employed for the destination list to be created by an overloaded destination selector. In the same way, the underloaded list is also generated by the underloaded destination selector. Finally, the container and VM migration is carried out by considering the multi‐objectives such as predicted load, migration cost, resource utilization, energy consumption, network, and bandwidth which are optimally selected by the proposed FHPO. Here, FHPO is the combination of Fractional Pelican Optimization (FPO) and Fire Hawk Optimizer (FHO). The designed model achieved the measures with minimum energy consumption, resource utilization, Service Level Agreement (SLA), and Makespan as 0.066, 0.019, 0.054, and 0.066, respectively for setup one. [ABSTRACT FROM AUTHOR]

Summary: Due to the high performance of cloud computing‐based microservices, a wide range of industries and fields rely on them. In a containerized cloud, traditional resource management strategies are typically used to allocate and migrate virtual machines. A major problem for cloud service providers is resource allocation for containers, which directly affects system performance and resource consumption. In this paper, we propose a joint optimization of load balancing and resource allocation in the cloud using an optimal container management strategy. We aim to enhance scheduling efficiency and reduce costs by improving the container's schedule requested digitally by users. An improved backtracking search optimization (IBSO) algorithm is used to allocate resources between end‐users/IoT devices and the cloud under the consideration of service‐level agreements. Mechanic quantum recurrent neural networks (MQ‐RNNs) are designed to allocate, consolidate, and migrate containers in cloud environments. The various simulation measures used to validate the proposed strategy are energy consumption, number of active servers, number of interruptions, total cost, runtime, and statistical measures. [ABSTRACT FROM AUTHOR]

In cloud computing environment, the workflow allocation mainly focuses on task assignment to satisfy the quality-of-service (QoS) standards and workload balancing by the best use of the demanded resources as a service on pay per use basis to optimize objectives from service level agreements. In secured workflow allocation (SWA), the intermediate data transfers in sensitive workflow tasks demand security requirements during the execution across the virtual machines. Security-aware workflow execution challenges result in a significant share of security overhead which affects the QoS performance. The aim of this work is to facilitate the researcher's selection of suitable SWA approaches from the existing approaches in the literature. To achieve this, we have comprehensively reviewed the current state-of-the-art approaches of the SWA models in cloud computing environments. Also, a secured workflow allocation challenges taxonomy by describing the security overhead model and QoS objectives and constraints is presented. Finally, it determines and discusses the existing open issues, challenges, and future research direction in the SWA model based on the study's literature review. The study will help researchers in the domain with current security challenges for SWA research trends. [ABSTRACT FROM AUTHOR]

The modern era has been ruled by federated cloud extensively owing to the upward popularity of real-time applications. However, balancing the load among the different service providers in the federation is a challenging task. This is because of the enlarged load of each service provider and the demand for Service Level Agreement (SLA) requirements. Live VM migration is an effective mechanism to balance the load among the different Cloud Service Providers (CSP). Yet, none of the works has focused on both inter- and intra- CSP-based live VM migration in the federated cloud. Besides, they utilized traditional techniques (Pre and Post-copy) to perform live VM migration. This increases migration and downtime in a federated cloud environment. Motivated by this, we propose matching-based data center allocation and Duple live VM migration in clustered federated cloud environment (DALMIG). The proposed DALMIG approach tackles the downsides faced in the federated cloud as yet. In this regard, three processes are contributed that are Matching-based Data Center Allocation, Two-Fold Clustering, and Duple live VM migration. Initially, brokers allocate apt data center to each incoming task using Auction-based Bipartite One to N (A-BON) matching algorithm. And then, we execute two-fold clustering process: Here, the data center is clustered using the Kernel Neutrosophic C-Means (kernel NCM) algorithm. And, we cluster VMs in each data center using the Credal C-Means (CCM) algorithm. To maintain the load of each CSP, we maintain two entities, such as a load monitor and a migrator. Here, the load monitor estimates a load of each data center and the migrator performs a live VM migration process. Here, the live VM migration process is performed using the Hybrid Copy technique. We execute both intra- and inter-CSP live VM migration. Here, intra-live VM migration is performed using the State Action Reward State Action (SARSA) algorithm. Inter-CSP live VM migration is accomplished using the Black Widow Optimization (BWO) algorithm. We show our DALMIG performance in the federation using the Cloudsim tool. The simulation results acquired from Cloudsim are auspicious in the context of metrics including latency, response time, number of VM migrations, Migration time, load fairness, and communication overhead. [ABSTRACT FROM AUTHOR]

Several real-world scientific and industrial workflow applications adopt elastic and cost-efficient cloud services to fulfill their requirement. There are two stakeholders in the system, namely the user and the provider each of which tries to maximize its profit and at the same time minimize their possible overall costs. Since task scheduling algorithms for workflow executions determine which task should be possibly executed on what resources, they have a drastic impact on both the user's quality of experiences and on the underlying resource utilization. Indeed, an efficient task scheduling algorithm can meet service-level agreement (SLA) for both sides. For the sake of the importance of the issue, this survey presents a subjective taxonomy on the task scheduling schemes in the literature for workflow executions in cloud computing environments to be a guideline for future improvement. It classifies the literature based on the proposed algorithms in the literature, objectives, stakeholders' requirements, and evaluation metrics. This survey highlights research trends, challenges, research gaps, potential solutions, and future direction. It can also pave the way for further processing, improvement and strengthening of existing approaches or devising novel ones for interested researchers in the field of task scheduling problems. [ABSTRACT FROM AUTHOR]

Fog computing has emerged as a decentralized computing paradigm that extends cloud services to the network edge, enabling faster data processing and real-time applications. The increasing popularity of fog computing has led to the emergence of a potential market involving users and providers of fog resources. However, both parties are driven by self-interest and seek to maximize their utility, giving rise to multiple conflicts extending beyond mere price considerations. Negotiations can play a crucial role in resolving conflicts and establishing mutually beneficial service level agreements. Moreover, in the heterogeneous fog environment, quality of service attributes, such as throughput, delay, trust, power dissipation, etc., vary significantly among different user-fog associations. These attributes, although non-negotiable, hold great importance for entities and directly influence partner selection. Entities may exhibit a preference for one another based on these non-negotiable attributes. To the best of our knowledge, no existing literature specifically addresses the issue of associating with a preferred trading partner at a negotiated value for multiple issues in the fog environment. This research aims to address this gap and provide insights into this unexplored area. This work presents a novel Preference-based Muti-Issue Negotiation Algorithm, PMINA, for many to many, bilateral and concurrent negotiations in the fog environment. The results confirm the significance of PMINA, demonstrating a substantial enhancement in user and fog utilities. [ABSTRACT FROM AUTHOR]

The article discusses the concept of servitization, which involves selling products as services. The author believes that this shift is inevitable due to various factors such as outsourcing non-core activities, the circular economy, and advances in technology. The article emphasizes the importance of focusing on value creation and not just changing the revenue model. It also discusses the journey of Rolls-Royce in adopting a services business model and highlights the importance of customer-driven value propositions and flexibility in adapting to customer operations and cultural situations. The future of advanced services in the manufacturing industry is moving towards servitization, driven by economic and environmental pressures and supported by digital technology and efficient asset sharing networks. Manufacturers should identify value creation opportunities and work in partnership with customers to pilot and scale their service offerings. [Extracted from the article]

The resilient concept of cloud computing (CC) enables individuals and businesses to access the necessary services following their requirements. The methodology presents various functionalities like storage solutions, deployment platforms, convenient admittance to online services, and more. A significant issue in CC is load balancing (LB), which reduces the performance and effectiveness of the resources. The LB can be attained through (Task / Virtual Machine) scheduling and allocation. This research considers virtual machine (VM) allocation, which allocates VM to a suitable physical machine (PM). Implementing an efficient VM allocation approach is essential for mitigating energy consumption (EC) and service level agreement (SLA) breaches. This study presents an effectual LB model for CC based on state and randomization. Based on host utilization, the proposed approach first identifies the PM's current state (high, medium, and low). Next, select the suitable PM for VM allocation using a randomization approach. The Cloudsim toolkit is employed for simulating the suggested technique, and the PlanetLab workload is used to evaluate the performance regarding EC and SLA violations. The proposed approach is compared with MFPED (Medium-Fit Power Efficient Decreasing) and HVMAP (Hybrid VM Allocation and Placement). The experimental outcome shows that the proposed technique significantly lowers EC by 40.81% and 39.76% and SLA violation by 96.81% and 95.58% for MFPED and HVMAP methods. [ABSTRACT FROM AUTHOR]

Summary: Web scraping is a widely used technique for decision‐making, collecting, and structuring public data from the internet. As the volume of data continues to grow, the need for more efficient methods of data extraction becomes crucial. This article introduces a novel web scraping framework that utilizes Burstable virtual machines (VMs) on Amazon Web Services with the objective of reducing the monetary cost of execution while ensuring compliance with service level agreements (SLAs). To achieve this, the framework utilizes a combination of fixed and temporary Burstable VMs in a mixed cluster, which can be elastically scaled up to fulfill the SLA and scaled down to minimize monetary costs. Two strategies for handling VM allocation are proposed and evaluated: (i) a queue and SLA‐based strategy that employs queue size information and SLA criteria to determine the required number of VMs for the current scraping requests, and (ii) a credit‐based strategy that incorporates information about Burstable VM credits to effectively manage instance creation and termination. Experimental tests show that the proposed framework meets the defined SLA while achieving cost reductions of up to 74% compared to an approach that executes on fixed‐size clusters of Burstable instances. [ABSTRACT FROM AUTHOR]

Virtual machine (VM) consolidation and migration that only consider current workload can result in excessive unnecessary migrations. To address this issue, a VM consolidation algorithm based on resource utilization prediction is proposed. An improved K-nearest neighbor (KNN) classification algorithm weighted by attribute inconsistency is proposed to predict the workload of both the host and the VMs. Firstly, two distributions are partitioned according to the neighboring relationship for comparing consistency. Then, an inconsistency evaluation function based on earth mover's distance (EMD) is designed to measure the inconsistency between the neighboring sample set of each sample under each attribute and the equivalent partition refined by the decision attribute. Finally, the inconsistency level of the neighboring samples is transformed into the importance of the corresponding attribute to implement the attribute weighting KNN classifier. When selecting the source host and target host for VM migration, both current and predicted overloads are considered to avoid unnecessary VM migrations. Simulation tests were performed with random and realistic workloads, and the results show that the proposed method can reduce the overall energy consumption of the host, while also reducing service level agreement (SLA) violations and VM migration. [ABSTRACT FROM AUTHOR]

As the application complexity increases, it is desired to offload the computationally intensive tasks from the end devices to the cloud. In the cloud, virtual machines (VM) conduct the computations on behalf of the end devices. To reduce the processing delay of the offloaded tasks at the cloud, VMs may be created in advance by anticipating the arrival of offload requests, which is termed 'VM pre-provisioning'. For certain types of applications, however, long delay or high cost for the communication between the end devices and the cloud is not acceptable, and edge computing is used as an alternative. Since the edge servers are typically equipped with far smaller resource capacity than the cloud servers, the conventional VM pre-provisioning becomes inefficient. In this paper, we focus on VM pre-provisioning for resource-constrained edge servers. The VMs to be pre-provisioned are carefully chosen to effectively utilize the limited resources of the edge servers. When there exist pre-provisioned VMs that match the offload requests, those VMs are cloned to serve the offload requests, which enables quick start of the VMs. Extensive evaluation shows that the proposed scheme outperforms the existing schemes in the resource-constrained edge computing environment, particularly when the application diversity is high. For realistic evaluation, we collect a large-scale measurement trace of global users and use it in conjunction with the existing Google cluster trace. [ABSTRACT FROM AUTHOR]

Cloud computing provides consumers and organizations with shared pools of resources for data storage and processing and its optimization is essential as 98% of the allocated resources have been utilized only 86% of 98%. Hence, we carry out optimization to automatically allocate resources. In a cloud data center, Virtual machine placement is essential, and choosing the optimal physical machine to host the virtual machine is a critical step. The efficacy of the Virtual machine placement strategy has a considerable impact on cloud computing efficiency. Today, cloud computing optimization is needed for business goals and competition in the digital landscape for cost reduction (20–28%) and Energy consumption (16–22%), improving performance (30–42%) and scaling (12–14%) to meet changing business needs. Virtual machine placement optimization problems are a class of problems that arise in cloud computing when allocating resources to virtual machines across a set of physical machines or hosts. The goal is to optimize resource utilization (12–16%) while satisfying various constraints, such as performance requirements, availability, and energy efficiency than non-metaheuristic optimization techniques. Several virtual machine placement optimization problems include placement, consolidation, migration, and scheduling. Virtualization facilitated by virtual machine placement and migration meets the ever-increasing demands of a dynamic workload by transferring virtual machines inside cloud data center. Many resource management goals, including power efficiency, load balancing, fault tolerance, and system maintenance, are aided by virtual machine placement and migration. To propose a multi-objective Mayfly virtual machine placement algorithm with a massive cloud data center with different and multi-dimensional resources to handle these issues. A multi-objective, dynamic virtual machine placement strategy simultaneously reduces resource wastage, overcommitment ratio, migration time, service level agreement violation, and energy consumption. This paper presents a dynamic, multi-objective virtual machine placement strategy in cloud data centers based on overcommitment resource allocation to influence Virtual machine Physical machine mapping and achieved an increase in the range of 12.5–14.89% in allocation than the existing works. We validated our method by conducting a performance evaluation study using the CloudSim tool. The experimental results demonstrate that this article improves resource usage while reducing energy consumption, makespan, over-commitment, and physical machine overload. [ABSTRACT FROM AUTHOR]

The progression in quantum computing and the rapid development of quantum computation hardware has raised expectations for its application to commercially relevant use cases in the future. However, the need for high-level quantum programming abstractions and targeted use cases paired with vertical applications, which can directly benefit from quantum computing, remains an open challenge. This paper presents our vision for a decentralized architecture for swarm based IoT systems that leverages a high-level continuous development and integration quantum programming suite to support edge processing capabilities for different use cases across the edge-fog-cloud continuum. The planned Quantum DevKit provides the necessary abstractions and low-level backend interfaces for quantum computing infrastructure, enabling edge computation using quantum processing, with extensions to efficient management of Service Level Agreements (SLAs). The paper focuses on the presentation of the development kit and its coupling swarm-based architecture that automates the orchestration of the cloud-to-edge continuum, showcasing the potential of quantum technology in edge processing. [ABSTRACT FROM AUTHOR]

The helpdesk work unit was formed as a basic implementation of Information Technology Service Management (ITSM) implementation in the Company. In order to be able to measure the implementation of ITSM in the company, standardization of service management is needed; currently, the standard used as a reference and widely used is ISO 20000. ISO 20000 is a reference or best practice used in the implementation of business process management, which is expected to be able to respond to globalization developments where the ultimate goal to be achieved is to improve the quality and service in the Company. Currently, the constraints and problems faced by the Guarantee Company when ITSM has not been implemented include poor IT management, reduced levels of trust from stakeholders, weak control functions so that many risks occur and service level agreements (SLA) become unclear or do not have indicators in providing services. [ABSTRACT FROM AUTHOR]

Microservices is a structural approach, where multiple small set of services are composed and processed independently with lightweight communication mechanism. To accomplish the end‐user demand in minimum delay and cost without violating the service level agreement (SLA) constraints and overhead is a challenging issue in cloud computing. In addition, existing framework tries to deploy the microservice over the best computing resource for latency‐sensitive applications, but long boot‐time, and low resource utilization still remains a challenging task. To find the solution for aforementioned issues, we propose a Quality of Service (QoS) aware resource allocation model based on a Fine‐tuned Sunflower Whale Optimization Algorithm (FSWOA) that find the best resources for microservice deployment and fulfill the objectives of users as well as service provider. The proposed technique deploys the container‐based services over the physical machine based upon the capacity, to execute the micro services by utilizing the CPU and memory maximally. The proposed work aims is to distribute the workload in efficient manner and avoid the wastage of resources that leads to optimize the QoS parameters. The experimental results conducted in simulation environment demonstrates that proposed approach perform superior over baseline approaches and reduces the time, memory consumption, CPU consumption, and service cost up to 4.26%, 11.29%, 17.07% and 24.22% compared to SFWAO, GA, PSO and ACO. [ABSTRACT FROM AUTHOR]

The evolvement of data intensive applications in the cloud computing platform increased the burden of the cloud and resulted in the violation of the service level agreement (SLA). Moreover, the single cloud scenario offers no flexibility to switch between cloud service providers (CPs), leading to limited resource utilization and slower execution. To resolve these issues, this article introduces a hierarchical framework for task scheduling in a federated cloud scenario with multiple datacenters (DCs). The dynamic tasks with different resource requirements are considered to arrive at the federated cloud environment for successful execution. Initially, the task agent receives the tasks and clusters them using the proposed enhanced density peaks clustering (EDPC) algorithm. The proposed clustering algorithm clusters the tasks based on their resource requirements and results in two clusters such as CPU demanding tasks and I/O demanding tasks. After clustering, the tasks are sent to the federation layer, where the scheduling decisions are taken for the tasks. The federated cloud manager (FCM) within the cloud service provider (CP) communicates with the available DCs regarding resource availability. If the resources cannot satisfy the needs, they extend communication with the neighboring FCMs within the federation to complete the execution of tasks. After acquiring the availability status of the entire federation, the FCM executes the African vultures optimization algorithm (AVOA) based task scheduling framework to make the scheduling decision. The performance of the proposed model is evaluated using the GWT T-12 Bit Brains dataset by implementation in the CloudSim tool. The proposed model proved its efficacy over the compared metaheuristics in different metrics. [ABSTRACT FROM AUTHOR]

A distributed research infrastructure such as LifeWatch ERIC necessitates close cooperation among the various components that constitute the research infrastructure and contribute to the overall services, facilities and resources it offers to the user community. In-kind contributions are non-monetary contributions, consisting of labour, services, facilities and access to resources, typically provided by the distributed components of the research infrastructure. The financing for in-kind contributions is usually provided by the national funding agency on behalf of the LifeWatch member country or by the member directly, with a value being accredited towards the member's funding obligations to LifeWatch ERIC. The management and validation of in-kind contributions towards shared objectives are integral to the legal framework supporting LifeWatch ERIC. This involves a rigorous assessment process to verify each in-kind contribution. This short article outlines that process and explores the function of the In-Kind Contributions Committee (IKCC) at LifeWatch ERIC. [ABSTRACT FROM AUTHOR]

In the dynamic landscape of cloud computing, efficient resource allocation and workload prediction are paramount for optimizing infrastructure utilization, cost management, and overall service quality. As a result, an efficient resource management approach involves pooling available resources among many customers in a manner that considers their energy usage, SLAs, and forecasting accuracy all at once. Based on the performance analysis results, it is determined that integrating these techniques would result in the most efficient and adaptable cloud data centre. We proposed a Model based on Deep Convolutional Neural Networks (DCNN) and Long Short-Term Memory (LSTM) for handling SLAs in the cloud from both the perspective of consumers and service providers. Subsequently, we delve into the methodology of applying deep CNN and LSTM models to the problem of workload prediction in cloud environments. This methodology encompasses data preprocessing, model architecture, training parameters, and the choice of performance metrics. To anticipate CPU consumption from time series data and detect SLA violations, we suggested a DCNN-LSTM model. The accuracy prediction, energy usage, CPU use, and Service Level Agreement monitoring are all a part of this model. The proposed method is effective in helping cloud providers cut down on service violations and associated fines. Regarding the composite metric of Energy SLA Violation, which assesses the combined aspects of energy use and adherence to Service Level Agreements (SLAs), DCNN-LSTM surpasses ARIMA-LSTM, CNN, LSTM, and ARIMA 6.8%, 10.88%, 16.6%, and 22.4%, respectively. [ABSTRACT FROM AUTHOR]

Cloud computing technology provides access on demand to virtualized resources, services, and applications via a distributed network. In cloud data centers effective energy utilization is a critical concern in today's technology-driven world. Cloud data centers (CDC) are massive facilities that host and manage an enormous amount of data and computing resources. This article addresses the growing significance and energy-intensive nature of data centers. Due to the rapid growth of cloud computing, it offers on-demand access to resources globally and leads to substantial power consumption and carbon impact on the environment. They consume substantial amounts of energy, and optimizing their energy utilization is essential for reducing operational costs, minimizing environmental impact, and ensuring sustainable growth. To combat this, efficient energy-saving approaches using machine learning methods have been researched. ML methods hold great potential for enhancing energy efficiency in CDCs by analysing data, detecting patterns, and optimizing resource usage. The focus areas include CPU usage prediction, overload finding, underload estimation, selection, migration, and relocation of VMs to attain resources and improve energy utilization. The paper compares energy-saving results achieved by different machine learning techniques in data centers to minimize energy usage and meet service level agreements (SLA). The machine learning approaches reduce energy consumption from 1.6% to 88.5% compared to the benchmark approach mentioned, considering various settings and parameters. [ABSTRACT FROM AUTHOR]

The similarity measure (SM) of fuzzy numbers is vital in decision-making, ranking, and risk analysis, particularly when dealing with qualitative information and fuzzy mathematical models. Traditional set theory struggles with such applications due to the significant presence of uncertainties. Although a wide array of methods are available for SMs applied to fuzzy numbers (FNs), some shortcomings persist. Few recent approaches involving magnitude, distance, and coordinates to measure similarity have demonstrated certain drawbacks when applied to arbitrary trapezoidal fuzzy numbers (TZFNs). These existing measures frequently fail to capture human intuitions, encounter division by zero issues, and account for the unique properties of TZFNs. The proposed work introduces a method to determine the center of gravity (CoG) of TZFNs, which is then utilized along with information on areas, perimeter, weights, and geometric distances of TZFNs to formulate a comprehensive SM. This new approach overcomes the limitations observed in the existing SMs for TZFNs. The mathematical properties of the proposed method are discussed, and the performance of the model is evaluated and compared with recent works. It demonstrates its superior handling of a standard set of 32 TZFNs and for various other special cases, shown numerically in the results section. Additionally, three case studies that involve risk assessment analysis applying the proposed SM are done. A specific scenario concerning service level agreement (SLA) violation in Fog-integrated Cloud is thoroughly explored. [ABSTRACT FROM AUTHOR]

Description Within the current climate of health care pressures, services are exploring ways in which to use resources to the best of their ability to ensure service users have timely, safe, and effective care as well as having positive outcomes and good experiences of the care they receive. The current paper explores a service level agreement between the private sector and a child and family service within the HSC. Within this agreement, has been the development of a pathway through which families on a psychological service waiting list, could be triaged, and allocated to a private clinic within which, psychological care could be provided from assessment through to discharge. The subjective experiences and initial outcomes of those involved have been largely positive. The paper describes, explores, and reflects on the process of collaborative practice. Terminology HSC – Health & Social Care. In England, Scotland and Wales, the National Health Service (NHS) provides health care services while local councils provide social care services. In Northern Ireland these services are combined under what is known as Health and Social Care (HSC) [ABSTRACT FROM AUTHOR]

Recently, low-cost, low-power sensors have made wireless communication possible for Wireless Sensor Networks (WSNs), which are growing in popularity and bringing about tremendous improvement. Nevertheless, their autonomy eventually wanes because WSN nodes run entirely on batteries. The network lifetime is affected by this energy constraint. Therefore, the proposed method uses Quantum-based Optical sensors with the CUCKOO-ANN method for Enhanced Energy-Efficient Wireless Communication to overcome the existing issues. Initially, the Quantum-based optical sensors helped collect the data from the location and were also used for energy-efficient communication. It transfers the data very quickly and securely without any interruption. Next, the CUCKOO-ANN method optimises the real-time data and provides efficiency. Therefore, it helps to find an optimal route between the user and the Base station. The concept that has been presented exhibits excellent potential as it can efficiently fulfil customer service level agreements, lower average execution times, and improve data centre energy efficiency. Experimental results demonstrate that the proposed energy-efficient method minimises energy consumption, optimises data transmission, improves the network lifetime, and enhances the scalability of the network. [ABSTRACT FROM AUTHOR]

Disaster Recovery (DR) providers define the main services as backup and restore, both with critical data and applications. Most clients of data and application backup, fully rely on DR's services to restore them in the disaster events. Furthermore, in the normal conditions, DR provider backs up data, sometimes with application in routine or regular basis. Thus, providers should deliver the services in sophisticated way through several Key Performance Indicators (KPIs). In this paper, development of KPIs is focused on performance and assurance, which a laboratory testing simulates backup and restore services. On the other hand, experimental data such as network and system parameters are analysed to build performance and assurance framework. Meanwhile, interdependence among each performance and assurance of DRCs was used to build a relationship model. It is acted as a part of service level agreement, that a DR providers can present services dynamically. They also should adapt the computing resources to deliver services to meet SLA (Service Level Agreement) in flexible strategy. In turn, they can negotiate SLA with the customers based on this new approach. [ABSTRACT FROM AUTHOR]

Summary: The expeditious deployment of Cloud applications and services on wide‐ranging Cloud Data Centres (CDC) gives rise to the utilization of many resources. Moreover, by the increase in resource utilization, virtualization also greatly impacts achieving desired performance. The major challenges in virtualization are detecting over‐utilized or under‐utilized hosts at the right time and the proper scaling of Virtual Machines (VM) on the accurate host. Auto‐scaling in Cloud Computing allows the service providers to scale up or down the resources automatically and provides on‐demand computing power and storage capacities. Effective utilization and autonomous scaling of resources eventually reduce the load, energy consumption, and operating costs. In this paper, an efficient auto‐scaling approach for predicting host load through VM migration has been proposed. The ensemble method using different time‐series forecasting models has been proposed to forecast the approaching workload on the host. Based on this predicted load, different algorithms have been devised to detect over‐utilized and under‐utilized hosts and VMs can be migrated. The designed approach has been validated by experimentation on a real‐time Google cluster dataset. The proposed technique significantly improves average CPU utilization and reduces over‐utilization and under‐utilization. It also minimizes response time, service level agreement violations, and the slighter number of migrations and scaling overhead. [ABSTRACT FROM AUTHOR]

One of the most challenging problems in cloud datacenters is the degradation of performance and energy efficiency due to the overutilization of hosts and their exposition to excessive workload. Virtual machine (VM) consolidation and migration from one host to another are strategies that have been proven to successfully bring about performance improvements and energy efficiency. These schemes help in energy optimization by moving VMs experiencing difficulty functioning on an overloaded host to another host. Similarly, by migrating VMs from an underloaded host and consolidating them, unnecessary resources have a chance to be shut down. This makes clear why the accurate detection of overloaded and underloaded hosts is of fundamental importance when energy consumption, quality of services, and service level agreements are targeted. In this paper, an energy-aware QoS-based consolidation algorithm is proposed to dynamically manage VMs in cloud datacenters. The proposed algorithm applies reinforcement learning and artificial neural networks. The first method is used to select a suitable VM for migration, while the latter helps to predict the future state of hosts and detect overloaded and underloaded hosts. We simulated the proposed algorithm using the CloudSim framework and compared it to the baselines and state-of-the-art algorithms. The results show that the proposed approach surpasses other methods in what concerns both performance and energy efficiency. [ABSTRACT FROM AUTHOR]

Efficient scheduling algorithms are necessary in the cloud paradigm to optimize service provision to clients while minimizing time duration, energy consumption, and violations of service level agreements (SLAs). Disregarding task appropriateness in resource scheduling can have a detrimental effect on the quality of service provided by cloud providers. Moreover, the utilization of resources in an ineffective manner will necessitate a substantial expenditure of energy to execute activities, leading to prolonged processing duration that adversely affect the temporal duration. Many research projects have focused on employment scheduling problems, and the algorithms used in these studies have offered answers that were deemed nearly flawless. This study presents a chaos bird swarm algorithm (Chaos BSA) approach that use machine learning to consider task priority while allocating tasks to the cloud platform. The method calculates the priorities of task virtual machines and incorporates these values into the scheduler. The scheduler will select tasks that align with the specified priorities and are compatible with the virtual machines. The implementation of the system utilized the openstack cloud platform and the cloudsim tool. The results and comparison with the baseline approach genetic algorithm (GA), ant colony optimization (ACO), and particle swarm optimization (PSO) clearly demonstrate that our Chaos BSA outperforms them by 18% in terms of efficiency. [ABSTRACT FROM AUTHOR]

Virtual machine (VM) integration methods have effectively proven an optimized load balancing in cloud data centers. The main challenge with VM integration methods is the trade-off among cost effectiveness, quality of service, performance, optimal resource utilization and compliance with service level agreement violations. Deep Learning methods are widely used in existing research on cloud load balancing. However, there is still a problem with acquiring noisy multilayered fluctuations in workload due to the limited resource-level provisioning. The long short-term memory (LSTM) model plays a vital role in the prediction of server load and workload provisioning. This research presents a hybrid model using deep learning with Particle Swarm Intelligence and Genetic Algorithm ("DPSO-GA") for dynamic workload provisioning in cloud computing. The proposed model works in two phases. The first phase utilizes a hybrid PSO-GA approach to address the prediction challenge by combining the benefits of these two methods in fine-tuning the Hyperparameters. In the second phase, CNN-LSTM is utilized. Before using the CNN-LSTM approach to forecast the consumption of resources, a hybrid approach, PSO-GA, is used for training it. In the proposed framework, a one-dimensional CNN and LSTM are used to forecast the cloud resource utilization at various subsequent time steps. The LSTM module simulates temporal information that predicts the upcoming VM workload, while a CNN module extracts complicated distinguishing features gathered from VM workload statistics. The proposed model simultaneously integrates the resource utilization in a multi-resource utilization, which helps overcome the load balancing and over-provisioning issues. Comprehensive simulations are carried out utilizing the Google cluster traces benchmarks dataset to verify the efficiency of the proposed DPSO-GA technique in enhancing the distribution of resources and load balancing for the cloud. The proposed model achieves outstanding results in terms of better precision, accuracy and load allocation. [ABSTRACT FROM AUTHOR]