SLA Violations Research Articles

An energy-efficient load balance strategy based on virtual machine consolidation in cloud environment

In the cloud computing environment, unbalanced utilization of multi-dimensional resources in physical servers generates resource fragmentation, leading to inefficient resource utilization and energy wastage in data centers. The inefficient utilization of resources makes high energy consumption and low quality of service (QoS) in resource management an urgent problem to be solved. We propose a load balancing strategy based on virtual machine consolidation (LBVMC), which aims to reduce the energy consumption and service level agreement (SLA) violation of data centers by balancing the multi-dimensional resource utilization in physical machines (PMs). First, we present a load state classification algorithm for PMs with load abnormality considering current and future loads to reduce unnecessary virtual machine (VM) migrations caused by occasional load fluctuations. Then, we propose a resource-weight based selection model for migratable VMs, which selects appropriate VMs to be migrated based on multi-dimensional resource utilization, and reduces resource fragmentation caused by load imbalance. Finally, we design a VM placement algorithm based on resource fitness and load correlation to deploy VMs on the optimal destination PMs to assure load balancing of the destination PMs after VM placement. We perform simulated experiments under homogeneous, heterogeneous, and bottleneck resource environments. Experimental results show that LBVMC is superior to other strategies in reducing energy consumption and SLA violations and achieves better overall performance.

Learning to Dynamically Select Cost Optimal Schedulers in Cloud Computing Environments

The operational cost of a cloud computing platform is one of the most significant Quality of Service (QoS) criteria for schedulers, crucial to keep up with the growing computational demands. Several data-driven deep neural network (DNN)-based schedulers have been proposed in recent years that outperform alternative approaches by providing scalable and effective resource management for dynamic workloads. However, state-of-the-art schedulers rely on advanced DNNs with high computational requirements, implying high scheduling costs. In non-stationary contexts, the most sophisticated schedulers may not always be required, and it may be sufficient to rely on low-cost schedulers to temporarily save operational costs. In this work, we propose MetaNet, a surrogate model that predicts the operational costs and scheduling overheads of a large number of DNNbased schedulers and chooses one on-the-fly to jointly optimize job scheduling and execution costs. This facilitates improvements in execution costs, energy usage and service level agreement violations of up to 11%, 43% and 13% compared to the state-of-the-art methods.

VMP-A3C: Virtual machines placement in cloud computing based on asynchronous advantage actor-critic algorithm

Virtualization technology represented through Virtual Machines (VMs) is recognized as a key infrastructure in cloud computing. This technology is developing rapidly and cloud data centers face challenges such as Virtual Machine Placement (VMP) for energy efficiency. VMP is defined as the efficient allocation of VMs to Host Machines (HMs) to achieve various objectives such as reducing energy consumption, load balancing and avoid Service Level Agreement Violations (SLAV). In this paper, VMP is addressed using a Deep Reinforcement Learning (DRL) based strategy to determine the best mapping between VMs and HMs. We present VMP-A3C, an effective strategy to solve VMP using Asynchronous Advantage Actor-Critic (A3C) algorithm as a new DRL approach. VMP-A3C aims at load balancing in HMs without SLAV, where energy consumption is reduced as much as possible. VMP-A3C learns to dynamically consolidate VMs using migration techniques to a minimum number of HMs. We believe that there is scope for improvements in shutting down little-workload HMs through VMs migration. The effectiveness of the proposed algorithm has been evaluated from various aspects such as the deployment rate, energy consumption, SLAV, the number of shutdown HMs and the number of migrated VMs. The main difference in terms of energy consumption and the number of required HMs between VMP-A3C and the best existing state-of-the-art method is 2.54% and 7.14%, respectively.

SLA Aware Task-Scheduling Algorithm in Cloud Computing Using Whale Optimization Algorithm

Task scheduling in Cloud Computing paradigm poses new challenges for cloud provider as heterogeneous, diversified tasks arrived on to cloud console. To schedule these type of tasks efficiently on to virtual resources in cloud paradigm, an effective scheduler is needed, which precisely maps tasks to virtual machines by considering priorities of both tasks and VMs. Existing scheduling algorithms failed to map tasks precisely to virtual resources due to high dynamic nature in cloud environment which leads to increase of makespan and SLA violations will be increased. In this paper, authors proposed a task-scheduling mechanism, which considers task priorities and VMs. To model this scheduling paradigm we have chosen whale optimization through which our scheduler will take decisions for scheduling tasks precisely onto virtual resources in cloud environment. Entire simulation was carried out on CloudSim. Initially we have chosen random generated workload to run simulation and after that, we have considered a real-time workload named as BigDataBench and ran our simulation. Finally, we compared our proposed work with classical baseline mechanisms. From simulations we observed that proposed whale scheduler improved makespan for PSO, ACO, GA, and W-schedulers by 20.07%, 17.55%, 19.9%, and 6.35%, respectively, and 17.3%, 17.86%, 17.64%, and 5.93%, respectively, for BigDataBench workloads. SLA violations improved over PSO, ACO, GA, and W-Scheduler by 56.76%, 42.17%, 35.29%, and 24.53%, respectively, and 63.42%, 23.33%, 55.51%, and 40.1%, respectively, for BigDataBench workloads. From extensive simulation results, our proposed scheduler using whale optimization approach minimizes makespan and SLA violations to a great extent.

Uniform distribution elephant herding optimization (UDEHO) based virtual machine consolidation for energy-efficient cloud data centres

Information technology (IT) providers should use cloud-based services due to their flexibility, reliability, and scalability to handle the rising requirement for processing capacity. The maintenance of dependable services between cloud providers and their customers in a cloud environment, on the other hand, depends on Quality of Service (QoS) assurance. Virtual machine (VM) consolidation is nondeterministic polynomial time (NP) hard issue, and numerous heuristic techniques have been suggested to solve it. In this work, the suggested VM consolidation technique takes into account both current and future uniform distribution elephant herding optimization (UDEHO) based VM consolidation approaches for resource utilization via host overload detection (utilization prediction based potential overload detection (UP-POD)) and host underload detection (UP-PUD). A UDEHO method efficiently predicts resource use in the future. Depending on the power utilization and the number of migrations, a power-saving value is advised for identifying under-loaded hosts. Furthermore, the CloudSim toolkit is used to construct and test these techniques using the same experimental parameters. Lastly, the findings demonstrate that the suggested methodologies considerably decrease the number of VM migrations by about 0.073%, the energy usage of about 11%, and SLA violations by 6.15% while retaining QoS guarantees when compared to conventional techniques.

Power and thermal-aware virtual machine scheduling optimization in cloud data center

Virtual machine (VM) consolidation technology is a commonly used energy-saving method in data centers. Most of the existing methods are committed to consolidating VMs to a small number of servers to improve the utilization of server resources and reduce the total energy consumption while preventing hotspots. However, energy and thermal-aware VM scheduling is a multi-objective optimization problem. Most of the existing related work cannot provide adequate means to adjust the impact of energy consumption and temperature on VM scheduling. Hence, this paper proposes a power and thermal-aware VM dynamic scheduling scheme (PTDS) for cloud data centers. The proposed PTDS dynamically adjusts the VM consolidation scheme by real-time detecting the server temperature and resource utilization rate under the premise of considering the thermal cycle effect of the data center computer room. Power and Thermal Objective Ant Colony Optimization (PTOACO) is proposed in the VM placement. PTOACO improves the defect that the ant colony algorithm easily falls into local optimization and adds control parameters to adjust the bias between sub-objectives. We performed extensive experiments by using real PlanetLab and random workloads. The performance results were compared with several advanced schemes regarding total energy consumption, hotspots, SLA violation rate, etc. The experimental results demonstrate that PTDS reduces energy consumption by 26.69% on average compared with other advanced schemes and ensures a meager SLA violation rate while avoiding hotspots.

A Multi-Objective Virtual Network Migration Algorithm Based on Reinforcement Learning

Virtual network migration (VNM) helps improve network performance by remapping a subset of virtual nodes or links to physical infrastructure, aligning the resource allocation to the virtual network&#x0027;s changing conditions. However, existing VNM methods neglect integrating multiple objectives that affect network performance, such as energy, communication, migration, and service level agreement violation (SLAV). It is challenging to make VNM decisions to optimize the overall objective in a large-scale cloud environment. This article establishes a multi-objective optimization model and proposes a multi-objective VNM algorithm called MiOvnm. The MiOvnm employs the double deep <inline-formula><tex-math notation="LaTeX"><?TeX $Q$?></tex-math></inline-formula>-learning approach to cope with ample state space. It also applies an action selection method called actfilter to deal with large-scale action space. The MiOvnm finds the migration action with optimal potential reward from the candidate action set. Simulation results demonstrate the superiority of our MiOvnm to the state-of-the-art methods. More specifically, MiOvnm reduces average SLAV, communication cost, and total cost by 24.32&#x0025;, 4.95&#x0025;, and 12.45&#x0025;, respectively. Furthermore, evaluation results in a real-world OpenStack platform reveal that making full use of computation and network resources, the MiOvnm reduces the completion time of computation- and network-intensive benchmarks by 11.35&#x0025; and 10.31&#x0025;, respectively, with a total cost reduction of 26.02&#x0025;.

Two-Phase Virtual Machine Placement in Cloud Computing Data Centers Using Extreme Learning Machine Prediction-Based Triggering Method

Background: Two-phase Optimization of Virtual Machine Placement (VMP) Problem considers both the Online Incremental VMP (iVMP) phase in which the new arrival of dynamic requests of Virtual Machines VMs are attended to and the Offline VMP reconfiguration (VMPr) phase that performs placement recalculation. In the two-phase scheme, the first part of the two-phase approach is the iVMP, where virtual machines (VMs) can be built, changed, or destroyed at runtime. While the second phase focuses on raising the standard of solutions produced by the iVMP, several studies have been done in different literature to solve the VMP problem. However, the methods used tend to be over-forecast and have long runs of a linear trend. This affects the prediction and produces a less optimal solution. Objective: The following four objective functions are optimized using the proposed Extreme Learning Machine Prediction-Based Triggering Method for Virtual Machine Placement in Cloud Computing Datacenters in Two-Phases, which combines the advantages of both online (dynamic) and static (offline) VMP formulations: the length of the reconfiguration process, the amount of energy used, the way resources are used, and the financial expenses. This study suggests a brand-new strategy for deciding when to start the VMP reconfiguration phase. Results: The Method provides more accuracy to the predicted requests as well as reduces the total economic penalties for service Level Agreement(SLA) violations. An experimental comparison with the existing approach is conducted utilizing 400 cases. Conclusion: The results demonstrated that, in comparison to the benchmark approach, the proposed work obtained a minimum cost function with a 10.5% improvement

Cloud computing virtual machine consolidation based on stock trading forecast techniques

In Cloud Computing, the virtual machine scheduling in datacenters becomes challenging when trying to optimize user-service requirements and, at the same time, efficient resource management. Clumsy load management results in host overloads that trigger a continuous flow of virtual machine (VM) migrations to correct this situation, thus negatively impacting the Service Level Agreement (SLA), resource availability and energy consumption. The present paper explores the combined use of trend analysis techniques with time series forecasting techniques broadly used in stock markets, to improve VM-to-host consolidation. The main goal is to provide an efficient estimate of the near future trend of virtual machine resource usage and host availability. This information improves the scheduler’s decisions when determining the correct VM to be migrated and the candidate host to allocate it to. The results have demonstrated that it is possible to reduce the number of migrations by up to 75% while obtaining a reduction in the SLA violations by up to 60%. The results also showed noticeable improvements regarding the reduction of energy consumption. The migration decisions based on predictions of near-future resource usage trends using stock trading techniques showed a decrease in network usage, thus obtaining an energy saving of up to 16%.

DRAGON: Decentralized Fault Tolerance in Edge Federations

Edge Federation is a new computing paradigm that seamlessly interconnects the resources of multiple edge service providers. A key challenge in such systems is the deployment of latency-critical and AI based resource-intensive applications in constrained devices. To address this challenge, we propose a novel memory-efficient deep learning based model, namely generative optimization networks (GON). Unlike GANs, GONs use a single network to both discriminate input and generate samples, significantly reducing their memory footprint. Leveraging the low memory footprint of GONs, we propose a decentralized fault-tolerance method called DRAGON that runs simulations (as per a digital modeling twin) to quickly predict and optimize the performance of the edge federation. Extensive experiments with real-world edge computing benchmarks on multiple Raspberry-Pi based federated edge configurations show that DRAGON can outperform the baseline methods in fault-detection and Quality of Service (QoS) metrics. Specifically, the proposed method gives higher F1 scores for fault-detection than the best deep learning (DL) method, while consuming lower memory than the heuristic methods. This allows for improvement in energy consumption, response time and service level agreement violations by up to 74, 63 and 82 percent, respectively.

An Efficient Virtual Machine Consolidation Algorithm for Cloud Computing

With the rapid development of integration in blockchain and IoT, virtual machine consolidation (VMC) has become a heated topic because it can effectively improve the energy efficiency and service quality of cloud computing in the blockchain. The current VMC algorithm is not effective enough because it does not regard the load of the virtual machine (VM) as an analyzed time series. Therefore, we proposed a VMC algorithm based on load forecast to improve efficiency. First, we proposed a migration VM selection strategy based on load increment prediction called LIP. Combined with the current load and load increment, this strategy can effectively improve the accuracy of selecting VM from the overloaded physical machines (PMs). Then, we proposed a VM migration point selection strategy based on the load sequence prediction called SIR. We merged VMs with complementary load series into the same PM, effectively improving the stability of the PM load, thereby reducing the service level agreement violation (SLAV) and the number of VM migrations due to the resource competition of the PM. Finally, we proposed a better virtual machine consolidation (VMC) algorithm based on the load prediction of LIP and SIR. The experimental results show that our VMC algorithm can effectively improve energy efficiency.

Using dynamic voltage frequency scaling and service‐level agreement to reduce energy consumption in cloud datacenters based on distance

AbstractEnergy consumption in cloud is an important topic. Previous works have reduced energy consumption, but have not consider and optimal selection of servers in a datacenter. In our paper, we try to elicit optimal selection of servers in a datacenter. We will modify the architecture of the system and the proposed algorithm using service‐level agreement (SLA) for datacenter servers in this selection, we consider the distance between the servers and the job and if there is a violation of SLA, the migration of virtual machines (VMs) occurs. On the other hand, unnecessary migration of VMs increases energy consumption. So we provide an algorithm for migrating VMs, and because we need task scheduling to perform tasks in the cloud, we will also provide algorithms for tasks migrating. Energy consumption is important for us, we use dynamic voltage frequency scaling to reduce energy consumption. On the other hand, the selection of service providers is done in the SLA management, and these service providers are located on different datacenters, we consider the parameter of the distance to the service consumer in selecting the appropriate datacenter.

Real-Time Virtual Machine Scheduling in Industry IoT Network: A Reinforcement Learning Method

The widespread adoption of Industrial Internet of Things (IIoT)-based applications has driven the emergence and development of cloud-related computing paradigms with the ability to seamlessly leverage cloud resources. Heterogeneous resources, mobility factors in IoT, and dynamic behavior make it challenging for the corresponding virtual machine (VM) scheduling problem to address the processing effectiveness of application requests in these kinds of cloud environments. Based on reinforcement learning theory, this article proposes an online VM scheduling scheme (OSEC) for joint energy consumption and cost optimization that divides the scheduling process into two parts: VM allocation and VM migration. First, all the VMs and the physical machines (PMs) are regarded as a set of states and actions in the cloud environment, and the Q-learning feedback is used to achieve the iterative computation of Q-values to obtain the optimal parallel allocation sequence for multiple VMs. Then, VMs are migrated among the active PMs according to a grouping policy and the best-fit principle to achieve dynamic consolidation of the resources in the data center. Finally, experimental results show that compared with state-of-the-art algorithms under different conditions, the proposed method reduces energy consumption by approximately 18.25%, VM execution costs by approximately 21.34%, and service level agreement (SLA) violations by approximately 90.51%.

Technical Study of Deep Learning in Cloud Computing for Accurate Workload Prediction

Proactive resource management in Cloud Services not only maximizes cost effectiveness but also enables issues such as Service Level Agreement (SLA) violations and the provisioning of resources to be overcome. Workload prediction using Deep Learning (DL) is a popular method of inferring complicated multidimensional data of cloud environments to meet this requirement. The overall quality of the model depends on the quality of the data as much as the architecture. Therefore, the data sourced to train the model must be of good quality. However, existing works in this domain have either used a singular data source or have not taken into account the importance of uniformity for unbiased and accurate analysis. This results in the efficacy of DL models suffering. In this paper, we provide a technical analysis of using DL models such as Recurrent Neural Networks (RNN), Multilayer Perception (MLP), Long Short-Term Memory (LSTM), and, Convolutional Neural Networks (CNN) to exploit the time series characteristics of real-world workloads from the Parallel Workloads Archive of the Standard Workload Format (SWF) with the aim of conducting an unbiased analysis. The robustness of these models is evaluated using the Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) error metrics. The findings of these highlight that the LSTM model exhibits the best performance compared to the other models. Additionally, to the best of our knowledge, insights of DL in workload prediction of cloud computing environments is insufficient in the literature. To address these challenges, we provide a comprehensive background on resource management and load prediction using DL. Then, we break down the models, error metrics, and data sources across different bodies of work.

Runtime Management of Service Level Agreements through Proactive Resource Provisioning for a Cloud Environment

By leveraging the Internet, cloud computing allows users to have on-demand access to large pools of configurable computing resources. PaaS (Platform as a Service), IaaS (Infrastructure as a Service), and SaaS (Software as a Service) are three basic categories for the services provided by cloud the computing environments. Quality of service (QoS) metrics like reliability, availability, performance, and cost determine which resources and services are available in a cloud computing scenario. Provider and the user-specified performance characteristics, such as, rejection rate, throughput, response time, financial cost, and energy consumption, form the basis for QoS. To fulfil the needs of its customers, cloud computing must ensure that its services are given with the appropriate quality of service QoS. A “A legally enforceable agreement known as a “Service Level Agreement” (SLA) between a service provider and a customer that outlines service objectives, quality of service requirements, and any associated financial penalties for falling short. We, therefore, presented “A Proactive Resource Supply based Run-time Monitoring of SLA in Cloud Computing”, which allows for the proactive management of SLAs during run-time via the provisioning of cloud services and resources. Within the framework of the proposed work, SLAs are negotiated between cloud users and providers at run-time utilizing SLA Manager. Resources are proactively allocated via the Resource Manager to cut down on SLA violations and misdetection costs. As metrics of performance, we looked at the frequency with which SLAs were broken and the money lost due to false positives. We compared the proposed PRP-RM-SLA model’s simulated performance to the popular existing SLA-based allocation strategy SCOOTER. According to simulation data, the suggested PRP-RM-SLA model is 25% more effective than the current work SCOOTER at reducing SLA breaches and the cost of misdetection.

Dynamic Virtual Machine Consolidation in the Cloud: A Cuckoo Search Approach

The practice of consolidating virtual machines onto a smaller number of physical servers is known as Virtual Machine (VM) consolidation. This allows for a reduction in the total number of physical servers that are required to host virtual machines with minimum consumption of energy. There are lots of mechanism already developed by the various researchers in this filed to optimize the energy consumption rate. Therefore, in this research work, Cuckoo Search (CS) based VMs consolidation approach for Cloud Computing (CC). It is impossible to effectively optimize energy efficiency without compromising some form of system performance cloud service quality. The majority of current VM consolidation approaches are based on the concept of load and threshold, which affects the system performance in terms of Quality of Service (QoS) metrics such as energy consumption, Service Level Agreement (SLA) violation, and task scheduling overhead. Because of this, optimizing energy efficiency is currently impossible. Therefore, proposed a CS-based VMs consolidation approach to achieve this goal of research. When CS-based method is compared against literature in terms of energy consumption and SLA violation, the model achieves a considerable reduction in energy consumption while concurrently achieving a reduction in the quantity of service violations.

An Autonomous Multi-Agent Framework using Quality of Service to prevent Service Level Agreement Violations in Cloud Environment

Cloud is a specialized computing technology accommodating several million users to provide seamless services via the internet. The extension of this reverenced technology is growing abruptly with the increase in the number of users. One of the major issues with the cloud is that it receives a huge volume of workloads requesting resources to complete their executions. While executing these workloads, the cloud suffers from the issue of service level agreement (SLA) violations which impacts the performance and reputation of the cloud. Therefore, there is a requirement for an effective design that supports faster and optimal execution of workloads without any violation of SLA. To fill this gap, this article proposes an automatic multi-agent framework that ensures the minimization of the SLA violation rate in workload execution. The proposed framework includes seven major agents such as user agent, system agent, negotiator agent, coordinator agent, monitoring agent, arbitrator agent and the history agent. All these agents work cooperatively to enable the effective execution of workloads irrespective of their dynamic nature. With effective execution of workloads, the proposed model also resulted in an advantage of minimized energy consumption in data centres. The inclusion of a history agent within the framework enabled the model to predict future requirements based on the records of resource utilization. The proposed model followed the Poisson distribution to generate random numbers that are further used for evaluation purposes. The simulations of the model proved that model is more reliable in reducing SLA violations compared to the existing works. The proposed method resulted in an average SLA violation rate of 55.71% for 1200 workloads and resulted in an average energy consumption of 47.84kWh for 1500 workloads.

Genetic-Based Virtual Machines Consolidation Strategy With Efficient Energy Consumption in Cloud Environment

In cloud computing environments, virtualization is used to share physical machine (PM) resources among multiple users by creating virtual machines (VMs). Running the PM consumes a large amount of energy. Additionally, the PM will be overloaded when the demand for resources exceeds the PM capacity. This overload on the PM leads to violations of Service Level Agreements (SLAs). Dynamic VM consolidation techniques use live migration of VMs to optimize resource utilization and minimize energy consumption. However, excessive migration of VMs impacts negatively the application performance due to the incurred overhead at the runtime. This paper presents a modified genetic-based VM consolidation (MGVMC) strategy that aims to replace VMs in an online manner taking into account energy consumption, SLA violations, and the number of VM migrations. The MGVMC strategy utilizes the genetic algorithm to migrate VMs to the appropriate PM in a way that minimizes the number of over-utilized and under-utilized physical machines (PMs) as low as possible. The performance of the MGVMC strategy was evaluated using the CloudSim Plus framework with a large number of VMs and workload traces from the PlanetLab platform. The experimental results revealed that the MGVMC strategy achieved a significant improvement in energy consumption, SLA violations, and the number of VM migrations compared to other recent approaches. These results demonstrate the effectiveness of the MGVMC strategy in optimizing VM consolidation in the cloud environment.

Utilisation aware virtual machine selection policy for workload consolidation in cloud data centres

Large-scale virtualised data centres have been constructed throughout the world in response to the rising demand for service-oriented computing and the expansion of cloud computing technologies. These vast data centres consume a significant amount of power and have a significant carbon footprint, which must be reduced to the greatest extent feasible. The dynamic virtual machine consolidation provided by live migration leads to significant energy savings. However, it also constitutes a violation of the service level agreement (SLA). The process of selecting virtual machine (VM) for migration is critical in the realm of energy-aware cloud computing. This study proposes a novel utilisation aware VM selection (UAVMS) policy that aids in VM selection for migration using server utilisation and skewness value. We use the CloudSim toolkit to build our UAVMS policy and compare its performance with existing methods. The experimental results shows that UAVMS reduces the energy usage and SLA violations.

DRL-Based Slice Admission Using Overbooking in 5G Networks

In 5G-and-beyond networks, the concept of Network Slicing is used to support multiple independent and co-existing logical networks on a physical network infrastructure. The infrastructure provider (InP) owns the set of virtual and physical resources that are used to support the tenant slice requests. Each slice request specifies a service level agreement (SLA) that contains the required slice-level resources (computation and communication) and the revenue provided by the tenant. Due to limited resources, the InP cannot accommodate all requests made by the tenants. In general, it has been found that tenants tend to overestimate their resource demands (e.g., for 5G Core computation) to reduce possible SLA violations. In this paper, we consider two major slice types: Elastic (low priority and low revenue) and Inelastic (high priority and high revenue). We apply the concept of overbooking, where the InP accepts more slices while considering slice priorities in order to maximize the overall revenue and utilization. We consider a multi-tenant environment and propose a slice admission system named PRLOV, which is based on Reinforcement Learning (RL) and prediction methods. The system predicts the future resource demands of Elastic slices and applies an opportunistic overbooking technique to overbook InP for accepting more slices. In addition, the admission decision is formulated as an Markov Decision Process (MDP) problem and solved using standard RL techniques (Policy Iteration, Q-Learning, DQN). The performance of our proposed work is compared against three other heuristics (Basic, Prediction, PRL) that do not use overbooking. Data traces from the Materna data center networks were used for prediction purposes. The important performance metrics measures include InP total revenue, the acceptance rate of respective slices and overall resource utilization for different slices. The results show that the proposed work significantly outperforms the other mechanisms in terms of revenue gain and resource utilization. Simulation results show that PRLOV provides a revenue gain of 6%, 26%, and 102% compared to the PRL, Prediction and Basic scheme.