Service Level Agreement Violation Rate Research Articles

Multi-resource predictive workload consolidation approach in virtualized environments

The revolution of virtualization technologies and Cloud computing solutions has emphasized the need for energy-efficient and Service level agreement (SLA)-aware resource management techniques in cloud data centers. Workload consolidation in Infrastructure-as-a-Service (IaaS) providers allows for efficient utilization of hardware resources and reduced energy consumption by consolidating workloads onto fewer physical servers. To ensure successful workload consolidation, it is crucial for IaaS providers to carefully estimate the host state and identify overloaded and underloaded hosts, thereby avoiding overly aggressive consolidation. Existing proposals determine the host state depending on its current resource utilization or a single anticipated resource utilization value, and often consider only a single resource type of the host, such as CPU. These limitations may lead to unreliable host state estimations, resulting in excessive and needless service migrations between physical machines (PMs). This, in turn, can lead to extra delays in service execution, degraded performance, increased power consumption, and SLA violations. To address these challenges, we propose a workload consolidation approach that leverages a multi-resource and multi-step resource utilization prediction model. Based on this model, our overload and underload decision-making algorithms consider the forecasted future trend (sequence of future value) of each host resource's utilization, including CPU, memory, and bandwidth. Through extensive experimentations conducted with two real-world datasets, we demonstrate that our approach can significantly reduce power consumption, SLA violation rate, and the number of migrations compared to existing benchmarks.

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.

Model-Based Reinforcement Learning With Kernels for Resource Allocation in RAN Slices

Network slicing is a key feature of 5G and beyond networks, allowing the deployment of separate logical networks (network slices), sharing a common underlying physical infrastructure, and characterized by distinct descriptors and behaviors. The dynamic allocation of physical network resources among coexisting slices should address a challenging trade-off: to use resources efficiently while assigning each slice sufficient resources to meet its service level agreement (SLA). We consider the allocation of time-frequency resources from a new perspective: to design a control algorithm capable of learning over the operating network, while keeping the SLA violation rate under an acceptable level during the learning process. For this purpose, traditional model-free reinforcement learning (RL) methods present several drawbacks: low sample efficiency, extensive exploration of the policy space, and inability to discriminate between conflicting objectives, causing inefficient use of the resources and/or frequent SLA violations during the learning process. To overcome these limitations, we propose a model-based RL approach built upon a novel modeling strategy that comprises a kernel-based classifier and a self-assessment mechanism. In numerical experiments, our proposal, referred to as kernel-based RL, clearly outperforms state-of-the-art RL algorithms in terms of SLA fulfillment, resource efficiency, and computational overhead.

Utilization prediction-based VM consolidation approach

Reducing energy consumption and optimizing resource usage in large cloud data centers is still an essential target for the current researchers and cloud providers. The state-of-the-art highlights the effectiveness of VM consolidation and live migrations in achieving reasonable solutions. However, most proposals consider only the real-time workload variations to decide whether a host is overloaded or underloaded, or to trigger migration actions. Such approaches may apply frequent and needless VM migrations leading to energy waste, performance degradation, and service-level agreement (SLA) violations. In this paper, we propose a consolidation approach based on the resource utilization prediction to determine the overloaded and underloaded hosts. The prediction method combines a Kalman filter and support vector regression (SVR) to forecast the host's future CPU utilization. Simulations are conducted on Cloudsim using real PlanetLab workloads to verify the performance of our proposal against existing benchmark algorithms. Experimental results demonstrate that our consolidation technique significantly reduces the SLA violation rate, number of VM migrations, and energy consumed in the datacenter.

A Fuzzy Grouping Genetic Algorithm for Solving a Real-World Virtual Machine Placement Problem in a Healthcare-Cloud

Due to the large-scale development of cloud computing, data center electricity energy costs have increased rapidly. Energy saving has become a major research direction of virtual machine placement problems. At the same time, the multi-dimensional resources on the cloud should be used in a balanced manner in order to avoid resources waste. In this context, this paper addresses a real-world virtual machine placement problem arising in a Healthcare-Cloud (H-Cloud) of hospitals chain in Saudi Arabia, considering server power consumption and resource utilization. As a part of optimizing both objectives, user service quality has to be taken into account. In fact, user quality of service (QoS) is also considered by measuring the Service-Level Agreement (SLA) violation rate. This problem is modeled as a multi-objective virtual machine placement problem with the objective of minimizing power consumption, resource utilization, and SLA violation rate. To solve this challenging problem, a fuzzy grouping genetic algorithm (FGGA) is proposed. Considering that multiple optimization objectives may have different degrees of influence on the problem, the fitness function of the proposed algorithm is calculated with fuzzy logic-based function. The experimental results show the effectiveness of the proposed algorithm.

Dynamic Resource Allocation Method for Load Balance Scheduling Over Cloud Data Center Networks

The cloud datacenter has numerous hosts as well as application requests where resources are dynamic. The demands placed on the resource allocation are diverse. These factors could lead to load imbalances, which affect scheduling efficiency and resource utilization. A scheduling method called Dynamic Resource Allocation for Load Balancing (DRALB) is proposed. The proposed solution constitutes two steps: First, the load manager analyzes the resource requirements such as CPU, Memory, Energy and Bandwidth usage and allocates an appropriate number of VMs for each application. Second, the resource information is collected and updated where resources are sorted into four queues according to the loads of resources i.e. CPU intensive, Memory intensive, Energy intensive and Bandwidth intensive. We demonstarate that SLA-aware scheduling not only facilitates the cloud consumers by resources availability and improves throughput, response time etc. but also maximizes the cloud profits with less resource utilization and SLA (Service Level Agreement) violation penalties. This method is based on diversity of client’s applications and searching the optimal resources for the particular deployment. Experiments were carried out based on following parameters i.e. average response time; resource utilization, SLA violation rate and load balancing. The experimental results demonstrate that this method can reduce the wastage of resources and reduces the traffic upto 44.89% and 58.49% in the network.

An energy-aware approach for resources allocating in the internet of things using a forest optimization algorithm

PurposeInternet of things (IoT) is essential in technical, social and economic domains, but there are many challenges that researchers are continuously trying to solve. Traditional resource allocation methods in IoT focused on the optimal resource selection process, but the energy consumption for allocating resources is not considered sufficiently. This paper aims to propose a resource allocation technique aiming at energy and delay reduction in resource allocation. Because of the non-deterministic polynomial-time hard nature of the resource allocation issue and the forest optimization algorithm’s success in complex problems, the authors used this algorithm to allocate resources in IoT.Design/methodology/approachFor the vast majority of IoT applications, energy-efficient communications, sustainable energy supply and reduction of latency have been major goals in resource allocation, making operating systems and applications more efficient. One of the most critical challenges in this field is efficient resource allocation. This paper has provided a new technique to solve the mentioned problem using the forest optimization algorithm. To simulate and analyze the proposed technique, the MATLAB software environment has been used. The results obtained from implementing the proposed method have been compared to the particle swarm optimization (PSO), genetic algorithm (GA) and distance-based algorithm.FindingsSimulation results show that the proper performance of the proposed technique. The proposed method, in terms of “energy” and “delay,” is better than other ones (GA, PSO and distance-based algorithm).Practical implicationsThe paper presents a useful method for improving resource allocation methods. The proposed method has higher efficiency compared to the previous methods. The MATLAB-based simulation results have indicated that energy consumption and delay have been improved compared to other algorithms, which causes the high application of this method in practical projects. In the future, the focus will be on resource failure and reducing the service level agreement violation rate concerning the number of resources.Originality/valueThe proposed technique can solve the mentioned problem in the IoT with the best resource utilization, low delay and reduced energy consumption. The proposed forest optimization-based algorithm is a promising technique to help enterprises participate in IoT initiatives and develop their business.

Efficient VM Selection Strategies in Cloud Datacenter Using Fuzzy Soft Set

A cloud data center is established to meet the storage demand due to the rate of growth of data. The inefficient use of resources causes an enormous amount of power consumption in data centers. In this paper, a fuzzy soft set-based virtual machine (FSS_VM) consolidation algorithm is proposed to address this problem. The algorithm uses four thresholds to detect overloaded hosts and applies fuzzy soft set approach to select appropriate VM for migration. It considers all factors: CPU utilization, memory usage, RAM usage, and correlation values. The algorithm is experimentally tested for 11 different combinations of choice parameters where each combination is considered as fuzzy soft set and compared with existing algorithms for various metrics. The experimental results show that proposed FSS_VM algorithm achieves significant improvement in optimizing the objectives such as power consumption, service level agreement violation rate, and VM migrations compared to all existing algorithms. Moreover, performance comparison among the fuzzy soft set-based VM selection methods are made, and Pareto-optimal fuzzy soft sets are identified. The results show that the Pareto-based VM selection improves the QoS. The time complexity of the proposed algorithm increases when it finds best VM for migration. The future work will reduce the time complexity and will concentrate on developing an efficient VM placement strategy for VM migration since it has the greater impact on improving QoS in VM placement.

An Adaptive Deployment Algorithm for IaaS Cloud Virtual Machines Based on Q Learning Mechanism

When deploying infrastructure as a service (IaaS) cloud virtual machines using the existing algorithms, the deployment process cannot be simplified, and the algorithm is difficult to be applied. This leads to the problems of high energy consumption, high number of migrations, and high average service‐level agreement (SLA) violation rate. In order to solve the above problems, an adaptive deployment algorithm for IaaS cloud virtual machines based on Q learning mechanism is proposed in this research. Based on the deployment principle, the deployment characteristics of the IaaS cloud virtual machines are analyzed. The virtual machine scheduling problem is replaced with the Markov process. The multistep Q learning algorithm is used to schedule the virtual machines based on the Q learning mechanism to complete the adaptive deployment of the IaaS cloud virtual machines. Experimental results show that the proposed algorithm has low energy consumption, small number of migrations, and low average SLA violation rate.

Efficient task optimization algorithm for green computing in cloud

Cloud infrastructure assets are accessed by all hooked heterogeneous network servers and applications to maintain entail reliability towards global subscribers with high performance and low cost is a tedious challenging task. Most of the extant techniques are considered limited constraints like task deadline, which leads Service Level Agreement (SLA) violation. In this manuscript, we develop Hadoop based Task Scheduling (HTS) algorithm which considers a task deadline time, completion time, migration time and future resource availability of each virtual machine. The Intelligent System (IS) enabled with adaptive neural computation method to assess all above attributes. Specifically, the result of Prophecy Resource Availability (PRA) method has been used to assess the status of each Virtual Machine (VM), which helps to streamline the resource wastage and increases the response time with low SLA violation rate.

SDN-cloud: a power aware resource management system for efficient energy optimization

PurposeIn modern cloud services, resource provisioning and allocation are significant for assigning the available resources in efficient way. Resource management in cloud becomes challenging due to high energy consumption at data center (DC), virtual machine (VM) migration, high operational cost and overhead on DC.Design/methodology/approachIn this paper, the authors proposed software-defined networking (SDN)-enabled cloud for resource management to reduce energy consumption in DC. SDN-cloud comprises four phases: (1) user authentication, (2) service-level agreement (SLA) constraints, (3) cloud interceder and (4) SDN-controller.FindingsResource management is significant for reducing power consumption in CDs that is based on scheduling, VM placement, with Quality of Service (QoS) requirements.Research limitations/implicationsThe main goal is to utilize the resources energy effectively for reducing power consumption in cloud environment. This method effectively increases the user service rate and reduces the unnecessary migration process.Originality/valueAs a result, the authors show a significant reduction in energy consumption by 20 KWh as well as over 60% power consumption in the presence of 500 VMs. In future, the authors have planned to concentrate the issues on resource failure and also SLA violation rate with respect to number of resources will be decreased.

ThermoSim: Deep learning based framework for modeling and simulation of thermal-aware resource management for cloud computing environments

Current cloud computing frameworks host millions of physical servers that utilize cloud computing resources in the form of different virtual machines. Cloud Data Center (CDC) infrastructures require significant amounts of energy to deliver large scale computational services. Moreover, computing nodes generate large volumes of heat, requiring cooling units in turn to eliminate the effect of this heat. Thus, overall energy consumption of the CDC increases tremendously for servers as well as for cooling units. However, current workload allocation policies do not take into account effect on temperature and it is challenging to simulate the thermal behavior of CDCs. There is a need for a thermal-aware framework to simulate and model the behavior of nodes and measure the important performance parameters which can be affected by its temperature. In this paper, we propose a lightweight framework, ThermoSim, for modeling and simulation of thermal-aware resource management for cloud computing environments. This work presents a Recurrent Neural Network based deep learning temperature predictor for CDCs which is utilized by ThermoSim for lightweight resource management in constrained cloud environments. ThermoSim extends the CloudSim toolkit helping to analyze the performance of various key parameters such as energy consumption, service level agreement violation rate, number of virtual machine migrations and temperature during the management of cloud resources for execution of workloads. Further, different energy-aware and thermal-aware resource management techniques are tested using the proposed ThermoSim framework in order to validate it against the existing framework (Thas). The experimental results demonstrate the proposed framework is capable of modeling and simulating the thermal behavior of a CDC and ThermoSim framework is better than Thas in terms of energy consumption, cost, time, memory usage and prediction accuracy.

Formulating monitoring strategy based on multiple SLA parameters for multi-tenant service-based systems

The widely used service-based systems (SBSs) are composed of individual services and delivered to multiple tenants. When runtime anomalies occurred, the violation rate of service level agreement (SLA) can increase remarkably. Therefore, service monitoring is crucial for meeting the tenant's quality of service (QoS) requirements. However, monitoring each service constantly wastes resources. In this paper, we propose a monitoring strategy based on multiple SLA parameters for multi-tenant SBSs (MMTS). The criticality of a service is evaluated to the importance of each service in a SBS based on QoS and tenants, tenants are ranked by multiple SLA parameters. Afterwards, monitoring benefit, resource cost, and system overload are weighed to formulate a cost-effective monitoring strategy. Finally, MMTS is evaluated with comprehensive experiments under multiple QoS dimensions and the results have shown that MMTS can outperform other six algorithms in the SLA violation rate and the cost-effectiveness of service monitoring.

CSL-driven and energy-efficient resource scheduling in cloud data center

High energy consumption in data center has serious impacts on environment. Energy-efficient scheduling under Customer Satisfaction Level (CSL) constraint has become a key problem of cloud computing in data centers. In this paper, a CSL-driven and energy-efficient resource scheduling (CDEERS) framework has been designed to optimize energy efficiency and CSL in cloud data centers. To achieve different goals in different CSL states, three scheduling strategies (aiming to minimize energy saving, maximize CSL and maximize CSL per energy, respectively) are auto-adaptively applied to consolidate resources according to CSL states. To identify CSL states, a metric based on Service Level Agreement (SLA) violation rate (MSVR) is designed and applied in the proposed method. Additionally, the Weighted Moving Average (WMA) model is applied to predict the future state of CSL and optimize the resource allocation. Simulation result shows that the MSVR improves energy efficiency (CSL per energy) by 51.1% and 30.4% compared with traditional metric based on workload (MW) and metric based on response time (MRT), respectively, during the whole test period. It reduces the SLA violation rate by 7.9% and the number of host overloads by 9.6%, respectively, while prediction mechanism is applied. It shows that CDEERS has better energy efficiency than these static scheduling methods with single objection in the condition of dynamic state of CSL.

VM Selection and Allocation Policy to Optimize VM Migration in Cloud Environment

Cloud computing, a metered based technology provides the services using virtualized technology over the internet. In the cloud environment, to improve the performance (such as utilization of the resources, energy minimization) extreme number of virtual machines (VMs) can be installed on the servers as per their resource capacity. In this way, servers can be overloaded. Overloaded servers consume more energythan normal status servers. VM migration (VMM) is an efficient technique to become a server in a normal state. VMM technique is used to consolidate the resources to increase resource utilization (RU) and reduceenergy usage. In the VMM technique, selection of VM such as which VM is migrated from one server to another server and allocation of VM on servers is an important aspect. Appropriate VM selection declines the numeral of VMMs and increasesenergy efficiency. Appropriate VM allocation declines the server to become overloaded. In this paper, the VM selection and allocation strategy is presented. CloudSim toolkit is used to verify the strength of proposed VM selection and allocation algorithm. Proposed VM Selection algorithm (MaMT) performs better than existing MiMT algorithm in terms of total energy consumption, number of hosts shut down, number of VMM, and average Service Level Agreement (SLA) violation rate. MaMT algorithm with resource aware provisioning (RAP) and MiMT+RAP algorithm combines both VM selection and allocation policies. RAP algorithm used both energy and RU parameters while allocating VM to the server.MaMTreduces the energy consumption up to 7.25% and reduces the SLA violation rate up-to 2.6% in comparison to MiMT algorithm. When VM selection and allocation policies combines together than more system performance is improved. MaMT+RAPreduces the energy consumption up to6.76% and reduces the SLA violation rate up-to 0.22% in comparison to MaMT algorithm.MiMT+RAPreduces the energy consumption up to15.23% and reduces the SLA violation rate up-to 0.95% in comparison to MiMT algorithm.

Resource Provisioning Based Scheduling Framework for Execution of Heterogeneous and Clustered Workloads in Clouds: from Fundamental to Autonomic Offering

Provisioning of adequate resources to cloud workloads depends on the Quality of Service (QoS) requirements of these cloud workloads. Based on workload requirements (QoS) of cloud users, discovery and allocation of best workload-resource pair is an optimization problem. Acceptable QoS can be offered only if provisioning of resources is appropriately controlled. So, there is a need for a QoS-based resource provisioning framework for the autonomic scheduling of resources to observe the behavior of the services and adjust it dynamically in order to satisfy the QoS requirements. In this paper, framework for self-management of cloud resources for execution of clustered workloads named as SCOOTER is proposed that efficiently schedules the provisioned cloud resources and maintains the Service Level Agreement (SLA) by considering properties of self-management and the maximum possible QoS parameters are required to improve cloud based services. Finally, the performance of SCOOTER has been evaluated in a cloud environment that demonstrates the optimized QoS parameters such as execution cost, energy consumption, execution time, SLA violation rate, fault detection rate, intrusion detection rate, resource utilization, resource contention, throughput and waiting time.

Minimizing SLA violation and power consumption in Cloud data centers using adaptive energy-aware algorithms

In this paper, we address the problem of reducing Cloud datacenter high energy consumption with minimal Service Level Agreement (SLA) violation. Although there are many energy-aware resource management solutions for Cloud datacenters, existing approaches focus on minimizing energy consumption while ignoring the SLA violation at the time of virtual machine (VM) deployment. Also, they do not consider the types of application running in the VMs and thus may not really reduce energy consumption with minimal SLA violation under a variety of workloads. In this paper, we propose two novel adaptive energy-aware algorithms for maximizing energy efficiency and minimizing SLA violation rate in Cloud datacenters. Unlike the existing approaches, the proposed energy-aware algorithms take into account the application types as well as the CPU and memory resources during the deployment of VMs. To study the efficacy of the proposed approaches, we performed extensive experimental analysis using real-world workload, which comes from more than a thousand PlanetLab VMs. The experimental results show that, compared with the existing energy-saving techniques, the proposed approaches can effectively decrease the energy consumption in Cloud datacenters while maintaining low SLA violation.

Energy-saving resource scheduling algorithm based on workload characteristic clustering

When infrastructure as a service( Iaa S) providers offer high performance services for users,they must think about how to reduce the energy cost of the cloud platform without violating the service level agreement( SLA). A resource scheduling algorithm to ensure SLA was proposed based on clustering analysis of the load characteristic. Ultimately,the targets of reducing SLA violation rate and saving energy were realized.The resource scheduling algorithm was analyzed based on improved K-means clustering analysis and extraction of workload characteristic according to energy consumption. Physical resources were effectively allocated to ensure the requirement of energy saving of Iaa S platform. Based on the extension of the Cloud Sim simulation platform,the algorithm proposed was compared with the optimized best fit decreasing( BFD) to show lower SLA violation rate and energy consumption.