SLA Violations Research Articles

VM Consolidation Plan for Improving the Energy Efficiency of Cloud

Abstract Achieving energy-efficiency with minimal Service Level Agreement (SLA) violation constraint is a major challenge in cloud datacenters owing to financial and environmental concerns. The static consolidation of Virtual Machines (VMs) is not much significant in recent time and has become outdated because of the unpredicted workload of cloud users. In this paper, a dynamic consolidation plan is proposed to optimize the energy consumption of the cloud datacenter. The proposed plan encompasses algorithms for VM selection and VM placement. The VM selection algorithm estimates power consumption of each VM to select the required VMs for migration from the overloaded Physical Machine (PM). The proposed VM allocation algorithm estimates the net increase in Imbalance Utilization Value (IUV) and power consumption of a PM, in advance before allocating the VM. The analysis of simulation results suggests that the proposed dynamic consolidation plan outperforms other state of arts.

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.

Distance Aware VM Allocation Process to Minimize Energy Consumption in Cloud Computing

Background: Cloud computing is considered as an on-demand service resource with the applications towards data center on pay per user basis. For allocating the resources appropriately for the satisfaction of user needs, an effective and reliable resource allocation method is required. Because of the enhanced user demand, the allocation of resources has now considered as a complex and challenging task when a physical machine is overloaded, Virtual Machines share its load by utilizing the physical machine resources. Previous studies lack in energy consumption and time management while keeping the Virtual Machine at the different server in turned on state. Aim and Objective: The main aim of this research work is to propose an effective resource allocation scheme for allocating the Virtual Machine from an ad hoc sub server with Virtual Machines. Methods: The execution of the research has been carried out into two sections, initially, the location of Virtual Machines and Physical Machine with the server has been taken place and subsequently, the cross-validation of allocation is addressed. For the sorting of Virtual Machines, Modified Best Fit Decreasing algorithm is used and Multi-Machine Job Scheduling is used while the placement process of jobs to an appropriate host. Results and Conclusion: Artificial Neural Network as a classifier, has allocated jobs to the hosts. Measures, viz. Service Level Agreement violation and energy consumption are considered and fruitful results have been obtained with a 37.7 of reduction in energy consumption and 15% improvement in Service Level Agreement violation.

A Systematic Review of Energy Management Strategies for Resource Allocation in the Cloud: Clustering, Optimization and Machine Learning

Nowadays, many organizations and individual users are employing cloud services extensively due to their efficiency, reliability and low cost. A key aspect for cloud data centers is to achieve management methods to reduce energy consumption, increasing the profit and reducing the environmental impact, which is critical in the deployment of leading-edge technologies today such as blockchain and digital finances, IoT, online gaming and video streaming. In this review, various clustering, optimization, and machine learning methods used in cloud resource allocation to increase the energy efficiency and performance are analyzed, compared and classified. Specifically, on the one hand, we discuss how clustering methods and optimization techniques are widely applied in energy management due to their capacity to provide solutions for energy consumption reduction. On the other hand, we study how multi-objective optimization methods focus on reducing energy consumption as well as service level agreement (SLA) violation, and improving quality of services (QoS) simultaneously. Also, we discuss how optimization methods such as the firefly algorithm, whale optimization algorithm (WOA), particle swarm optimization (PSO) and genetic algorithm (GA) provide the highest performance in the field. Moreover, we analyze how machine learning methods such as deep neural network (DNN), random forest, and support vector machine (SVM) are applied to the prediction of energy consumption in the cloud, showing an accurate performance in this prediction. Nevertheless, we study how the existing methods still have limitations of low convergence, trap into local optima and overfitting.

Alts: An Adaptive Load Balanced Task Scheduling Approach for Cloud Computing

According to the research, many task scheduling approaches have been proposed like GA, ACO, etc., which have improved the performance of the cloud data centers concerning various scheduling parameters. The task scheduling problem is NP-hard, as the key reason is the number of solutions/combinations grows exponentially with the problem size, e.g., the number of tasks and the number of computing resources. Thus, it is always challenging to have complete optimal scheduling of the user tasks. In this research, we proposed an adaptive load-balanced task scheduling (ALTS) approach for cloud computing. The proposed task scheduling algorithm maps all incoming tasks to the available VMs in a load-balanced way to reduce the makespan, maximize resource utilization, and adaptively minimize the SLA violation. The performance of the proposed task scheduling algorithm is evaluated and compared with the state-of-the-art task scheduling ACO, GA, and GAACO approaches concerning average resource utilization (ARUR), Makespan, and SLA violation. The proposed approach has revealed significant improvements concerning the makespan, SLA violation, and resource utilization against the compared approaches.

A proactive fog service provisioning framework for Internet of Things applications: An autonomic approach

AbstractIn recent years, Internet of Things (IoT) services have expanded to promote the quality of life in different areas. Cloud connectivity services are so popular now that they have prompted the experts to enhance cloud computing for its utilization in IoT, making everything online in the next few decades. For reducing latency, immediate processing, and network congestion, fog computing has emerged in which cloud computing is expanded to the edge of the network. On the other hand, concerning the limitations in fog hardware resources compared with the cloud, and the dynamic and unpredictable fog environment, the provision of dynamic fog services is a challenge. Automatic matching of the resources based on the workload oscillations of IoT applications leads to allocating minimum fog resources to IoT devices, therefore, the satisfaction of service level agreement (SLA) and quality of service (QoS) parameters.The present article introduces a method based on the control monitoring‐analysis‐planning‐execution having shared knowledge‐base loop and presents an approach for dynamic resource provisioning based on autonomic computing and reinforcement learning techniques. The proposed scheme uses learning automata as a decision‐maker in the planning phase and time series prediction model in the analysis phase. The simulation test results indicated a reduced delay in service provisioning, total cost, and SLA violation compared with other approaches, highlighting the potential of fog computing in ensuring the QoS.

Multi-Tenant RDBMS Migration in the Cloud Environment

In a multi-tenant business environment, tenants share the same applications and databases to store their data. Due to the widespread use of a multi-tenant environment, the service providers face difficult challenges daily. These challenges are condensed in how to guarantee the quality of service provided to tenants, which are documented in a formal document known as a Service Level Agreement (SLA). In addition, SLA should consider the irregular patterns of workload of tenants which may affect the level of guarantee. In this research, an Enhanced Multi-Tenant Database Management System (EMT DBMS) is proposed. In addition, an Enhanced Multi-tenant Migration Algorithm called EMT-M is presented, which aims to migrate the violated tenants depending on both the number of SLA violations and the variance rate. Experimental results prove that the proposed EMT-M algorithm is ideal for migrating violated tenants, as it reduces the number of SLA violations compared to previous migration algorithms.

Load balancing in virtualized environments using virtual machine migration: A comprehensive survey

Virtualization is a core and requisite technology in Cloud Computing that provisions scalable virtual resources for execution of varied applications. It enables the cloud datacenter resources to be multiplexed within numerous virtual computing environments recognized as virtual machines. These virtual machines consolidates varied applications with diversified resource requirements. It prompts to increase in load imbalance level leading to reduced performance and SLA violations. In order to achieve load balancing across virtual machines varied approaches are presented in literature and virtual machine migration based load balancing is a popular move in this direction. In this work, recent literature on different migration based load balancing schemes are reviewed. The objective of the work is highlight the features, advantages and shortcomings of the considered literature. Alongside that, the effort is conferred to provide an analytical view over different perspectives which will motivate the research in this area.

Energy-efficient VM scheduling based on deep reinforcement learning

Achieving data center resource optimization and QoS guarantee driven by high energy efficiency has become a research hotspot. However, QoS information directly sampled from the cloud environment will inevitably be affected by a small amount of structured noise. This paper proposes a deep reinforcement learning model based on QoS feature learning to optimize data center resource scheduling. In the deep learning stage, we propose a QoS feature learning method based on improved stacked denoising autoencoders to extract more robust QoS characteristic information. In the reinforcement learning stage, we propose a multi-power machines (PMs) collaborative resource scheduling algorithm based on reinforcement learning. Extensive experiments show that compared with other excellent resource scheduling strategies, our method can effectively reduce the energy consumption of cloud data centers while maintaining the lowest service level agreement (SLA) violation rate. A good balance is achieved between energy-saving and QoS optimization.

Multiphase fault tolerance genetic algorithm for vm and task scheduling in datacenter

Cloud datacenter (Dc) have become popular in recent years with the rising popularity and high performance of cloud computing. The multi-step of data computation and diverse task dependencies fail in the task, energy consumption, overloading of Virtual Machines (VMs), and violation of the agreement. To overcome these challenges, we propose a genetic algorithm (GA) based multiphase fault tolerance (MFTGA) approach for intelligently schedule the tasks over the VMs for multiuser. This MFTGA approach efficiently maps optimal VMs with users according to the service level agreement (SLA). The presented approach comprises four phases namely individual phase, local phase, global phase, and fault tolerance phase. In the individual phase of the MFTGA algorithm, we calculate the local fitness (fl) of each user. Then calculate the global fitness (fg) of multiuser according to the SLA in the global fitness phase. After mapping the optimal VMs with the multiuser, we check the status of task execution in the fault tolerance phase. MFTGA method is used to improve the reliability, latency, and reduce the failure of the task in the cloud computing environment. The proposed MFTGA scheme is compared against the GA and Adoptive Incremental Genetic Algorithm (AIGA). The simulation results validate that the proposed method exhibits better performance than GA and AIGA in terms of execution time, memory utilization, cost, SLA violation, and energy consumption.

Adaptive Energy-Aware Algorithms to Minimize Power Consumption and SLA Violation in Cloud Computing

Objective: With the establishment of virtualized datacenters on a large scale, cuttingedge technology requires more energy to deliver the services 24*7 hours. With this expansion and accumulation of information on a massive scale on data centers, the consumption of an excessive amount of power results in high operational costs. Therefore, there is an urgent need to make the environment more adaptive and dynamic, where the overutilization and underutilization of hosts are well known to the system and active measures can be taken accordingly. To serve this purpose, an energy-efficient method, for the detection of overloaded and under-loaded hosts, has been proposed in this paper. For implementing VM migration, VM placement decision has also been taken to save energy and reduce SLA (Service Level Agreement) rate over the cloud. Methods: In the paper, a novel adaptive heuristics approach has been presented that concerns with the utilization of resources for dynamic consolidation of VMs based on the mustered data from the usage of resources by VMs, while ensuring the high level of relevancy to the SLA. After identification of under-load and overload hosts, VM placement decision has been taken in the way that takes minimum energy consumption. A minimum migration policy has been adopted in the proposed methodology to minimize execution time. The validation of the effectiveness and efficiency of the suggested approach has been performed by using real-world workload traces in the CloudSim simulator. Results: The results shows that the proposed methodology is ideal for SLA, but costs more for VM migration. Conclusion: A deep analysis must be done in existing energy efficient approaches and a new platform should be suggested to save energy in real life.

SLA amongst Users and Providers in Multi-Cloud Environment Through Negotiation Model

<p class="0abstract">Cloud Computing conducts application, infrastructure services or platform to a very large amount of users with more choices and continuous changing requirements. Cloud providers are occupied in organizing data warehouses to arrange the continuous growth in cloud user’s acceptance. Features of cloud computing services have afforded an important tendency of companies choosing these services. In this case, many cloud users, who intend a certain service, and many cloud providers, who provision those services, create a competitive market. When constructing a Cloud scheduling scheme, the project trade-offs of the Cloud architecture should be rated. The simplest manner to rate this infrastructure is to use a simulation tool called CloudSim simulation toolkit. To reduce/avoid SLA violations in the cloud computing system, in our work we propose a SLA reduction framework in which we have considered three steps: Migration control of VMs, Energy efficiency and VmScheduling. It is also implemented in Cloudsimulator.</p>

Conceptual Service Level Agreement Mechanism to Minimize the SLA Violation with SLA Negotiation Process in Cloud Computing Environment

Online service is used to be as Pay-Per-Use in Cloud computing. Service user need not be in a long time contract with cloud service providers. Service level agreements (SLAs) are understandings marked between a cloud service providers and others, for example, a service user, intermediary operator, or observing operators. Since cloud computing is an ongoing technology giving numerous services to basic business applications and adaptable systems to manage online agreements are significant. SLA maintains the quality-of-service to the cloud user. If service provider fails to maintain the required service SLA is considered to be SLA violated. The main aim is to minimize the SLA violations for maintain the QoS of their cloud users. In this research article, a toolbox is proposed to help the procedure of exchanging of a SLA with the service providers that will enable the cloud client in indicating service quality demands and an algorithm as well as Negotiation model is also proposed to negotiate the request with the service providers to produce a better agreement between service provider and cloud service consumer. Subsequently, the discussed framework can reduce SLA violations as well as negotiation disappointments and have expanded cost-adequacy. Moreover, the suggested SLA toolkit is additionally productive to clients so clients can secure a sensible value repayment for diminished QoS or conceding time. This research shows the assurance level in the cloud service providers can be kept up by as yet conveying the services with no interruption from the client's perspective

A new weighted fuzzy C-means clustering for workload monitoring in cloud datacenter platforms.

The rapid growth in virtualization solutions has driven the widespread adoption of cloud computing paradigms among various industries and applications. This has led to a growing need for XaaS solutions and equipment to enable teleworking. To meet this need, cloud operators and datacenters have to overtake several challenges related to continuity, the quality of services provided, data security, and anomaly detection issues. Mainly, anomaly detection methods play a critical role in detecting virtual machines’ abnormal behaviours that can potentially violate service level agreements established with users. Unsupervised machine learning techniques are among the most commonly used technologies for implementing anomaly detection systems. This paper introduces a novel clustering approach for analyzing virtual machine behaviour while running workloads in a system based on resource usage details (such as CPU utilization and downtime events). The proposed algorithm is inspired by the intuitive mechanism of flocking birds in nature to form reasonable clusters. Each starling movement’s direction depends on self-information and information provided by other close starlings during the flight. Analogically, after associating a weight with each data sample to guide the formation of meaningful groups, each data element determines its next position in the feature space based on its current position and surroundings. Based on a realistic dataset and clustering validity indices, the experimental evaluation shows that the new weighted fuzzy c-means algorithm provides interesting results and outperforms the corresponding standard algorithm (weighted fuzzy c-means).

A novel service level agreement model using blockchain and smart contract for cloud manufacturing in industry 4.0

ABSTRACT The number of service offerings in cloud manufacturing continues to grow, so that service level agreement (SLA) within cloud manufacturing is increasingly used to ensure the coordination of the cross-organisational business processes between the cloud service providers (CSP) and cloud service consumers (CSC). The definition of SLA and monitoring SLA’s performance are significant and should be highly valued to ensure the effectiveness and efficiency of business services. At present, the third-party monitoring methods based on service credit are mainly applied in cloud manufacturing environments. There is no effective supervision mechanism to monitor the third party and no efficient punishment mechanism on SLA violation. To address aforementioned issues, this paper proposes a novel Service Level Agreement model by integrating Blockchain and Smart Contract (SLABSC), in which trust issues among CSP, CSC, and the third-party monitoring can be addressed through Blockchain technologies. The method proposed in this paper realises the full functionality of SLABSC and facilitates data security, and it can be used to supervise CSP to provide better services. Experiments results show that the performance of the proposed model is valid and efficient.

AFED-EF: An Energy-Efficient VM Allocation Algorithm for IoT Applications in a Cloud Data Center

Cloud Data Centers (CDCs) have become a vital computing infrastructure for enterprises. However, CDCs consume substantial energy due to the increased demand for computing power, especially for the Internet of Things (IoT) applications. Although a great deal of research in green resource allocation algorithms have been proposed to reduce the energy consumption of the CDCs, existing approaches mostly focus on minimizing the number of active Physical Machines (PMs) and rarely address the issue of load fluctuation and energy efficiency of the Virtual Machine (VM) provisions jointly. Moreover, existing approaches lack mechanisms to consider and redirect the incoming traffics to appropriate resources to optimize the Quality of Services (QoSs) provided by the CDCs. We propose a novel adaptive energy-aware VM allocation and deployment mechanism called AFED-EF for IoT applications to handle these problems. The proposed algorithm can efficiently handle the fluctuation of load and has good performance during the VM allocation and placement. We carried out extensive experimental analysis using a real-world workload based on more than a thousand PlanetLab VMs. The experimental results illustrate that AFED-EF outperforms other energy-aware algorithms in energy consumption, Service Level Agreements (SLA) violation, and energy efficiency.

Intelligent SLA-Aware VM Allocation and Energy Minimization Approach with EPO Algorithm for Cloud Computing Environment

Cloud computing is the most prominent established framework; it offers access to resources and services based on large-scale distributed processing. An intensive management system is required for the cloud environment, and it should gather information about all phases of task processing and ensuring fair resource provisioning through the levels of Quality of Service (QoS). Virtual machine allocation is a major issue in the cloud environment that contributes to energy consumption and asset utilization in distributed cloud computing. Subsequently, in this paper, a multiobjective Emperor Penguin Optimization (EPO) algorithm is proposed to allocate the virtual machines with power utilization in a heterogeneous cloud environment. The proposed method is analyzed to make it suitable for virtual machines in the data center through Binary Gravity Search Algorithm (BGSA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO). To compare with other strategies, EPO is energy-efficient and there are significant differences. The results of the proposed system have been evaluated through the JAVA simulation platform. The exploratory outcome presents that the proposed EPO-based system is very effective in limiting energy consumption, SLA violation (SLAV), and enlarging QoS requirements for giving capable cloud service.

DAWM: Cost-Aware Asset Claim Analysis Approach on Big Data Analytic Computation Model for Cloud Data Centre

The heterogeneous resource-required application tasks increase the cloud service provider (CSP) energy cost and revenue by providing demand resources. Enhancing CSP profit and preserving energy cost is a challenging task. Most of the existing approaches consider task deadline violation rate rather than performance cost and server size ratio during profit estimation, which impacts CSP revenue and causes high service cost. To address this issue, we develop two algorithms for profit maximization and adequate service reliability. First, a belief propagation-influenced cost-aware asset scheduling approach is derived based on the data analytic weight measurement (DAWM) model for effective performance and server size optimization. Second, the multiobjective heuristic user service demand (MHUSD) approach is formulated based on the CPS profit estimation model and the user service demand (USD) model with dynamic acyclic graph (DAG) phenomena for adequate service reliability. The DAWM model classifies prominent servers to preserve the server resource usage and cost during an effective resource slicing process by considering each machine execution factor (remaining energy, energy and service cost, workload execution rate, service deadline violation rate, cloud server configuration (CSC), service requirement rate, and service level agreement violation (SLAV) penalty rate). The MHUSD algorithm measures the user demand service rate and cost based on the USD and CSP profit estimation models by considering service demand weight, tenant cost, and energy cost. The simulation results show that the proposed system has accomplished the average revenue gain of 35%, cost of 51%, and profit of 39% than the state-of-the-art approaches.

A new SLA-aware method for discovering the cloud services using an improved nature-inspired optimization algorithm.

Cloud computing is one of the most important computing patterns that use a pay-as-you-go manner to process data and execute applications. Therefore, numerous enterprises are migrating their applications to cloud environments. Not only do intensive applications deal with enormous quantities of data, but they also demonstrate compute-intensive properties very frequently. The dynamicity, coupled with the ambiguity between marketed resources and resource requirement queries from users, remains important issues that hamper efficient discovery in a cloud environment. Cloud service discovery becomes a complex problem because of the increase in network size and complexity. Complexity and network size keep increasing dynamically, making it a complex NP-hard problem that requires effective service discovery approaches. One of the most famous cloud service discovery methods is the Ant Colony Optimization (ACO) algorithm; however, it suffers from a load balancing problem among the discovered nodes. If the workload balance is inefficient, it limits the use of resources. This paper solved this problem by applying an Inverted Ant Colony Optimization (IACO) algorithm for load-aware service discovery in cloud computing. The IACO considers the pheromones’ repulsion instead of attraction. We design a model for service discovery in the cloud environment to overcome the traditional shortcomings. Numerical results demonstrate that the proposed mechanism can obtain an efficient service discovery method. The algorithm is simulated using a CloudSim simulator, and the result shows better performance. Reducing energy consumption, mitigate response time, and better Service Level Agreement (SLA) violation in the cloud environments are the advantages of the proposed method.

An improved multi-objective genetic algorithm with heuristic initialization for service placement and load distribution in edge computing

Edge Computing (EC) is a promising concept to overcome some obstacles of traditional cloud data centers to support Internet of Things (IoT) applications, especially time-sensitive applications. However, EC faces some challenges, including the resource allocation for heterogeneous applications at a network edge composed of distributed and resource-restricted nodes. A relevant issue that needs to be addressed by a resource manager is the service placement problem, which is the decision-making process of determining where to place different services (or applications). A related issue of service placement is how to distribute workloads of an application placed on multiple locations. Hence, we jointly investigate the load distribution and placement of IoT applications to minimize Service Level Agreement (SLA) violations due to the limitations of EC resources and other conflicting objectives. In order to handle the computational complexity of the formulated problem, we propose a multi-objective genetic algorithm with the initial population based on random and heuristic solutions to obtain near-optimal solutions. Evaluation results show that our proposal outperforms other benchmark algorithms in terms of response deadline violation, as well as terms of other conflicting objectives, such as operational cost and service availability.