Auto-scaling Approach Research Articles

Impact of Autoscaling on Application Performance in Cloud Environments

The current paper studies the impact of Autoscaling on application performance in Cloud computing environments. Cloud computing is one of the most encouraging innovations due to its vast applications. Predictive autoscaling is an advanced technique that aims to address the challenges in the autoscaling trends for large-scale systems. To account for the Quality of Service to the customer, features like load balancing according to the workload demand, understanding resource allocation and utilization, and dynamic decision-making are vital to any cloud computing application. This paper interprets these challenges and reviews a meta-reinforcement learning approach for predictive autoscaling in cloud environments. A novel RL-based predictive autoscaling approach on a popular large-scale digital payment platform system, Alipay, is compared with the existing models such as Autopilot and FIRM. The aim is to conduct a detailed analysis of performance metrics before and after autoscaling actions, aiming to identify optimal scaling strategies that minimize response time and maximize resource utilization without over- provisioning.

Learning-driven hybrid scaling for multi-type services in cloud

In order to deal with the fast changing requirements of container based services in clouds, auto-scaling is used as an essential mechanism for adapting the number of provisioned resources with the variable service workloads. However, the latest auto-scaling approaches lack the comprehensive consideration of variable workloads and hybrid auto-scaling for multi-type services. Firstly, the historical data based proactive approaches are widely used to handle complex and variable workloads in advance. The decision-making accuracy of proactive approaches depends on the prediction algorithm, which is affected by the anomalies, missing values and errors in the historical workload data, and the unexpected workload cannot be handled. Secondly, the trigger based reactive approaches are seriously affected by workload fluctuation which causes the frequent invalid scaling of service resources. Besides, due to the existence of scaling time, there are different completion delays of different scaling actions. Thirdly, the latest approaches also ignore the different scaling time of hybrid scaling for multi-type services including stateful services and stateless services. Especially, when the stateful services are scaled horizontally, the neglected long scaling time causes the untimely supply and withdrawal of resources. Consequently, all three issues above can lead to the degradation of Quality of Services (QoS) and the inefficient utilization of resources. This paper proposes a new hybrid auto-scaling approach for multi-type services to resolve the impact of service scaling time on decision making. We combine the proactive scaling strategy with the reactive anomaly detection and correction mechanism. For making a proactive decision, the ensemble learning model with the structure improved deep network is designed to predict the future workload. On the basis of the predicted results and the scaling time of different types of services, the auto-scaling decisions are made by a Deep Reinforcement Learning (DRL) model with heterogeneous action space, which integrates horizontal and vertical scaling actions. Meanwhile, with the anomaly detection and correction mechanism, the workload fluctuation and unexpected workload can be detected and handled. We evaluate our approach against three different proactive and reactive auto-scaling approaches in the cloud environment, and the experimental results show the proposed approach can achieve the better scaling behavior compared to state-of-the-art approaches.

A Q-learning based auto-scaling approach for provisioning big data analysis services in cloud environments

Currently, analyzing big data to capture hidden values in various fields is one of the most popular research directions. Analytics-as-a-Service (AaaS) providers typically construct a common platform by renting virtual machine (VM) resources from Infrastructure-as-a-Service (IaaS) providers instead of owning their own physical resources, to provision big data analysis services to end users. However, due to the fact that service demands from AaaS users tend to fluctuate over time in reality, how to dynamically adjust the type and quantity of VMs rented from IaaS providers has become an urgent problem to be solved. In this article, we assume that IaaS providers can offer VM resources in two charging modes, and service demand workloads arrive stochastically. We formulate the problem as a Markov decision process and propose a Q-Learning based auto-scaling method, which explores the trade-off between AaaS providers’ cost and VM resource utilization. Eventually, we evaluate our method on both real traces and simulated traces and compare it with some existing methods. Experimental results demonstrate that our method can not only achieve the goal of reducing cost, but also ensure the high utilization of VM resources in the long run.

An Auto-Scaling Approach for Microservices in Cloud Computing Environments

An Auto-Scaling Approach for Microservices in Cloud Computing Environments

Graph neural networks for representing multivariate resource usage: A multiplayer mobile gaming case-study

The emergence of Multiplayer Mobile Gaming (MMG) applications is intertwined with a plethora of Quality of Service and Quality of Experience requirements. Resource usage prediction can provide valuable insights into the corresponding orchestration and management process in the form of several proactive functionalities in resource scaling, service migration, task offloading and scheduling. These processes are crucial in the Cloud and Edge environments exploited by MMG applications. Thus, producing accurate resource usage predictions concerning these types of applications is of paramount importance. To that end, we propose a resource usage representation paradigm based on Graph Neural Networks (GNNs). The novelty of this approach is based on the process of leveraging the dependencies that exist among the various types of computational resources. Furthermore, we expand upon this representation approach to develop a GNN-based Encoder-Decoder model that caters to the complexities of resource usage and can provide multi-step resource usage predictions. This model is compared against numerous well-established Encoder-Decoder and Deep Learning prediction models to assess its efficiency. Finally, the proposed model is incorporated in a proactive Horizontal Autoscaling solution that manages to outperform a standard reactive Horizontal Autoscaling approach in the context of a large-scale simulation, in terms of various performance metrics, while keeping the volume of the required computational resources to a minimum. The findings of this work showcase the importance of developing novel approaches in order to represent resource usage and the numerous benefits in the context of application performance and resource consumption that may derive from such scientific endeavors.

Scalable Edge Computing Environment Based on the Containerized Microservices and Minikube

The growing number of connected IoT devices and their continuous data collection will generate huge amounts of data in the near future. Edge computing has emerged as a new paradigm in recent years for reducing network congestion and offering real-time IoT applications. Processing the large amount of data generated by such IoT devices requires the development of a scalable edge computing environment. Accordingly, applications deployed in an edge computing environment need to be scalable enough to handle the enormous amount of data generated by IoT devices. The performance of MSA and monolithic architecture is analyzed and compared to develop a scalable edge computing environment. An auto-scaling approach is described to handle multiple concurrent requests at runtime. Minikube is used to perform auto-scaling operation of containerized microservices on resource constraint edge node. Considering performance of both the architecture and according to the results and discussions, MSA is a better choice for building scalable edge computing environment.

Cost-efficient auto-scaling of container-based elastic processes

In business process landscapes, a common challenge is to provide the necessary computational resources to enact the single process steps. One well-known approach to solve this issue in a cost-efficient way is to use the notion of elasticity, i.e., to provide cloud-based computational resources in a rapid fashion and to enact the single process steps on these resources. Existing approaches to provide elastic processes are mostly based on Virtual Machines (VMs). Utilizing container technologies could enable a more fine-grained allocation of process steps to computational resources, leading to a better resource utilization and improved cost efficiency.In this paper, we propose an approach to optimize resource allocation for elastic processes by applying a four-fold auto-scaling approach. The main goal is to minimize the cost of process enactments by using containers. To this end, we formulate and implement a multi-objective optimization problem applying Mixed-Integer Linear Programming and use a transformation step to allocate software services to containers. We thoroughly evaluate the optimization problem and show that it can lead to significant cost savings while maintaining Service Level Agreements, compared to approaches that only use VMs.

Intelligent Horizontal Autoscaling in Edge Computing using a Double Tower Neural Network

Edge computing is characterized by varying workload intensities that have a strong effect on applications’ performance and requirements in terms of resources. Thus, in order to maintain a sustainable performance a resource autoscaling mechanism that will automatically add or remove computational nodes is needed. This autoscaling mechanism must ensure that the user’s requirements in terms of Quality of Service (QoS) are satisfied, while respecting the Service Level Agreements (SLAs) and delivering always-on services using affordable on-demand computing solutions. The autoscaling could take place proactively or reactively and the adjustability of the resources could be static or dynamic. To this end, the research goal of this paper is to design a novel autoscaling mechanism that minimizes the task execution times and maximizes the corresponding throughput while using the minimum number of resources. In this direction, we propose an Intelligent Horizontal Proactive Autoscaling (IHPA) mechanism that leverages resource usage metrics of processing edge nodes such as CPU, RAM and Bandwidth in order to provide timely scale up and scale down decisions. The IHPA is based on a double tower Deep Learning (DL) architecture. In order to find a close to optimal DL architecture and guarantee the generality of our approach we also propose the innovative hybrid Bayesian Evolution Strategy method. We conduct an extensive simulation of the IHPA with three baseline task offloading mechanisms keeping the same statistical task generation distribution. We also compare the performance of our proposed IHPA against a reactive autoscaling approach and the Kubernetes Horizontal autoscaling mechanism. The results show that our framework shows significant improvements in terms of latency and throughput, while making an optimal use of the available resources.

Hybrid Auto-Scaled Service-Cloud-Based Predictive Workload Modeling and Analysis for Smart Campus System

The Internet of Things is an emerging technology used in cloud computing and provides many services of the cloud. The cloud services users mostly suffer from service delays and disruptions due to service cloud resource management based on vertical and horizontal scalable systems. Adding more resources to a single cloud server is called vertical scaling, and an increasing number of servers is known as horizontal scaling. The service-bursts significantly impact the vertical scaled environment where the scale-up degrades the service quality and users' trust after reaching the server's maximum capacity. Besides, the horizontally scaled environment, though being resilient, is cost-inefficient. It is also hard to detect and manage bursts online to sustain application efficiency for complex workloads. Burst detection in real-time workloads is a complicated issue because even in the presence of auto-scaling methods, it can dramatically degrade the application's efficiency. This research study presents a new bursts-aware auto-scaling approach that detects bursts in dynamic workloads using resource estimation, decision-making scaling, and workload forecasting while reducing response time. This study proposes a hybrid auto-scaled service cloud model that ensures the best approximation of vertical and horizontal scalable systems to ensure Quality of Service (QoS) for smart campus-based applications. This study carries out the workload prediction and auto-scaling employing an ensemble algorithm. The model pre-scales the scalable vertical system by leveraging the service-load predictive modeling using an ensemble classification of defined workload estimation. The prediction of the upcoming workload helped scale-up the system, and auto-scaling dynamically scaled the assigned resources to many users' service requests. The proposed model efficiently managed service-bursts by addressing load balancing challenges through horizontal auto-scaling to ensure application consistency and service availability. The study simulated the smart campus environment model to monitor the time-stamped diverse service-requests appearing with different workloads.

HSOP: A Hybrid Service Orchestration Platform for Internet-Telephony Networks

Nowadays Telecom service providers are seeking new paradigms of service creation and execution platform to reduce new services’ time to market and increase profitability. However, the existing static services orchestration approaches cannot meet the dynamic complicated business demands. This paper proposes a hybrid service orchestration platform for Internet-Telephony networks. Firstly, designs a hybrid service orchestration language for developers to achieve dynamic and rapid orchestration of new hybrid services over the Internet-Telephony networks. Secondly, proposes an event-driven and component-based hybrid service orchestration container to meet the asynchronous dynamic interactions between hybrid services. Thirdly, proposes a cost-aware auto-scaling approach, including the pre-scaling and real-time scaling stages, to dynamically scale the required resources at different levels. Finally, illustrates the hybrid voice chatting services orchestration scenario, and also the effectiveness and practicability of the proposed platform are validated through extensive experiments.

Optimizing the performance of optimization in the cloud environment–An intelligent auto-scaling approach

Abstract The cloud computing paradigm has gained wide acceptance in the scientific community, taking a significant share from fields previously reserved exclusively for High Performance Computing (HPC). On-demand access to a large amount of computing resources provided by Cloud makes it ideal for executing large-scale optimizations using evolutionary algorithms without the need for owning any computing infrastructure. In this regard, we extended WoBinGO, an existing parallel software framework for genetic algorithm based optimization, to be used in Cloud. With these extensions, the framework is capable of elastically and frugally utilizing the underlying cloud computing infrastructure for performing computationally expensive fitness evaluations. We studied two issues that are pertinent when dealing with large-scale optimization in the elastic cloud environment: the computing instance launching overhead and the price of engaging Cloud for solving optimization problems, in terms of the instances’ cumulative uptime. To explain the usability limits of WoBinGO framework running in the IaaS environment, a comprehensive analysis of the framework’s performance was given. Optimization of both total optimization time and total cumulative uptime, leads to minimizing the cost of cloud resources utilization. In this way, we are proposing an intelligent decision support engine based on artificial neural networks and metaheuristics to provide the user with an assessment of the framework’s behavior on the underlying infrastructure in terms of optimization duration and the cost of resource consumption. According to a given assessment, the user can decide upon faster delivery of results or lower infrastructure costs. The proposed software framework has been used to solve a complex real-world optimization problem of a subsurface rock mass model calibration. The results obtained from the private OpenStack deployment show that by using the proposed decision support engine, significant savings can be achieved in both optimization time and optimization cost.

IBrownout: An Integrated Approach for Managing Energy and Brownout in Container-Based Clouds

Energy consumption of Cloud data centers has been a major concern of many researchers, and one of the reasons for huge energy consumption of Clouds lies in the inefficient utilization of computing resources. Besides energy consumption, another challenge of data centers is the unexpected loads, which leads to the overloads and performance degradation. Compared with VM consolidation and Dynamic Voltage Frequency Scaling that cannot function well when the whole data center is overloaded, brownout has shown to be a promising technique to handle both overloads and energy consumption through dynamically deactivating application optional components, which are also identified as containers/microservices. In this work, we propose an integrated approach to manage energy consumption and brownout in container-based cloud data centers. \color{black} We also evaluate our proposed scheduling policies with real traces in a prototype system. The results show that our approach reduces about 40%, 20% and 10% energy than the approach without power-saving techniques, brownout-overbooking approach and auto-scaling approach respectively while ensuring Quality of Service.

Auto-Scaling Approach for Cloud based Mobile Learning Applications

In the last decade, mobile learning applications have attracted a significant amount of attention. Huge investments have been made to develop educational applications that can be implemented on mobile devices. However, mobile learning applications have some limitations, such as storage space and battery life. Cloud computing provides a new idea to solve some limitations of mobile learning applications. However, there are other limitations, like scalability, that must be solved before mobile cloud learning can become completely operational. There are two main problems with scalability. The first occurs when the application server’s performance declines due to an increase in the number of requests, which affects usability. The second is that a decrease in the number of requests makes most application servers idle and therefore wastes money. These two problems can be avoided or minimized by provisioning auto-scaling techniques that permit the acquisition and release of resources dynamically to accommodate demand. In this paper, we propose an intelligent neuro-fuzzy reinforcement learning approach to solve the scalability problem in mobile cloud learning applications, and evaluate the proposed approach against some of the existing approaches via MATLAB. The large state space and long training time required to find the optimal policy are the main problems of reinforcement learning. We use fuzzy Q-learning to solve the large state space problem by grouping similar variables in the same state; there is then no need to use large look-up tables. The use of parallel learning agents reduces the training time needed to determine optimal policies. The experimental results prove that the proposed approach is able to increase learning speed and reduce the training time needed to determine optimal policies.

WITHDRAWN: A fuzzy auto-scaling approach using workload prediction for MMOG application in a cloud environment

WITHDRAWN: A fuzzy auto-scaling approach using workload prediction for MMOG application in a cloud environment

Automatic Scaling Hadoop in the Cloud for Efficient Process of Big Geospatial Data

Efficient processing of big geospatial data is crucial for tackling global and regional challenges such as climate change and natural disasters, but it is challenging not only due to the massive data volume but also due to the intrinsic complexity and high dimensions of the geospatial datasets. While traditional computing infrastructure does not scale well with the rapidly increasing data volume, Hadoop has attracted increasing attention in geoscience communities for handling big geospatial data. Recently, many studies were carried out to investigate adopting Hadoop for processing big geospatial data, but how to adjust the computing resources to efficiently handle the dynamic geoprocessing workload was barely explored. To bridge this gap, we propose a novel framework to automatically scale the Hadoop cluster in the cloud environment to allocate the right amount of computing resources based on the dynamic geoprocessing workload. The framework and auto-scaling algorithms are introduced, and a prototype system was developed to demonstrate the feasibility and efficiency of the proposed scaling mechanism using Digital Elevation Model (DEM) interpolation as an example. Experimental results show that this auto-scaling framework could (1) significantly reduce the computing resource utilization (by 80% in our example) while delivering similar performance as a full-powered cluster; and (2) effectively handle the spike processing workload by automatically increasing the computing resources to ensure the processing is finished within an acceptable time. Such an auto-scaling approach provides a valuable reference to optimize the performance of geospatial applications to address data- and computational-intensity challenges in GIScience in a more cost-efficient manner.

NASLA: Novel Auto Scaling Approach based on Learning Automata for Web Application in Cloud Computing Environment

Considering the growing interest in using cloud services, the accessibility and the effective management of the required resources, irrespective of the time and place, seems to be of great importance both to the service providers and users. One of the best ways for increasing utilization and improving the performance of the cloud systems is the auto-scaling of the applications; this is because of the fact that, due to the scalability of cloud computing, on the one hand, cloud providers believe that sufficient resources have to be prepared for the users, and on the other, the users also have a tendency towards the “pay as you go” system of payment for the resources. This paper seeks to offer an approach, based on the learning automata, for the scalability of the web applications, which combines virtual machine clusters and the learning automata in order to provide the best possible way for the scaling up and scaling down of the virtual machines. The results of this study indicate that the proposed approach has decreased the number of SLA violations (in percentage), while it has a smaller load of scalability compared to the other approaches in this regard.

A cost-aware auto-scaling approach using the workload prediction in service clouds

Service clouds are distributed infrastructures which deploys communication services in clouds. The scalability is an important characteristic of service clouds. With the scalability, the service cl...