Autoscaling Method for Docker Swarm Towards Bursty Workload

The autoscaling mechanism of cloud computing can automatically adjust computing resources according to user needs, improve quality of service (QoS) and avoid over-provision. However, the traditional autoscaling methods suffer from oscillation and degradation of QoS when dealing with burstiness. Therefore, the autoscaling algorithm should consider the effect of bursty workloads. In this paper, we propose a novel AmRP (an autoscaling method that combines reactive and proactive mechanisms) that uses proactive scaling to launch some containers in advance, and then the reactive module performs vertical scaling based on existing containers to increase resources rapidly. Our method also integrates burst detection to alleviate the oscillation of the scaling algorithm and improve the QoS. Finally, we evaluated our approach with state-of-the-art baseline scaling methods under different workloads in a Docker Swarm cluster. Compared with the baseline methods, the experimental results show that AmRP has fewer SLA violations when dealing with bursty workloads, and its resource cost is also lower

Self-managing cloud-native applications : design, implementation and experience

Running applications in the cloud efficiently requires much more than deploying software in virtual machines. Cloud applications have to be continuously managed: (1) to adjust their resources to the incoming load and (2) to face transient failures replicating and restarting components to provide resiliency on unreliable infrastructure. Continuous management monitors application and infrastructural metrics to provide automated and responsive reactions to failures (health management) and changing environmental conditions (auto-scaling) minimizing human intervention. In the current practice, management functionalities are provided as infrastructural or third party services. In both cases they are external to the application deployment. We claim that this approach has intrinsic limits, namely that separating management functionalities from the application prevents them from naturally scaling with the application and requires additional management code and human intervention. Moreover, using infrastructure provider services for management functionalities results in vendor lock-in effectively preventing cloud applications to adapt and run on the most effective cloud for the job. In this paper we discuss the main characteristics of cloud native applications, propose a novel architecture that enables scalable and resilient self-managing applications in the cloud, and relate on our experience in porting a legacy application to the cloud applying cloud-native principles

AUTO-SCALING AND ADJUSTMENT PLATFORM FOR CLOUD-BASED SYSTEMS

For customers of cloud-computing platforms it is important to minimize the infrastructure footprint and associated costs while providing required levels of Quality of Service (QoS) and Quality of Experience (QoE) dictated by the Service Level Agreement (SLA). To assist with that cloud service providers are offering: (1) horizontal resource scaling through provisioning and destruction of virtual machines and containers, (2) vertical scaling through changing the capacity of individual cloud nodes. Existing scaling solutions mostly concentrate on low-level metrics like CPU load and memory consumption which doesn’t always correlate with the level of SLA conformity. Such technical measures should be preprocessed and viewed from a higher level of abstraction. Application level metrics should also be considered when deciding upon scaling the cloud-based solution. Existing scaling platforms are mostly proprietary technologies owned by cloud service providers themselves or by third parties and offered as Software as a Service. Enterprise applications could span infrastructures of multiple public and private clouds, dictating that the auto-scaling solution should not be isolated inside a single cloud infrastructure. The goal of this paper is to address the challenges above by presenting the architecture of Auto-scaling and Adjustment Platform for Cloud-based Systems (ASAPCS). It is based on open-source technologies and supports integration of various low and high level performance metrics, providing higher levels of abstraction for design of scaling algorithms. ASAPCS can be used with any cloud service provider and guarantees that move from one cloud platform to another will not result in complete redesign of the scaling algorithm. ASAPCS itself is horizontally scalable and can process large amounts of real-time data which is particularly important for applications developed following the microservices architectural style. ASAPCS approaches the scaling problem in a nonstandard way by considering real-time adjustments of the application logic to be part of the scalability strategy if it can result in performance improvements

Toward Bio-Inspired Auto-Scaling Algorithms: An Elasticity Approach for Container Orchestration Platforms

(c) 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.[EN] The wide adoption of microservices architectures has introduced an unprecedented granularisation of computing that requires the coordinated execution of multiple containers with diverse lifetimes and with potentially different auto-scaling requirements. These applications are managed by means of container orchestration platforms and existing centralised approaches for auto-scaling face challenges when used for the timely adaptation of the elasticity required for the different application components. This paper studies the impact of integrating bio-inspired approaches for dynamic distributed auto-scaling on container orchestration platforms. With a focus on running self-managed containers, we compare alternative configuration options for the container life cycle. The performance of the proposed models is validated through simulations subjected to both synthetic and real-world workloads. Also, multiple scaling options are assessed with the purpose of identifying exceptional cases and improvement areas. Furthermore, a nontraditional metric for scaling measurement is introduced to substitute classic analytical approaches. We found out connections for two related worlds (biological systems and software container elasticity procedures) and we open a new research area in software containers that features potential self-guided container elasticity activities.This work was supported by the Ministerio de Economía, Industria y Competitividad, Spanish Government, for the Project BigCLOE under Grant TIN2016-79951-RHerrera, J.; Moltó, G. (2020). Toward Bio-Inspired Auto-Scaling Algorithms: An Elasticity Approach for Container Orchestration Platforms. IEEE Access. 8:52139-52150. https://doi.org/10.1109/ACCESS.2020.2980852S5213952150

Burst-aware predictive autoscaling for containerized microservices

Autoscaling methods are used for cloud-hosted applications to dynamically scale the allocated resources for guaranteeing Quality-of-Service (QoS). The public-facing application serves dynamic workloads, which contain bursts and pose challenges for autoscaling methods to ensure application performance. Existing State-of-the-art autoscaling methods are burst-oblivious to determine and provision the appropriate resources. For dynamic workloads, it is hard to detect and handle bursts online for maintaining application performance. In this article, we propose a novel burst-aware autoscaling method which detects burst in dynamic workloads using workload forecasting, resource prediction, and scaling decision making while minimizing response time service-level objectives (SLO) violations. We evaluated our approach through a trace-driven simulation, using multiple synthetic and realistic bursty workloads for containerized microservices, improving performance when comparing against existing state-of-the-art autoscaling methods. Such experiments show an increase of × 1.09 in total processed requests, a reduction of × 5.17 for SLO violations, and an increase of × 0.767 cost as compared to the baseline method.This work was partially supported by the European Research Council (ERC) under the EU Horizon 2020 programme (GA 639595), the Spanish Ministry of Economy, Industry and Competitiveness (TIN2015-65316-P and IJCI2016-27485) and the Generalitat de Catalunya (2014-SGR-1051).Peer ReviewedPostprint (author's final draft

Theta-Scan: Leveraging behavior-driven forecasting for vertical auto-scaling in container cloud

Detection of behavior patterns on resource usage in containerized Cloud applications is necessary for proper resource provisioning. Applications can use CPU/Memory with repetitive patterns, following a trend over time independently. By identifying such patterns, resource forecasting models can be fit better, reducing over/under-provisioning via fewer resizing operations. Here we present ThetaScan, a time-series analysis method for vertical auto-scaling of containers in the Cloud, based on the detection of stationarity/trending and periodicity on resource consumption. Our method leverages the Theta Forecaster algorithm with deseasonalization that, in our provisioning scenario, only requires the estimated periodicity for resource consumption as principal hyper-parameter. Commonly used behavior detection methods require manual hyper-parameter tuning, making them infeasible for automation. Besides, it can be used at multi-scales (minute/hour/day), detecting hourly and daily patterns to improve resource usage prediction. Experiments show that we can detect behaviors in resource consumption that common methods miss, without requiring extensive manual tuning. We can reduce the resizing triggers compared to fixed-size scheduling around ~ 10% – 15%, reduce over-provisioning of CPU and Memory through periodic-based provisioning. Also a ~ 60% on multiscale resource forecasting for traces showing periodicity at different levels in respect to single-scale.This work has been partially supported by the Spanish Government (contract PID2019-107255GB) and by Generalitat de Catalunya (contract 2014-SGR-1051).Peer ReviewedPostprint (author's final draft

Orchestrated Platform for Cyber-Physical Systems

One of the main driving forces in the era of cyber-physical systems (CPSs) is the introduction of massive sensor networks (or nowadays various Internet of things solutions as well) into manufacturing processes, connected cars, precision agriculture, and so on. Therefore, large amounts of sensor data have to be ingested at the server side in order to generate and make the "twin digital model" or virtual factory of the existing physical processes for (among others) predictive simulation and scheduling purposes usable. In this paper, we focus on our ultimate goal, a novel software container-based approach with cloud agnostic orchestration facilities that enable the system operators in the industry to create and manage scalable, virtual IT platforms on-demand for these two typical major pillars of CPS: (1) server-side (i.e., back-end) framework for sensor networks and (2) configurable simulation tool for predicting the behavior of manufacturing systems. The paper discusses the scalability of the applied discrete-event simulation tool and the layered back-end framework starting from simple virtual machine-level to sophisticated multilevel autoscaling use case scenario. The presented achievements and evaluations leverage on (among others) the synergy of the existing EasySim simulator, our new CQueue software container manager, the continuously developed Octopus cloud orchestrator tool, and the latest version of the evolving MiCADO framework for integrating such tools into a unified platform