Job-queuing and Auto-scaling in Container-based Cloud Environments

Many applications process large quantities of data that takes significant time and requires big amount of compu- tational resources. Optimising the execution of such applications in a cloud computing environment by keeping costs at minimum but still completing the task by a set deadline has paramount importance. As container-based technologies are becoming more widespread, support for job-queuing and auto-scaling in such environments is becoming important. Current container tech- nologies, such as Docker or Kubernetes provide limited support in this area. This paper presents JQueuer and CAutoScaler, a couple of cloud-independent solutions that offer job-queuing and automated scalability at the level of containers. Applying these solutions leads to more cloud-aware applications providing transparent auto-scaling for end-users and optimising execution time and costs. Business and science gateways will benefit from using an orchestrator combined with JQueuer and CAutoScaler since it will provide the layers needed to auto-scale the containers and to batch/sweep the jobs from a queue depending on a user- defined policy

A cloud-agnostic queuing system to support the implementation of deadline-based application execution policies

There are many scientific and commercial applications that require the execution of a large number of independent jobs resulting in significant overall execution time. Therefore, such applications typically require distributed computing infrastructures and science gateways to run efficiently and to be easily accessible for end-users. Optimising the execution of such applications in a cloud computing environment by keeping resource utilisation at minimum but still completing the experiment by a set deadline has paramount importance. As container-based technologies are becoming more widespread, support for job-queuing and auto-scaling in such environments is becoming important. Current container management technologies, such as Docker Swarm or Kubernetes, while provide auto-scaling based on resource consumption, do not support job queuing and deadline-based execution policies directly. This paper presents JQueuer, a cloud-agnostic queuing system that supports the scheduling of a large number of jobs in containerised cloud environments. The paper also demonstrates how JQueuer, when integrated with a cloud application-level orchestrator and auto-scaling framework, called MiCADO, can be used to implement deadline-based execution policies. This novel technical solution provides an important step towards the cost-optimisation of batch processing and job submission applications. In order to test and prove the effectiveness of the solution, the paper presents experimental results when executing an agent-based simulation application using the open source REPAST simulation framework

Innovations in Simulation: Experiences with Cloud-based Simulation Experimentation

The amount of simulation experimentation that can be performed in a project can be restricted by time, especially if a model takes a long time to simulate and many replications are required. Cloud Computing presents an attractive proposition to speeding up, or extending, simulation experimentation as computing resources can be hired on demand rather than having to invest in costly infrastructure. However, it is not common practice for simulation users to take advantage of this and, arguably, rather than speeding up simulation experimentation users tend to make compromises by using unnecessary model simplification techniques. This may be due to a lack of awareness of what Cloud Computing can offer. Based on several years’ experience of innovation in this area, this article presents our experiences in developing Cloud Computing applications for simulation experimentation and discusses what future innovations might be created for the widespread benefit of our simulation community

Towards a Deadline-Based Simulation Experimentation Framework Using Micro-Services Auto-Scaling Approach

There is growing number of research efforts in developing auto-scaling algorithms and tools for cloud resources. Traditional performance metrics such as CPU, memory and bandwidth usage for scaling up or down resources are not sufficient for all applications. For example, modeling and simulation experimentation is usually expected to yield results within a specific timeframe. In order to achieve this, often the quality of experiments is compromised either by restricting the parameter space to be explored or by limiting the number of replications required to give statistical confidence. In this paper, we present early stages of a deadline-based simulation experimentation framework using a micro-services auto-scaling approach. A case study of an agent-based simulation of a population physical activity behavior is used to demonstrate our framework

Towards auto-scaling in the cloud: online resource allocation techniques

Cloud computing provides an easy access to computing resources. Customers can acquire and release resources any time. However, it is not trivial to determine when and how many resources to allocate. Many applications running in the cloud face workload changes that affect their resource demand. The first thought is to plan capacity either for the average load or for the peak load. In the first case there is less cost incurred, but performance will be affected if the peak load occurs. The second case leads to money wastage, since resources will remain underutilized most of the time. Therefore there is a need for a more sophisticated resource provisioning techniques that can automatically scale the application resources according to workload demand and performance constrains. Large cloud providers such as Amazon, Microsoft, RightScale provide auto-scaling services. However, without the proper configuration and testing such services can do more harm than good. In this work I investigate application specific online resource allocation techniques that allow to dynamically adapt to incoming workload, minimize the cost of virtual resources and meet user-specified performance objectives

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