Evolutionary computation for optimal component deployment with multitenancy isolation in cloud-hosted applications.

A multitenant cloud-application that is designed to use several components needs to implement the required degree of isolation between the components when the workload changes. The highest degree of isolation results in high resource consumption and running cost per component. A low degree of isolation allows sharing of resources, but leads to degradation in performance and to increased security vulnerability. This paper presents a simulation-based approach operating on computational metaheuristics that search for optimal ways of deploying components of a cloud-hosted application to guarantee multitenancy isolation When the workload changes, an open multiclass Queuing Network model is used to determine the average number of component access requests, followed by a metaheuristic search for the optimal deployment solutions of the components in question. The simulation-based evaluation of optimization performance showed that the solutions obtained were very close to the target solution. Various recommendations and best practice guidelines for deploying components in a way that guarantees the required degree of isolation are also provided

Optimal deployment of components of cloud-hosted application for guaranteeing multitenancy isolation

One of the challenges of deploying multitenant cloud-hosted services that are designed to use (or be integrated with) several components is how to implement the required degree of isolation between the components when there is a change in the workload. Achieving the highest degree of isolation implies deploying a component exclusively for one tenant; which leads to high resource consumption and running cost per component. A low degree of isolation allows sharing of resources which could possibly reduce cost, but with known limitations of performance and security interference. This paper presents a model-based algorithm together with four variants of a metaheuristic that can be used with it, to provide near-optimal solutions for deploying components of a cloud-hosted application in a way that guarantees multitenancy isolation. When the workload changes, the model based algorithm solves an open multiclass QN model to determine the average number of requests that can access the components and then uses a metaheuristic to provide near-optimal solutions for deploying the components. Performance evaluation showed that the obtained solutions had low variability and percent deviation when compared to the reference/optimal solution. We also provide recommendations and best practice guidelines for deploying components in a way that guarantees the required degree of isolation

A framework for achieving the required degree of multitenancy isolation for deploying components of a cloud-hosted service.

Multitenancy allows multiple tenants to access a single instance of a cloud offering. While several approaches exist for implementing multitenancy, little attention has been paid to implementing the required degree of isolation since there are varying degrees of isolation that can be implemented for each tenant. This paper presents a framework for achieving the required degree of isolation between tenants accessing a cloud offering so that the required performance, resource utilisation and access privilege of one tenant does not affect other tenants when there are workload changes. The framework is composed of two main constituents: 1) component-based approach to multitenancy isolation through request re-routing (COMITRE); 2) an optimisation model for providing optimal solutions for deploying components of a cloud-hosted service. We demonstrate using a case study of: 1) a cloud-hosted bug tracking system; 2) a synthetic dataset, the possibility of providing the required degree of isolation and optimal deployment solutions

A model for optimising the deployment of cloud-hosted application components for guaranteeing multitenancy isolation.

Tenants associated with a cloud-hosted application seek to reduce running costs and minimize resource consumption by sharing components and resources. However, despite the benefits, sharing resources can affect tenant’s access and overall performance if one tenant abruptly experiences a significant workload, particularly if the application fails to accommodate this sudden increase in workload. In cases where a there is a higher or varying degree of isolation between components, this issue can become severe. This paper aims to present novel solutions for deploying components of a cloud-hosted application with the purpose of guaranteeing the required degree of multitenancy isolation through a mathematical optimization model and metaheuristic algorithm. Research conducted through this paper demonstrates that, when compared, optimal solutions achieved through the model had low variability levels and percent deviation. This paper additionally provides areas of application of our optimization model as well as challenges and recommendations for deploying components associated with varying degrees of isolation

Architecting the deployment of cloud-hosted services for guaranteeing multitenancy isolation.

In recent years, software tools used for Global Software Development (GSD) processes (e.g., continuous integration, version control and bug tracking) are increasingly being deployed in the cloud to serve multiple users. Multitenancy is an important architectural property in cloud computing in which a single instance of an application is used to serve multiple users. There are two key challenges of implementing multitenancy: (i) ensuring isolation either between multiple tenants accessing the service or components designed (or integrated) with the service; and (ii) resolving trade-offs between varying degrees of isolation between tenants or components. The aim of this thesis is to investigate how to architect the deployment of cloud-hosted service while guaranteeing the required degree of multitenancy isolation. Existing approaches for architecting the deployment of cloud-hosted services to serve multiple users have paid little attention to evaluating the effect of the varying degrees of multitenancy isolation on the required performance, resource consumption and access privilege of tenants (or components). Approaches for isolating tenants (or components) are usually implemented at lower layers of the cloud stack and often apply to the entire system and not to individual tenants (or components). This thesis adopts a multimethod research strategy to providing a set of novel approaches for addressing these problems. Firstly, a taxonomy of deployment patterns and a general process, CLIP (CLoud-based Identification process for deployment Patterns) was developed for guiding architects in selecting applicable cloud deployment patterns (together with the supporting technologies) using the taxonomy for deploying services to the cloud. Secondly, an approach named COMITRE (COmponent-based approach to Multitenancy Isolation Through request RE-routing) was developed together with supporting algorithms and then applied to three case studies to empirically evaluate the varying degrees of isolation between tenants enabled by multitenancy patterns for three different cloud-hosted GSD processes, namely-continuous integration, version control, and bug tracking. After that, a synthesis of findings from the three case studies was carried out to provide an explanatory framework and new insights about varying degrees of multitenancy isolation. Thirdly, a model-based decision support system together with four variants of a metaheuristic solution was developed for solving the model to provide an optimal solution for deploying components of a cloud-hosted application with guarantees for multitenancy isolation. By creating and applying the taxonomy, it was learnt that most deployment patterns are related and can be implemented by combining with others, for example, in hybrid deployment scenarios to integrate data residing in multiple clouds. It has been argued that the shared component is better for reducing resource consumption while the dedicated component is better in avoiding performance interference. However, as the experimental results show, there are certain GSD processes where that might not necessarily be so, for example, in version control, where additional copies of the files are created in the repository, thus consuming more disk space. Over time, performance begins to degrade as more time is spent searching across many files on the disk. Extensive performance evaluation of the model-based decision support system showed that the optimal solutions obtained had low variability and percent deviation, and were produced with low computational effort when compared to a given target solution

User-centric Adaptation Analysis of Multi-tenant Services

Multi-tenancy is a key pillar of cloud services. It allows different users to share computing and virtual resources transparently, meanwhile guaranteeing substantial cost savings. Due to the tradeoff between scalability and customization, one of the major drawbacks of multi-tenancy is limited configurability. Since users may often have conflicting configuration preferences, offering the best user experience is an open challenge for service providers. In addition, the users, their preferences, and the operational environment may change during the service operation, thus jeopardizing the satisfaction of user preferences. In this article, we present an approach to support user-centric adaptation of multi-tenant services. We describe how to engineer the activities of the Monitoring, Analysis, Planning, Execution (MAPE) loop to support user-centric adaptation, and we focus on adaptation analysis. Our analysis computes a service configuration that optimizes user satisfaction, complies with infrastructural constraints, and minimizes reconfiguration obtrusiveness when user- or service-related changes take place. To support our analysis, we model multitenant services and user preferences by using feature and preference models, respectively. We illustrate our approach by utilizing different cases of virtual desktops. Our results demonstrate the effectiveness of the analysis in improving user preferences satisfaction in negligible time.Ministerio de Economía y Competitividad TIN2012-32273Junta de Andalucía P12--TIC--1867Junta de Andalucía TIC-590

OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System

Automated machine learning (AutoML) seeks to build ML models with minimal human effort. While considerable research has been conducted in the area of AutoML in general, aiming to take humans out of the loop when building artificial intelligence (AI) applications, scant literature has focused on how AutoML works well in open-environment scenarios such as the process of training and updating large models, industrial supply chains or the industrial metaverse, where people often face open-loop problems during the search process: they must continuously collect data, update data and models, satisfy the requirements of the development and deployment environment, support massive devices, modify evaluation metrics, etc. Addressing the open-environment issue with pure data-driven approaches requires considerable data, computing resources, and effort from dedicated data engineers, making current AutoML systems and platforms inefficient and computationally intractable. Human-computer interaction is a practical and feasible way to tackle the problem of open-environment AI. In this paper, we introduce OmniForce, a human-centered AutoML (HAML) system that yields both human-assisted ML and ML-assisted human techniques, to put an AutoML system into practice and build adaptive AI in open-environment scenarios. Specifically, we present OmniForce in terms of ML version management; pipeline-driven development and deployment collaborations; a flexible search strategy framework; and widely provisioned and crowdsourced application algorithms, including large models. Furthermore, the (large) models constructed by OmniForce can be automatically turned into remote services in a few minutes; this process is dubbed model as a service (MaaS). Experimental results obtained in multiple search spaces and real-world use cases demonstrate the efficacy and efficiency of OmniForce

A Survey and Taxonomy of Self-Aware and Self-Adaptive Cloud Autoscaling Systems

Autoscaling system can reconfigure cloud-based services and applications, through various configurations of cloud software and provisions of hardware resources, to adapt to the changing environment at runtime. Such a behavior offers the foundation for achieving elasticity in a modern cloud computing paradigm. Given the dynamic and uncertain nature of the shared cloud infrastructure, the cloud autoscaling system has been engineered as one of the most complex, sophisticated, and intelligent artifacts created by humans, aiming to achieve self-aware, self-adaptive, and dependable runtime scaling. Yet the existing Self-aware and Self-adaptive Cloud Autoscaling System (SSCAS) is not at a state where it can be reliably exploited in the cloud. In this article, we survey the state-of-the-art research studies on SSCAS and provide a comprehensive taxonomy for this field. We present detailed analysis of the results and provide insights on open challenges, as well as the promising directions that are worth investigated in the future work of this area of research. Our survey and taxonomy contribute to the fundamentals of engineering more intelligent autoscaling systems in the cloud

Management of generic and multi-platform workflows for exploiting heterogeneous environments on e-Science

Scientific Workflows (SWFs) are widely used to model applications in e-Science. In this programming model, scientific applications are described as a set of tasks that have dependencies among them. During the last decades, the execution of scientific workflows has been successfully performed in the available computing infrastructures (supercomputers, clusters and grids) using software programs called Workflow Management Systems (WMSs), which orchestrate the workload on top of these computing infrastructures. However, because each computing infrastructure has its own architecture and each scientific applications exploits efficiently one of these infrastructures, it is necessary to organize the way in which they are executed. WMSs need to get the most out of all the available computing and storage resources. Traditionally, scientific workflow applications have been extensively deployed in high-performance computing infrastructures (such as supercomputers and clusters) and grids. But, in the last years, the advent of cloud computing infrastructures has opened the door of using on-demand infrastructures to complement or even replace local infrastructures. However, new issues have arisen, such as the integration of hybrid resources or the compromise between infrastructure reutilization and elasticity, everything on the basis of cost-efficiency. The main contribution of this thesis is an ad-hoc solution for managing workflows exploiting the capabilities of cloud computing orchestrators to deploy resources on demand according to the workload and to combine heterogeneous cloud providers (such as on-premise clouds and public clouds) and traditional infrastructures (supercomputers and clusters) to minimize costs and response time. The thesis does not propose yet another WMS, but demonstrates the benefits of the integration of cloud orchestration when running complex workflows. The thesis shows several configuration experiments and multiple heterogeneous backends from a realistic comparative genomics workflow called Orthosearch, to migrate memory-intensive workload to public infrastructures while keeping other blocks of the experiment running locally. The running time and cost of the experiments is computed and best practices are suggested.Los flujos de trabajo científicos son comúnmente usados para modelar aplicaciones en e-Ciencia. En este modelo de programación, las aplicaciones científicas se describen como un conjunto de tareas que tienen dependencias entre ellas. Durante las últimas décadas, la ejecución de flujos de trabajo científicos se ha llevado a cabo con éxito en las infraestructuras de computación disponibles (supercomputadores, clústers y grids) haciendo uso de programas software llamados Gestores de Flujos de Trabajos, los cuales distribuyen la carga de trabajo en estas infraestructuras de computación. Sin embargo, debido a que cada infraestructura de computación posee su propia arquitectura y cada aplicación científica explota eficientemente una de estas infraestructuras, es necesario organizar la manera en que se ejecutan. Los Gestores de Flujos de Trabajo necesitan aprovechar el máximo todos los recursos de computación y almacenamiento disponibles. Habitualmente, las aplicaciones científicas de flujos de trabajos han sido ejecutadas en recursos de computación de altas prestaciones (tales como supercomputadores y clústers) y grids. Sin embargo, en los últimos años, la aparición de las infraestructuras de computación en la nube ha posibilitado el uso de infraestructuras bajo demanda para complementar o incluso reemplazar infraestructuras locales. No obstante, este hecho plantea nuevas cuestiones, tales como la integración de recursos híbridos o el compromiso entre la reutilización de la infraestructura y la elasticidad, todo ello teniendo en cuenta que sea eficiente en el coste. La principal contribución de esta tesis es una solución ad-hoc para gestionar flujos de trabajos explotando las capacidades de los orquestadores de recursos de computación en la nube para desplegar recursos bajo demando según la carga de trabajo y combinar proveedores de computación en la nube heterogéneos (privados y públicos) e infraestructuras tradicionales (supercomputadores y clústers) para minimizar el coste y el tiempo de respuesta. La tesis no propone otro gestor de flujos de trabajo más, sino que demuestra los beneficios de la integración de la orquestación de la computación en la nube cuando se ejecutan flujos de trabajo complejos. La tesis muestra experimentos con diferentes configuraciones y múltiples plataformas heterogéneas, haciendo uso de un flujo de trabajo real de genómica comparativa llamado Orthosearch, para traspasar cargas de trabajo intensivas de memoria a infraestructuras públicas mientras se mantienen otros bloques del experimento ejecutándose localmente. El tiempo de respuesta y el coste de los experimentos son calculados, además de sugerir buenas prácticas.Els fluxos de treball científics són comunament usats per a modelar aplicacions en e-Ciència. En aquest model de programació, les aplicacions científiques es descriuen com un conjunt de tasques que tenen dependències entre elles. Durant les últimes dècades, l'execució de fluxos de treball científics s'ha dut a terme amb èxit en les infraestructures de computació disponibles (supercomputadors, clústers i grids) fent ús de programari anomenat Gestors de Fluxos de Treballs, els quals distribueixen la càrrega de treball en aquestes infraestructures de computació. No obstant açò, a causa que cada infraestructura de computació posseeix la seua pròpia arquitectura i cada aplicació científica explota eficientment una d'aquestes infraestructures, és necessari organitzar la manera en què s'executen. Els Gestors de Fluxos de Treball necessiten aprofitar el màxim tots els recursos de computació i emmagatzematge disponibles. Habitualment, les aplicacions científiques de fluxos de treballs han sigut executades en recursos de computació d'altes prestacions (tals com supercomputadors i clústers) i grids. No obstant açò, en els últims anys, l'aparició de les infraestructures de computació en el núvol ha possibilitat l'ús d'infraestructures sota demanda per a complementar o fins i tot reemplaçar infraestructures locals. No obstant açò, aquest fet planteja noves qüestions, tals com la integració de recursos híbrids o el compromís entre la reutilització de la infraestructura i l'elasticitat, tot açò tenint en compte que siga eficient en el cost. La principal contribució d'aquesta tesi és una solució ad-hoc per a gestionar fluxos de treballs explotant les capacitats dels orquestadors de recursos de computació en el núvol per a desplegar recursos baix demande segons la càrrega de treball i combinar proveïdors de computació en el núvol heterogenis (privats i públics) i infraestructures tradicionals (supercomputadors i clústers) per a minimitzar el cost i el temps de resposta. La tesi no proposa un gestor de fluxos de treball més, sinó que demostra els beneficis de la integració de l'orquestració de la computació en el núvol quan s'executen fluxos de treball complexos. La tesi mostra experiments amb diferents configuracions i múltiples plataformes heterogènies, fent ús d'un flux de treball real de genòmica comparativa anomenat Orthosearch, per a traspassar càrregues de treball intensives de memòria a infraestructures públiques mentre es mantenen altres blocs de l'experiment executant-se localment. El temps de resposta i el cost dels experiments són calculats, a més de suggerir bones pràctiques.Carrión Collado, AA. (2017). Management of generic and multi-platform workflows for exploiting heterogeneous environments on e-Science [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/86179TESI

Performance Isolation in Multi-Tenant Applications

The thesis presents methods to isolate different tenants, sharing one application instance, with regards to he performance they observe. Therefore, a request based admission control is introduced. Furthermore, the publication presents methods and novel metrics to evaluate the degree of isolation a system achieves. These insights are used to evaluate the developed isolation methods, resulting in recommendations of methods for various scenarios