Image Transfer and Storage Cost Aware Brokering Strat. for Multiple Clouds

Cloud Brokering, Resource Allocation, Storage, Data Transfer, SimGrid Cloud BrokerNowadays, Clouds are used for hosting a large range of services. But between different Cloud Service Providers, the pricing model and the price of individual resources are very different. Furthermore hosting a service in one Cloud is the major cause of service outage. To increase resiliency and minimize the monetary cost of running a service, it becomes mandatory to span it between different Clouds. Moreover, due to dynamicity of both the service and Clouds, it could be required to migrate a service at run time. Accordingly, this ability must be integrated into the multi-Cloud resource manager, i.e. the Cloud broker. But, when migrating a VM to a new Cloud Service Provider, the VM disk image must be migrated too. Accordingly, data storage and transfer must be taken into account when choosing if and where an application will be migrated. In this paper, we extend a cost-optimization algorithm to take into account storage costs to approximate the optimal placement of a service. The data storage management consists in taking two decisions: where to upload an image, and keep it on-line during the experiment lifetime or delete it when unused. Although the default approach can be to upload an image on demand and delete it when it is no more used, we demonstrate that by adopting other policies the user can achieve better economical results.De nos jours, les Clouds sont utilisés pour héberger un grand ensemble de services. Mais entre les différents fournisseurs de service Cloud, les modéles de prix et le prix de chaque ressource sont très différents. De plus, héberger un service dans un unique Cloud est une des causes principales d'interruption de service. Pour améliorer la résistance et diminuer le coût monétaire d'une application, il devient obligatoire de la distribuer dans plusieurs Clouds. En outre, à cause de la dynamicité de l'application et des Clouds, il peut être nécessaire de migrer l'application pendant l'exécution. Par conséquence, cette capacité doit être intégrée dans le gestionnaire de ressources multi-Cloud i.e. le Cloud Broker. Mais, quand une VM migre vers un nouveau fournisseur de service Cloud, l'image disque de la VM doit être migrée également. Par conséquence, le stockage et transfert de donnée doivent être pris en compte quand il est choisi si une application doit migrer et où. Dans ce papier, nous étendons un algorithme d'optimisation de coût pour prendre en compte le coût du stockage afin d'approximer le placement optimal d'une application. La gestion du stockage de donnée consiste à devoir prendre 2 décisions: où l'image doit être envoyée et doit-elle être conservée ou supprimée quand elle n'est plus utilisée. Même si l'approche par défaut peut être d'envoyer l'image à la demande et la supprimer quand elle n'est plus utilisée, nous démontrons qu'en adoptant d'autres politiques l'utilisateur peut réussir à atteindre de meilleurs résultats économiques

A service broker for Intercloud computing

This thesis aims at assisting users in finding the most suitable Cloud resources taking into account their functional and non-functional SLA requirements. A key feature of the work is a Cloud service broker acting as mediator between consumers and Clouds. The research involves the implementation and evaluation of two SLA-aware match-making algorithms by use of a simulation environment. The work investigates also the optimal deployment of Multi-Cloud workflows on Intercloud environments

Next Generation Cloud Computing: New Trends and Research Directions

The landscape of cloud computing has significantly changed over the last decade. Not only have more providers and service offerings crowded the space, but also cloud infrastructure that was traditionally limited to single provider data centers is now evolving. In this paper, we firstly discuss the changing cloud infrastructure and consider the use of infrastructure from multiple providers and the benefit of decentralising computing away from data centers. These trends have resulted in the need for a variety of new computing architectures that will be offered by future cloud infrastructure. These architectures are anticipated to impact areas, such as connecting people and devices, data-intensive computing, the service space and self-learning systems. Finally, we lay out a roadmap of challenges that will need to be addressed for realising the potential of next generation cloud systems.Comment: Accepted to Future Generation Computer Systems, 07 September 201

Trusted resource allocation in volunteer edge-cloud computing for scientific applications

Data-intensive science applications in fields such as e.g., bioinformatics, health sciences, and material discovery are becoming increasingly dynamic and demanding with resource requirements. Researchers using these applications which are based on advanced scientific workflows frequently require a diverse set of resources that are often not available within private servers or a single Cloud Service Provider (CSP). For example, a user working with Precision Medicine applications would prefer only those CSPs who follow guidelines from HIPAA (Health Insurance Portability and Accountability Act) for implementing their data services and might want services from other CSPs for economic viability. With the generation of more and more data these workflows often require deployment and dynamic scaling of multi-cloud resources in an efficient and high-performance manner (e.g., quick setup, reduced computation time, and increased application throughput). At the same time, users seek to minimize the costs of configuring the related multi-cloud resources. While performance and cost are among the key factors to decide upon CSP resource selection, the scientific workflows often process proprietary/confidential data that introduces additional constraints of security postures. Thus, users have to make an informed decision on the selection of resources that are most suited for their applications while trading off between the key factors of resource selection which are performance, agility, cost, and security (PACS). Furthermore, even with the most efficient resource allocation across multi-cloud, the cost to solution might not be economical for all users which have led to the development of new paradigms of computing such as volunteer computing where users utilize volunteered cyber resources to meet their computing requirements. For economical and readily available resources, it is essential that such volunteered resources can integrate well with cloud resources for providing the most efficient computing infrastructure for users. In this dissertation, individual stages such as user requirement collection, user's resource preferences, resource brokering and task scheduling, in lifecycle of resource brokering for users are tackled. For collection of user requirements, a novel approach through an iterative design interface is proposed. In addition, fuzzy interference-based approach is proposed to capture users' biases and expertise for guiding their resource selection for their applications. The results showed improvement in performance i.e. time to execute in 98 percent of the studied applications. The data collected on user's requirements and preferences is later used by optimizer engine and machine learning algorithms for resource brokering. For resource brokering, a new integer linear programming based solution (OnTimeURB) is proposed which creates multi-cloud template solutions for resource allocation while also optimizing performance, agility, cost, and security. The solution was further improved by the addition of a machine learning model based on naive bayes classifier which captures the true QoS of cloud resources for guiding template solution creation. The proposed solution was able to improve the time to execute for as much as 96 percent of the largest applications. As discussed above, to fulfill necessity of economical computing resources, a new paradigm of computing viz-a-viz Volunteer Edge Computing (VEC) is proposed which reduces cost and improves performance and security by creating edge clusters comprising of volunteered computing resources close to users. The initial results have shown improved time of execution for application workflows against state-of-the-art solutions while utilizing only the most secure VEC resources. Consequently, we have utilized reinforcement learning based solutions to characterize volunteered resources for their availability and flexibility towards implementation of security policies. The characterization of volunteered resources facilitates efficient allocation of resources and scheduling of workflows tasks which improves performance and throughput of workflow executions. VEC architecture is further validated with state-of-the-art bioinformatics workflows and manufacturing workflows.Includes bibliographical references

Cloud brokering : nouveaux services de valeur ajoutée et politique de prix

Cloud brokering is a service paradigm that provides interoperability and portability of applications across multiple Cloud providers. The attractiveness of Cloud brokering relies on the new services and extended computing facilities that enhance or complement those already offered by isolated Cloud providers. These services provide new value to Small and Medium-sized Businesses (SMBs) and large enterprises and make Cloud providers more competitive. Nowadays, at the infrastructure level, Cloud brokers act as an intermediary between the end-users and the Cloud providers. A Cloud broker provides a single point for service consumption in order to avoid vendor lock-in, increase application resilience, provide a unified billing, and simplify governance, procurement and settlement processes across multiple Cloud providers. In the future, Cloud brokers will provide advanced valueadded services and will use attractive pricing models to capture potential Cloud consumers. The aim of this thesis is to propose advanced value-added services and a pricing model for Cloud brokers.Le « Cloud brokering » est un paradigme de service qui fournit interopérabilité et portabilité des applications à travers plusieurs fournisseurs de Cloud. Les nouveaux services et capacités étendues qui améliorent ou complètent celles déjà offertes par les fournisseurs de Cloud sont la caractéristique principale des « Cloud brokers ». Actuellement, d’un point de vue de l’infrastructure Cloud, les Cloud brokers jouent un rôle d’agents intermédiaires entre les utilisateurs et les fournisseurs, agissant ainsi comme un point commun pour la consommation des services Cloud. Parmi les avantages les plus notables liés à ce point d’accès commun on trouve : l’augmentation de la résilience en allouant l’infrastructure chez de multiples fournisseurs ; la délivrance d’une facturation unifiée ; la simplification des processus de gouvernance ; l’approvisionnement et le règlement à travers de multiples fournisseurs. Dans le futur, les Cloud brokers fourniront des services avancés de valeur ajoutée et vendront des services Cloud en utilisant d’attractives politiques de prix. Le but de cette thèse est de proposer deux services avancés de valeur ajoutée et une politique de prix pour les Cloud broker

Cloud computing : developing a cost estimation model for customers

Cloud computing is an essential part of the digital transformation journey. It offers many benefits to organisations, including the advantages of scalability and agility. Cloud customers see cloud computing as a moving train that every organisation needs to catch. This means that adoption decisions are made quickly in order to keep up with the new trend. Such quick decisions have led to many disappointments for cloud customers and have questioned the cost of the cloud. This is also because there is a lack of criteria or guidelines to help cloud customers get a complete picture of what is required of them before they go to the cloud. From another perspective, as new technologies force changes to the organizational structure and business processes, it is important to understand how cloud computing changes the IT and non-IT departments and how can this be translated into costs. Accordingly, this research uses the total cost of ownership approach and transaction cost theory to develop a customer-centric model to estimate the cost of cloud computing. The Research methodology used the Design Science Research approach. Expert interviews were used to develop the model. The model was then validated using four case studies. The model, named Sunny, identifies many costs that need to be estimated, which will help to make the cloud-based digital transformation journey less cloudy. The costs include Meta Services, Continuous Contract management, Monitoring and ITSM Adjustment. From an academic perspective, this research highlights the management efforts required for cloud computing and how misleading the rapid provision potential of the cloud resources can be. From a business perspective, proper estimation of these costs would help customers make informed decisions and vendors make realistic promises.Cloud Computing ist ein wesentlicher Bestandteil der Digitalisierung. Es bietet Unternehmen viele Vorteile, wie Skalierbarkeit und Agilität. Cloud-Kunden sehen Cloud Computing als einen Zug, auf den jedes Unternehmen aufspringen muss. Das bedeutet, dass Einführungsentscheidungen schnell getroffen werden, um mit dem neuen Trend Schritt zu halten. Solche Schnellschüsse haben zu vielen Enttäuschungen bei Cloud-Kunden geführt und die Kosten der Cloud in Frage gestellt. Dies ist auch darauf zurückzuführen, dass es keine Kriterien oder Leitlinien gibt, die den Cloud-Kunden helfen, sich ein vollständiges Bild davon zu machen, was von ihnen erwartet wird, bevor sie in die Cloud gehen. Aus einem anderen Blickwinkel ist es wichtig zu verstehen, wie Cloud Computing IT- und Nicht-IT-Abteilungen verändert und wie sich dies auf die Kosten auswirkt, da neue Technologien Veränderungen in der Organisationsstruktur und den Geschäftsprozessen erzwingen. Dementsprechend werden in dieser Forschungsarbeit der Total Cost of Ownership-Ansatz und die Transaktionskostentheorie verwendet, um ein kundenorientiertes Modell zur Schätzung der Kosten von Cloud Computing zu entwickeln. Die Forschungsmethodik basiert auf dem Design Science Research Ansatz. Zur Entwicklung des Modells wurden Experteninterviews durchgeführt. Anschließend wurde das Modell anhand von vier Fallstudien validiert. Das Modell mit dem Namen Sunny identifiziert viele Kosten, die geschätzt werden müssen, um die Reise zur digitalen Transformation in der Cloud weniger wolkig zu gestalten. Zu diesen Kosten gehören Meta-Services, kontinuierliches Vertragsmanagement, Überwachung und ITSM-Anpassung. Aus akademischer Sicht verdeutlicht diese Forschung, welcher Verwaltungsaufwand für Cloud Computing erforderlich ist und wie irreführend das schnelle Bereitstellungspotenzial von Cloud-Ressourcen sein kann. Aus Unternehmenssicht würde eine korrekte Einschätzung dieser Kosten den Kunden helfen, fundierte Entscheidungen zu treffen, und den Anbietern, realistische Versprechungen zu machen