Multi-Criteria Service Selection Agent for Federated Cloud

Federated cloud interconnects small and medium-sized cloud service providers for service enhancement to meet demand spikes. The service bartering technique in the federated cloud enables service providers to exchange their services. Selecting an optimal service provider to share services is challenging in the cloud federation. Agent-based and Reciprocal Resource Fairness (RRF) based models are used in the federated cloud for service selection. The agent-based model selects the best service provider using Quality of Service (quality of service). RRF model chooses fair service providers based on service providers\u27 previous service contribution to the federation. However, the models mentioned above fail to address free rider and poor performer problems during the service provider selection process. To solve the above issue, we propose a Multi-criteria Service Selection (MCSS) algorithm for effectively selecting a service provider using quality of service, Performance-Cost Ratio (PCR), and RRF. Comprehensive case studies are conducted to prove the effectiveness of the proposed algorithm. Extensive simulation experiments are conducted to compare the proposed algorithm performance with the existing algorithm. The evaluation results demonstrated that MCSS provides 10% more services selection efficiency than Cloud Resource Bartering System (CRBS) and provides 16% more service selection efficiency than RPF

Hybrid clouds for data-Intensive, 5G-Enabled IoT applications: an overview, key issues and relevant architecture

Hybrid cloud multi-access edge computing (MEC) deployments have been proposed as efficient means to support Internet of Things (IoT) applications, relying on a plethora of nodes and data. In this paper, an overview on the area of hybrid clouds considering relevant research areas is given, providing technologies and mechanisms for the formation of such MEC deployments, as well as emphasizing several key issues that should be tackled by novel approaches, especially under the 5G paradigm. Furthermore, a decentralized hybrid cloud MEC architecture, resulting in a Platform-as-a-Service (PaaS) is proposed and its main building blocks and layers are thoroughly described. Aiming to offer a broad perspective on the business potential of such a platform, the stakeholder ecosystem is also analyzed. Finally, two use cases in the context of smart cities and mobile health are presented, aimed at showing how the proposed PaaS enables the development of respective IoT applications.Peer ReviewedPostprint (published version

Cloud provider capacity augmentation through automated resource bartering

© 2017 Elsevier B.V. Growing interest in Cloud Computing places a heavy workload on cloud providers which is becoming increasingly difficult for them to manage with their primary data centre infrastructures. Resource scarcity can make providers vulnerable to significant reputational damage and it often forces customers to select services from the larger, more established companies, sometimes at a higher price. Funding limitations, however, commonly prevent emerging and even established providers from making a continual investment in hardware speculatively assuming a certain level of growth in demand. As an alternative, they may opt to use the current inter-cloud resource sharing systems which mainly rely on monetary payments and thus put pressure on already stretched cash flows. To address such issues, a new multi-agent based Cloud Resource Bartering System (CRBS) is implemented in this work that fosters the management and bartering of pooled resources without requiring costly financial transactions between IAAS cloud providers. Agents in CRBS collaborate to facilitate bartering among providers which not only strengthens their trading relationships but also enables them to handle surges in demand with their primary setup. Unlike existing systems, CRBS assigns resources by considering resource urgency which comparatively improves customers’ satisfaction and the resource utilization rate by more than 50%. The evaluation results verify that our system assists providers to timely acquire the additional resources and to maintain sustainable service delivery. We conclude that the existence of such a system is economically beneficial for cloud providers and enables them to adapt to fluctuating workloads

Automated and dynamic multi-level negotiation framework applied to an efficient cloud provisioning

L’approvisionnement du Cloud est le processus de déploiement et de gestion des applications sur les infrastructures publiques du Cloud. Il est de plus en plus utilisé car il permet aux fournisseurs de services métiers de se concentrer sur leurs activités sans avoir à gérer et à investir dans l’infrastructure. Il comprend deux niveaux d’interaction : (1) entre les utilisateurs finaux et les fournisseurs de services pour l’approvisionnement des applications, et (2) entre les fournisseurs de services et les fournisseurs de ressources pour l’approvisionnement des ressources virtuelles. L’environnement Cloud est devenu un marché complexe où tout fournisseur veut maximiser son profit monétaire et où les utilisateurs finaux recherchent les services les plus efficaces tout en minimisant leurs coûts. Avec la croissance de la concurrence dans le Cloud, les fournisseurs de services métiers doivent assurer un approvisionnement efficace qui maximise la satisfaction de la clientèle et optimise leurs profits.Ainsi, les fournisseurs et les utilisateurs doivent être satisfaits en dépit de leurs besoins contradictoires. La négociation est une solution prometteuse qui permet de résoudre les conflits en comblant le gap entre les capacités des fournisseurs et les besoins des utilisateurs. Intuitivement, la négociation automatique des contrats (SLA) permet d’aboutir à un compromis qui satisfait les deux parties. Cependant, pour être efficace, la négociation automatique doit considérer les propriétés de l’approvisionnement du Cloud et les complexités liées à la dynamicité (dynamicité de la disponibilité des ressources, dynamicité des prix). En fait ces critères ont un impact important sur le succès de la négociation. Les principales contributions de cette thèse répondant au défi de la négociation multi-niveau dans un contexte dynamique sont les suivantes: (1) Nous proposons un modèle de négociateur générique qui considère la nature dynamique de l’approvisionnement du Cloud et son impact potentiel sur les résultats décisionnels. Ensuite, nous construisons un cadre de négociation multicouche fondé sur ce modèle en l’instanciant entre les couches du Cloud. Le cadre comprend des agents négociateurs en communication avec les modules en relation avec la qualité et le prix du service à fournir (le planificateur, le moniteur, le prospecteur de marché). (2) Nous proposons une approche de négociation bilatérale entre les utilisateurs finaux et les fournisseurs de service basée sur une approche d’approvisionnement existante. Les stratégies de négociation sont basées sur la communication avec les modules d’approvisionnement (le planificateur et l’approvisionneur de machines virtuelles) afin d’optimiser les bénéfices du fournisseur de service et de maximiser la satisfaction du client. (3) Afin de maximiser le nombre de clients, nous proposons une approche de négociation adaptative et simultanée comme extension de la négociation bilatérale. Nous proposons d’exploiter les changements de charge de travail en termes de disponibilité et de tarification des ressources afin de renégocier simultanément avec plusieurs utilisateurs non acceptés (c’est-à-dire rejetés lors de la première session de négociation) avant la création du contrat SLA. (4) Afin de gérer toute violation possible de SLA, nous proposons une approche proactive de renégociation après l’établissement de SLA. La renégociation est lancée lors de la détection d’un événement inattendu (par exemple, une panne de ressources) pendant le processus d’approvisionnement. Les stratégies de renégociation proposées visent à minimiser la perte de profit pour le fournisseur et à assurer la continuité du service pour le consommateur. Les approches proposées sont mises en œuvre et les expériences prouvent les avantages d’ajouter la (re)négociation au processus d’approvisionnement. L’utilisation de la (re)négociation améliore le bénéfice du fournisseur, le nombre de demandes acceptées et la satisfaction du client.Cloud provisioning is the process of deployment and management of applications on public cloud infrastructures. Cloud provisioning is used increasingly because it enables business providers to focus on their business without having to manage and invest in infrastructure. Cloud provisioning includes two levels of interaction: (1) between end-users and business providers for application provisioning; and (2) between business providers and resource providers for virtual resource provisioning.The cloud market nowadays is a complex environment where business providers need to maximize their monetary profit, and where end-users look for the most efficient services with the lowest prices. With the growth of competition in the cloud, business providers must ensure efficient provisioning that maximizes customer satisfaction and optimizes the providers’ profit. So, both providers and users must be satisfied in spite of their conflicting needs. Negotiation is an appealing solution to solve conflicts and bridge the gap between providers’ capabilities and users’ requirements. Intuitively, automated Service Level Agreement (SLA) negotiation helps in reaching an agreement that satisfies both parties. However, to be efficient, automated negotiation should consider the properties of cloud provisioning mainly the two interaction levels, and complexities related to dynamicity (e.g., dynamically-changing resource availability, dynamic pricing, dynamic market factors related to offers and demands), which greatly impact the success of the negotiation. The main contributions of this thesis tackling the challenge of multi-level negotiation in a dynamic context are as follows: (1) We propose a generic negotiator model that considers the dynamic nature of cloud provisioning and its potential impact on the decision-making outcome. Then, we build a multi-layer negotiation framework built upon that model by instantiating it among Cloud layers. The framework includes negotiator agents. These agents are in communication with the provisioning modules that have an impact on the quality and the price of the service to be provisioned (e.g, the scheduler, the monitor, the market prospector). (2) We propose a bilateral negotiation approach between end-users and business providers extending an existing provisioning approach. The proposed decision-making strategies for negotiation are based on communication with the provisioning modules (the scheduler and the VM provisioner) in order to optimize the business provider’s profit and maximize customer satisfaction. (3) In order to maximize the number of clients, we propose an adaptive and concurrent negotiation approach as an extension of the bilateral negotiation. We propose to harness the workload changes in terms of resource availability and pricing in order to renegotiate simultaneously with multiple non-accepted users (i.e., rejected during the first negotiation session) before the establishment of the SLA. (4) In order to handle any potential SLA violation, we propose a proactive renegotiation approach after SLA establishment. The renegotiation is launched upon detecting an unexpected event (e.g., resource failure) during the provisioning process. The proposed renegotiation decision-making strategies aim to minimize the loss in profit for the provider and to ensure the continuity of the service for the consumer. The proposed approaches are implemented and experiments prove the benefits of adding (re)negotiation to the provisioning process. The use of (re)negotiation improves the provider’s profit, the number of accepted requests, and the client’s satisfaction

Towards Automated and Optimized Security Orchestration in Cloud SLA

In cloud computing, providers pool their resources and make them available to customers. Next-generation computer scientists are flocking to the cutting-edge field of cloud computing for their research and exploration of uncharted territory. There are still several barriers that cloud service providers must overcome in order to provide cloud services in accordance with service level agreements. Each cloud service provider aspires to achieve maximum performance as per Service Level Agreements (SLAs), and this is especially true when it comes to the delivery of services. A cloud service level agreement (SLA) guarantees that cloud service providers will satisfy the needs of large businesses and offer their clients with a specified list of services. The authors offer a web service level agreement–inspired approach for cloud service agreements. We adopt patterns and antipatterns to symbolize the best and worst practices of OCCI (Open Cloud Computing Interface Standard), REST (Representational State Transfer), and TOSCA (Topology and Orchestration Specification for Cloud Applications) with DevOps solutions, all of which API developers should bear in mind when designing APIs. When using this method, everything pertaining to the cloud service, from creation to deployment to measurement to evaluation to management to termination, may be handled mechanically. When distributing resources to cloud apps, our system takes into account the likelihood of SLA breaches and responds by providing more resources if necessary. We say that for optimal performance, our suggested solution should be used in a private cloud computing setting. As more and more people rely on cloud computing for their day-to-day workloads, there has been a corresponding rise in the need for efficient orchestration and management strategies that foster interoperability

Design and implementation of a multi-agent opportunistic grid computing platform

Opportunistic Grid Computing involves joining idle computing resources in enterprises into a converged high performance commodity infrastructure. The research described in this dissertation investigates the viability of public resource computing in offering a plethora of possibilities through seamless access to shared compute and storage resources. The research proposes and conceptualizes the Multi-Agent Opportunistic Grid (MAOG) solution in an Information and Communication Technologies for Development (ICT4D) initiative to address some limitations prevalent in traditional distributed system implementations. Proof-of-concept software components based on JADE (Java Agent Development Framework) validated Multi-Agent Systems (MAS) as an important tool for provisioning of Opportunistic Grid Computing platforms. Exploration of agent technologies within the research context identified two key components which improve access to extended computer capabilities. The first component is a Mobile Agent (MA) compute component in which a group of agents interact to pool shared processor cycles. The compute component integrates dynamic resource identification and allocation strategies by incorporating the Contract Net Protocol (CNP) and rule based reasoning concepts. The second service is a MAS based storage component realized through disk mirroring and Google file-system’s chunking with atomic append storage techniques. This research provides a candidate Opportunistic Grid Computing platform design and implementation through the use of MAS. Experiments conducted validated the design and implementation of the compute and storage services. From results, support for processing user applications; resource identification and allocation; and rule based reasoning validated the MA compute component. A MAS based file-system that implements chunking optimizations was considered to be optimum based on evaluations. The findings from the undertaken experiments also validated the functional adequacy of the implementation, and show the suitability of MAS for provisioning of robust, autonomous, and intelligent platforms. The context of this research, ICT4D, provides a solution to optimizing and increasing the utilization of computing resources that are usually idle in these contexts

Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration

Future AI applications require performance, reliability and privacy that the existing, cloud-dependant system architectures cannot provide. In this article, we study orchestration in the device-edge-cloud continuum, and focus on AI for edge, that is, the AI methods used in resource orchestration. We claim that to support the constantly growing requirements of intelligent applications in the device-edge-cloud computing continuum, resource orchestration needs to embrace edge AI and emphasize local autonomy and intelligence. To justify the claim, we provide a general definition for continuum orchestration, and look at how current and emerging orchestration paradigms are suitable for the computing continuum. We describe certain major emerging research themes that may affect future orchestration, and provide an early vision of an orchestration paradigm that embraces those research themes. Finally, we survey current key edge AI methods and look at how they may contribute into fulfilling the vision of future continuum orchestration.Comment: 50 pages, 8 figures (Revised content in all sections, added figures and new section

Adaptive learning-based resource management strategy in fog-to-cloud

Technology in the twenty-first century is rapidly developing and driving us into a new smart computing world, and emerging lots of new computing architectures. Fog-to-Cloud (F2C) is among one of them, which emerges to ensure the commitment for bringing the higher computing facilities near to the edge of the network and also help the large-scale computing system to be more intelligent. As the F2C is in its infantile state, therefore one of the biggest challenges for this computing paradigm is to efficiently manage the computing resources. Mainly, to address this challenge, in this work, we have given our sole interest for designing the initial architectural framework to build a proper, adaptive and efficient resource management mechanism in F2C. F2C has been proposed as a combined, coordinated and hierarchical computing platform, where a vast number of heterogeneous computing devices are participating. Notably, their versatility creates a massive challenge for effectively handling them. Even following any large-scale smart computing system, it can easily recognize that various kind of services is served for different purposes. Significantly, every service corresponds with the various tasks, which have different resource requirements. So, knowing the characteristics of participating devices and system offered services is giving advantages to build effective and resource management mechanism in F2C-enabled system. Considering these facts, initially, we have given our intense focus for identifying and defining the taxonomic model for all the participating devices and system involved services-tasks. In any F2C-enabled system consists of a large number of small Internet-of-Things (IoTs) and generating a continuous and colossal amount of sensing-data by capturing various environmental events. Notably, this sensing-data is one of the key ingredients for various smart services which have been offered by the F2C-enabled system. Besides that, resource statistical information is also playing a crucial role, for efficiently providing the services among the system consumers. Continuous monitoring of participating devices generates a massive amount of resource statistical information in the F2C-enabled system. Notably, having this information, it becomes much easier to know the device's availability and suitability for executing some tasks to offer some services. Therefore, ensuring better service facilities for any latency-sensitive services, it is essential to securely distribute the sensing-data and resource statistical information over the network. Considering these matters, we also proposed and designed a secure and distributed database framework for effectively and securely distribute the data over the network. To build an advanced and smarter system is necessarily required an effective mechanism for the utilization of system resources. Typically, the utilization and resource handling process mainly depend on the resource selection and allocation mechanism. The prediction of resources (e.g., RAM, CPU, Disk, etc.) usage and performance (i.e., in terms of task execution time) helps the selection and allocation process. Thus, adopting the machine learning (ML) techniques is much more useful for designing an advanced and sophisticated resource allocation mechanism in the F2C-enabled system. Adopting and performing the ML techniques in F2C-enabled system is a challenging task. Especially, the overall diversification and many other issues pose a massive challenge for successfully performing the ML techniques in any F2C-enabled system. Therefore, we have proposed and designed two different possible architectural schemas for performing the ML techniques in the F2C-enabled system to achieve an adaptive, advance and sophisticated resource management mechanism in the F2C-enabled system. Our proposals are the initial footmarks for designing the overall architectural framework for resource management mechanism in F2C-enabled system.La tecnologia del segle XXI avança ràpidament i ens condueix cap a un nou món intel·ligent, creant nous models d'arquitectures informàtiques. Fog-to-Cloud (F2C) és un d’ells, i sorgeix per garantir el compromís d’acostar les instal·lacions informàtiques a prop de la xarxa i també ajudar el sistema informàtic a gran escala a ser més intel·ligent. Com que el F2C es troba en un estat preliminar, un dels majors reptes d’aquest paradigma tecnològic és gestionar eficientment els recursos informàtics. Per fer front a aquest repte, en aquest treball hem centrat el nostre interès en dissenyar un marc arquitectònic per construir un mecanisme de gestió de recursos adequat, adaptatiu i eficient a F2C.F2C ha estat concebut com una plataforma informàtica combinada, coordinada i jeràrquica, on participen un gran nombre de dispositius heterogenis. La seva versatilitat planteja un gran repte per gestionar-los de manera eficaç. Els serveis que s'hi executen consten de diverses tasques, que tenen requisits de recursos diferents. Per tant, conèixer les característiques dels dispositius participants i dels serveis que ofereix el sistema és un requisit per dissenyar mecanismes eficaços i de gestió de recursos en un sistema habilitat per F2C. Tenint en compte aquests fets, inicialment ens hem centrat en identificar i definir el model taxonòmic per a tots els dispositius i sistemes implicats en l'execució de tasques de serveis. Qualsevol sistema habilitat per F2C inclou en un gran nombre de dispositius petits i connectats (conegut com a Internet of Things, o IoT) que generen una quantitat contínua i colossal de dades de detecció capturant diversos events ambientals. Aquestes dades són un dels ingredients clau per a diversos serveis intel·ligents que ofereix F2C. A més, el seguiment continu dels dispositius participants genera igualment una gran quantitat d'informació estadística. En particular, en tenir aquesta informació, es fa molt més fàcil conèixer la disponibilitat i la idoneïtat dels dispositius per executar algunes tasques i oferir alguns serveis. Per tant, per garantir millors serveis sensibles a la latència, és essencial distribuir de manera equilibrada i segura la informació estadística per la xarxa. Tenint en compte aquests assumptes, també hem proposat i dissenyat un entorn de base de dades segura i distribuïda per gestionar de manera eficaç i segura les dades a la xarxa. Per construir un sistema avançat i intel·ligent es necessita un mecanisme eficaç per a la gestió de l'ús dels recursos del sistema. Normalment, el procés d’utilització i manipulació de recursos depèn principalment del mecanisme de selecció i assignació de recursos. La predicció de l’ús i el rendiment de recursos (per exemple, RAM, CPU, disc, etc.) en termes de temps d’execució de tasques ajuda al procés de selecció i assignació. Adoptar les tècniques d’aprenentatge automàtic (conegut com a Machine Learning, o ML) és molt útil per dissenyar un mecanisme d’assignació de recursos avançat i sofisticat en el sistema habilitat per F2C. L’adopció i la realització de tècniques de ML en un sistema F2C és una tasca complexa. Especialment, la diversificació general i molts altres problemes plantegen un gran repte per realitzar amb èxit les tècniques de ML. Per tant, en aquesta recerca hem proposat i dissenyat dos possibles esquemes arquitectònics diferents per realitzar tècniques de ML en el sistema habilitat per F2C per aconseguir un mecanisme de gestió de recursos adaptatiu, avançat i sofisticat en un sistema F2C. Les nostres propostes són els primers passos per dissenyar un marc arquitectònic general per al mecanisme de gestió de recursos en un sistema habilitat per F2C.Postprint (published version