A Socio-Technical Analysis of Challenges in Managing Multi-Clouds

Today’s cloud market consists of numerous providers competing and offering new services and features on an almost daily basis. From an organization\u27s perspective, it can therefore be beneficial to consider multiple providers in their cloud strategy to exploit possibilities for differentiation and specialization, ensure service availability, or realize cost savings. However, the resulting multi-cloud environment becomes highly complex and difficult to manage, which leads organizations to hold back from an implementation. Specifically, there is no common ground on what challenges organizations need to address when managing a multi-cloud environment. In this study, we derive a taxonomy of multi-cloud management challenges deductively through a structured literature review and inductively through an analysis of common multi-cloud broker and expert knowledge. Our taxonomy provides organizations with a holistic overview of challenges in managing multi-clouds and is intended to help initiate new interdisciplinary research in the scientific community

Multicloud Resource Allocation:Cooperation, Optimization and Sharing

Nowadays our daily life is not only powered by water, electricity, gas and telephony but by "cloud" as well. Big cloud vendors such as Amazon, Microsoft and Google have built large-scale centralized data centers to achieve economies of scale, on-demand resource provisioning, high resource availability and elasticity. However, those massive data centers also bring about many other problems, e.g., bandwidth bottlenecks, privacy, security, huge energy consumption, legal and physical vulnerabilities. One of the possible solutions for those problems is to employ multicloud architectures. In this thesis, our work provides research contributions to multicloud resource allocation from three perspectives of cooperation, optimization and data sharing. We address the following problems in the multicloud: how resource providers cooperate in a multicloud, how to reduce information leakage in a multicloud storage system and how to share the big data in a cost-effective way. More specifically, we make the following contributions: Cooperation in the decentralized cloud. We propose a decentralized cloud model in which a group of SDCs can cooperate with each other to improve performance. Moreover, we design a general strategy function for SDCs to evaluate the performance of cooperation based on different dimensions of resource sharing. Through extensive simulations using a realistic data center model, we show that the strategies based on reciprocity are more effective than other strategies, e.g., those using prediction based on historical data. Our results show that the reciprocity-based strategy can thrive in a heterogeneous environment with competing strategies. Multicloud optimization on information leakage. In this work, we firstly study an important information leakage problem caused by unplanned data distribution in multicloud storage services. Then, we present StoreSim, an information leakage aware storage system in multicloud. StoreSim aims to store syntactically similar data on the same cloud, thereby minimizing the user's information leakage across multiple clouds. We design an approximate algorithm to efficiently generate similarity-preserving signatures for data chunks based on MinHash and Bloom filter, and also design a function to compute the information leakage based on these signatures. Next, we present an effective storage plan generation algorithm based on clustering for distributing data chunks with minimal information leakage across multiple clouds. Finally, we evaluate our scheme using two real datasets from Wikipedia and GitHub. We show that our scheme can reduce the information leakage by up to 60% compared to unplanned placement. Furthermore, our analysis in terms of system attackability demonstrates that our scheme makes attacks on information much more complex. Smart data sharing. Moving large amounts of distributed data into the cloud or from one cloud to another can incur high costs in both time and bandwidth. The optimization on data sharing in the multicloud can be conducted from two different angles: inter-cloud scheduling and intra-cloud optimization. We first present CoShare, a P2P inspired decentralized cost effective sharing system for data replication to optimize network transfer among small data centers. Then we propose a data summarization method to reduce the total size of dataset, thereby reducing network transfer

Resource Management Techniques in Cloud-Fog for IoT and Mobile Crowdsensing Environments

The unpredictable and huge data generation nowadays by smart devices from IoT and mobile Crowd Sensing applications like (Sensors, smartphones, Wi-Fi routers) need processing power and storage. Cloud provides these capabilities to serve organizations and customers, but when using cloud appear some limitations, the most important of these limitations are Resource Allocation and Task Scheduling. The resource allocation process is a mechanism that ensures allocation virtual machine when there are multiple applications that require various resources such as CPU and I/O memory. Whereas scheduling is the process of determining the sequence in which these tasks come and depart the resources in order to maximize efficiency. In this paper we tried to highlight the most relevant difficulties that cloud computing is now facing. We presented a comprehensive review of resource allocation and scheduling techniques to overcome these limitations. Finally, the previous techniques and strategies for allocation and scheduling have been compared in a table with their drawbacks

Clouds + Games: A multifaceted approach

The computer game landscape is changing: people play games on multiple computing devices with heterogeneous form-factors, capability, and connectivity. Providing high playability on such devices concurrently is difficult. To enhance the gaming experience, designers could leverage abundant and elastic cloud resources, but current cloud platforms aren't optimized for highly interactive games. Existing studies focus on streaming-based cloud gaming, which is a special case for the more general cloud game architecture. The authors explain how to integrate techniques from the cloud and game research communities into a complete architecture for enhanced online gaming quality. They examine several open issues that appear only when clouds and games are put together. © 2014 IEEE

Resource Management Techniques in Cloud-Fog for IoT and Mobile Crowdsensing Environments

The unpredictable and huge data generation nowadays by smart devices from IoT and mobile Crowd Sensing applications like (Sensors, smartphones, Wi-Fi routers) need processing power and storage. Cloud provides these capabilities to serve organizations and customers, but when using cloud appear some limitations, the most important of these limitations are Resource Allocation and Task Scheduling. The resource allocation process is a mechanism that ensures allocation virtual machine when there are multiple applications that require various resources such as CPU and I/O memory. Whereas scheduling is the process of determining the sequence in which these tasks come and depart the resources in order to maximize efficiency. In this paper we tried to highlight the most relevant difficulties that cloud computing is now facing. We presented a comprehensive review of resource allocation and scheduling techniques to overcome these limitations. Finally, the previous techniques and strategies for allocation and scheduling have been compared in a table with their drawbacks

Financial evaluation of SLA-based VM scheduling strategies for cloud federations

In recent years, cloud federations have gained popularity. Small as well as big cloud service providers (CSPs) join federations to reduce their costs, and also cloud management software like OpenStack offers support for federations. In a federation, individual CSPs cooperate such that they can move load to partner clouds at high peaks and possibly offer a wider range of services to their customers. Research in this area addresses the organization of such federations and strategies that CSPs can apply to increase their profit. In this paper we present the latest extensions to the FederatedCloudSim framework that considerably improve the simulation and evaluation of cloud federations. These simulations include service-level agreements (SLAs), scheduling and brokering strategies on various levels, the use of real-world cloud workload traces and a fine-grained financial evaluation using the new CloudAccount module. We use FederatedCloudSim to compare scheduling and brokering strategies on the federation level. Among them are new strategies that conduct auctions or consult a reliance factor to select an appropriate federated partner for running outsourced virtual machines. Our results show that choosing the right strategy has a significant impact on SLA compliance and revenue

Contribución a la estimulación del uso de soluciones Cloud Computing: Diseño de un intermediador de servicios Cloud para fomentar el uso de ecosistemas distribuidos digitales confiables, interoperables y de acuerdo a la legalidad. Aplicación en entornos multi-cloud.

184 p.El objetivo del trabajo de investigación presentado en esta tesis es facilitar a los desarrolladores y operadores de aplicaciones desplegadas en múltiples Nubes el descubrimiento y la gestión de los diferentes servicios de Computación, soportando su reutilización y combinación, para generar una red de servicios interoperables, que cumplen con las leyes y cuyos acuerdos de nivel de servicio pueden ser evaluados de manera continua. Una de las contribuciones de esta tesis es el diseño y desarrollo de un bróker de servicios de Computación llamado ACSmI (Advanced Cloud Services meta-Intermediator). ACSmI permite evaluar el cumplimiento de los acuerdos de nivel de servicio incluyendo la legislación. ACSmI también proporciona una capa de abstracción intermedia para los servicios de Computación donde los desarrolladores pueden acceder fácilmente a un catálogo de servicios acreditados y compatibles con los requisitos no funcionales establecidos.Además, este trabajo de investigación propone la caracterización de las aplicaciones nativas multiNube y el concepto de "DevOps extendido" especialmente pensado para este tipo de aplicaciones. El concepto "DevOps extendido" pretende resolver algunos de los problemas actuales del diseño, desarrollo, implementación y adaptación de aplicaciones multiNube, proporcionando un enfoque DevOps novedoso y extendido para la adaptación de las prácticas actuales de DevOps al paradigma multiNube