Testing a Cloud Provider Network for Hybrid P2P and Cloud Streaming Architectures

The number of online real-time streaming services deployed over network topologies like P2P or centralized ones has remarkably increased in the recent years. This has revealed the lack of networks that are well prepared to respond to this kind of traffic. A hybrid distribution network can be an efficient solution for real-time streaming services. This paper contains the experimental results of streaming distribution in a hybrid architecture that consist of mixed connections among P2P and Cloud nodes that can interoperate together. We have chosen to represent the P2P nodes as Planet Lab machines over the world and the cloud nodes using a Cloud provider's network. First we present an experimental validation of the Cloud infrastructure's ability to distribute streaming sessions with respect to some key streaming QoS parameters: jitter, throughput and packet losses. Next we show the results obtained from different test scenarios, when a hybrid distribution network is used. The scenarios measure the improvement of the multimedia QoS parameters, when nodes in the streaming distribution network (located in different continents) are gradually moved into the Cloud provider infrastructure. The overall conclusion is that the QoS of a streaming service can be efficiently improved, unlike in traditional P2P systems and CDN, by deploying a hybrid streaming architecture. This enhancement can be obtained by strategic placing of certain distribution network nodes into the Cloud provider infrastructure, taking advantage of the reduced packet loss and low latency that exists among its datacenters.

Business model with discount incentive in a P2P-cloud multimedia streaming system

Today P2P faces two important challenges: design of mechanisms to encourage users' collaboration in multimedia live streaming services; design of reliable algorithms with QoS provision, to encourage the multimedia providers employ the P2P topology in commercial live streaming systems. We believe that these two challenges are tightly-related and there is much to be done with respect. This paper analyzes the effect of user behavior in a multi-tree P2P overlay and describes a business model based on monetary discount as incentive in a P2P-Cloud multimedia streaming system. We believe a discount model can boost up users' cooperation and loyalty and enhance the overall system integrity and performance. Moreover the model bounds the constraints for a provider's revenue and cost if the P2P system is leveraged on a cloud infrastructure. Our case study shows that a streaming system provider can establish or adapt his business model by applying the described bounds to achieve a good discount-revenue trade-off and promote the system to the users.

Cloud Computing Service for Managing Large Medical Image Data-Sets Using Balanced Collaborative Agents

Managing large medical image collections is an increasingly demanding important issue in many hospitals and other medical settings. A huge amount of this information is daily generated, which requires robust and agile systems. In this paper we present a distributed multi-agent system capable of managing very large medical image datasets. In this approach, agents extract low-level information from images and store them in a data structure implemented in a relational database. The data structure can also store semantic information related to images and particular regions. A distinctive aspect of our work is that a single image can be divided so that the resultant sub-images can be stored and managed separately by different agents to improve performance in data accessing and processing. The system also offers the possibility of applying some region-based operations and filters on images, facilitating image classification. These operations can be performed directly on data structures in the database.

Cloud Computing en salud: Sistema para Administrar Imagenes Biomedicas

En el campo de la biomedicina se genera una inmensa cantidad de imágenes diariamente. Para administrarlas es necesaria la creación de sistemas informáticos robustos y ágiles, que necesitan gran cantidad de recursos computacionales. El presente artículo presenta un servicio de cloud computing capaz de manejar grandes colecciones de imágenes biomédicas. Gracias a este servicio organizaciones y usuarios podrían administrar sus imágenes biomédicas sin necesidad de poseer grandes recursos informáticos. El servicio usa un sistema distribuido multi agente donde las imágenes son procesadas y se extraen y almacenan en una estructura de datos las regiones que contiene junto con sus características. Una característica novedosa del sistema es que una misma imagen puede ser dividida, y las sub-imágenes resultantes pueden ser almacenadas por separado por distintos agentes. Esta característica ayuda a mejorar el rendimiento del sistema a la hora de buscar y recuperar las imágenes almacenadas.

Reliability issues related to the usage of Cloud Computing in Critical Infrastructures

The use of cloud computing is extending to all kind of systems, including the ones that are part of Critical Infrastructures, and measuring the reliability is becoming more difficult. Computing is becoming the 5th utility, in part thanks to the use of cloud services. Cloud computing is used now by all types of systems and organizations, including critical infrastructure, creating hidden inter-dependencies on both public and private cloud models. This paper investigates the use of cloud computing by critical infrastructure systems, the reliability and continuity of services risks associated with their use by critical systems. Some examples are presented of their use by different critical industries, and even when the use of cloud computing by such systems is not widely extended, there is a future risk that this paper presents. The concepts of macro and micro dependability and the model we introduce are useful for inter-dependency definition and for analyzing the resilience of systems that depend on other systems, specifically in the cloud model.