Fawaz Paraiso

A Federated Multi-cloud PaaS Infrastructure

Cloud platforms are increasingly being used for hosting a broad diversity of services from traditional e-commerce applications to interactive web-based Ides. How-ever, we observe that the proliferation of offers by cloud providers raises several challenges. Developers will not only have to deploy applications for a specific cloud, but will also have to consider migrating services from one cloud to another, and to manage distributed applications spanning multiple clouds. In this paper, we present our federated multi-cloud PaaS infrastructure for addressing these challenges. This infrastructure is based on three foundations: i) an open service model used to design and implement both our multi-cloud PaaSand the SaaS applications running on top of it, ii) a configurable architecture of the federated PaaS, and iii) some infrastructure services for managing both our multi-cloud PaaS and the SaaS applications. We then show how this multi-cloud PaaS can be deployed on top of thirteen existing IaaS/PaaS. We finally report on three distributed SaaS applications developed with and deployed on our federated multi-cloud PaaS infrastructure.

soCloud: a service-oriented component-based PaaS for managing portability, provisioning, elasticity, and high availability across multiple clouds

Multi-cloud computing is a promising paradigm to support very large scale world wide distributed applications. Multi-cloud computing is the usage of multiple, independent cloud environments, which assumed no priori agreement between cloud providers or third party. However, multi-cloud computing has to face several key challenges such as portability, provisioning, elasticity, and high availability. Developers will not only have to deploy applications to a specific cloud, but will also have to consider application portability from one cloud to another, and to deploy distributed applications spanning multiple clouds. This article presents soCloud a service-oriented component-based Platform as a Service for managing portability, elasticity, provisioning, and high availability across multiple clouds. soCloud is based on the OASIS Service Component Architecture standard in order to address portability. soCloud provides services for managing provisioning, elasticity, and high availability across multiple clouds. soCloud has been deployed and evaluated on top of ten existing cloud providers: Windows Azure, DELL KACE, Amazon EC2, CloudBees, OpenShift, dotCloud, Jelastic, Heroku, Appfog, and an Eucalyptus private cloud.

Managing elasticity across multiple cloud providers

In the context of cloud computing, elasticity is the capacity to scale computing resources up and down easily. Currently, most Platforms as a Service (PaaS) manage application elasticity within a single cloud provider. However, the not so infrequent issue of cloud outages has become a concern that hinders the availability of cloud-based applications. The most promising solutions to this issue are those based on the federation of multiple clouds. In this paper, we present a Multi-Cloud-PaaS architecture. We show how this architecture can be used for managing elasticity across multiple cloud providers.

A Middleware Platform to Federate Complex Event Processing

Distributed systems like crisis management are subject to the dissemination of a huge volume of heterogeneous events, ranging from low level network data to high level crisis management intelligence, depending on the role of the rescue teams involved. In such systems, Complex Event Processing (CEP) has emerged as a solution to detect and react (in real-time) to complex events, which are correlations of more primitive events. Although various CEP engines implement the support for dealing with the business heterogeneity of events, the technological integration of these events remains uncovered. Therefore, in this paper we introduce DiCEPE (Distributed Complex Event Processing Engine), a platform which focuses on the integration of CEP engines in distributed systems. DiCEPE provides a native support for various communication protocols in order to federate CEP engines and ease the deployment of complex systems-of-systems. We illustrate our proposal using a nuclear crisis management scenario and show how DiCEPE leverages the coordination and the federation of different CEP engines.

Model-Driven Management of Docker Containers

With the emergence of Docker, it becomes easier to encapsulate applications and their dependencies into lightweight Linux containers and make them available to the world by deploying them in the cloud. Compared to hypervisor-based virtualization approaches, the use of containers provides faster start-ups times and reduces the consumption of computer resources. However, Docker lacks of deployability verification tool for containers at design time. Currently, the only way to be sure that the designed containers will execute well is to test them in a running system. If errors occur, a correction is made but this operation can be repeated several times before the deployment becomes operational. Docker does not provide a solution to increase or decrease the size of container resources in demand. Besides the deployment of containers, Docker lacks of synchronization between the designed containers and those deployed. Moreover, container management with Docker is done at low level, and therefore requires users to focus on low level system issues. In this paper we focus on these issues related to the management of Docker containers. In particular, we propose an approach for modeling Docker containers. We provide tooling to ensure the deployability and the management of Docker containers. We illustrate our proposal using an event processing application and show how our solution provides a significantly better compromise between performance and development costs than the basic Docker container solution.

A Study of Virtual Machine Placement Optimization in Data Centers

In recent years, cloud computing has shown a valuable way for accommodating and providing services over the Internet such that data centers rely increasingly on this platform to host a large amount of applications (web hosting, e-commerce, social networking, etc.). Thus, the utilization of servers in most data centers can be improved by adding virtualization and selecting the most suitable host for each Virtual Machine (VM). The problem of VM placement is an optimization problem aiming for multiple goals. It can be covered through various approaches. Each approach aims to simultaneously reduce power consumption, maximize resource utilization and avoid traffic congestion. The main goal of this literature survey is to provide a better understanding of existing approaches and algorithms that ensure better VM placement in the context of cloud computing and to identify future directions.

Autonomic Vertical Elasticity of Docker Containers with ELASTICDOCKER

Elasticity is the key feature of cloud computing to scale computing resources according to application workloads timely. In the literature as well as in industrial products, much attention was given to the elasticity of virtual machines, but much less to the elasticity of containers. However, containers are the new trend for packaging and deploying microservices-based applications. Moreover, most of approaches focus on horizontal elasticity, fewer works address vertical elasticity. In this paper, we propose ELASTICDOCKER, the first system powering vertical elasticity of Docker containers autonomously. Based on the well-known IBMs autonomic computing MAPE-K principles, ELASTICDOCKER scales up and down both CPU and memory assigned to each container according to the application workload. As vertical elasticity is limited to the host machine capacity, ELASTICDOCKER does container live migration when there is no enough resources on the hosting machine. Our experiments show that ELASTICDOCKER helps to reduce expenses for container customers, make better resource utilization for container providers, and improve Quality of Experience for application end-users. In addition, based on the observed migration performance metrics, the experiments reveal a high efficient live migration technique. As compared to horizontal elasticity, ELASTICDOCKER outperforms Kubernetes elasticity by 37.63%.

A Cloud-based Infrastructure for Crowdsourcing Data from Mobile Devices

In the vast galaxy of crowdsourcing activities, crowd-sensing consists in using users’ cellphones for collecting large sets of data. In this chapter, we present the APISENSE distributed crowd-sensing platform. In particular, APISENSE provides a participative environment to easily deploy sensing experiments in the wild. Beyond the scientific contributions of this platform, the technical originality of APISENSE lies in its Cloud orientation, which is built on top of the soCloud distributed multi-cloud platform, and the remote deployment of scripts within the mobile devices of the participants. We validate this solution by reporting on various crowd-sensing experiments we deployed using Android smartphones and comparing our solution to existing crowd-sensing platforms.

Towards Formal-Based Semantic Interoperability in Multi-Clouds: The FCLOUDS Framework

Multi-cloud computing has been proposed as a way to reduce vendor lock-in, to improve resiliency during outages and geo-presence, to boost performance and to lower costs. However, semantic differences between cloud providers, as well as their heterogeneous management interfaces, make changing from one provider to another very complex and costly. This is quite challenging for the implementation of multi-cloud systems. In this paper, we aim to take advantage of formal methods to define a precise semantics for multi-clouds. We propose fclouds, a formal-based framework for semantic interoperability in multi-clouds. This framework contains a catalogue of formal models that mathematically describe cloud APIs and reason over them. A precise alignment can be described between their concepts, which promotes semantic interoperability.

CloudGC: Recycling Idle Virtual Machines in the Cloud

Cloud computing conveys the image of a pool of unlimited virtual resources that can be quickly and easily provisioned to accommodate the user requirements. However, this flexibility may require to adjust physical resources at the infrastructure level to keep the pace of user requests. While elasticity can be considered as the de facto solution to support this issue, this elasticity can still be broken by budget requirements or physical limitations of a private cloud. In this paper, we therefore explore an alternative, yet complementary, solution to the problem of resource provisioning by adopting the principles of garbage collection in the context of cloud computing. In particular, our approach consists in detecting idle virtual machines to recycle their resources when the cloud infrastructure reaches its limits. We implement this approach, named CloudGC, as a new middleware service integrated within OpenStack and we demonstrate its capacity to stop the waste of cloud resources. CloudGC periodically recycles idle VM instances and automatically recovers them whenever needed. Thanks to CloudGC, cloud infrastructures can even switch between operational configurations depending on periods of activities.