Alain Tchana

Roboconf: A Hybrid Cloud Orchestrator to Deploy Complex Applications

This paper presents Roboconf, an open-source distributed application orchestration framework for multi-cloud platforms, designed to solve challenges of current Autonomic Computing Systems in the era of Cloud computing. It provides a Domain Specific Language (DSL) which allows to describe applications and their execution environments (cloud platforms) in a hierarchical way in order to provide a fine-grained management. Roboconf implements an asynchronous and parallel deployment protocol which accelerates and makes resilient the deployment process. Intensive experiments with different type of applications over different cloud models (e.g. Private, hybrid, and multi-cloud) validate the genericity of Roboconf. These experiments also demonstrate its efficiency comparing to existing frameworks such as Right Scale, Scalr, and Cloudify.

An adaptable framework to deploy complex applications onto multi-cloud platforms

Cloud computing is nowadays a popular technology for hosting IT services. However, deploying and reconfiguring complex applications involving multiple software components, which are distributed on many virtual machines running on single or multi-cloud platforms, is error-prone and time-consuming for human administrators. Existing deployment frameworks are most of the time either dedicated to a unique type of applica- tion (e.g. JEE applications) or address a single cloud platform (e.g. Amazon EC2). This paper presents a novel distributed application management framework for multi-cloud platforms. It provides a Domain Specific Language (DSL) which allows to describe applications and their execution environments (cloud platforms) in a hierarchical way in order to provide a fine-grained management. This framework implements an asynchronous and parallel deployment protocol which accelerates and make resilient the deployment process. A prototype has been developed to serve conducting intensive experiments with different type of applications (e.g. OSGi application and ubiquitous big data analytics for IoT) over disparate cloud models (e.g. private, hybrid, and multi-cloud), which validate the genericity of the framework. These experiments also demonstrate its efficiency comparing to existing frameworks such as Cloudify.

Coordinating self-sizing and self-repair managers for multi-tier systems

Computing systems have become more and more distributed and heterogeneous, making their manual administration difficult and error-prone. The Autonomic Computing approach has been proposed to overcome this issue, by automating the administration of computing systems with the help of control loops called autonomic managers. Many research works have investigated the automation of the administration functions of computing systems and today many autonomic managers are available. However the existing autonomic manages are mostly specialized in the management of few administration concerns such as self-repair which handles server failures, and self-sizing which deals with dynamic server allocation. This makes necessary the coexistence of multiple autonomic managers for a complete system management. The coexistence of several such managers is required to handle multiple concerns, yet requires coordination mechanisms to avoid incoherent administration decisions. We investigate the use of control techniques for the design of coordination controllers, for which we exercise synchronous programming that provide formal semantics, and discrete controller synthesis to automate the construction of the controller. The paper details an application of the latter approach for the design of a coordination controller to orchestrate the execution of four self-repair and two self-sizing managers that address the availability and performance of a multi-tier replication-based system. We evaluate and demonstrate the benefits of our coordination solution by executing the RUBiS Benchmark web application.

Software consolidation as an efficient energy and cost saving solution

Virtual machines (VM) are used in cloud computing environments to isolate different software. They also support live migration, and thus dynamic VM consolidation. This possibility can be used to reduce power consumption in the cloud. However, consolidation in cloud environments is limited due to reliance on VMs, mainly due to their memory overhead. For instance, over a 4-month period in a real cloud located in Grenoble (France), we observed that 805 VMs used less than 12% of the CPU (of the active physical machines). This paper presents a solution introducing dynamic software consolidation. Software consolidation makes it possible to dynamically collocate several software applications on the same VM to reduce the number of VMs used. This approach can be combined with VM consolidation which collocates multiple VMs on a reduced number of physical machines. Software consolidation can be used in a private cloud to reduce power consumption, or by a client of a public cloud to reduce the number of VMs used, thus reducing costs. The solution was tested with a cloud hosting JMS messaging and Internet servers. The evaluations were performed using both the SPECjms2007 benchmark and an enterprise LAMP benchmark on both a VMware private cloud and Amazon EC2 public cloud. The results show that our approach can reduce the energy consumed in our private cloud by about 40% and the charge for VMs on Amazon EC2 by about 40.5%.

A High Level Approach for Generating Model's Graphical Editors

Domain Specific Languages (DSL) are increasingly used in software engineering and other domains. The result is an increasing need of appropriate DSLs tools, especially platform for building, editors and runtime associated with DSLs. Different experiences show that existent DSL tools are generally not user friendly enough, or simply unadapted for the generation of graphical DSL editors. In this paper we present a higher level and effortless framework for generating graphical DSL editors. This framework was designed and experienced in the context of an autonomic management system based on a component model.

Software Consolidation as an Efficient Energy and Cost Saving Solution for a SaaS/PaaS Cloud Model

Virtual machines (VM) are used in cloud computing environments to isolate different software. Virtualization enables live migration, and thus dynamic VM consolidation. This possibility can be used to reduce power consumption in the cloud. However, consolidation in cloud environments is limited due to reliance on VMs, mainly due to their memory overhead. For instance, over a 4-month period in a real cloud located in Grenoble (France), we observed that 805 VMs used less than 12 % of the CPU (of the active physical machines). This paper presents a solution introducing dynamic software consolidation. Software consolidation makes it possible to dynamically collocate several software applications on the same VM to reduce the number of VMs used. This approach can be combined with VM consolidation which collocates multiple VMs on a reduced number of physical machines. Software consolidation can be used in a private cloud to reduce power consumption, or by a client of a public cloud to reduce the number of VMs used, thus reducing costs. The evaluation was performed using both the SPECjms2007 benchmark and an enterprise LAMP benchmark on both a VMware private cloud and Amazon EC2 public cloud. The results show that our approach can reduce the energy consumed in our private cloud by about 40 % and the charge for VMs on Amazon EC2 by about 40.5 %.

A self-scalable load injection service

Load testing of applications is an important and costly activity for software provider companies. Classical solutions are very difficult to set up statically, and their cost is prohibitive in terms of both human and hardware resources. Virtualized cloud computing platforms provide new opportunities for stressing an applications scalability, by providing a large range of flexible and less expensive (pay‐per‐use model) computation units. On the basis of these advantages, load testing solutions could be provided on demand in the cloud. This paper describes a Benchmark‐as‐a‐Service solution that automatically scales the load injection platform and facilitates its setup according to load profiles. Our approach is based on: (i) virtualization of the benchmarking platform to create self‐scaling injectors; (ii) online calibration to characterize the injectors capacity and impact on the benched application; and (iii) a provisioning solution to appropriately scale the load injection platform ahead of time. We also report experiments on a benchmark illustrating the benefits of this system in terms of cost and resource reductions. Copyright

The lock holder and the lock waiter pre-emption problems: nip them in the bud using informed spinlocks (I-Spinlock)

In native Linux systems, spinlocks implementation relies on the assumption that both the lock holder thread and lock waiter threads cannot be preempted. However, in a virtualized environment, these threads are scheduled on top of virtual CPUs (vCPU) that can be preempted by the hypervisor at any time, thus forcing lock waiter threads on other vCPUs to busy wait and to waste CPU cycles. This leads to the well-known Lock Holder Preemption (LHP) and Lock Waiter Preemption (LWP) issues. In this paper, we propose I-Spinlock (for Informed Spinlock), a new spinlock implementation for virtualized environments. Its main principle is to only allow a thread to acquire a lock if and only if the remaining time-slice of its vCPU is sufficient to enter and leave the critical section. This is possible if the spinlock primitive is aware (informed) of its time-to-preemption (by the hypervisor). We implemented I-Spinlock in the Xen virtualization system. We show that our solution is compliant with both para-virtual and hardware virtualization modes. We performed extensive performance evaluations with various reference benchmarks and compared our solution to previous solutions. The evaluations demonstrate that I-Spinlock outperforms other solutions, and more significantly when the number of core increases.

A Self-Scalable and Auto-Regulated Request Injection Benchmarking Tool for Automatic Saturation Detection

Software applications providers have always been required to perform load testing prior to launching new applications. This crucial test phase is expensive in human and hardware terms, and the solutions generally used would benefit from further development. In particular, designing an appropriate load profile to stress an application is difficult and must be done carefully to avoid skewed testing. In addition, static testing platforms are exceedingly complex to set up. New opportunities to ease load testing solutions are becoming available thanks to cloud computing. This paper describes a Benchmark-as-a-Service platform based on: (i) intelligent generation of traffic to the benched application without inducing thrashing (avoiding predefined load profiles), (ii) a virtualized and self-scalable load injection system. The platform developed was experimented using two use cases based on the reference JEE benchmark RUBiS. This involved detecting bottleneck tiers, and tuning servers to improve performance. This platform was found to reduce the cost of testing by 50 percent compared to more commonly used solutions.

Dynamic Scalability of a Consolidation Service

In the coming years, cloud environments will increasingly face energy saving issues. While consolidating the virtual machines running in a cloud is a well-accepted solution to reduce the energy consumption, ensuring the scalability of the consolidation service remains a challenging issue. In this paper, we propose an elastic consolidation service that scales according to the dynamic needs of the cloud environment. Our proposition is based on (i) virtualizing the consolidation manager, (ii) partitioning the consolidation work and (iii) regulating the consolidation scalability through an autonomic control loop. Our proposition has been tested and validated through several experiments.