Daniel Hagimont

Approaches to cloud computing fault tolerance

Fault tolerance in cloud computing platforms and applications is a crucial issue. This issue is especially difficult since cloud computing relies by nature on a complex splitting into many layers. From a technical point of view, most cloud computing platforms exploit virtualization, which implies that they are split into 3 layers: hosts, virtual machines and applications. From an administration point of view, they are split into 2 layers: the cloud provider who manages the hosting center and the customer who manages his application in the cloud. This paper analyses the implementation of fault tolerance in such a complex cloud computing environment with a focus on autonomic repair. We show that in most of current approaches, fault tolerance is exclusively handled by the provider or the customer which leads to partial or inefficient solutions, while collaborative solutions are much promising. We illustrate this discussion with experiments where exclusive and collaborative fault tolerance solutions are implemented in an autonomic cloud infrastructure that we prototyped.

DVFS Aware CPU Credit Enforcement in a Virtualized System

Nowadays, virtualization is present in almost all computing infrastructures. Thanks to VM migration and server consolidation, virtualization helps reducing power consumption in distributed environments. On another side, Dynamic Voltage and Frequency Scaling (DVFS) allows servers to dynamically modify the processor frequency (according to the CPU load) in order to achieve less energy consumption. We observed that these two techniques have several incompatibilities. For instance, if two virtual machines VM1 and VM2 are running on the same physical host (with their respective allocated credits), VM1 being overloaded and VM2 being underloaded, the host may be globally underloaded leading to a reduction of the processor frequency, which would penalize VM1 even if VM1’s owner booked a given CPU capacity. In this paper, we analyze the compatibility of available VM schedulers with DVFS management in virtualized environments, we identify key issues and finally propose a DVFS aware VM scheduler which addresses these issues. We implemented and evaluated our prototype in the Xen virtualized environment.

Two levels autonomic resource management in virtualized IaaS

Virtualized cloud infrastructures are very popular as they allow resource mutualization and therefore cost reduction. For cloud providers, minimizing the number of used resources is one of the main services that such environments must ensure. Cloud customers are also concerned with the minimization of used resources in the cloud since they want to reduce their invoice. Thus, resource management in the cloud should be considered by the cloud provider at the virtualization level and by the cloud customers at the application level. Many research works investigate resource management strategies in these two levels. Most of them study virtual machine consolidation (according to the virtualized infrastructure utilization rate) at the virtualized level and dynamic application sizing (according to its workload) at the application level. However, these strategies are studied separately. In this article, we show that virtual machine consolidation and dynamic application sizing are complementary. We show the efficiency of the combination of these two strategies, in reducing resource usage and keeping an applications Quality of Service. Our demonstration is done by comparing the evaluation of three resource management strategies (implemented at the virtualization level only, at the application level only, or complementary at both levels) in a private cloud infrastructure, hosting typical JEE web applications (evaluated with the RUBiS benchmark).

Self-Protection in a Clustered Distributed System

Self-protection refers to the ability for a system to detect illegal behaviors and to fight-back intrusions with counter-measures. This article presents the design, the implementation, and the evaluation of a self-protected system which targets clustered distributed applications. Our approach is based on the structural knowledge of the cluster and of the distributed applications. This knowledge allows to detect known and unknown attacks if an illegal communication channel is used. The current prototype is a self-protected JEE infrastructure (Java 2 Enterprise Edition) with firewall-based intrusion detection. Our prototype induces low-performance penalty for applications.

Non-Intrusive Autonomic Approach with Self-Management Policies Applied to Legacy Infrastructures for Performance Improvements

The growing complexity of large IT facilities involves important time and effort costs to operate and maintain. Autonomic computing gives a new approach in designing distributed architectures that manage themselves in accordance with high-level objectives. The main issue is that existing architectures do not necessarily follow this new approach. The motivation is to implement a system that can interface heterogeneous components and platforms supplied by different vendors in a non-intrusive and generic manner. The goal is to increase the intelligence of the system by actively monitoring its state and autonomously taking corrective actions without the need to modify the managed system. In this paper, the authors focus on modeling software and hardware architectures as well as describing administration policies using a graphical language inspired from UML. The paper demonstrates that this language is powerful enough to describe complex scenarios and evaluates some self-management policies for performance improvement on a distributed computational jobs load balancer over a grid.

Autonomic Management of Internet Services: Experience with Self-Optimization

Autonomic computing is an appealing approach to facilitate the management of distributed systems. However, several challenges remain open among which: (i) How to manage legacy systems? (ii) How to manage complex distributed systems? In this paper, we precisely address these issues. The paper presents the design, implementation and evaluation of Jade, an environment for autonomic management of legacy computing systems. This environment relies on two main frameworks: a framework for administrable elements, which provides the administered legacy system’s elements with a uniform management interface; and a framework for autonomic managers, which regulates a set of managed elements for a specific management aspect. The aspect that we consider in this paper is self-optimization, i.e. preserving the system’s performance in response to wide variations of the load. The main contribution of this paper is a generic architectural model that eases the construction of autonomically managed systems. Thus, integrating a new managed legacy system only necessitates a few hundred lines of code. Another contribution of the paper is the application of this model to self-optimization of complex and multi-tier Internet services. Our experiments on an auction site show that Jade is able to maintain the client perceived performance stable when the workload varies, while, without Jade, the performance continuously decreases. Furthermore, in these experiments Jade allows twice as many client requests to be served as a system with no autonomic management.

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%.

Large Scale Deployment

Current computing platforms become more and more complex for users to use. To simplify configuration and deployment of applications on these infrastructures tools are necessary. Current deployment tools lack of maturity for large-scale deployment. For example a grid environment leads to a great diversity. Deploying an application on a grid from a single centralized machine is not adapt. So, decentralization of management is needed. We present in this paper a hierarchical deployment with the TUNe autonomic management system. This approach is based on UML formalisms.

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 %.

Self-sizing of clustered databases

Distributed software environments are increasingly difficult to manage. This paper presents a middleware for the development of self-manageable and autonomic systems. Preliminary experiments for automatically adapting a cluster of replicated databases according to QoS requirements are reported