Proactive software rejuvenation solution for web enviroments on virtualized platforms

The availability of the Information Technologies for everything, from everywhere, at all times is a growing requirement. We use information Technologies from common and social tasks to critical tasks like managing nuclear power plants or even the International Space Station (ISS). However, the availability of IT infrastructures is still a huge challenge nowadays. In a quick look around news, we can find reports of corporate outage, affecting millions of users and impacting on the revenue and image of the companies. It is well known that, currently, computer system outages are more often due to software faults, than hardware faults. Several studies have reported that one of the causes of unplanned software outages is the software aging phenomenon. This term refers to the accumulation of errors, usually causing resource contention, during long running application executions, like web applications, which normally cause applications/systems to hang or crash. Gradual performance degradation could also accompany software aging phenomena. The software aging phenomena are often related to memory bloating/ leaks, unterminated threads, data corruption, unreleased file-locks or overruns. We can find several examples of software aging in the industry. The work presented in this thesis aims to offer a proactive and predictive software rejuvenation solution for Internet Services against software aging caused by resource exhaustion. To this end, we first present a threshold based proactive rejuvenation to avoid the consequences of software aging. This first approach has some limitations, but the most important of them it is the need to know a priori the resource or resources involved in the crash and the critical condition values. Moreover, we need some expertise to fix the threshold value to trigger the rejuvenation action. Due to these limitations, we have evaluated the use of Machine Learning to overcome the weaknesses of our first approach to obtain a proactive and predictive solution. Finally, the current and increasing tendency to use virtualization technologies to improve the resource utilization has made traditional data centers turn into virtualized data centers or platforms. We have used a Mathematical Programming approach to virtual machine allocation and migration to optimize the resources, accepting as many services as possible on the platform while at the same time, guaranteeing the availability (via our software rejuvenation proposal) of the services deployed against the software aging phenomena. The thesis is supported by an exhaustive experimental evaluation that proves the effectiveness and feasibility of our proposals for current systems

A Taxonomy of Self-configuring Service Discovery Systems

We analyze the fundamental concepts and issues in service discovery. This analysis places service discovery in the context of distributed systems by describing service discovery as a third generation naming system. We also describe the essential architectures and the functionalities in service discovery. We then proceed to show how service discovery fits into a system, by characterizing operational aspects. Subsequently, we describe how existing state of the art performs service discovery, in relation to the operational aspects and functionalities, and identify areas for improvement

MACHS: Mitigating the Achilles Heel of the Cloud through High Availability and Performance-aware Solutions

Cloud computing is continuously growing as a business model for hosting information and communication technology applications. However, many concerns arise regarding the quality of service (QoS) offered by the cloud. One major challenge is the high availability (HA) of cloud-based applications. The key to achieving availability requirements is to develop an approach that is immune to cloud failures while minimizing the service level agreement (SLA) violations. To this end, this thesis addresses the HA of cloud-based applications from different perspectives. First, the thesis proposes a component’s HA-ware scheduler (CHASE) to manage the deployments of carrier-grade cloud applications while maximizing their HA and satisfying the QoS requirements. Second, a Stochastic Petri Net (SPN) model is proposed to capture the stochastic characteristics of cloud services and quantify the expected availability offered by an application deployment. The SPN model is then associated with an extensible policy-driven cloud scoring system that integrates other cloud challenges (i.e. green and cost concerns) with HA objectives. The proposed HA-aware solutions are extended to include a live virtual machine migration model that provides a trade-off between the migration time and the downtime while maintaining HA objective. Furthermore, the thesis proposes a generic input template for cloud simulators, GITS, to facilitate the creation of cloud scenarios while ensuring reusability, simplicity, and portability. Finally, an availability-aware CloudSim extension, ACE, is proposed. ACE extends CloudSim simulator with failure injection, computational paths, repair, failover, load balancing, and other availability-based modules