Snapshot Provisioning of Cloud Application Stacks to Face Traffic Surges

Traffic surges, like the Slashdot effect, occur when a web application is overloaded by a huge number of requests, potentially leading to unavailability. Unfortunately, such traffic variations are generally totally unplanned, of great amplitude, within a very short period, and a variable delay to return to a normal regime. In this report, we introduce PeakForecast as an elastic middleware solution to detect and absorb a traffic surge. In particular, PeakForecast can, from a trace of queries received in the last seconds, minutes or hours, to detect if the underlying system is facing a traffic surge or not, and then estimate the future traffic using a forecast model with an acceptable precision, thereby calculating the number of resources required to absorb the remaining traffic to come. We validate our solution by experimental results demonstrating that it can provide instantaneous elasticity of resources for traffic surges observed on the Japanese version of Wikipedia during the Fukushima Daiichi nuclear disaster in March 2011.Les pics de trafic, tels que l'effet Slashdot, apparaissent lorsqu'une application web doit faire face un nombre important de requêtes qui peut potentiellement entraîner une mise hors service de l'application. Malheureusement, de telles variations de traffic sont en général totalement imprévues et d'une grande amplitude, arrivent pendant une très courte période de temps et le retour à un régime normal prend un délai variable. Dans ce rapport, nous présentons PeakForecast qui est une solution intergicielle élastique pour détecter et absorber les pics de trafic. En particulier, PeakForecast peut, à partir des traces de requêtes reçues dans les dernières secondes, minutes ou heures, détecter si le système sous-jacent fait face ou non à un pic de trafic, estimer le trafic futur en utilisant un modèle de prédiction suffisamment précis, et calculer le nombre de ressources nécessaires à l'absorption du trafic restant à venir. Nous validons notre solution avec des résultats expérimentaux qui démontrent qu'elle fournit une élasticité instantanée des ressources pour des pics de trafic qui ont été observés sur la version japonaise de Wikipedia lors de l'accident nucléaire de Fukushima Daiichi en mars 2011

Snapshot Provisioning of Cloud Application Stacks to Face Traffic Surges

Traffic surges, like the Slashdot effect, occur when a web application is overloaded by a huge number of requests, potentially leading to unavailability. Unfortunately, such traffic variations are generally totally unplanned, of great amplitude, within a very short period, and a variable delay to return to a normal regime. In this report, we introduce PeakForecast as an elastic middleware solution to detect and absorb a traffic surge. In particular, PeakForecast can, from a trace of queries received in the last seconds, minutes or hours, to detect if the underlying system is facing a traffic surge or not, and then estimate the future traffic using a forecast model with an acceptable precision, thereby calculating the number of resources required to absorb the remaining traffic to come. We validate our solution by experimental results demonstrating that it can provide instantaneous elasticity of resources for traffic surges observed on the Japanese version of Wikipedia during the Fukushima Daiichi nuclear disaster in March 2011.Les pics de trafic, tels que l'effet Slashdot, apparaissent lorsqu'une application web doit faire face un nombre important de requêtes qui peut potentiellement entraîner une mise hors service de l'application. Malheureusement, de telles variations de traffic sont en général totalement imprévues et d'une grande amplitude, arrivent pendant une très courte période de temps et le retour à un régime normal prend un délai variable. Dans ce rapport, nous présentons PeakForecast qui est une solution intergicielle élastique pour détecter et absorber les pics de trafic. En particulier, PeakForecast peut, à partir des traces de requêtes reçues dans les dernières secondes, minutes ou heures, détecter si le système sous-jacent fait face ou non à un pic de trafic, estimer le trafic futur en utilisant un modèle de prédiction suffisamment précis, et calculer le nombre de ressources nécessaires à l'absorption du trafic restant à venir. Nous validons notre solution avec des résultats expérimentaux qui démontrent qu'elle fournit une élasticité instantanée des ressources pour des pics de trafic qui ont été observés sur la version japonaise de Wikipedia lors de l'accident nucléaire de Fukushima Daiichi en mars 2011

Feature-Model-Guided Online Learning for Self-Adaptive Systems

A self-adaptive system can modify its own structure and behavior at runtime based on its perception of the environment, of itself and of its requirements. To develop a self-adaptive system, software developers codify knowledge about the system and its environment, as well as how adaptation actions impact on the system. However, the codified knowledge may be insufficient due to design time uncertainty, and thus a self-adaptive system may execute adaptation actions that do not have the desired effect. Online learning is an emerging approach to address design time uncertainty by employing machine learning at runtime. Online learning accumulates knowledge at runtime by, for instance, exploring not-yet executed adaptation actions. We address two specific problems with respect to online learning for self-adaptive systems. First, the number of possible adaptation actions can be very large. Existing online learning techniques randomly explore the possible adaptation actions, but this can lead to slow convergence of the learning process. Second, the possible adaptation actions can change as a result of system evolution. Existing online learning techniques are unaware of these changes and thus do not explore new adaptation actions, but explore adaptation actions that are no longer valid. We propose using feature models to give structure to the set of adaptation actions and thereby guide the exploration process during online learning. Experimental results involving four real-world systems suggest that considering the hierarchical structure of feature models may speed up convergence by 7.2% on average. Considering the differences between feature models before and after an evolution step may speed up convergence by 64.6% on average. [...

Workload Interleaving with Performance Guarantees in Data Centers

In the era of global, large scale data centers residing in clouds, many applications and users share the same pool of resources for the purposes of reducing energy and operating costs, and of improving availability and reliability. Along with the above benefits, resource sharing also introduces performance challenges: when multiple workloads access the same resources concurrently, contention may occur and introduce delays in the performance of individual workloads. Providing performance isolation to individual workloads needs effective management methodologies. The challenges of deriving effective management methodologies lie in finding accurate, robust, compact metrics and models to drive algorithms that can meet different performance objectives while achieving efficient utilization of resources. This dissertation proposes a set of methodologies aiming at solving the challenging performance isolation problem in workload interleaving in data centers, focusing on both storage components and computing components. at the storage node level, we focus on methodologies for better interleaving user traffic with background workloads, such as tasks for improving reliability, availability, and power savings. More specifically, a scheduling policy for background workload based on the statistical characteristics of the system busy periods and a methodology that quantitatively estimates the performance impact of power savings are developed. at the storage cluster level, we consider methodologies on how to efficiently conduct work consolidation and schedule asynchronous updates without violating user performance targets. More specifically, we develop a framework that can estimate beforehand the benefits and overheads of each option in order to automate the process of reaching intelligent consolidation decisions while achieving faster eventual consistency. at the computing node level, we focus on improving workload interleaving at off-the-shelf servers as they are the basic building blocks of large-scale data centers. We develop priority scheduling middleware that employs different policies to schedule background tasks based on the instantaneous resource requirements of the high priority applications running on the server node. Finally, at the computing cluster level, we investigate popular computing frameworks for large-scale data intensive distributed processing, such as MapReduce and its Hadoop implementation. We develop a new Hadoop scheduler called DyScale to exploit capabilities offered by heterogeneous cores in order to achieve a variety of performance objectives