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

An Analysis of Linux Scalability to Many Cores

URL to paper from conference siteThis paper analyzes the scalability of seven system applications (Exim, memcached, Apache, PostgreSQL, gmake, Psearchy, and MapReduce) running on Linux on a 48- core computer. Except for gmake, all applications trigger scalability bottlenecks inside a recent Linux kernel. Using mostly standard parallel programming techniques— this paper introduces one new technique, sloppy counters— these bottlenecks can be removed from the kernel or avoided by changing the applications slightly. Modifying the kernel required in total 3002 lines of code changes. A speculative conclusion from this analysis is that there is no scalability reason to give up on traditional operating system organizations just yet.Quanta Computer (Firm)National Science Foundation (U.S.) (0834415)National Science Foundation (U.S.) (0915164)Microsoft Research (Fellowship)Irwin Mark Jacobs and Joan Klein Jacobs Presidential Fellowshi

Investigation of High-Level Language Support in a Resource-Constrained Embedded Environment

Personal computers have gained a significant boost in computational power and digital storage space at a reduced cost in the last decade. In the search of increased programmer productivity and cross platform portability, language popularity has shifted from lower level languages such as C to higher level languages such as Java and C#. Many of today’s embedded systems are experiencing the same development as the personal computers did. However, most companies dealing with embedded devices still use C. We investigated what effect a shift like this would have at Axis Communications. The study was done by setting up C# and Java on a camera and conducting performance tests on it. The analysis showed that when using C# as a replacement for C, we saw improvements in programmer productivity whilst still upholding performance for some applications. For the most performance intense use cases, the performance requirements were not satisfied. With the growth of high-level languages, we do see a bright future for the support for them in embedded systems

Achieving Continuous Delivery of Immutable Containerized Microservices with Mesos/Marathon

In the recent years, DevOps methodologies have been introduced to extend the traditional agile principles which have brought up on us a paradigm shift in migrating applications towards a cloud-native architecture. Today, microservices, containers, and Continuous Integration/Continuous Delivery have become critical to any organization’s transformation journey towards developing lean artifacts and dealing with the growing demand of pushing new features, iterating rapidly to keep the customers happy. Traditionally, applications have been packaged and delivered in virtual machines. But, with the adoption of microservices architectures, containerized applications are becoming the standard way to deploy services to production. Thanks to container orchestration tools like Marathon, containers can now be deployed and monitored at scale with ease. Microservices and Containers along with Container Orchestration tools disrupt and redefine DevOps, especially the delivery pipeline. This Master’s thesis project focuses on deploying highly scalable microservices packed as immutable containers onto a Mesos cluster using a container orchestrating framework called Marathon. This is achieved by implementing a CI/CD pipeline and bringing in to play some of the greatest and latest practices and tools like Docker, Terraform, Jenkins, Consul, Vault, Prometheus, etc. The thesis is aimed to showcase why we need to design systems around microservices architecture, packaging cloud-native applications into containers, service discovery and many other latest trends within the DevOps realm that contribute to the continuous delivery pipeline. At BetterDoctor Inc., it is observed that this project improved the avg. release cycle, increased team members’ productivity and collaboration, reduced infrastructure costs and deployment failure rates. With the CD pipeline in place along with container orchestration tools it has been observed that the organisation could achieve Hyperscale computing as and when business demands

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