Quantifying Eventual Consistency with PBS

Data replication results in a fundamental trade-off between operation latency and consistency. At the weak end of the spectrum of possible consistency models is eventual consistency, which provides no limit to the staleness of data returned. However, anecdotally, eventual consistency is often “good enough ” for practitioners given its latency and availability benefits. In this work, we explain this phenomenon and demonstrate that, despite their weak guarantees, eventually consistent systems regularly return consistent data while providing lower latency than their strongly consistent counterparts. To quantify the behavior of eventually consistent stores, we introduce Probabilistically Bounded Staleness (PBS), a consistency model that provides expected bounds on data staleness with respect to both versions and wall clock time. We derive a closed-form solution for version-based staleness and model real-time staleness for a large class of quorum replicated, Dynamo-style stores. Using PBS, we measure the trade-off between latency and consistency for partial, non-overlapping quorum systems under Internet production workloads. We quantitatively demonstrate how and why eventually consistent systems frequently return consistent data within tens of milliseconds while offering large latency benefits. 1

A Holistic Approach to Lowering Latency in Geo-distributed Web Applications

User perceived end-to-end latency of web applications have a huge impact on the revenue for many businesses. The end-to-end latency of web applications is impacted by: (i) User to Application server (front-end) latency which includes downloading and parsing web pages, retrieving further objects requested by javascript executions; and (ii) Application and storage server(back-end) latency which includes retrieving meta-data required for an initial rendering, and subsequent content based on user actions. Improving the user-perceived performance of web applications is challenging, given their complex operating environments involving user-facing web servers, content distribution network (CDN) servers, multi-tiered application servers, and storage servers. Further, the application and storage servers are often deployed on multi-tenant cloud platforms that show high performance variability. While many novel approaches like SPDY and geo-replicated datastores have been developed to improve their performance, many of these solutions are specific to certain layers, and may have different impact on user-perceived performance. The primary goal of this thesis is to address the above challenges in a holistic manner, focusing specifically on improving the end-to-end latency of geo-distributed multi-tiered web applications. This thesis makes the following contributions: (i) First, it reduces user-facing latency by helping CDNs identify and map objects that are more critical for page-load latency to the faster CDN cache layers. Through controlled experiments on real-world web pages, we show the potential of our approach to reduce hundreds of milliseconds in latency without affecting overall CDN miss rates. (ii) Next, it reduces back-end latency by optimally adapting the datastore replication policies (including number and location of replicas) to the heterogeneity in workloads. We show the benefits of our replication models using real-world traces of Twitter, Wikipedia and Gowalla on a 8 datacenter Cassandra cluster deployed on EC2. (iii) Finally, it makes multi-tier applications resilient to the inherent performance variability in the cloud through fine-grained request redirection. We highlight the benefits of our approach by deploying three real-world applications on commercial cloud platforms

Resolving data center power bill disputes: the energy-performance trade-offs of consolidation

This is the author accepted manuscript. The final version is available from ACM via http://dx.doi.org/10.1145/2768510.2770933In this paper we challenge the common evaluation practices used for Virtual Machine (VM) consolidation, such as simulation and small testbeds, which fail to capture the fundamental trade-off between energy consumption and performance. We identify a number of over-simplifying assumptions which are typically made about the energy consumption and performance characteristics of modern networked systems. In response, we describe how more accurate models for data-center systems can be designed and used in order to create an evaluation framework that allows the more reliable exploration of the energy-performance trade-off for VM consolidation strategies.This work was jointly supported by by MINECO (grant TEC2014- 55713-R), the EPSRC INTERNET Project EP / H040536/1, and the Defense Advanced Research Projects Agency (DARPA) and the Air Force Research Laboratory (AFRL), under contract FA8750-11-C-0249. The views, opinions, and/or findings contained in this article/presentation are those of the author/presenter and should not be interpreted as representing the official views or policies, either expressed or implied, of the Defense Advanced Research Projects Agency or the Department of Defense

A Self-Organising Distributed Location Server for Ad Hoc Networks

Wireless networks allow communication between multiple devices (nodes) without the use of wires. Range in such networks is often limited restricting the use of networks to small offices and homes; however, it is possible to use nodes to forward packets for others thereby extending the communication range of individual nodes. Networks employing such forwarding are called Multi-Hop Ad Hoc Networks (MANETS) Discovering routes in MANETS is a challenging task given that the topology is flat and node addresses reveal nothing about their place in the network. In addition, nodes may move or leave changing the network topology quickly. Existing approaches to discovering locations involve either broadcast dissemination or broadcast route discovery throughout the entire network. The reliance on the use of techniques that use broadcast schemes restricts the size of network that the techniques are applicable to. Routing in large scale ad hoc networks is therefore achieved by the use of geographical forwarding. Each node is required to know its location and that of its neighbours so that it may use this information for forward packets. The next hop chosen is the neighbour that is closest to the destination and a number of techniques are used to handle scenarios here the network has areas void of nodes. Use of such geographical routing techniques requires knowledge of the destination's location. This is provided by location servers and the literature proposes a number of methods of providing them. Unfortunately many of the schemes are limited by using a proportion of the network that increases with size, thereby immediately limiting the scalability. Only one technique is surveyed that provides high scalability but it has a number of limitations in terms of handling node mobility and failure. Ad hoc networks have limited capacity and so the inspiration for a technique to address these shortcomings comes from observations of nature. Birds and ants are able to organise themselves without direct communication through the observation of their environment and their peers. They provide an emergent intelligence based on individual actions rather than group collaboration. This thesis attempts to discover whether software agents can mimic this by creating a group of agents to store location information in a specific location. Instead of requiring central co-ordination, the agents observe one another and make individual decisions to create an emergent intelligence that causes them to resist mobility and node failures. The new technique is called a Self Organising Location Server (SOLS) and is compared against existing approaches to location servers. Most existing techniques do not scale well whereas SOLS uses a new idea of a home location. The use of this idea and the self organising behaviour of the agents that store the information results in significant benefits in performance. SOLS significantly out performs Terminode home region, the only other scalable approach surveyed. SOLS is able to tolerate much higher node failure rates than expected in likely implementations of large scale ad hoc networks. In addition, SOLS successfully mitigates node mobility which is likely to be encountered in an ad hoc network

Contributions to High-Throughput Computing Based on the Peer-to-Peer Paradigm

XII, 116 p.This dissertation focuses on High Throughput Computing (HTC) systems and how to build a working HTC system using Peer-to-Peer (P2P) technologies. The traditional HTC systems, designed to process the largest possible number of tasks per unit of time, revolve around a central node that implements a queue used to store and manage submitted tasks. This central node limits the scalability and fault tolerance of the HTC system. A usual solution involves the utilization of replicas of the master node that can replace it. This solution is, however, limited by the number of replicas used. In this thesis, we propose an alternative solution that follows the P2P philosophy: a completely distributed system in which all worker nodes participate in the scheduling tasks, and with a physically distributed task queue implemented on top of a P2P storage system. The fault tolerance and scalability of this proposal is, therefore, limited only by the number of nodes in the system. The proper operation and scalability of our proposal have been validated through experimentation with a real system. The data availability provided by Cassandra, the P2P data management framework used in our proposal, is analysed by means of several stochastic models. These models can be used to make predictions about the availability of any Cassandra deployment, as well as to select the best possible con guration of any Cassandra system. In order to validate the proposed models, an experimentation with real Cassandra clusters is made, showing that our models are good descriptors of Cassandra's availability. Finally, we propose a set of scheduling policies that try to solve a common problem of HTC systems: re-execution of tasks due to a failure in the node where the task was running, without additional resource misspending. In order to reduce the number of re-executions, our proposals try to nd good ts between the reliability of nodes and the estimated length of each task. An extensive simulation-based experimentation shows that our policies are capable of reducing the number of re-executions, improving system performance and utilization of nodes

Towards Building Wind Tunnels for Data Center Design

Data center design is a tedious and expensive process. Recently, this process has become even more challenging as users of cloud services expect to have guaranteed levels of availability, durability and performance. A new challenge for the service providers is to find the most cost-effective data center design and configuration that will accommodate the users ’ expectations, on ever-changing workloads, and constantly evolving hardware and software components. In this paper, we argue that data center design should become a systematic process. First, it should be done using an integrated approach that takes into account both the hardware and the software interdependencies, and their impact on users ’ expectations. Second, it should be performed in a “wind tunnel”, which uses large-scale simulation to systematically explore the impact of a data center configuration on both the users ’ and the service providers ’ requirements. We believe that this is the first step towards systematic data center design – an exciting area for future research. 1

Byzantine fault tolerance from static selection to dynamic switching

La Tolérance aux pannes Byzantines (BFT) est de plus en plus crucial avec l'évolution d'applications et en raison de la croissance de l'innovation technologique en informatique. Bien que des dizaines de protocoles BFT aient été introduites dans les années précédentes, leur mise en œuvre ne semble pas satisfaisant. Pour faire face à cette complexité, due à la dependence d'un protocol d'une situation, nous tentons une approche qui permettra de sélectionner un protocole en fonction d'une situation. Ceci nous paraît, en s'inspirant de tout système d'encrage, comme une démarche nécessaire pour aborder la problématique de la BFT. Dans cette thèse, nous introduisons un modèle de sélection ainsi que l'algorithme qui permet de simplifier et d'automatiser le processus d'élection d'un protocole. Ce mécanisme est conçu pour fonctionner selon 3 modes : statique, dynamique et heuristique. Les deux derniers modes, nécessitent l'introduction d'un système réactif, nous ont conduits à présenter un nouveau modèle BFT : Adapt. Il réagit à tout changement et effectue, d'une manière adaptée, la commutation entre les protocoles d'une façon dynamique. Le mode statique permet aux utilisateurs de BFT de choisir un protocole BFT en une seule fois. Ceci est très utile dans les services Web et les " Clouds " où le BFT peut être fournit comme un service inclut dans le contrat (SLA). Ce mode est essentiellement conçu pour les systèmes qui n'ont pas trop d'états fluctuants. Pour ce faire, un processus d'évaluation est en charge de faire correspondre, à priori, les préférences de l'utilisateur aux profils du protocole BFT nommé, en fonction des critères de fiabilité et de performance. Le protocole choisi est celui qui réalise le meilleur score d'évaluation. Le mécanisme est bien automatisé à travers des matrices mathématiques, et produit des sélections qui sont raisonnables. D'autres systèmes peuvent cependant avoir des conditions flottantes, il s'agit de la variation des charges ou de la taille de message qui n'est pas fixe. Dans ce cas, le mode statique ne peut continuer à être efficace et risque de ne pas pouvoir s'adapter aux nouvelles conditions. D'où la nécessité de trouver un moyen permettant de répondre aux nouvelles exigences d'une façon dynamique. Adapt combine un ensemble de protocoles BFT ainsi que leurs mécanismes de commutation pour assurer l'adaptation à l'évolution de l'état du système. Par conséquent, le "Meilleur" protocole est toujours sélectionné selon l'état du système. On obtient ainsi une qualité optimisée de service, i.e., la fiabilité et la performance. Adapt contrôle l'état du système grâce à ses mécanismes d'événements, et utilise une méthode de "Support Vecor Regrssion" pour conduire aux prédictions en temps réel pour l'exécution des protocoles (par exemple, débit, latence, etc.). Ceci nous conduit aussi à un mode heuristique. En utilisant des heuristiques prédéfinies, on optimise les préférences de l'utilisateur afin d'améliorer le processus de sélection. L'évaluation de notre approche montre que le choix du "meilleur" protocole est automatisé et proche de la réalité de la même façon que dans le mode statique. En mode dynamique, Adapt permet toujours d'obtenir la performance optimale des protocoles disponibles. L'évaluation démontre, en plus, que la performance globale du système peut être améliorée de manière significative. Explorer d'autres cas qui ne conduisent pas de basculer entre les protocoles. Ceci est rendu possible grâce à la réalisation des prévisions d'une grande precision qui peuvent atteindre plus de 98% dans de nombreux cas. La thèse montre que cette adaptabilité est rendue possible grâce à l'utilisation des heuristiques dans un mode dynamique.Byzantine Fault Tolerance (BFT) is becoming crucial with the revolution of online applications and due to the increasing number of innovations in computer technologies. Although dozens of BFT protocols have been introduced in the previous decade, their adoption by practitioners sounds disappointing. To some extant, this indicates that existing protocols are, perhaps, not yet too convincing or satisfactory. The problem is that researchers are still trying to establish 'the best protocol' using traditional methods, e.g., through designing new protocols. However, theoretical and experimental analyses demonstrate that it is hard to achieve one-size-fits-all BFT protocols. Indeed, we believe that looking for smarter tac-tics like 'fasten fragile sticks with a rope to achieve a solid stick' is necessary to circumvent the issue. In this thesis, we introduce the first BFT selection model and algorithm that automate and simplify the election process of the 'preferred' BFT protocol among a set of candidate ones. The selection mechanism operates in three modes: Static, Dynamic, and Heuristic. For the two latter modes, we present a novel BFT system, called Adapt, that reacts to any potential changes in the system conditions and switches dynamically between existing BFT protocols, i.e., seeking adaptation. The Static mode allows BFT users to choose a single BFT protocol only once. This is quite useful in Web Services and Clouds where BFT can be sold as a service (and signed in the SLA contract). This mode is basically designed for systems that do not have too fuctuating states. In this mode, an evaluation process is in charge of matching the user preferences against the profiles of the nominated BFT protocols considering both: reliability, and performance. The elected protocol is the one that achieves the highest evaluation score. The mechanism is well automated via mathematical matrices, and produces selections that are reasonable and close to reality. Some systems, however, may experience fluttering conditions, like variable contention or message payloads. In this case, the static mode will not be e?cient since a chosen protocol might not fit the new conditions. The Dynamic mode solves this issue. Adapt combines a collection of BFT protocols and switches between them, thus, adapting to the changes of the underlying system state. Consequently, the 'preferred' protocol is always polled for each system state. This yields an optimal quality of service, i.e., reliability and performance. Adapt monitors the system state through its Event System, and uses a Support Vector Regression method to conduct run time predictions for the performance of the protocols (e.g., throughput, latency, etc). Adapt also operates in a Heuristic mode. Using predefined heuristics, this mode optimizes user preferences to improve the selection process. The evaluation of our approach shows that selecting the 'preferred' protocol is automated and close to reality in the static mode. In the Dynamic mode, Adapt always achieves the optimal performance among available protocols. The evaluation demonstrates that the overall system performance can be improved significantly too. Other cases explore that it is not always worthy to switch between protocols. This is made possible through conducting predictions with high accuracy, that can reach more than 98% in many cases. Finally, the thesis shows that Adapt can be smarter through using heursitics