OverFlow: Multi-Site Aware Big Data Management for Scientific Workflows on Clouds

International audienceThe global deployment of cloud datacenters is enabling large scale scientific workflows to improve performance and deliver fast responses. This unprecedented geographical distribution of the computation is doubled by an increase in the scale of the data handled by such applications, bringing new challenges related to the efficient data management across sites. High throughput, low latencies or cost-related trade-offs are just a few concerns for both cloud providers and users when it comes to handling data across datacenters. Existing solutions are limited to cloud-provided storage, which offers low performance based on rigid cost schemes. In turn, workflow engines need to improvise substitutes, achieving performance at the cost of complex system configurations, maintenance overheads, reduced reliability and reusability. In this paper, we introduce OverFlow, a uniform data management system for scientific workflows running across geographically distributed sites, aiming to reap economic benefits from this geo-diversity. Our solution is environment-aware, as it monitors and models the global cloud infrastructure, offering high and predictable data handling performance for transfer cost and time, within and across sites. OverFlow proposes a set of pluggable services, grouped in a data scientist cloud kit. They provide the applications with the possibility to monitor the underlying infrastructure, to exploit smart data compression, deduplication and geo-replication, to evaluate data management costs, to set a tradeoff between money and time, and optimize the transfer strategy accordingly. The system was validated on the Microsoft Azure cloud across its 6 EU and US datacenters. The experiments were conducted on hundreds of nodes using synthetic benchmarks and real-life bio-informatics applications (A-Brain, BLAST). The results show that our system is able to model accurately the cloud performance and to leverage this for efficient data dissemination, being able to reduce the monetary costs and transfer time by up to 3 times

Squeezing the most benefit from network parallelism in datacenters

One big non-blocking switch is one of the most powerful and pervasive abstractions in datacenter networking. As Moore's law begins to wane, using parallelism to scale out processing units, vs. scale them up, is becoming exceedingly popular. The one-big-switch abstraction, for example, is typically implemented via leveraging massive degrees of parallelism behind the scene. In particular, in today's datacenters that exhibit a high degree of multi-pathing, each logical path between a communicating pair in the one-big-switch abstraction is mapped to a set of paths that can carry traffic in parallel. Similarly, each one-big-switch abstraction function, such as the firewall functionality, is mapped to a set of distributed hardware and software switches. Efficiently deploying this pool of networking connectivity and preserving the functional correctness of network functions, in spite of the parallelism, are challenging. Efficiently balancing the load among multiple paths is challenging because microbursts, responsible for the majority of packet loss in datacenters today, usually last for only a few microseconds. Even the fastest traffic engineering schemes today have control loops that are several orders of magnitude slower (a few milliseconds to a few seconds), and are therefore ineffective in controlling microbursts. Correctly implementing network functions in the face of parallelism is hard because the distributed set of elements that in parallel implement a one-big-switch abstraction can inevitably have inconsistent states that may cause them to behave differently than one physical switch. The first part of this thesis presents DRILL, a datacenter fabric for Clos networks which performs micro load balancing to distribute load as evenly as possible on microsecond timescales. To achieve this, DRILL employs packet-level decisions at each switch based on local queue occupancies and randomized algorithms to distribute load. Despite making per-packet forwarding decisions, by enforcing a tight control on queue occupancies, DRILL manages to keep the degree of packet reordering low. DRILL adapts to topological asymmetry (e.g. failures) in Clos networks by decomposing the network into symmetric components. Using a detailed switch hardware model, we simulate DRILL and show it outperforms recent edge-based load balancers particularly in the tail latency under heavy load, e.g., under 80% load, it reduces the 99.99th percentile of flow completion times of Presto and CONGA by 32% and 35%, respectively. Finally, we analyze DRILL's stability and throughput-efficiency. In the second part, we focus on the correctness of one-big-switch abstraction's implementation. We first show that naively using parallelism to scale networking elements can cause incorrect behavior. For example, we show that an IDS system which operates correctly as a single network element can erroneously and permanently block hosts when it is replicated. We then provide a system, COCONUT, for seamless scale-out of network forwarding elements; that is, an SDN application programmer can program to what functionally appears to be a single forwarding element, but which may be replicated behind the scenes. To do this, we identify the key property for seamless scale out, weak causality, and guarantee it through a practical and scalable implementation of vector clocks in the data plane. We build a prototype of COCONUT and experimentally demonstrate its correct behavior. We also show that its abstraction enables a more efficient implementation of seamless scale-out compared to a naive baseline. Finally, reasoning about network behavior requires a new model that enables us to distinguish between observable and unobservable events. So in the last part, we present the Input/Output Automaton (IOA) model and formalize networks' behaviors. Using this framework, we prove that COCONUT enables seamless scale out of networking elements, i.e., the user-perceived behavior of any COCONUT element implemented with a distributed set of concurrent replicas is provably indistinguishable from its singleton implementation

Anales del XIII Congreso Argentino de Ciencias de la Computación (CACIC)

Contenido: Arquitecturas de computadoras Sistemas embebidos Arquitecturas orientadas a servicios (SOA) Redes de comunicaciones Redes heterogéneas Redes de Avanzada Redes inalámbricas Redes móviles Redes activas Administración y monitoreo de redes y servicios Calidad de Servicio (QoS, SLAs) Seguridad informática y autenticación, privacidad Infraestructura para firma digital y certificados digitales Análisis y detección de vulnerabilidades Sistemas operativos Sistemas P2P Middleware Infraestructura para grid Servicios de integración (Web Services o .Net)Red de Universidades con Carreras en Informática (RedUNCI

Anales del XIII Congreso Argentino de Ciencias de la Computación (CACIC)

Contenido: Arquitecturas de computadoras Sistemas embebidos Arquitecturas orientadas a servicios (SOA) Redes de comunicaciones Redes heterogéneas Redes de Avanzada Redes inalámbricas Redes móviles Redes activas Administración y monitoreo de redes y servicios Calidad de Servicio (QoS, SLAs) Seguridad informática y autenticación, privacidad Infraestructura para firma digital y certificados digitales Análisis y detección de vulnerabilidades Sistemas operativos Sistemas P2P Middleware Infraestructura para grid Servicios de integración (Web Services o .Net)Red de Universidades con Carreras en Informática (RedUNCI