Slicing as a Distributed Systems Primitive

Maia, Francisco; Matos, Miguel; Oliveira, Rui; Rivière, Étienne

doi:10.1109/ladc.2013.21

Cited by 6 publications

(5 citation statements)

References 27 publications

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“…If the error is higher than L, then the system is considered unstable (line 9) and BUZZPSS should adjust T using gradient descent. In other words, BUZZPSS performs Algorithm 1 (lines [8][9][10][11][12][13][14][15][16][17][18][19]. Otherwise, the system is considered stable for that iteration (line 22).…”

Section: Improving Bandwidth Efficiency By Rewarding Stabilitymentioning

confidence: 99%

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BuzzPSS: A Dependable and Adaptive Peer Sampling Service

Machado

Maia

Matos

et al. 2016

2016 Seventh Latin-American Symposium on Dependable Computing (LADC)

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A distributed system is often built on top of an overlay network. Overlay networks enable network topology transparency while, at the same time, can be designed to provide efficient data dissemination, load balancing, and even fault tolerance. They are constructed by defining logical links between nodes creating a node graph. In practice, this is materialized by a Peer Sampling Service (PSS) that provides references to other nodes to communicate with. Depending on the configuration of the PSS, the characteristics of the overlay can be adjusted to cope with application requirements and performance concerns.Unfortunately, overlay efficiency comes at the expense of dependability. To overcome this, one often deploys an application overlay focused on efficiency, along with a safety-net overlay to ensure dependability. However, this approach results in significant resource waste since safety-net overlays are seldom used.In this paper, we focus on safety-net overlay networks and propose an adaptable mechanism to minimize resource usage while maintaining dependability guarantees. In detail, we consider a random overlay network, known to be highly dependable, and propose BUZZPSS, a new Peer Sampling Service that is able to autonomously fine-tune its resource consumption usage according to the observed system stability. When the system is stable and connectivity is not at risk, BUZZPSS autonomously changes its behavior to save resources. Alongside, it is also able to detect system instability and act accordingly to guarantee that the overlay remains operational. Through an experimental evaluation, we show that BUZZPSS is able to autonomously adapt to the system stability levels, consuming up to 6x less resources than a static approach.

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Section: Improving Bandwidth Efficiency By Rewarding Stabilitymentioning

confidence: 99%

“…There is an extensive body of work on PSS algorithms covering aspects such as topology-awareness [13], security [14] or slicing [15]. Several approaches explicitly maintain two overlays with different goals.…”

Section: R Elated Workmentioning

confidence: 99%

BuzzPSS: A Dependable and Adaptive Peer Sampling Service

Machado

Maia

Matos

et al. 2016

2016 Seventh Latin-American Symposium on Dependable Computing (LADC)

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“…Desgraciadamente no hay ninguna primitiva de sistema distribuido de slicing que soporte un particionamiento del estado interno [33] junto con un cambio en caliente, además de que los sistemas cloud actuales como OpenStack u OpenNebula no ofrecen directamente una monitorización elástica. Esto implica que hay un problema bilateral: cómo puede una aplicación escalar de forma elástica y cómo monitorizar esa aplicación para saber cuándo escalarla horizontalmente.…”

Section: VIIunclassified

“…Obviously the ultimate goal would be the creation of a Slice as distributed system primitive [33] that can be invoked by the application to scale and thus separate and externalize all the complexity of the scaling process the resource provisioning system instead of handling internally.…”

Section: Hypothesis and Contributionmentioning

confidence: 99%

“…Today's response to the above need is cloud computing. However, cloud computing applications need to be able to scale elastically [22].Unfortunately there is no slicing, as distributed system primitives that support internal state partitioning [33] and hot swapping and current cloud systems like OpenStack or OpenNebula do not provide elastic monitoring out of the box. This means there is a two-sided problem: 1) how to scale an application elastically and 2) how to monitor the application and know when it should scale in or out.…”

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confidence: 99%

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A Novel Approach to Context-Aware Content-Based Middleware

Vavassori¹

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Realizado el acto de defensa y lectura de la Tesis el día de de 2016 en la E.T.S.I. Informáticos EL PRESIDENTE LOS VOCALESLA SECRETARIA AcknowledgementsI would like to express my gratitude to all those people who directly, indirectly or unconsciously have pushed me to improve myself continuously. AbstractThe application of pervasive computing is extending from field-specific to everyday use. The Internet of Things (IoT) is the shiniest example of its application and of its intrinsic complexity compared with classical application development.The main characteristic that differentiates pervasive from other forms of computing lies in the use of contextual information. Some classical applications do not use any contextual information whatsoever. Others, on the other hand, use only part of the contextual information, which is integrated in an ad hoc fashion using an application-specific implementation. This information is handled in a one-off manner because of the difficulty of sharing context across applications. As a matter of fact, the application type determines what the contextual information is. For instance, for an imaging editor, the image is the information and its meta-data, like the time of the shot or camera settings, are the context, whereas, for a file-system application, the image, including its camera settings, is the information and the meta-data external to the file, like the modification date or the last accessed timestamps, constitute the context. This means that contextual information is hard to share. A communication middleware that supports context decidedly eases application development in pervasive computing. However, the use of context should not be mandatory; otherwise, the communication middleware would be reduced to a context middleware and no longer be compatible with non-context-aware applications.SilboPS, our implementation of content-based publish/subscribe inspired by SIENA [11,9], solves this problem by adding two new elements to the paradigm: the context and the context function. Context represents the actual contextual information specific to the message to be sent or that needs to be notified to the subscriber, whereas the context function is evaluated using the publisher's context and the subscriber's context to decide whether the current message and context are useful for the subscriber. In this manner, context logic management is decoupled from context management, increasing the flexibility of communication and usage across different applications. IIISince the default context is empty, context-aware and classical applications can use the same SilboPS, resolving the syntactic mismatch that there is between the two categories. In any case, the possible semantic mismatch is still present because it depends on how each application interprets the data, and it cannot be resolved by an agnostic third party.The IoT environment introduces not only context but scaling challenges too. The number of sensors, the volume of the data that they produce and the number of applications that coul...

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DATAFLASKS: An epidemic dependable key-value substrate

Maia

Matos

Vilaça

et al. 2013

2013 43rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)

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Recently, tuple-stores have become pivotal structures in many information systems. Their ability to handle large datasets makes them important in an era with unprecedented amounts of data being produced and exchanged. However, these tuple-stores typically rely on structured peer-to-peer protocols which assume moderately stable environments. Such assumption does not always hold for very large scale systems sized in the scale of thousands of machines. In this paper we present a novel approach to the design of a tuple-store. Our approach follows a stratified design based on an unstructured substrate. We focus on this substrate and how the use of epidemic protocols allow reaching high dependability and scalability.

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Slicing as a Distributed Systems Primitive

Cited by 6 publications

References 27 publications

BuzzPSS: A Dependable and Adaptive Peer Sampling Service

BuzzPSS: A Dependable and Adaptive Peer Sampling Service

A Novel Approach to Context-Aware Content-Based Middleware

DATAFLASKS: An epidemic dependable key-value substrate

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