Latency-Aware Strategies for Placing Data Stream Analytics onto Edge Computing

International audienceMuch of the "big data" generated today is received in near real-time and requires quick analysis. In Internet of Things (IoT) [1, 9], for instance, continuous data streams produced by multiple sources must be handled under very short delays. As a result, several stream processing engines have been proposed. Under several engines, a stream processing application is a directed graph or dataflow whose vertices are operators that execute a function over the incoming data and edges that define how data flows between them. A dataflow has one or multiple sources (i.e., sensors, gateways or actuators), operators that perform transformations on the data (e.g., filtering, mapping, and aggregation) and sinks (i.e., queries that consume or store the data). In a traditional cloud deployment, the whole application is placed in the cloud computing to benefit from virtually unlimited resources. However, processing all the data in the cloud can introduce latency due to data transfer, which makes near real-time processing difficult to achieve. In contrast, edge computing has become an attractive solution for performing certain stream processing operators, as many edge devices have non-trivial compute capacity. The deployment of data stream processing applications onto heterogeneous infrastructure has been proved to be NP-hard [2]. Moving operators from cloud to edge devices is challenging due to limitations of edge devices [5]. Existing work often proposes placements strategies considering user intervention [8]. Many models do not support memory and communication constraints [6, 4] while others consider all data sinks to be located in the cloud, with no feedback loop to actua-tors located at the edge of the network [3, 7]. There is a lack of solutions covering scenarios involving smart cities, precision agriculture, and smart homes comprising various heterogeneous sensors and actuators, as well as, time-constraint applications. We model the data stream processing placement problem considering heterogeneous computational and network resources, and computing and communication as M/M/1 queues (i.e., Poisson arrival distribution, exponential service time and single server). Events are handled in a First-Come, First-Served fashion both by the computation and communication services, guaranteeing the time order of events; an important requirement in many data stream processing applications. The model allows us to calculate the waiting and service times for each message in computation and communication queues allowing for estimating the response time. We then propose two strategies to minimize the application response time by splitting the dataflow graph dynamically and distributing the operators across cloud and edge infrastructure. We focus on real-time analytics applications with multiple sources and sinks distributed across resources. In particular, we first decompose the application graph by considering behaviors such as forks and joins (i.e., split points), and by identifying the operator dependencies recursively. The Response Time Rate (RTR) strategy takes the decomposed graph and organizes the deployment sequence and consecutively calculates the response time for each operator by considering the previous mappings, resource capabilities, and operator requirements. RTR with Region Patterns (RTR+RP) strategy extends RTR by exploiting the split points to first find candidate operators for edge or cloud and then estimates the response time for the edge operators. Comprehensive simulations considering multiple application configurations demonstrate that our approach can improve the response time up to 50%. For future work, we will investigate further techniques to deal with CPU-intensive operators and their energy consumption

Boosting big data streaming applications in clouds with burstFlow

The rapid growth of stream applications in financial markets, health care, education, social media, and sensor networks represents a remarkable milestone for data processing and analytic in recent years, leading to new challenges to handle Big Data in real-time. Traditionally, a single cloud infrastructure often holds the deployment of Stream Processing applications because it has extensive and adaptative virtual computing resources. Hence, data sources send data from distant and different locations of the cloud infrastructure, increasing the application latency. The cloud infrastructure may be geographically distributed and it requires to run a set of frameworks to handle communication. These frameworks often comprise a Message Queue System and a Stream Processing Framework. The frameworks explore Multi-Cloud deploying each service in a different cloud and communication via high latency network links. This creates challenges to meet real-time application requirements because the data streams have different and unpredictable latencies forcing cloud providers' communication systems to adjust to the environment changes continually. Previous works explore static micro-batch demonstrating its potential to overcome communication issues. This paper introduces BurstFlow, a tool for enhancing communication across data sources located at the edges of the Internet and Big Data Stream Processing applications located in cloud infrastructures. BurstFlow introduces a strategy for adjusting the micro-batch sizes dynamically according to the time required for communication and computation. BurstFlow also presents an adaptive data partition policy for distributing incoming streams across available machines by considering memory and CPU capacities. The experiments use a real-world multi-cloud deployment showing that BurstFlow can reduce the execution time up to 77% when compared to the state-of-the-art solutions, improving CPU efficiency by up to 49%

Mécanismes prenant en compte la qualité de service pour la (re)configuration d’applications de traitement de flux de données sur une infrastructure hautement distribuée

A large part of this big data is most valuable when analysed quickly, as it is generated. Under several emerging application scenarios, such as in smart cities, operational monitoring of large infrastructure, and Internet of Things (IoT), continuous data streams must be processed under very short delays. In multiple domains, there is a need for processing data streams to detect patterns, identify failures, and gain insights. Data is often gathered and analysed by Data Stream Processing Engines (DSPEs).A DSPE commonly structures an application as a directed graph or dataflow. A dataflow has one or multiple sources (i.e., gateways or actuators); operators that perform transformations on the data (e.g., filtering); and sinks (i.e., queries that consume or store the data). Most complex operator transformations store information about previously received data as new data is streamed in. Also, a dataflow has stateless operators that consider only the current data. Traditionally, Data Stream Processing (DSP) applications were conceived to run in clusters of homogeneous resources or on the cloud. In a cloud deployment, the whole application is placed on a single cloud provider to benefit from virtually unlimited resources. This approach allows for elastic DSP applications with the ability to allocate additional resources or release idle capacity on demand during runtime to match the application requirements.We introduce a set of strategies to place operators onto cloud and edge while considering characteristics of resources and meeting the requirements of applications. In particular, we first decompose the application graph by identifying behaviours such as forks and joins, and then dynamically split the dataflow graph across edge and cloud. Comprehensive simulations and a real testbed considering multiple application settings demonstrate that our approach can improve the end-to-end latency in over 50% and even other QoS metrics. The solution search space for operator reassignment can be enormous depending on the number of operators, streams, resources and network links. Moreover, it is important to minimise the cost of migration while improving latency. Reinforcement Learning (RL) and Monte-Carlo Tree Search (MCTS) have been used to tackle problems with large search spaces and states, performing at human-level or better in games such as Go. We model the application reconfiguration problem as a Markov Decision Process (MDP) and investigate the use of RL and MCTS algorithms to devise reconfiguring plans that improve QoS metrics.Une grande partie de ces données volumineuses ont plus de valeur lorsqu'elles sont analysées rapidement, au fur et à mesure de leur génération. Dans plusieurs scénarios d'application émergents, tels que les villes intelligentes, la surveillance opérationnelle de grandes infrastructures et l'Internet des Objets (Internet of Things), des flux continus de données doivent être traités dans des délais très brefs. Dans plusieurs domaines, ce traitement est nécessaire pour détecter des modèles, identifier des défaillances et pour guider la prise de décision. Les données sont donc souvent rassemblées et analysées par des environnements logiciels conçus pour le traitement de flux continus de données. Ces environnements logiciels pour le traitement de flux de données déploient les applications sous-la forme d'un graphe orienté ou de dataflow. Un dataflow contient une ou plusieurs sources (i.e. capteurs, passerelles ou actionneurs); opérateurs qui effectuent des transformations sur les données (e.g., filtrage et agrégation); et des sinks (i.e., éviers qui consomment les requêtes ou stockent les données). Nous proposons dans cette thèse un ensemble de stratégies pour placer les opérateurs dans une infrastructure massivement distribuée cloud-edge en tenant compte des caractéristiques des ressources et des exigences des applications. En particulier, nous décomposons tout d'abord le graphe d'application en identifiant quelques comportements tels que des forks et des joints, puis nous le plaçons dynamiquement sur l'infrastructure. Des simulations et un prototype prenant en compte plusieurs paramètres d'application démontrent que notre approche peut réduire la latence de bout en bout de plus de 50% et aussi améliorer d'autres métriques de qualité de service. L'espace de recherche de solutions pour la reconfiguration des opérateurs peut être énorme en fonction du nombre d'opérateurs, de flux, de ressources et de liens réseau. De plus, il est important de minimiser le coût de la migration tout en améliorant la latence. Des travaux antérieurs, Reinforcement Learning (RL) et Monte-Carlo Tree Searh (MCTS) ont été utilisés pour résoudre les problèmes liés aux grands nombres d’actions et d’états de recherche. Nous modélisons le problème de reconfiguration d'applications sous la forme d'un processus de décision de Markov (MDP) et étudions l'utilisation des algorithmes RL et MCTS pour concevoir des plans de reconfiguration améliorant plusieurs métriques de qualité de service

Assessing the Impact of Network Bandwidth and Operator Placement on Data Stream Processing for Edge Computing Environments

National audienceA substantial part of the " big data " generated today is received in near real time and must be promptly processed. Cloud-based architectures for data stream processing comprise multiple software modules or frameworks for data collection, message queueing, and stream processing itself. This modular approach allows each component to grow independently from one another and accommodate changes, but it may increase the end-to-end latency when data events are processed in the cloud. Recent solutions intend to explore the edges of the Internet (i.e. edge computing) to perform certain data processing tasks and hence better utilise network resources. This work evaluates the impact regarding network bandwidth while employing frameworks that are commonly used to build cloud and edge-based stream processing solutions. Mots-clés : Traitement de flux de données, méta-données, analyse de performance, systèmes distribués, informatique en nuage

Monte-Carlo Tree Search and Reinforcement Learning for Reconfiguring Data Stream Processing on Edge Computing

International audienceDistributed Stream Processing (DSP) applications are increasingly used in new pervasive services that process enormous amounts of data in a seamless and near real-time fashion. Edge computing has emerged as a means to minimise the time to handle events by enabling processing (i.e., operators) to be offloaded from the Cloud to the edges of the Internet, where the data is often generated. Deciding where to execute such operations (i.e., edge or cloud) during application deployment or at runtime is not a trivial problem. In this work, we employ Reinforcement Learning (RL) and Monte-Carlo Tree Search (MCTS) to reassign operators during application runtime. Experimental results show that RL and MCTS algorithms perform better than traditional placement techniques. We also introduce an optimisation to a MCTS algorithm, called MCTS-Best-UCT,that achieves similar latency with fewer operator migrations and faster execution time. In certain scenarios, the time needed by MCTS-Best-UCT to find the best end-to-end latency is at least 33% smaller than the time required by the other algorithms

Distributed Data Stream Processing and Edge Computing: A Survey on Resource Elasticity and Future Directions

International audienceUnder several emerging application scenarios, such as in smart cities, operational monitoring of large infrastructure, wearable assistance, and Internet of Things, continuous data streams must be processed under very short delays. Several solutions, including multiple software engines, have been developed for processing unbounded data streams in a scalable and efficient manner. More recently, architecture has been proposed to use edge computing for data stream processing. This paper surveys state of the art on stream processing engines and mechanisms for exploiting resource elasticity features of cloud computing in stream processing. Resource elasticity allows for an application or service to scale out/in according to fluctuating demands. Although such features have been extensively investigated for enterprise applications, stream processing poses challenges on achieving elastic systems that can make efficient resource management decisions based on current load. Elasticity becomes even more challenging in highly distributed environments comprising edge and cloud computing resources. This work examines some of these challenges and discusses solutions proposed in the literature to address them

Latency-Aware Strategies for Deploying Data Stream Processing Applications on Large Cloud-Edge Infrastructure

International audienceInternet of Things (IoT) applications often require the processing of data streams generated by devices dispersed over a large geographical area. Traditionally, these data streams are forwarded to a distant cloud for processing, thus resulting in high application end-to-end latency. Recent work explores the combination of resources located in clouds and at the edges of the Internet, called cloud-edge infrastructure, for deploying Data Stream Processing (DSP) applications. Most previous work, however, fails to scale to very large IoT settings. This paper introduces deployment strategies for the placement of DSP applications on to cloud-edge infrastructure. The strategies split an application graph into regions and consider regions with stringent time requirements for edge placement. The proposed Aggregate End-to-End Latency Strategy with Region Patterns and Latency Awareness (AELS+RP+LA) decreases the number of evaluated resources when computing an operator’s placement by considering the communication overhead across computing resources. Simulation results show that, unlike the state-of-the-art, AELS+RP+LA scales to environments with more than 100k resources with negligible impact on the application end-to-end latency

Assessing the Impact of Network Bandwidth and Operator Placement on Data Stream Processing for Edge Computing Environments

National audienceA substantial part of the " big data " generated today is received in near real time and must be promptly processed. Cloud-based architectures for data stream processing comprise multiple software modules or frameworks for data collection, message queueing, and stream processing itself. This modular approach allows each component to grow independently from one another and accommodate changes, but it may increase the end-to-end latency when data events are processed in the cloud. Recent solutions intend to explore the edges of the Internet (i.e. edge computing) to perform certain data processing tasks and hence better utilise network resources. This work evaluates the impact regarding network bandwidth while employing frameworks that are commonly used to build cloud and edge-based stream processing solutions. Mots-clés : Traitement de flux de données, méta-données, analyse de performance, systèmes distribués, informatique en nuage

An Optimal Model for Optimizing the Placement and Parallelism of Data Stream Processing Applications on Cloud-Edge Computing

International audienceThe Internet of Things has enabled many application scenarios where a large number of connected devices generate unbounded streams of data, often processed by data stream processing frameworks deployed in the cloud. Edge computing enables offloading processing from the cloud and placing it close to where the data is generated, thereby reducing the time to process data events and deployment costs. However, edge resources are more computationally constrained than their cloud counterparts, raising two interrelated issues, namely deciding on the parallelism of processing tasks (a.k.a. operators) and their mapping onto available resources. In this work, we formulate the scenario of operator placement and parallelism as an optimal mixed-integer linear programming problem. The proposed model is termed as Cloud-Edge data Stream Placement (CESP). Experimental results using discrete-event simulation demonstrate that CESP can achieve an end-to-end latency at least 80% and monetary costs at least 30% better than traditional cloud deployment

Strategies for Big Data Analytics through Lambda Architectures in Volatile Environments

International audienceExpectations regarding the future growth of Internet of Things (IoT)-related technologies are high. These expectations require the realization of a sustainable general purpose application framework that is capable to handle these kinds of environments with their complexity in terms of heterogeneity and volatility. The paradigm of the Lambda architecture features key characteristics (such as robustness, fault tolerance, scalability, generalization, extensibility, ad-hoc queries, minimal maintenance, and low-latency reads and updates) to cope with this complexity. The paper at hand suggest a basic set of strategies to handle the arising challenges regarding the volatility, heterogeneity, and desired low latency execution by reducing the overall system timing (scheduling, execution, monitoring, and faults recovery) as well as possible faults (churn, no answers to executions). The proposed strategies make use of services such as migration, replication, MapReduce simulation, and combined processing methods (batch- and streaming-based). Via these services, a distribution of tasks for the best balance of computational resources is achieved, while monitoring and management can be performed asynchronously in the background. %An application of batch and stream-based methods are proposed to reduce the latency