The Experts below are selected from a list of 84816 Experts worldwide ranked by ideXlab platform
Da Silva Veith Alexandre - One of the best experts on this subject based on the ideXlab platform.
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Latency-Aware Strategies for Deploying Data Stream Processing Applications on Large Cloud-Edge Infrastructure
'Institute of Electrical and Electronics Engineers (IEEE)', 2021Co-Authors: Da Silva Veith Alexandre, Dias De Assuncao, Marcos, Lefèvre LaurentAbstract: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
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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
HAL CCSD, 2019Co-Authors: Da Silva Veith AlexandreAbstract: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
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Latency-Aware Strategies for Placing Data Stream Analytics onto Edge Computing
HAL CCSD, 2018Co-Authors: Da Silva Veith Alexandre, Dias De Assuncao, Marcos, Lefèvre LaurentAbstract: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
Jian Tang - One of the best experts on this subject based on the ideXlab platform.
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performance modeling and predictive scheduling for distributed stream data processing
IEEE Transactions on Big Data, 2016Co-Authors: Jian TangAbstract:In a distributed stream data processing system, an Application is usually modeled using a directed Graph, in which each vertex corresponds to a data source or a processing unit, and edges indicate data flow. In this paper, we propose a novel predictive scheduling framework to enable fast and distributed stream data processing, which features topology-aware modeling for performance prediction and predictive scheduling. For prediction, we present a topology-aware method to accurately predict the average tuple processing time of an Application for a given scheduling solution, according to the topology of the Application Graph and runtime statistics. For scheduling, we present an effective algorithm to assign threads to machines under the guidance of prediction results. To validate and evaluate the proposed framework, we implemented it based on a highly-regarded distributed stream data processing platform, Storm, and tested it with 3 representative Applications: word count (stream version), log stream processing and continuous query. Extensive experimental results show 1) The topology-aware prediction method offers an average accuracy of 84.2 percent. 2) The predictive scheduling framework reduces the average tuple processing time by 24.9 percent on average, compared to Storm's default scheduler.
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a predictive scheduling framework for fast and distributed stream data processing
International Conference on Big Data, 2015Co-Authors: Jian TangAbstract:In a distributed stream data processing system, an Application is usually modeled using a directed Graph, in which each vertex corresponds to a data source or a processing unit, and edges indicate data flow. In this paper, we propose a novel predictive scheduling framework to enable fast and distributed stream data processing, which features topology-aware performance prediction and predictive scheduling. For prediction, we present a topology-aware method to accurately predict the average tuple processing time of an Application for a given scheduling solution, according to the topology of the Application Graph and runtime statistics. For scheduling, we present an effective algorithm to assign threads to machines under the guidance of prediction results. To validate and evaluate the proposed framework, we implemented it based on a highly-regarded distributed stream data processing platform, Storm, and tested it with two representative Applications: word count (stream version) and log stream processing. Extensive experimental results show 1) The topology-aware prediction method offers an average accuracy of 83.7%. 2) The predictive scheduling framework reduces the average tuple processing time by 25.9% on average, compared to Storm's default scheduler.
Yalda Aryan - One of the best experts on this subject based on the ideXlab platform.
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a bi objective workflow Application scheduling in cloud computing systems
International Journal on Integrating Technology in Education, 2014Co-Authors: Yalda Aryan, Arash Ghorbannia DelavarAbstract:The task scheduling is a key process in large-scale distributed systems like cloud computing infrastructures which can have much impressed on system performance. This problem is referred to as a NP-hard problem because of some reasons such as heterogeneous and dynamic features and dependencies among the requests. Here, we proposed a bi-objective method called DWSGA to obtain a proper solution for allocating the requests on resources. The purpose of this algorithm is to earn the response quickly, with some goal-oriented operations. At first, it makes a good initial population by a special way that uses a bidirectional tasks prioritization. Then the algorithm moves to get the most appropriate possible solution in a conscious manner by focus on optimizing the makespan, and considering a good distribution of workload on resources by using efficient parameters in the mentioned systems. Here, the experiments indicate that the DWSGA amends the results when the numbers of tasks are increased in Application Graph, in order to mentioned objectives. The results are compared with other studied algorithms.
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hsga a hybrid heuristic algorithm for workflow scheduling in cloud systems
Cluster Computing, 2014Co-Authors: Arash Ghorbannia Delavar, Yalda AryanAbstract:In heterogeneous distributed computing systems like cloud computing, the problem of mapping tasks to resources is a major issue which can have much impact on system performance. For some reasons such as heterogeneous and dynamic features and the dependencies among requests, task scheduling is known to be a NP-complete problem. In this paper, we proposed a hybrid heuristic method (HSGA) to find a suitable scheduling for workflow Graph, based on genetic algorithm in order to obtain the response quickly moreover optimizes makespan, load balancing on resources and speedup ratio. At first, the HSGA algorithm makes tasks prioritization in complex Graph considering their impact on others, based on Graph topology. This technique is efficient to reduction of completion time of Application. Then, it merges Best-Fit and Round Robin methods to make an optimal initial population to obtain a good solution quickly, and apply some suitable operations such as mutation to control and lead the algorithm to optimized solution. This algorithm evaluates the solutions by considering efficient parameters in cloud environment. Finally, the proposed algorithm presents the better results with increasing number of tasks in Application Graph in contrast with other studied algorithms.
Arash Ghorbannia Delavar - One of the best experts on this subject based on the ideXlab platform.
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a bi objective workflow Application scheduling in cloud computing systems
International Journal on Integrating Technology in Education, 2014Co-Authors: Yalda Aryan, Arash Ghorbannia DelavarAbstract:The task scheduling is a key process in large-scale distributed systems like cloud computing infrastructures which can have much impressed on system performance. This problem is referred to as a NP-hard problem because of some reasons such as heterogeneous and dynamic features and dependencies among the requests. Here, we proposed a bi-objective method called DWSGA to obtain a proper solution for allocating the requests on resources. The purpose of this algorithm is to earn the response quickly, with some goal-oriented operations. At first, it makes a good initial population by a special way that uses a bidirectional tasks prioritization. Then the algorithm moves to get the most appropriate possible solution in a conscious manner by focus on optimizing the makespan, and considering a good distribution of workload on resources by using efficient parameters in the mentioned systems. Here, the experiments indicate that the DWSGA amends the results when the numbers of tasks are increased in Application Graph, in order to mentioned objectives. The results are compared with other studied algorithms.
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hsga a hybrid heuristic algorithm for workflow scheduling in cloud systems
Cluster Computing, 2014Co-Authors: Arash Ghorbannia Delavar, Yalda AryanAbstract:In heterogeneous distributed computing systems like cloud computing, the problem of mapping tasks to resources is a major issue which can have much impact on system performance. For some reasons such as heterogeneous and dynamic features and the dependencies among requests, task scheduling is known to be a NP-complete problem. In this paper, we proposed a hybrid heuristic method (HSGA) to find a suitable scheduling for workflow Graph, based on genetic algorithm in order to obtain the response quickly moreover optimizes makespan, load balancing on resources and speedup ratio. At first, the HSGA algorithm makes tasks prioritization in complex Graph considering their impact on others, based on Graph topology. This technique is efficient to reduction of completion time of Application. Then, it merges Best-Fit and Round Robin methods to make an optimal initial population to obtain a good solution quickly, and apply some suitable operations such as mutation to control and lead the algorithm to optimized solution. This algorithm evaluates the solutions by considering efficient parameters in cloud environment. Finally, the proposed algorithm presents the better results with increasing number of tasks in Application Graph in contrast with other studied algorithms.
Lefèvre Laurent - One of the best experts on this subject based on the ideXlab platform.
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Latency-Aware Strategies for Deploying Data Stream Processing Applications on Large Cloud-Edge Infrastructure
'Institute of Electrical and Electronics Engineers (IEEE)', 2021Co-Authors: Da Silva Veith Alexandre, Dias De Assuncao, Marcos, Lefèvre LaurentAbstract: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
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Latency-Aware Strategies for Placing Data Stream Analytics onto Edge Computing
HAL CCSD, 2018Co-Authors: Da Silva Veith Alexandre, Dias De Assuncao, Marcos, Lefèvre LaurentAbstract: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