This document discusses enhancing performance and fault tolerance in Hadoop clusters. It proposes a method to identify faulty nodes in the cluster that are decreasing job execution efficiency. The method monitors node performance and categorizes nodes as active or blacklisted based on the number of task failures. If a node is frequently blacklisted, it is considered faulty. The experiment shows that removing faulty nodes identified by this method improves overall cluster efficiency by reducing job execution time.
A popular programming model for running data intensive applications on the cloud is map reduce. In
the Hadoop usually, jobs are scheduled in FIFO order by default. There are many map reduce
applications which require strict deadline. In Hadoop framework, scheduler wi t h deadline
con s t ra in t s has not been implemented. Existing schedulers d o not guarantee that the job will be
completed by a specific deadline. Some schedulers address the issue of deadlines but focus more on
improving s y s t em utilization. We have proposed an algorithm which facilitates the user to
specify a jobs deadline and evaluates whether the job can be finished before the deadline.
Scheduler with deadlines for Hadoop, which ensures that only jobs, whose deadlines can be met are
scheduled for execution. If the job submitted does not satisfy the specified deadline, physical or
virtual nodes can be added dynamically to complete the job within deadline[8].
And introdution to MR and Hadoop and an view on the opportunities to use MR with databases i.e., SQL-MapReduce by Teradata and In-database MR by Oracle.
The presentation was used during a class of Datenbanken Implementierungstechniken in 2013.
This document provides an overview of Hadoop MapReduce scheduling algorithms. It discusses several commonly used algorithms like FIFO, fair scheduling, and capacity scheduler. It also introduces more advanced algorithms such as LATE, SAMR, ESAMR, locality-aware scheduling, and center-of-gravity scheduling that aim to improve metrics like fairness, throughput, response time, and resource utilization. The document concludes by listing references for further reading on MapReduce scheduling techniques.
This document describes the MapReduce programming model for processing large datasets in a distributed manner. MapReduce allows users to write map and reduce functions that are automatically parallelized and run across large clusters. The input data is split and the map tasks run in parallel, producing intermediate key-value pairs. These are shuffled and input to the reduce tasks, which produce the final output. The system handles failures, scheduling and parallelization transparently, making it easy for programmers to write distributed applications.
In this presentation , i provide in depth information about the how MapReduce works. It contains many details about the execution steps , Fault tolerance , master / worker responsibilities.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Hadoop Mapreduce Performance Enhancement Using In-Node Combinersijcsit
This document summarizes a research paper that proposes using in-node combiners to improve the performance of Hadoop MapReduce jobs. It discusses how MapReduce jobs are I/O intensive and describes two common bottlenecks: during the map phase when data is loaded from disks, and during the shuffle phase when intermediate results are transferred over the network. The paper introduces an in-node combiner approach to optimize I/O by locally aggregating intermediate results within nodes to reduce network traffic between mappers and reducers. It evaluates this approach through an experiment counting word occurrences in Twitter messages.
This document compares approaches to large-scale data analysis using MapReduce and parallel database management systems (DBMSs). It presents results from running a benchmark of tasks on an open-source MapReduce system (Hadoop) and two parallel DBMSs using a cluster of 100 nodes. The parallel DBMSs showed significantly better performance than MapReduce for the tasks, but took much longer to load data and tune executions. The document discusses architectural differences between the approaches and their performance implications.
A popular programming model for running data intensive applications on the cloud is map reduce. In
the Hadoop usually, jobs are scheduled in FIFO order by default. There are many map reduce
applications which require strict deadline. In Hadoop framework, scheduler wi t h deadline
con s t ra in t s has not been implemented. Existing schedulers d o not guarantee that the job will be
completed by a specific deadline. Some schedulers address the issue of deadlines but focus more on
improving s y s t em utilization. We have proposed an algorithm which facilitates the user to
specify a jobs deadline and evaluates whether the job can be finished before the deadline.
Scheduler with deadlines for Hadoop, which ensures that only jobs, whose deadlines can be met are
scheduled for execution. If the job submitted does not satisfy the specified deadline, physical or
virtual nodes can be added dynamically to complete the job within deadline[8].
And introdution to MR and Hadoop and an view on the opportunities to use MR with databases i.e., SQL-MapReduce by Teradata and In-database MR by Oracle.
The presentation was used during a class of Datenbanken Implementierungstechniken in 2013.
This document provides an overview of Hadoop MapReduce scheduling algorithms. It discusses several commonly used algorithms like FIFO, fair scheduling, and capacity scheduler. It also introduces more advanced algorithms such as LATE, SAMR, ESAMR, locality-aware scheduling, and center-of-gravity scheduling that aim to improve metrics like fairness, throughput, response time, and resource utilization. The document concludes by listing references for further reading on MapReduce scheduling techniques.
This document describes the MapReduce programming model for processing large datasets in a distributed manner. MapReduce allows users to write map and reduce functions that are automatically parallelized and run across large clusters. The input data is split and the map tasks run in parallel, producing intermediate key-value pairs. These are shuffled and input to the reduce tasks, which produce the final output. The system handles failures, scheduling and parallelization transparently, making it easy for programmers to write distributed applications.
In this presentation , i provide in depth information about the how MapReduce works. It contains many details about the execution steps , Fault tolerance , master / worker responsibilities.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Hadoop Mapreduce Performance Enhancement Using In-Node Combinersijcsit
This document summarizes a research paper that proposes using in-node combiners to improve the performance of Hadoop MapReduce jobs. It discusses how MapReduce jobs are I/O intensive and describes two common bottlenecks: during the map phase when data is loaded from disks, and during the shuffle phase when intermediate results are transferred over the network. The paper introduces an in-node combiner approach to optimize I/O by locally aggregating intermediate results within nodes to reduce network traffic between mappers and reducers. It evaluates this approach through an experiment counting word occurrences in Twitter messages.
This document compares approaches to large-scale data analysis using MapReduce and parallel database management systems (DBMSs). It presents results from running a benchmark of tasks on an open-source MapReduce system (Hadoop) and two parallel DBMSs using a cluster of 100 nodes. The parallel DBMSs showed significantly better performance than MapReduce for the tasks, but took much longer to load data and tune executions. The document discusses architectural differences between the approaches and their performance implications.
Parallel Data Processing with MapReduce: A SurveyKyong-Ha Lee
This document summarizes a survey on parallel data processing with MapReduce. It provides an overview of the MapReduce framework, including its architecture, key concepts of Map and Reduce functions, and how it handles parallel processing. It also discusses some inherent pros and cons of MapReduce, such as its simplicity but also performance limitations. Finally, it outlines approaches studied in recent literature to improve and optimize the MapReduce framework.
Map Reduce Workloads: A Dynamic Job Ordering and Slot Configurationsdbpublications
MapReduce is a popular parallel computing paradigm for large-scale data processing in clusters and data centers. A MapReduce workload generally contains a set of jobs, each of which consists of multiple map tasks followed by multiple reduce tasks. Due to 1) that map tasks can only run in map slots and reduce tasks can only run in reduce slots, and 2) the general execution constraints that map tasks are executed before reduce tasks, different job execution orders and map/reduce slot configurations for a MapReduce workload have significantly different performance and system utilization. This survey proposes two classes of algorithms to minimize the make span and the total completion time for an offline MapReduce workload. Our first class of algorithms focuses on the job ordering optimization for a MapReduce workload under a given map/reduce slot configuration. In contrast, our second class of algorithms considers the scenario that we can perform optimization for map/reduce slot configuration for a MapReduce workload. We perform simulations as well as experiments on Amazon EC2 and show that our proposed algorithms produce results that are up to 15 - 80 percent better than currently unoptimized Hadoop, leading to significant reductions in running time in practice.
HDFS-HC: A Data Placement Module for Heterogeneous Hadoop ClustersXiao Qin
An increasing number of popular applications become data-intensive in nature. In the past decade, the World Wide Web has been adopted as an ideal platform for developing data-intensive applications, since the communication paradigm of the Web is sufficiently open and powerful. Data-intensive applications like data mining and web indexing need to access ever-expanding data sets ranging from a few gigabytes to several terabytes or even petabytes. Google leverages the MapReduce model to process approximately twenty petabytes of data per day in a parallel fashion. In this talk, we introduce the Google’s MapReduce framework for processing huge datasets on large clusters. We first outline the motivations of the MapReduce framework. Then, we describe the dataflow of MapReduce. Next, we show a couple of example applications of MapReduce. Finally, we present our research project on the Hadoop Distributed File System.
The current Hadoop implementation assumes that computing nodes in a cluster are homogeneous in nature. Data locality has not been taken into
account for launching speculative map tasks, because it is
assumed that most maps are data-local. Unfortunately, both
the homogeneity and data locality assumptions are not satisfied
in virtualized data centers. We show that ignoring the datalocality issue in heterogeneous environments can noticeably
reduce the MapReduce performance. In this paper, we address
the problem of how to place data across nodes in a way that
each node has a balanced data processing load. Given a dataintensive application running on a Hadoop MapReduce cluster,
our data placement scheme adaptively balances the amount of
data stored in each node to achieve improved data-processing
performance. Experimental results on two real data-intensive
applications show that our data placement strategy can always
improve the MapReduce performance by rebalancing data
across nodes before performing a data-intensive application
in a heterogeneous Hadoop cluster.
Python in an Evolving Enterprise System (PyData SV 2013)PyData
The document evaluates different solutions for integrating Python with Hadoop to enable data modeling on Hadoop clusters. It tests various frameworks like Native Java, Streaming, mrjob, PyCascading, and Pig using a sample budget aggregation problem. Pig and PyCascading allow complex pipelines to be expressed simply, while Pig is more performant and mature, making it the most viable option for ad-hoc analysis on Hadoop from Python.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
MapReduce is a software framework introduced by Google that enables automatic parallelization and distribution of large-scale computations. It hides the details of parallelization, data distribution, load balancing, and fault tolerance. MapReduce allows programmers to specify a map function that processes key-value pairs to generate intermediate key-value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key. It then automatically parallelizes the computation across large clusters of machines.
The document discusses network performance profiling of Hadoop jobs. It presents results from running two common Hadoop benchmarks - Terasort and Ranked Inverted Index - on different Amazon EC2 instance configurations. The results show that the shuffle phase accounts for a significant portion (25-29%) of total job runtime. They aim to reproduce existing findings that network performance is a key bottleneck for shuffle-intensive Hadoop jobs. Some questions are also raised about inconsistencies in reported network bandwidth capabilities for EC2.
Cloud schedulers and Scheduling in HadoopPallav Jha
The document discusses scheduling in Hadoop. It describes how Hadoop chooses tasks for empty task slots, prioritizing failed tasks first, then unscheduled tasks with local data, and finally speculative tasks. It explains how Hadoop calculates progress scores for tasks to select speculative tasks. It also lists assumptions in Hadoop's scheduler and describes RDDs in Spark and how they provide fault tolerance through lineage tracking.
Novel Scheduling Algorithms for Efficient Deployment of Map Reduce Applicatio...IRJET Journal
This document discusses novel scheduling algorithms for efficiently deploying MapReduce applications in heterogeneous computing environments. It proposes dynamically allocating computing resources like slots between map and reduce tasks to minimize the completion time (makespan) of MapReduce jobs. The key idea is to leverage task status information from recently completed jobs to dynamically adjust the slot allocation ratio between map and reduce phases. This aims to better pipeline the job stages and reduce makespan, compared to Hadoop's static slot configuration.
Optimization of Continuous Queries in Federated Database and Stream Processin...Zbigniew Jerzak
The constantly increasing number of connected devices and sensors results in increasing volume and velocity of sensor-based streaming data. Traditional approaches for processing high velocity sensor data rely on stream processing engines. However, the increasing complexity of continuous queries executed on top of high velocity data has resulted in growing demand for federated systems composed of data stream processing engines and database engines. One of major challenges for such systems is to devise the optimal query execution plan to maximize the throughput of continuous queries.
In this paper we present a general framework for federated database and stream processing systems, and introduce the design and implementation of a cost-based optimizer for optimizing relational continuous queries in such systems. Our optimizer uses characteristics of continuous queries and source data streams to devise an optimal placement for each operator of a continuous query. This fine level of optimization, combined with the estimation of the feasibility of query plans, allows our optimizer to devise query plans which result in 8 times higher throughput as compared to the baseline approach which uses only stream processing engines. Moreover, our experimental results showed that even for simple queries, a hybrid execution plan can result in 4 times and 1.6 times higher throughput than a pure stream processing engine plan and a pure database engine plan, respectively.
MapReduce: Distributed Computing for Machine Learningbutest
This document summarizes research using the MapReduce framework for machine learning tasks on modest compute clusters. It benchmarks MapReduce performance on search and sort tasks using an 80-node cluster. It finds that MapReduce is suitable for basic operations on large datasets but has complications for more complex machine learning. It also discusses classes of machine learning algorithms that can be addressed in MapReduce, including single-pass, iterative, and query-based learning techniques.
Adaptive Replication for Elastic Data Stream ProcessingZbigniew Jerzak
A major challenge for cloud-based systems is to be fault tolerant so as to cope with an increasing probability of faults in cloud environments. This is especially true for in-memory computing solutions like data stream processing systems, where a single host failure might result in an unrecoverable information loss.
In state of the art data streaming systems either active replication or upstream backup are applied to ensure fault tolerance, which have a high resource overhead or a high recovery time respectively. This paper combines these two fault tolerance mechanisms in one system to minimize the number of violations of a user-defined recovery time threshold and to reduce the overall resource consumption compared to active replication. The system switches for individual operators between both replication techniques dynamically based on the current workload characteristics. Our approach is implemented as an extension of an elastic data stream processing engine, which is able to reduce the number of used hosts due to the smaller replication overhead. Based on a real-world evaluation we show that our system is able to reduce the resource usage by up to 19% compared to an active replication scheme.
The MapReduce job begins when a client program uploads configuration files to HDFS and notifies the JobTracker. The JobTracker assigns map tasks to idle TaskTrackers and the tasks extract input data, invoke the user-provided map function, and output intermediate key-value pairs. When the map tasks complete, reduce tasks are assigned to TaskTrackers to download intermediate data and invoke the reduce function to generate the final output. The framework is resilient to failures and can re-execute failed tasks as needed.
MapReduce: Simplified Data Processing on Large ClustersAshraf Uddin
This document summarizes the MapReduce programming model and its implementation for processing large datasets in parallel across clusters of computers. The key points are:
1) MapReduce expresses computations as two functions - Map and Reduce. Map processes input key-value pairs and generates intermediate output. Reduce combines these intermediate values to form the final output.
2) The implementation automatically parallelizes programs by partitioning work across nodes, scheduling tasks, and handling failures transparently. It optimizes data locality by scheduling tasks on machines containing input data.
3) The implementation provides fault tolerance by reexecuting failed tasks, guaranteeing the same output as non-faulty execution. Status information and counters help monitor progress and collect metrics.
MapReduce: Simplified Data Processing On Large Clusterskazuma_sato
The document discusses the MapReduce programming model which has been successfully used at Google. It describes how MapReduce simplifies data processing on large clusters by hiding the complexities of parallelization, fault tolerance, locality optimization, and load balancing. Computation is expressed as two functions - Map and Reduce. The Map function produces intermediate key-value pairs from input pairs, and the Reduce function merges all intermediate values associated with the same key.
"MapReduce: Simplified Data Processing on Large Clusters" Paper Presentation ...Adrian Florea
MapReduce is a programming model and implementation for processing large datasets across clusters of computers. It allows users to specify map and reduce functions. The map function processes input key-value pairs to generate intermediate pairs, while the reduce function combines intermediate values into final output. Google developed MapReduce to simplify distributed computing on large datasets, addressing issues like parallelization, fault tolerance, and load balancing. It works by splitting input data into blocks and assigning them to worker nodes that apply the user-defined map and reduce functions to process the data in parallel.
Auto-scaling Techniques for Elastic Data Stream ProcessingZbigniew Jerzak
An elastic data stream processing system is able to handle changes in workload by dynamically scaling out and
scaling in. This allows for handling of unexpected load spikes without the need for constant overprovisioning. One of the major challenges for an elastic system is to find the right point in time to scale in or to scale out. Finding such a point is difficult as it depends on constantly changing workload and system characteristics. In this paper we investigate the application of different auto-scaling techniques for solving this problem. Specifically: (1) we formulate basic requirements for an autoscaling technique used in an elastic data stream processing system, (2) we use the formulated requirements to select the best auto scaling techniques, and (3) we perform evaluation of the selected auto scaling techniques using the real world data. Our experiments show that the auto scaling techniques used in existing elastic data stream processing systems are performing worse than the strategies used in our work.
Mapreduce - Simplified Data Processing on Large ClustersAbhishek Singh
The document describes MapReduce, a programming model and implementation for processing large datasets across clusters of computers. It allows users to write map and reduce functions to parallelize tasks. The MapReduce library automatically parallelizes jobs, distributes data and tasks, handles failures and coordinates communication between machines. It is scalable, processing terabytes of data on thousands of machines, and easy for programmers without parallel experience to use.
The document discusses MapReduce, including its programming model, internal framework, and improvements. It describes MapReduce as a programming model and framework that allows parallel processing of large datasets across commodity machines. The map function processes input key-value pairs to generate intermediate pairs, and the reduce function combines values for each key. The framework automatically parallelizes jobs and provides fault tolerance.
IRJET - Evaluating and Comparing the Two Variation with Current Scheduling Al...IRJET Journal
This document presents two variations of a job-driven scheduling scheme called JOSS for efficiently executing MapReduce jobs on remote outsourced data across multiple data centers. The goal of JOSS is to improve data locality for map and reduce tasks, avoid job starvation, and improve job performance. Extensive experiments show that the two JOSS variations, called JOSS-T and JOSS-J, outperform other scheduling algorithms in terms of data locality and network overhead without significant overhead. JOSS-T performs best for workloads of small jobs, while JOSS-J provides the shortest workload time for jobs of varying sizes distributed across data centers.
A simulation-based approach for straggler tasks detection in Hadoop MapReduceIRJET Journal
This document proposes a simulation-based approach using machine learning to identify straggler tasks in Hadoop MapReduce jobs. Straggler tasks significantly increase overall job completion time. The existing approach in Hadoop uses simple profiling to detect stragglers, but this does not account for hardware variations. The proposed approach uses k-means clustering to classify tasks as fast or slow based on their expected completion time, as determined through simulation. This has the potential to improve job completion time by enabling more effective speculative execution of straggler tasks on alternative nodes. The approach groups simulated task completion times into two clusters representing fast and slow tasks. This allows accurate identification of straggler tasks to target for speculative execution.
Parallel Data Processing with MapReduce: A SurveyKyong-Ha Lee
This document summarizes a survey on parallel data processing with MapReduce. It provides an overview of the MapReduce framework, including its architecture, key concepts of Map and Reduce functions, and how it handles parallel processing. It also discusses some inherent pros and cons of MapReduce, such as its simplicity but also performance limitations. Finally, it outlines approaches studied in recent literature to improve and optimize the MapReduce framework.
Map Reduce Workloads: A Dynamic Job Ordering and Slot Configurationsdbpublications
MapReduce is a popular parallel computing paradigm for large-scale data processing in clusters and data centers. A MapReduce workload generally contains a set of jobs, each of which consists of multiple map tasks followed by multiple reduce tasks. Due to 1) that map tasks can only run in map slots and reduce tasks can only run in reduce slots, and 2) the general execution constraints that map tasks are executed before reduce tasks, different job execution orders and map/reduce slot configurations for a MapReduce workload have significantly different performance and system utilization. This survey proposes two classes of algorithms to minimize the make span and the total completion time for an offline MapReduce workload. Our first class of algorithms focuses on the job ordering optimization for a MapReduce workload under a given map/reduce slot configuration. In contrast, our second class of algorithms considers the scenario that we can perform optimization for map/reduce slot configuration for a MapReduce workload. We perform simulations as well as experiments on Amazon EC2 and show that our proposed algorithms produce results that are up to 15 - 80 percent better than currently unoptimized Hadoop, leading to significant reductions in running time in practice.
HDFS-HC: A Data Placement Module for Heterogeneous Hadoop ClustersXiao Qin
An increasing number of popular applications become data-intensive in nature. In the past decade, the World Wide Web has been adopted as an ideal platform for developing data-intensive applications, since the communication paradigm of the Web is sufficiently open and powerful. Data-intensive applications like data mining and web indexing need to access ever-expanding data sets ranging from a few gigabytes to several terabytes or even petabytes. Google leverages the MapReduce model to process approximately twenty petabytes of data per day in a parallel fashion. In this talk, we introduce the Google’s MapReduce framework for processing huge datasets on large clusters. We first outline the motivations of the MapReduce framework. Then, we describe the dataflow of MapReduce. Next, we show a couple of example applications of MapReduce. Finally, we present our research project on the Hadoop Distributed File System.
The current Hadoop implementation assumes that computing nodes in a cluster are homogeneous in nature. Data locality has not been taken into
account for launching speculative map tasks, because it is
assumed that most maps are data-local. Unfortunately, both
the homogeneity and data locality assumptions are not satisfied
in virtualized data centers. We show that ignoring the datalocality issue in heterogeneous environments can noticeably
reduce the MapReduce performance. In this paper, we address
the problem of how to place data across nodes in a way that
each node has a balanced data processing load. Given a dataintensive application running on a Hadoop MapReduce cluster,
our data placement scheme adaptively balances the amount of
data stored in each node to achieve improved data-processing
performance. Experimental results on two real data-intensive
applications show that our data placement strategy can always
improve the MapReduce performance by rebalancing data
across nodes before performing a data-intensive application
in a heterogeneous Hadoop cluster.
Python in an Evolving Enterprise System (PyData SV 2013)PyData
The document evaluates different solutions for integrating Python with Hadoop to enable data modeling on Hadoop clusters. It tests various frameworks like Native Java, Streaming, mrjob, PyCascading, and Pig using a sample budget aggregation problem. Pig and PyCascading allow complex pipelines to be expressed simply, while Pig is more performant and mature, making it the most viable option for ad-hoc analysis on Hadoop from Python.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
MapReduce is a software framework introduced by Google that enables automatic parallelization and distribution of large-scale computations. It hides the details of parallelization, data distribution, load balancing, and fault tolerance. MapReduce allows programmers to specify a map function that processes key-value pairs to generate intermediate key-value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key. It then automatically parallelizes the computation across large clusters of machines.
The document discusses network performance profiling of Hadoop jobs. It presents results from running two common Hadoop benchmarks - Terasort and Ranked Inverted Index - on different Amazon EC2 instance configurations. The results show that the shuffle phase accounts for a significant portion (25-29%) of total job runtime. They aim to reproduce existing findings that network performance is a key bottleneck for shuffle-intensive Hadoop jobs. Some questions are also raised about inconsistencies in reported network bandwidth capabilities for EC2.
Cloud schedulers and Scheduling in HadoopPallav Jha
The document discusses scheduling in Hadoop. It describes how Hadoop chooses tasks for empty task slots, prioritizing failed tasks first, then unscheduled tasks with local data, and finally speculative tasks. It explains how Hadoop calculates progress scores for tasks to select speculative tasks. It also lists assumptions in Hadoop's scheduler and describes RDDs in Spark and how they provide fault tolerance through lineage tracking.
Novel Scheduling Algorithms for Efficient Deployment of Map Reduce Applicatio...IRJET Journal
This document discusses novel scheduling algorithms for efficiently deploying MapReduce applications in heterogeneous computing environments. It proposes dynamically allocating computing resources like slots between map and reduce tasks to minimize the completion time (makespan) of MapReduce jobs. The key idea is to leverage task status information from recently completed jobs to dynamically adjust the slot allocation ratio between map and reduce phases. This aims to better pipeline the job stages and reduce makespan, compared to Hadoop's static slot configuration.
Optimization of Continuous Queries in Federated Database and Stream Processin...Zbigniew Jerzak
The constantly increasing number of connected devices and sensors results in increasing volume and velocity of sensor-based streaming data. Traditional approaches for processing high velocity sensor data rely on stream processing engines. However, the increasing complexity of continuous queries executed on top of high velocity data has resulted in growing demand for federated systems composed of data stream processing engines and database engines. One of major challenges for such systems is to devise the optimal query execution plan to maximize the throughput of continuous queries.
In this paper we present a general framework for federated database and stream processing systems, and introduce the design and implementation of a cost-based optimizer for optimizing relational continuous queries in such systems. Our optimizer uses characteristics of continuous queries and source data streams to devise an optimal placement for each operator of a continuous query. This fine level of optimization, combined with the estimation of the feasibility of query plans, allows our optimizer to devise query plans which result in 8 times higher throughput as compared to the baseline approach which uses only stream processing engines. Moreover, our experimental results showed that even for simple queries, a hybrid execution plan can result in 4 times and 1.6 times higher throughput than a pure stream processing engine plan and a pure database engine plan, respectively.
MapReduce: Distributed Computing for Machine Learningbutest
This document summarizes research using the MapReduce framework for machine learning tasks on modest compute clusters. It benchmarks MapReduce performance on search and sort tasks using an 80-node cluster. It finds that MapReduce is suitable for basic operations on large datasets but has complications for more complex machine learning. It also discusses classes of machine learning algorithms that can be addressed in MapReduce, including single-pass, iterative, and query-based learning techniques.
Adaptive Replication for Elastic Data Stream ProcessingZbigniew Jerzak
A major challenge for cloud-based systems is to be fault tolerant so as to cope with an increasing probability of faults in cloud environments. This is especially true for in-memory computing solutions like data stream processing systems, where a single host failure might result in an unrecoverable information loss.
In state of the art data streaming systems either active replication or upstream backup are applied to ensure fault tolerance, which have a high resource overhead or a high recovery time respectively. This paper combines these two fault tolerance mechanisms in one system to minimize the number of violations of a user-defined recovery time threshold and to reduce the overall resource consumption compared to active replication. The system switches for individual operators between both replication techniques dynamically based on the current workload characteristics. Our approach is implemented as an extension of an elastic data stream processing engine, which is able to reduce the number of used hosts due to the smaller replication overhead. Based on a real-world evaluation we show that our system is able to reduce the resource usage by up to 19% compared to an active replication scheme.
The MapReduce job begins when a client program uploads configuration files to HDFS and notifies the JobTracker. The JobTracker assigns map tasks to idle TaskTrackers and the tasks extract input data, invoke the user-provided map function, and output intermediate key-value pairs. When the map tasks complete, reduce tasks are assigned to TaskTrackers to download intermediate data and invoke the reduce function to generate the final output. The framework is resilient to failures and can re-execute failed tasks as needed.
MapReduce: Simplified Data Processing on Large ClustersAshraf Uddin
This document summarizes the MapReduce programming model and its implementation for processing large datasets in parallel across clusters of computers. The key points are:
1) MapReduce expresses computations as two functions - Map and Reduce. Map processes input key-value pairs and generates intermediate output. Reduce combines these intermediate values to form the final output.
2) The implementation automatically parallelizes programs by partitioning work across nodes, scheduling tasks, and handling failures transparently. It optimizes data locality by scheduling tasks on machines containing input data.
3) The implementation provides fault tolerance by reexecuting failed tasks, guaranteeing the same output as non-faulty execution. Status information and counters help monitor progress and collect metrics.
MapReduce: Simplified Data Processing On Large Clusterskazuma_sato
The document discusses the MapReduce programming model which has been successfully used at Google. It describes how MapReduce simplifies data processing on large clusters by hiding the complexities of parallelization, fault tolerance, locality optimization, and load balancing. Computation is expressed as two functions - Map and Reduce. The Map function produces intermediate key-value pairs from input pairs, and the Reduce function merges all intermediate values associated with the same key.
"MapReduce: Simplified Data Processing on Large Clusters" Paper Presentation ...Adrian Florea
MapReduce is a programming model and implementation for processing large datasets across clusters of computers. It allows users to specify map and reduce functions. The map function processes input key-value pairs to generate intermediate pairs, while the reduce function combines intermediate values into final output. Google developed MapReduce to simplify distributed computing on large datasets, addressing issues like parallelization, fault tolerance, and load balancing. It works by splitting input data into blocks and assigning them to worker nodes that apply the user-defined map and reduce functions to process the data in parallel.
Auto-scaling Techniques for Elastic Data Stream ProcessingZbigniew Jerzak
An elastic data stream processing system is able to handle changes in workload by dynamically scaling out and
scaling in. This allows for handling of unexpected load spikes without the need for constant overprovisioning. One of the major challenges for an elastic system is to find the right point in time to scale in or to scale out. Finding such a point is difficult as it depends on constantly changing workload and system characteristics. In this paper we investigate the application of different auto-scaling techniques for solving this problem. Specifically: (1) we formulate basic requirements for an autoscaling technique used in an elastic data stream processing system, (2) we use the formulated requirements to select the best auto scaling techniques, and (3) we perform evaluation of the selected auto scaling techniques using the real world data. Our experiments show that the auto scaling techniques used in existing elastic data stream processing systems are performing worse than the strategies used in our work.
Mapreduce - Simplified Data Processing on Large ClustersAbhishek Singh
The document describes MapReduce, a programming model and implementation for processing large datasets across clusters of computers. It allows users to write map and reduce functions to parallelize tasks. The MapReduce library automatically parallelizes jobs, distributes data and tasks, handles failures and coordinates communication between machines. It is scalable, processing terabytes of data on thousands of machines, and easy for programmers without parallel experience to use.
The document discusses MapReduce, including its programming model, internal framework, and improvements. It describes MapReduce as a programming model and framework that allows parallel processing of large datasets across commodity machines. The map function processes input key-value pairs to generate intermediate pairs, and the reduce function combines values for each key. The framework automatically parallelizes jobs and provides fault tolerance.
IRJET - Evaluating and Comparing the Two Variation with Current Scheduling Al...IRJET Journal
This document presents two variations of a job-driven scheduling scheme called JOSS for efficiently executing MapReduce jobs on remote outsourced data across multiple data centers. The goal of JOSS is to improve data locality for map and reduce tasks, avoid job starvation, and improve job performance. Extensive experiments show that the two JOSS variations, called JOSS-T and JOSS-J, outperform other scheduling algorithms in terms of data locality and network overhead without significant overhead. JOSS-T performs best for workloads of small jobs, while JOSS-J provides the shortest workload time for jobs of varying sizes distributed across data centers.
A simulation-based approach for straggler tasks detection in Hadoop MapReduceIRJET Journal
This document proposes a simulation-based approach using machine learning to identify straggler tasks in Hadoop MapReduce jobs. Straggler tasks significantly increase overall job completion time. The existing approach in Hadoop uses simple profiling to detect stragglers, but this does not account for hardware variations. The proposed approach uses k-means clustering to classify tasks as fast or slow based on their expected completion time, as determined through simulation. This has the potential to improve job completion time by enabling more effective speculative execution of straggler tasks on alternative nodes. The approach groups simulated task completion times into two clusters representing fast and slow tasks. This allows accurate identification of straggler tasks to target for speculative execution.
This document discusses a proposed data-aware caching framework called Dache that could be used with big data applications built on MapReduce. Dache aims to cache intermediate data generated during MapReduce jobs to avoid duplicate computations. When tasks run, they would first check the cache for existing results before running the actual computations. The goal is to improve efficiency by reducing redundant work. The document outlines the objectives and scope of extending MapReduce with Dache, provides background on MapReduce and Hadoop, and concludes that initial experiments show Dache can eliminate duplicate tasks in incremental jobs.
This document provides an overview of MapReduce and Apache Hadoop. It discusses the history and components of Hadoop, including HDFS and MapReduce. It then walks through an example MapReduce job, the WordCount algorithm, to illustrate how MapReduce works. The WordCount example counts the frequency of words in documents by having mappers emit <word, 1> pairs and reducers sum the counts for each word.
Big data refers to large volumes of unstructured or semi-structured data that is difficult to process using traditional databases and analysis tools. The amount of data generated daily is growing exponentially due to factors like increased internet usage and data collection by organizations. Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It uses HDFS for reliable storage and MapReduce as a programming model to process data in parallel across nodes.
Earlier stage for straggler detection and handling using combined CPU test an...IJECEIAES
This document summarizes a research paper that proposes a new framework called the combinatory late-machine (CLM) framework to facilitate early detection and handling of straggler tasks in MapReduce jobs. Straggler tasks significantly increase job execution time and energy consumption. The CLM framework combines CPU testing and the Longest Approximate Time to End (LATE) methodology to calculate a straggler tolerance threshold earlier. This allows for prompt mitigation actions. The paper reviews related work on straggler detection techniques and discusses the proposed methodology, which estimates task finish times based on progress scores. It aims to correlate straggler detection with system attributes like resource utilization that could cause delays.
Hadoop is an open- source implementation of MapReduce. Hadoop is useful for storing the large volume
of data into Hadoop Distributed File System (In distribute Manner) and that data get processed by MapReduce model
in parallel. MapReduce is scalable and efficient programming model to perform large scale data intensive application.
In Homogeneous Environment, it decreases the data transmission overhead because Hadoop schedules data location
and all nodes have ideal work with computing speed in a cluster. Unhappily, it’s difficult to take data locality in
heterogeneous or shared environments. The performance of MapReduce is improved using data Prefetching
mechanism which can fetch the data from slower remote node to faster node and as a result job execution time is
reduced. This mechanism hides the overhead of data processing and data transmission when data is not local. Data
prefetching design reduces data transmission overhead effectively.
The document discusses big data and distributed computing. It explains that big data refers to large, unstructured datasets that are too large for traditional databases. Distributed computing uses multiple computers connected via a network to process large datasets in parallel. Hadoop is an open-source framework for distributed computing that uses MapReduce and HDFS for parallel processing and storage across clusters. HDFS stores data redundantly across nodes for fault tolerance.
LOAD BALANCING LARGE DATA SETS IN A HADOOP CLUSTERijdpsjournal
With the interconnection of one to many computers online, the data shared by users is multiplying daily. As
a result, the amount of data to be processed by dedicated servers rises very quickly. However, the
instantaneous increase in the volume of data to be processed by the server comes up against latency during
processing. This requires a model to manage the distribution of tasks across several machines. This article
presents a study of load balancing for large data sets on a cluster of Hadoop nodes. In this paper, we use
Mapreduce to implement parallel programming and Yarn to monitor task execution and submission in a
node cluster.
This document summarizes a proposal to improve fault tolerance in Hadoop clusters. It proposes adding a "Backup" state to store intermediate MapReduce data, so reducers can continue working even if mappers fail. It also proposes a "supernode" protocol where neighboring slave nodes communicate task information. If one node fails, a neighbor can take over its tasks without involving the JobTracker. This would improve fault tolerance by allowing computation to continue locally between nodes after failures.
This paper addresses the issue of accumulated computational and communication skew in time-stepped scientific applications running on cloud environments. It proposes a new approach called AsyTick that fully exploits parallelism among application ticks to resist skew accumulation. AsyTick uses a data-centric programming model and runtime system to allow decomposing computational parts of objects into asynchronous sub-processes. Experimental results show the proposed approach improves performance over state-of-the-art skew-resistant approaches by up to 2.53 times for time-stepped applications in the cloud.
Sharing of cluster resources among multiple Workflow Applicationsijcsit
Many computational solutions can be expressed as workflows. A Cluster of processors is a shared
resource among several users and hence the need for a scheduler which deals with multi-user jobs
presented as workflows. The scheduler must find the number of processors to be allotted for each workflow
and schedule tasks on allotted processors. In this work, a new method to find optimal and maximum
number of processors that can be allotted for a workflow is proposed. Regression analysis is used to find
the best possible way to share available processors, among suitable number of submitted workflows. An
instance of a scheduler is created for each workflow, which schedules tasks on the allotted processors.
Towards this end, a new framework to receive online submission of workflows, to allot processors to each
workflow and schedule tasks, is proposed and experimented using a discrete-event based simulator. This
space-sharing of processors among multiple workflows shows better performance than the other methods
found in literature. Because of space-sharing, an instance of a scheduler must be used for each workflow
within the allotted processors. Since the number of processors for each workflow is known only during
runtime, a static schedule can not be used. Hence a hybrid scheduler which tries to combine the advantages
of static and dynamic scheduler is proposed. Thus the proposed framework is a promising solution to
multiple workflows scheduling on cluster.
1. The document discusses concepts related to managing big data using Hadoop including data formats, analyzing data with MapReduce, scaling out, data flow, Hadoop streaming, and Hadoop pipes.
2. Hadoop allows for distributed processing of large datasets across clusters of computers using a simple programming model. It scales out to large clusters of commodity hardware and manages data processing and storage automatically.
3. Hadoop streaming and Hadoop pipes provide interfaces for running MapReduce jobs using any programming language, such as Python or C++, instead of just Java. This allows developers to use the language of their choice.
This document proposes a new scheduler called Synchronized and Comparative Queue Capacity Scheduler (SCQ) to improve the performance of the default Capacity Scheduler in Hadoop. It describes the methodology used, which involves installing Hadoop, configuring the Capacity Scheduler, and adding the SCQ scheduler. Experiments were conducted on a single node and 4-node cluster using benchmark applications like Pi, WordCount, and TestDFSIO. The results show that the proposed SCQ scheduler reduces execution time compared to the default Capacity Scheduler, especially for larger problem sizes.
Map-Reduce Synchronized and Comparative Queue Capacity Scheduler in Hadoop fo...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
IRJET- Review of Existing Methods in K-Means Clustering AlgorithmIRJET Journal
The document reviews existing methods for the k-means clustering algorithm. It discusses how k-means clustering works and some of its limitations when dealing with large datasets, such as being dependent on the initial choice of centroids. It then proposes using Hadoop to overcome big data challenges and calculate preliminary centroids for k-means clustering in a distributed manner. Finally, it reviews different techniques that have been proposed in other research to improve k-means clustering, such as methods for selecting better initial centroids or determining the optimal number of clusters.
Hadoop is a framework for distributed storage and processing of large datasets across clusters of computers. It addresses problems like hardware failure and combining data after analysis. The core components are HDFS for distributed storage and MapReduce for distributed processing. HDFS stores data as blocks across nodes and handles replication for reliability. The Namenode manages the file system namespace and metadata, while Datanodes store and retrieve blocks. Hadoop supports reliable analysis of large datasets in a distributed manner through its scalable architecture.
This document provides an introduction and overview of Hadoop, an open-source framework for distributed storage and processing of large datasets across clusters of computers. It discusses how Hadoop uses MapReduce and HDFS to parallelize workloads and store data redundantly across nodes to solve issues around hardware failure and combining results. Key aspects covered include how HDFS distributes and replicates data, how MapReduce isolates processing into mapping and reducing functions to abstract communication, and how Hadoop moves computation to the data to improve performance.
The document proposes a system called Twiche that uses caching to improve the efficiency of incremental MapReduce jobs. Twiche indexes cached items from the map phase by their original input and applied operations. This allows it to identify duplicate computations and avoid reprocessing the same data. The experimental results show that Twiche can eliminate all duplicate tasks in incremental MapReduce jobs, reducing execution time and CPU utilization compared to traditional MapReduce.
A NOVEL METHODOLOGY FOR TASK DISTRIBUTION IN HETEROGENEOUS RECONFIGURABLE COM...ijesajournal
Modern embedded systems are being modeled as Heterogeneous Reconfigurable Computing Systems
(HRCS) where Reconfigurable Hardware i.e. Field Programmable Gate Array (FPGA) and soft core
processors acts as computing elements. So, an efficient task distribution methodology is essential for
obtaining high performance in modern embedded systems. In this paper, we present a novel methodology
for task distribution called Minimum Laxity First (MLF) algorithm that takes the advantage of runtime
reconfiguration of FPGA in order to effectively utilize the available resources. The MLF algorithm is a list
based dynamic scheduling algorithm that uses attributes of tasks as well computing resources as cost
function to distribute the tasks of an application to HRCS. In this paper, an on chip HRCS computing
platform is configured on Virtex 5 FPGA using Xilinx EDK. The real time applications JPEG, OFDM
transmitters are represented as task graph and then the task are distributed, statically as well dynamically,
to the platform HRCS in order to evaluate the performance of the designed task distribution model. Finally,
the performance of MLF algorithm is compared with existing static scheduling algorithms. The comparison
shows that the MLF algorithm outperforms in terms of efficient utilization of resources on chip and also
speedup an application execution.
Similar to Enhancing Performance and Fault Tolerance of Hadoop Cluster (20)
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
This study compares the use of Stark Steel and TMT Steel as reinforcement materials in a two-way reinforced concrete slab. Mechanical testing is conducted to determine the tensile strength, yield strength, and other properties of each material. A two-way slab design adhering to codes and standards is executed with both materials. The performance is analyzed in terms of deflection, stability under loads, and displacement. Cost analyses accounting for material, durability, maintenance, and life cycle costs are also conducted. The findings provide insights into the economic and structural implications of each material for reinforcement selection and recommendations on the most suitable material based on the analysis.
Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
This document discusses a study analyzing the effect of camber, position of camber, and angle of attack on the aerodynamic characteristics of airfoils. Sixteen modified asymmetric NACA airfoils were analyzed using computational fluid dynamics (CFD) by varying the camber, camber position, and angle of attack. The results showed the relationship between these parameters and the lift coefficient, drag coefficient, and lift to drag ratio. This provides insight into how changes in airfoil geometry impact aerodynamic performance.
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
This document reviews the progress and challenges of aluminum-based metal matrix composites (MMCs), focusing on their fabrication processes and applications. It discusses how various aluminum MMCs have been developed using reinforcements like borides, carbides, oxides, and nitrides to improve mechanical and wear properties. These composites have gained prominence for their lightweight, high-strength and corrosion resistance properties. The document also examines recent advancements in fabrication techniques for aluminum MMCs and their growing applications in industries such as aerospace and automotive. However, it notes that challenges remain around issues like improper mixing of reinforcements and reducing reinforcement agglomeration.
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
This document discusses research on using graph neural networks (GNNs) for dynamic optimization of public transportation networks in real-time. GNNs represent transit networks as graphs with nodes as stops and edges as connections. The GNN model aims to optimize networks using real-time data on vehicle locations, arrival times, and passenger loads. This helps increase mobility, decrease traffic, and improve efficiency. The system continuously trains and infers to adapt to changing transit conditions, providing decision support tools. While research has focused on performance, more work is needed on security, socio-economic impacts, contextual generalization of models, continuous learning approaches, and effective real-time visualization.
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
This document summarizes a research project that aims to compare the structural performance of conventional slab and grid slab systems in multi-story buildings using ETABS software. The study will analyze both symmetric and asymmetric building models under various loading conditions. Parameters like deflections, moments, shears, and stresses will be examined to evaluate the structural effectiveness of each slab type. The results will provide insights into the comparative behavior of conventional and grid slabs to help engineers and architects select appropriate slab systems based on building layouts and design requirements.
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
This document summarizes and reviews a research paper on the seismic response of reinforced concrete (RC) structures with plan and vertical irregularities, with and without infill walls. It discusses how infill walls can improve or reduce the seismic performance of RC buildings, depending on factors like wall layout, height distribution, connection to the frame, and relative stiffness of walls and frames. The reviewed research paper analyzes the behavior of infill walls, effects of vertical irregularities, and seismic performance of high-rise structures under linear static and dynamic analysis. It studies response characteristics like story drift, deflection and shear. The document also provides literature on similar research investigating the effects of infill walls, soft stories, plan irregularities, and different
This document provides a review of machine learning techniques used in Advanced Driver Assistance Systems (ADAS). It begins with an abstract that summarizes key applications of machine learning in ADAS, including object detection, recognition, and decision-making. The introduction discusses the integration of machine learning in ADAS and how it is transforming vehicle safety. The literature review then examines several research papers on topics like lightweight deep learning models for object detection and lane detection models using image processing. It concludes by discussing challenges and opportunities in the field, such as improving algorithm robustness and adaptability.
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...IRJET Journal
The document analyzes temperature and precipitation trends in Asosa District, Benishangul Gumuz Region, Ethiopia from 1993 to 2022 based on data from the local meteorological station. The results show:
1) The average maximum and minimum annual temperatures have generally decreased over time, with maximum temperatures decreasing by a factor of -0.0341 and minimum by -0.0152.
2) Mann-Kendall tests found the decreasing temperature trends to be statistically significant for annual maximum temperatures but not for annual minimum temperatures.
3) Annual precipitation in Asosa District showed a statistically significant increasing trend.
The conclusions recommend development planners account for rising summer precipitation and declining temperatures in
P.E.B. Framed Structure Design and Analysis Using STAAD ProIRJET Journal
This document discusses the design and analysis of pre-engineered building (PEB) framed structures using STAAD Pro software. It provides an overview of PEBs, including that they are designed off-site with building trusses and beams produced in a factory. STAAD Pro is identified as a key tool for modeling, analyzing, and designing PEBs to ensure their performance and safety under various load scenarios. The document outlines modeling structural parts in STAAD Pro, evaluating structural reactions, assigning loads, and following international design codes and standards. In summary, STAAD Pro is used to design and analyze PEB framed structures to ensure safety and code compliance.
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...IRJET Journal
This document provides a review of research on innovative fiber integration methods for reinforcing concrete structures. It discusses studies that have explored using carbon fiber reinforced polymer (CFRP) composites with recycled plastic aggregates to develop more sustainable strengthening techniques. It also examines using ultra-high performance fiber reinforced concrete to improve shear strength in beams. Additional topics covered include the dynamic responses of FRP-strengthened beams under static and impact loads, and the performance of preloaded CFRP-strengthened fiber reinforced concrete beams. The review highlights the potential of fiber composites to enable more sustainable and resilient construction practices.
Survey Paper on Cloud-Based Secured Healthcare SystemIRJET Journal
This document summarizes a survey on securing patient healthcare data in cloud-based systems. It discusses using technologies like facial recognition, smart cards, and cloud computing combined with strong encryption to securely store patient data. The survey found that healthcare professionals believe digitizing patient records and storing them in a centralized cloud system would improve access during emergencies and enable more efficient care compared to paper-based systems. However, ensuring privacy and security of patient data is paramount as healthcare incorporates these digital technologies.
Review on studies and research on widening of existing concrete bridgesIRJET Journal
This document summarizes several studies that have been conducted on widening existing concrete bridges. It describes a study from China that examined load distribution factors for a bridge widened with composite steel-concrete girders. It also outlines challenges and solutions for widening a bridge in the UAE, including replacing bearings and stitching the new and existing structures. Additionally, it discusses two bridge widening projects in New Zealand that involved adding precast beams and stitching to connect structures. Finally, safety measures and challenges for strengthening a historic bridge in Switzerland under live traffic are presented.
React based fullstack edtech web applicationIRJET Journal
The document describes the architecture of an educational technology web application built using the MERN stack. It discusses the frontend developed with ReactJS, backend with NodeJS and ExpressJS, and MongoDB database. The frontend provides dynamic user interfaces, while the backend offers APIs for authentication, course management, and other functions. MongoDB enables flexible data storage. The architecture aims to provide a scalable, responsive platform for online learning.
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...IRJET Journal
This paper proposes integrating Internet of Things (IoT) and blockchain technologies to help implement objectives of India's National Education Policy (NEP) in the education sector. The paper discusses how blockchain could be used for secure student data management, credential verification, and decentralized learning platforms. IoT devices could create smart classrooms, automate attendance tracking, and enable real-time monitoring. Blockchain would ensure integrity of exam processes and resource allocation, while smart contracts automate agreements. The paper argues this integration has potential to revolutionize education by making it more secure, transparent and efficient, in alignment with NEP goals. However, challenges like infrastructure needs, data privacy, and collaborative efforts are also discussed.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.IRJET Journal
This document provides a review of research on the performance of coconut fibre reinforced concrete. It summarizes several studies that tested different volume fractions and lengths of coconut fibres in concrete mixtures with varying compressive strengths. The studies found that coconut fibre improved properties like tensile strength, toughness, crack resistance, and spalling resistance compared to plain concrete. Volume fractions of 2-5% and fibre lengths of 20-50mm produced the best results. The document concludes that using a 4-5% volume fraction of coconut fibres 30-40mm in length with M30-M60 grade concrete would provide benefits based on previous research.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...IRJET Journal
The document discusses optimizing business management processes through automation using Microsoft Power Automate and artificial intelligence. It provides an overview of Power Automate's key components and features for automating workflows across various apps and services. The document then presents several scenarios applying automation solutions to common business processes like data entry, monitoring, HR, finance, customer support, and more. It estimates the potential time and cost savings from implementing automation for each scenario. Finally, the conclusion emphasizes the transformative impact of AI and automation tools on business processes and the need for ongoing optimization.
Multistoried and Multi Bay Steel Building Frame by using Seismic DesignIRJET Journal
The document describes the seismic design of a G+5 steel building frame located in Roorkee, India according to Indian codes IS 1893-2002 and IS 800. The frame was analyzed using the equivalent static load method and response spectrum method, and its response in terms of displacements and shear forces were compared. Based on the analysis, the frame was designed as a seismic-resistant steel structure according to IS 800:2007. The software STAAD Pro was used for the analysis and design.
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...IRJET Journal
This research paper explores using plastic waste as a sustainable and cost-effective construction material. The study focuses on manufacturing pavers and bricks using recycled plastic and partially replacing concrete with plastic alternatives. Initial results found that pavers and bricks made from recycled plastic demonstrate comparable strength and durability to traditional materials while providing environmental and cost benefits. Additionally, preliminary research indicates incorporating plastic waste as a partial concrete replacement significantly reduces construction costs without compromising structural integrity. The outcomes suggest adopting plastic waste in construction can address plastic pollution while optimizing costs, promoting more sustainable building practices.
Determination of Equivalent Circuit parameters and performance characteristic...pvpriya2
Includes the testing of induction motor to draw the circle diagram of induction motor with step wise procedure and calculation for the same. Also explains the working and application of Induction generator
This presentation is about Food Delivery Systems and how they are developed using the Software Development Life Cycle (SDLC) and other methods. It explains the steps involved in creating a food delivery app, from planning and designing to testing and launching. The slide also covers different tools and technologies used to make these systems work efficiently.
Build the Next Generation of Apps with the Einstein 1 Platform.
Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
AI in customer support Use cases solutions development and implementation.pdfmahaffeycheryld
AI in customer support will integrate with emerging technologies such as augmented reality (AR) and virtual reality (VR) to enhance service delivery. AR-enabled smart glasses or VR environments will provide immersive support experiences, allowing customers to visualize solutions, receive step-by-step guidance, and interact with virtual support agents in real-time. These technologies will bridge the gap between physical and digital experiences, offering innovative ways to resolve issues, demonstrate products, and deliver personalized training and support.
https://www.leewayhertz.com/ai-in-customer-support/#How-does-AI-work-in-customer-support
Tools & Techniques for Commissioning and Maintaining PV Systems W-Animations ...Transcat
Join us for this solutions-based webinar on the tools and techniques for commissioning and maintaining PV Systems. In this session, we'll review the process of building and maintaining a solar array, starting with installation and commissioning, then reviewing operations and maintenance of the system. This course will review insulation resistance testing, I-V curve testing, earth-bond continuity, ground resistance testing, performance tests, visual inspections, ground and arc fault testing procedures, and power quality analysis.
Fluke Solar Application Specialist Will White is presenting on this engaging topic:
Will has worked in the renewable energy industry since 2005, first as an installer for a small east coast solar integrator before adding sales, design, and project management to his skillset. In 2022, Will joined Fluke as a solar application specialist, where he supports their renewable energy testing equipment like IV-curve tracers, electrical meters, and thermal imaging cameras. Experienced in wind power, solar thermal, energy storage, and all scales of PV, Will has primarily focused on residential and small commercial systems. He is passionate about implementing high-quality, code-compliant installation techniques.
Height and depth gauge linear metrology.pdfq30122000
Height gauges may also be used to measure the height of an object by using the underside of the scriber as the datum. The datum may be permanently fixed or the height gauge may have provision to adjust the scale, this is done by sliding the scale vertically along the body of the height gauge by turning a fine feed screw at the top of the gauge; then with the scriber set to the same level as the base, the scale can be matched to it. This adjustment allows different scribers or probes to be used, as well as adjusting for any errors in a damaged or resharpened probe.