The document proposes a new preemption and priority-based scheduling algorithm for Hadoop Distributed File System (HDFS). It begins with an introduction to scheduling in Hadoop and describes existing scheduling algorithms like FIFO, fair, and capacity schedulers. It then discusses the limitations of these schedulers in handling priorities and preemption. The proposed algorithm allows the scheduler to make more efficient decisions by prioritizing jobs with high priority and preempting low priority jobs. Finally, a comparison table summarizes the different scheduling strategies for HDFS in terms of their scheduling methodology, benefits, limitations, and behaviors with priority and non-priority tasks.
Cache mechanism to avoid dulpication of same thing in hadoop system to speed ...eSAT Journals
This document proposes mechanisms to improve the efficiency of the Hadoop distributed file system and MapReduce framework. It suggests using locality-sensitive hashing to colocate related files on the same data nodes, which would improve data locality. It also proposes implementing a cache to store the results of MapReduce tasks, so that duplicate computations can be avoided when the same task is run again on the same data. Implementing these mechanisms could help speed up execution times in Hadoop by reducing unnecessary data transmission and repetitive task executions.
The document discusses MapReduce 2.0 and how it separates the programming model from resource management and scheduling using YARN. It explains that MapReduce 2.0 focuses only on the programming aspect, while YARN handles resource management and scheduling. It provides an overview of how MapReduce programs are executed in parallel across large clusters, with automatic parallelization, fault tolerance for node failures, and inter-machine communication. It also gives examples of how MapReduce can be used to solve problems like word counting in large documents.
In the era of big data, even though we have large infrastructure, storage data varies in size,
formats, variety, volume and several platforms such as hadoop, cloud since we have problem associated
with an application how to process the data which is varying in size and format. Data varying in
application and resources available during run time is called dynamic workflow. Using large
infrastructure and huge amount of resources for the analysis of data is time consuming and waste of
resources, it’s better to use scheduling algorithm to analyse the given data set, for efficient execution of
data set without time consuming and evaluate which scheduling algorithm is best and suitable for the
given data set. We evaluate with different data set understand which is the most suitable algorithm for
analysis of data being efficient execution of data set and store the data after analysis
Survey on Job Schedulers in Hadoop ClusterIOSR Journals
Abstract : Hadoop-MapReduce is a powerful parallel processing technique large for big data analysis on
distributed commodity hardware clusters such as Clouds. For job scheduling Hadoop provides default FIFO
scheduler where jobs are scheduled in FIFO order. But this scheduler might not be a good choice for some jobs.
So in such situations one should choose an alternate scheduler. In this paper we conduct a study on various
schedulers. Hopefully this study will stimulate other designers and experienced end users understand the details
of particular schedulers, enabling them to make the best choices for their particular research interests.
Keywords: Cloud Computing, Hadoop, HDFS, MapReduce, schedulers
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.
Abstract: Efficient task scheduling method can meet users' requirements, and improve the resource utilization, then increase the overall performance of the cloud computing environment. Cloud computing has new features, such as flexibility, Virtualization and etc., in this paper we propose a two levels task scheduling method based on load balancing in cloud computing. This task scheduling method meet user's requirements and get high resource utilization that simulation results in Cloud Sim simulator prove this.Keywords: cloud computing; task scheduling; virtualization.
Title: A Task Scheduling Algorithm in Cloud Computing
Author: Ali Bagherinia
ISSN 2350-1022
International Journal of Recent Research in Mathematics Computer Science and Information Technology
Paper Publications
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.
Resource Scheduling and Evaluation of Heuristics with Resource Reservation in...Eswar Publications
The "cloud" is a combination of various hardware and software that work jointly to bring many aspects of computing to the users as an online service. Some uniqueness of Cloud Computing is pay-per-use, elastic capacity, misapprehension of unlimited resources, self-service interface, virtualized resources etc. Various applications running on cloud environment would expect better Quality of Service (QoS) from Cloud environment. Improvement in Quality of Service (QoS) is possible through better job scheduling and reservation of resources in advance for execution of jobs. In this paper effects of Reservation Rate and Time Factor on the performance parameters like Resource Utilization, Waiting Time, Minimum Execution Time and Success Rate of Reserved
jobs have been studied for various job scheduling algorithms and their performance have been calculated in resource reservation environment in Cloud.
Cache mechanism to avoid dulpication of same thing in hadoop system to speed ...eSAT Journals
This document proposes mechanisms to improve the efficiency of the Hadoop distributed file system and MapReduce framework. It suggests using locality-sensitive hashing to colocate related files on the same data nodes, which would improve data locality. It also proposes implementing a cache to store the results of MapReduce tasks, so that duplicate computations can be avoided when the same task is run again on the same data. Implementing these mechanisms could help speed up execution times in Hadoop by reducing unnecessary data transmission and repetitive task executions.
The document discusses MapReduce 2.0 and how it separates the programming model from resource management and scheduling using YARN. It explains that MapReduce 2.0 focuses only on the programming aspect, while YARN handles resource management and scheduling. It provides an overview of how MapReduce programs are executed in parallel across large clusters, with automatic parallelization, fault tolerance for node failures, and inter-machine communication. It also gives examples of how MapReduce can be used to solve problems like word counting in large documents.
In the era of big data, even though we have large infrastructure, storage data varies in size,
formats, variety, volume and several platforms such as hadoop, cloud since we have problem associated
with an application how to process the data which is varying in size and format. Data varying in
application and resources available during run time is called dynamic workflow. Using large
infrastructure and huge amount of resources for the analysis of data is time consuming and waste of
resources, it’s better to use scheduling algorithm to analyse the given data set, for efficient execution of
data set without time consuming and evaluate which scheduling algorithm is best and suitable for the
given data set. We evaluate with different data set understand which is the most suitable algorithm for
analysis of data being efficient execution of data set and store the data after analysis
Survey on Job Schedulers in Hadoop ClusterIOSR Journals
Abstract : Hadoop-MapReduce is a powerful parallel processing technique large for big data analysis on
distributed commodity hardware clusters such as Clouds. For job scheduling Hadoop provides default FIFO
scheduler where jobs are scheduled in FIFO order. But this scheduler might not be a good choice for some jobs.
So in such situations one should choose an alternate scheduler. In this paper we conduct a study on various
schedulers. Hopefully this study will stimulate other designers and experienced end users understand the details
of particular schedulers, enabling them to make the best choices for their particular research interests.
Keywords: Cloud Computing, Hadoop, HDFS, MapReduce, schedulers
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.
Abstract: Efficient task scheduling method can meet users' requirements, and improve the resource utilization, then increase the overall performance of the cloud computing environment. Cloud computing has new features, such as flexibility, Virtualization and etc., in this paper we propose a two levels task scheduling method based on load balancing in cloud computing. This task scheduling method meet user's requirements and get high resource utilization that simulation results in Cloud Sim simulator prove this.Keywords: cloud computing; task scheduling; virtualization.
Title: A Task Scheduling Algorithm in Cloud Computing
Author: Ali Bagherinia
ISSN 2350-1022
International Journal of Recent Research in Mathematics Computer Science and Information Technology
Paper Publications
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.
Resource Scheduling and Evaluation of Heuristics with Resource Reservation in...Eswar Publications
The "cloud" is a combination of various hardware and software that work jointly to bring many aspects of computing to the users as an online service. Some uniqueness of Cloud Computing is pay-per-use, elastic capacity, misapprehension of unlimited resources, self-service interface, virtualized resources etc. Various applications running on cloud environment would expect better Quality of Service (QoS) from Cloud environment. Improvement in Quality of Service (QoS) is possible through better job scheduling and reservation of resources in advance for execution of jobs. In this paper effects of Reservation Rate and Time Factor on the performance parameters like Resource Utilization, Waiting Time, Minimum Execution Time and Success Rate of Reserved
jobs have been studied for various job scheduling algorithms and their performance have been calculated in resource reservation environment in Cloud.
Hadoop Training, Enhance your Big data subject knowledge with Online Training without wasting your time. Register for Free LIVE DEMO Class.
For more info: http://www.hadooponlinetutor.com
Contact Us:
8121660044
732-419-2619
http://www.hadooponlinetutor.com
Task scheduling is an important aspect to improve the utilization of resources in the Cloud Computing. This paper proposes a Divide and Conquer based approach for heterogeneous earliest finish time algorithm. The proposed system works in two phases. In the first phase it assigns the ranks to the incoming tasks with respect to size of it. In the second phase, we properly assign and manage the task to the virtual machine with the consideration of ideal time of respective virtual machine. This helps to get more effective resource utilization in Cloud Computing. The experimental results using Cybershake Scientific Workflow shows that the proposed Divide and Conquer HEFT performs better than HEFT in terms of task's finish time and response time. The result obtained by experimentally demonstrate that the proposed DCHEFT performance superiorly.
The document proposes a Twiche framework for caching intermediate data from MapReduce jobs processing large amounts of Twitter data. Twiche would cache intermediate results on the reduce tasks to eliminate duplicate computations. It requires minimal changes to the original MapReduce model. The authors implemented Twiche in Hadoop by extending relevant components. Experiments showed Twiche could eliminate all duplicate tasks in incremental MapReduce jobs with minimal application code changes.
This document discusses and compares various load balancing techniques in cloud computing. It begins by introducing load balancing as an important issue in cloud computing for efficiently scheduling user requests and resources. Several load balancing algorithms are then described, including honeybee foraging algorithm, biased random sampling, active clustering, OLB+LBMM, and Min-Min. Metrics for evaluating and comparing load balancing techniques are defined, such as throughput, overhead, fault tolerance, migration time, response time, resource utilization, scalability, and performance. The algorithms are then analyzed based on these metrics.
A Survey on Service Request Scheduling in Cloud Based ArchitectureIJSRD
Cloud computing has become quite popular now-a-days. It facilitates the users to store and process their data which is stored in 3rd party data centers. Today in IT sector everything is run and managed on the cloud environment. As the number of users is increasing day by day, faster and efficient processing of large volume of data and resources is desired at all levels. So the management of resources attains prime importance. While using cloud computing various issues are encountered like load balancing, traffic while computation etc. Job scheduling is one of the solution of these problems which reduces the waiting time and maximizes the quality of services. In job scheduling “priority†is an important factor. In this paper, we will be discussing various scheduling algorithms and a review on dynamic priority scheduling algorithm.
A Comparative Study of Load Balancing Algorithms for Cloud ComputingIJERA Editor
Cloud Computing is fast growing technology in both industry research and academy. User can access the cloud
service and pay based on the usage of resource. Balancing the load is major task of cloud service provider with
minimum response time, maximum throughput and better resource utilization. There are many load balancing
algorithms proposed to assign a user request to cloud resource in efficient manner. In this paper three load balancing
algorithms are simulated in Cloud Analyst and results are compared.
GROUPING BASED JOB SCHEDULING ALGORITHM USING PRIORITY QUEUE AND HYBRID ALGOR...ijgca
This document describes a proposed grouping based job scheduling algorithm for grid computing that aims to maximize resource utilization and minimize job processing times. It discusses related work on job scheduling algorithms and then presents the steps of the proposed algorithm. The algorithm uses shortest job first, first-in first-out, and round robin scheduling to process jobs in groups. The algorithm is evaluated experimentally in MATLAB and shown to reduce total job processing time compared to using only first-in first-out scheduling. Graphs demonstrate the processing time improvements achieved by the combined scheduling approach.
VIRTUAL MACHINE SCHEDULING IN CLOUD COMPUTING ENVIRONMENTijmpict
Cloud computing is an upcoming technology in dispersed computing facilitating paying for each model as
for each user demand and need. Cloud incorporates a set of virtual machine which comprises both storage
and computational facility. The fundamental goal of cloud computing is to offer effective access to isolated
and geographically circulated resources. Cloud is growing every day and experiences numerous problems
such as scheduling. Scheduling means a collection of policies to regulate the order of task to be executed
by a computer system. An excellent scheduler derives its scheduling plan in accordance with the type of
work and the varying environment. This research paper demonstrates a generalized precedence algorithm
for effective performance of work and contrast with Round Robin and FCFS Scheduling. Algorithm needs
to be tested within CloudSim toolkit and outcome illustrates that it provide good presentation compared
some customary scheduling algorithm.
Featuring a brief overview of fault-tolerant mechanisms across various Big Data systems such as Google File system (GFS), Amazon Dynamo, Bigtable, Hadoop - Map Reduce, Facebook Cassandra along with description of an existing fault tolerant model
High Dimensionality Structures Selection for Efficient Economic Big data usin...IRJET Journal
This document proposes a new framework for efficient analysis of high-dimensional economic big data using feature selection and k-means clustering algorithms. It introduces challenges in analyzing large volumes of economic data with high dimensionality. The framework combines methods for economic feature selection and model construction to identify patterns for economic development. It uses novel data preprocessing, distributed feature identification to select important indicators, and new econometric models to capture hidden patterns for economic analysis. The results on economic data sets demonstrate superior performance of the proposed methods.
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 proposes a fair scheduling algorithm with dynamic load balancing for grid computing. It begins by introducing grid computing and the need for efficient load balancing algorithms to distribute tasks. It then describes dynamic load balancing approaches, including information, triggering, resource type, location, and selection policies. The proposed algorithm uses a fair scheduling approach that assigns tasks to processors based on their estimated fair completion times to ensure tasks receive equal shares of computing resources. It also includes a dynamic load balancing component that migrates tasks between processors to maintain balanced loads across all resources. Simulation results demonstrated the algorithm achieved balanced loads across processors and reduced overall task completion times.
LOAD BALANCING ALGORITHM TO IMPROVE RESPONSE TIME ON CLOUD COMPUTINGijccsa
Load balancing techniques in cloud computing can be applied at different levels. There are two main
levels: load balancing on physical server and load balancing on virtual servers. Load balancing on a
physical server is policy of allocating physical servers to virtual machines. And load balancing on virtual
machines is a policy of allocating resources from physical server to virtual machines for tasks or
applications running on them. Depending on the requests of the user on cloud computing is SaaS (Software
as a Service), PaaS (Platform as a Service) or IaaS (Infrastructure as a Service) that has a proper load
balancing policy. When receiving the task, the cloud data center will have to allocate these tasks efficiently
so that the response time is minimized to avoid congestion. Load balancing should also be performed
between different datacenters in the cloud to ensure minimum transfer time. In this paper, we propose a
virtual machine-level load balancing algorithm that aims to improve the average response time and
average processing time of the system in the cloud environment. The proposed algorithm is compared to the
algorithms of Avoid Deadlocks [5], Maxmin [6], Throttled [8] and the results show that our algorithms
have optimized response times.
Fault Tolerance in Big Data Processing Using Heartbeat Messages and Data Repl...IJSRD
Big data is a popular term used to define the exponential evolution and availability of data, includes both structured and unstructured data. The volatile progression of demands on big data processing imposes heavy burden on computation, communication and storage in geographically distributed data centers. Hence it is necessary to minimize the cost of big data processing, which also includes fault tolerance cost. Big Data processing involves two types of faults: node failure and data loss. Both the faults can be recovered using heartbeat messages. Here heartbeat messages acts as an acknowledgement messages between two servers. This paper depicts about the study of node failure and recovery, data replication and heartbeat messages.
This document discusses various load balancing algorithms that can be applied in cloud computing. It begins with an introduction to cloud computing models including infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). It then discusses the goals of load balancing in cloud computing. The main part of the document describes and provides examples of several load balancing algorithms: Round Robin, Opportunistic Load Balancing, Minimum Completion Time, and Minimum Execution Time. For each algorithm, it explains the basic approach and provides an example to illustrate how it works.
The document summarizes two papers about MapReduce frameworks for cloud computing. The first paper describes Hadoop, which uses MapReduce and HDFS to process large amounts of distributed data across clusters. HDFS stores data across cluster nodes in a fault-tolerant manner, while MapReduce splits jobs into parallel map and reduce tasks. The second paper discusses P2P-MapReduce, which allows for a dynamic cloud environment where nodes can join and leave. It uses a peer-to-peer model where nodes can be masters or slaves, and maintains backup masters to prevent job loss if the primary master fails.
The document discusses using a genetic algorithm to schedule tasks in a cloud computing environment. It aims to minimize task execution time and reduce computational costs compared to the traditional Round Robin scheduling algorithm. The proposed genetic algorithm mimics natural selection and genetics to evolve optimal task schedules. It was tested using the CloudSim simulation toolkit and results showed the genetic algorithm provided better performance than Round Robin scheduling.
Application of selective algorithm for effective resource provisioning in clo...ijccsa
Modern day continued demand for resource hungry services and applications in IT sector has led to
development of Cloud computing. Cloud computing environment involves high cost infrastructure on one
hand and need high scale computational resources on the other hand. These resources need to be
provisioned (allocation and scheduling) to the end users in most efficient manner so that the tremendous
capabilities of cloud are utilized effectively and efficiently. In this paper we discuss a selective algorithm
for allocation of cloud resources to end-users on-demand basis. This algorithm is based on min-min and
max-min algorithms. These are two conventional task scheduling algorithm. The selective algorithm uses
certain heuristics to select between the two algorithms so that overall makespan of tasks on the machines is
minimized. The tasks are scheduled on machines in either space shared or time shared manner. We
evaluate our provisioning heuristics using a cloud simulator, called CloudSim. We also compared our
approach to the statistics obtained when provisioning of resources was done in First-Cum-First-
Serve(FCFS) manner. The experimental results show that overall makespan of tasks on given set of VMs
minimizes significantly in different scenarios.
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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.
Hourglass: a Library for Incremental Processing on HadoopMatthew Hayes
Hadoop enables processing of large data sets through its relatively easy-to-use semantics. However, jobs are often written inefficiently for tasks that could be computed incrementally due to the burdensome incremental state management for the programmer. This paper introduces Hourglass, a library for developing incremental monoid computations on Hadoop. It runs on unmodified Hadoop and provides an accumulator-based interface for programmers to store and use state across successive runs; the framework ensures that only the necessary subcomputations are performed. It is successfully used at LinkedIn, one of the largest online social networks, for many use cases in dashboarding and machine learning. Hourglass is open source and freely available.
1. The document discusses various ways to manage resource assignments and costs in Microsoft Project, including delaying resource start times, applying work contours, setting different cost rates, and assigning material resources.
2. It provides exercises for applying predefined contours to assignments, manually editing assignment values, changing cost rate tables, and addressing overallocation through reassignment.
3. The summary examines resource availability, scheduling unassigned tasks, and addressing overallocation issues visible in the timeline view.
Hadoop Training, Enhance your Big data subject knowledge with Online Training without wasting your time. Register for Free LIVE DEMO Class.
For more info: http://www.hadooponlinetutor.com
Contact Us:
8121660044
732-419-2619
http://www.hadooponlinetutor.com
Task scheduling is an important aspect to improve the utilization of resources in the Cloud Computing. This paper proposes a Divide and Conquer based approach for heterogeneous earliest finish time algorithm. The proposed system works in two phases. In the first phase it assigns the ranks to the incoming tasks with respect to size of it. In the second phase, we properly assign and manage the task to the virtual machine with the consideration of ideal time of respective virtual machine. This helps to get more effective resource utilization in Cloud Computing. The experimental results using Cybershake Scientific Workflow shows that the proposed Divide and Conquer HEFT performs better than HEFT in terms of task's finish time and response time. The result obtained by experimentally demonstrate that the proposed DCHEFT performance superiorly.
The document proposes a Twiche framework for caching intermediate data from MapReduce jobs processing large amounts of Twitter data. Twiche would cache intermediate results on the reduce tasks to eliminate duplicate computations. It requires minimal changes to the original MapReduce model. The authors implemented Twiche in Hadoop by extending relevant components. Experiments showed Twiche could eliminate all duplicate tasks in incremental MapReduce jobs with minimal application code changes.
This document discusses and compares various load balancing techniques in cloud computing. It begins by introducing load balancing as an important issue in cloud computing for efficiently scheduling user requests and resources. Several load balancing algorithms are then described, including honeybee foraging algorithm, biased random sampling, active clustering, OLB+LBMM, and Min-Min. Metrics for evaluating and comparing load balancing techniques are defined, such as throughput, overhead, fault tolerance, migration time, response time, resource utilization, scalability, and performance. The algorithms are then analyzed based on these metrics.
A Survey on Service Request Scheduling in Cloud Based ArchitectureIJSRD
Cloud computing has become quite popular now-a-days. It facilitates the users to store and process their data which is stored in 3rd party data centers. Today in IT sector everything is run and managed on the cloud environment. As the number of users is increasing day by day, faster and efficient processing of large volume of data and resources is desired at all levels. So the management of resources attains prime importance. While using cloud computing various issues are encountered like load balancing, traffic while computation etc. Job scheduling is one of the solution of these problems which reduces the waiting time and maximizes the quality of services. In job scheduling “priority†is an important factor. In this paper, we will be discussing various scheduling algorithms and a review on dynamic priority scheduling algorithm.
A Comparative Study of Load Balancing Algorithms for Cloud ComputingIJERA Editor
Cloud Computing is fast growing technology in both industry research and academy. User can access the cloud
service and pay based on the usage of resource. Balancing the load is major task of cloud service provider with
minimum response time, maximum throughput and better resource utilization. There are many load balancing
algorithms proposed to assign a user request to cloud resource in efficient manner. In this paper three load balancing
algorithms are simulated in Cloud Analyst and results are compared.
GROUPING BASED JOB SCHEDULING ALGORITHM USING PRIORITY QUEUE AND HYBRID ALGOR...ijgca
This document describes a proposed grouping based job scheduling algorithm for grid computing that aims to maximize resource utilization and minimize job processing times. It discusses related work on job scheduling algorithms and then presents the steps of the proposed algorithm. The algorithm uses shortest job first, first-in first-out, and round robin scheduling to process jobs in groups. The algorithm is evaluated experimentally in MATLAB and shown to reduce total job processing time compared to using only first-in first-out scheduling. Graphs demonstrate the processing time improvements achieved by the combined scheduling approach.
VIRTUAL MACHINE SCHEDULING IN CLOUD COMPUTING ENVIRONMENTijmpict
Cloud computing is an upcoming technology in dispersed computing facilitating paying for each model as
for each user demand and need. Cloud incorporates a set of virtual machine which comprises both storage
and computational facility. The fundamental goal of cloud computing is to offer effective access to isolated
and geographically circulated resources. Cloud is growing every day and experiences numerous problems
such as scheduling. Scheduling means a collection of policies to regulate the order of task to be executed
by a computer system. An excellent scheduler derives its scheduling plan in accordance with the type of
work and the varying environment. This research paper demonstrates a generalized precedence algorithm
for effective performance of work and contrast with Round Robin and FCFS Scheduling. Algorithm needs
to be tested within CloudSim toolkit and outcome illustrates that it provide good presentation compared
some customary scheduling algorithm.
Featuring a brief overview of fault-tolerant mechanisms across various Big Data systems such as Google File system (GFS), Amazon Dynamo, Bigtable, Hadoop - Map Reduce, Facebook Cassandra along with description of an existing fault tolerant model
High Dimensionality Structures Selection for Efficient Economic Big data usin...IRJET Journal
This document proposes a new framework for efficient analysis of high-dimensional economic big data using feature selection and k-means clustering algorithms. It introduces challenges in analyzing large volumes of economic data with high dimensionality. The framework combines methods for economic feature selection and model construction to identify patterns for economic development. It uses novel data preprocessing, distributed feature identification to select important indicators, and new econometric models to capture hidden patterns for economic analysis. The results on economic data sets demonstrate superior performance of the proposed methods.
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 proposes a fair scheduling algorithm with dynamic load balancing for grid computing. It begins by introducing grid computing and the need for efficient load balancing algorithms to distribute tasks. It then describes dynamic load balancing approaches, including information, triggering, resource type, location, and selection policies. The proposed algorithm uses a fair scheduling approach that assigns tasks to processors based on their estimated fair completion times to ensure tasks receive equal shares of computing resources. It also includes a dynamic load balancing component that migrates tasks between processors to maintain balanced loads across all resources. Simulation results demonstrated the algorithm achieved balanced loads across processors and reduced overall task completion times.
LOAD BALANCING ALGORITHM TO IMPROVE RESPONSE TIME ON CLOUD COMPUTINGijccsa
Load balancing techniques in cloud computing can be applied at different levels. There are two main
levels: load balancing on physical server and load balancing on virtual servers. Load balancing on a
physical server is policy of allocating physical servers to virtual machines. And load balancing on virtual
machines is a policy of allocating resources from physical server to virtual machines for tasks or
applications running on them. Depending on the requests of the user on cloud computing is SaaS (Software
as a Service), PaaS (Platform as a Service) or IaaS (Infrastructure as a Service) that has a proper load
balancing policy. When receiving the task, the cloud data center will have to allocate these tasks efficiently
so that the response time is minimized to avoid congestion. Load balancing should also be performed
between different datacenters in the cloud to ensure minimum transfer time. In this paper, we propose a
virtual machine-level load balancing algorithm that aims to improve the average response time and
average processing time of the system in the cloud environment. The proposed algorithm is compared to the
algorithms of Avoid Deadlocks [5], Maxmin [6], Throttled [8] and the results show that our algorithms
have optimized response times.
Fault Tolerance in Big Data Processing Using Heartbeat Messages and Data Repl...IJSRD
Big data is a popular term used to define the exponential evolution and availability of data, includes both structured and unstructured data. The volatile progression of demands on big data processing imposes heavy burden on computation, communication and storage in geographically distributed data centers. Hence it is necessary to minimize the cost of big data processing, which also includes fault tolerance cost. Big Data processing involves two types of faults: node failure and data loss. Both the faults can be recovered using heartbeat messages. Here heartbeat messages acts as an acknowledgement messages between two servers. This paper depicts about the study of node failure and recovery, data replication and heartbeat messages.
This document discusses various load balancing algorithms that can be applied in cloud computing. It begins with an introduction to cloud computing models including infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). It then discusses the goals of load balancing in cloud computing. The main part of the document describes and provides examples of several load balancing algorithms: Round Robin, Opportunistic Load Balancing, Minimum Completion Time, and Minimum Execution Time. For each algorithm, it explains the basic approach and provides an example to illustrate how it works.
The document summarizes two papers about MapReduce frameworks for cloud computing. The first paper describes Hadoop, which uses MapReduce and HDFS to process large amounts of distributed data across clusters. HDFS stores data across cluster nodes in a fault-tolerant manner, while MapReduce splits jobs into parallel map and reduce tasks. The second paper discusses P2P-MapReduce, which allows for a dynamic cloud environment where nodes can join and leave. It uses a peer-to-peer model where nodes can be masters or slaves, and maintains backup masters to prevent job loss if the primary master fails.
The document discusses using a genetic algorithm to schedule tasks in a cloud computing environment. It aims to minimize task execution time and reduce computational costs compared to the traditional Round Robin scheduling algorithm. The proposed genetic algorithm mimics natural selection and genetics to evolve optimal task schedules. It was tested using the CloudSim simulation toolkit and results showed the genetic algorithm provided better performance than Round Robin scheduling.
Application of selective algorithm for effective resource provisioning in clo...ijccsa
Modern day continued demand for resource hungry services and applications in IT sector has led to
development of Cloud computing. Cloud computing environment involves high cost infrastructure on one
hand and need high scale computational resources on the other hand. These resources need to be
provisioned (allocation and scheduling) to the end users in most efficient manner so that the tremendous
capabilities of cloud are utilized effectively and efficiently. In this paper we discuss a selective algorithm
for allocation of cloud resources to end-users on-demand basis. This algorithm is based on min-min and
max-min algorithms. These are two conventional task scheduling algorithm. The selective algorithm uses
certain heuristics to select between the two algorithms so that overall makespan of tasks on the machines is
minimized. The tasks are scheduled on machines in either space shared or time shared manner. We
evaluate our provisioning heuristics using a cloud simulator, called CloudSim. We also compared our
approach to the statistics obtained when provisioning of resources was done in First-Cum-First-
Serve(FCFS) manner. The experimental results show that overall makespan of tasks on given set of VMs
minimizes significantly in different scenarios.
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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.
Hourglass: a Library for Incremental Processing on HadoopMatthew Hayes
Hadoop enables processing of large data sets through its relatively easy-to-use semantics. However, jobs are often written inefficiently for tasks that could be computed incrementally due to the burdensome incremental state management for the programmer. This paper introduces Hourglass, a library for developing incremental monoid computations on Hadoop. It runs on unmodified Hadoop and provides an accumulator-based interface for programmers to store and use state across successive runs; the framework ensures that only the necessary subcomputations are performed. It is successfully used at LinkedIn, one of the largest online social networks, for many use cases in dashboarding and machine learning. Hourglass is open source and freely available.
1. The document discusses various ways to manage resource assignments and costs in Microsoft Project, including delaying resource start times, applying work contours, setting different cost rates, and assigning material resources.
2. It provides exercises for applying predefined contours to assignments, manually editing assignment values, changing cost rate tables, and addressing overallocation through reassignment.
3. The summary examines resource availability, scheduling unassigned tasks, and addressing overallocation issues visible in the timeline view.
PrintNetwork Diagrams and Resource UtilizationIntroduction B.docxChantellPantoja184
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Network Diagrams and Resource Utilization
Introduction | Building a Network Diagram | Building an AIB Manually | AIB Simulation | Resource Constrained Planning | Resource Leveling | Adding or Modifying Resources | Summary
Introduction
Back to Top
Last week, we learned more about how to begin planning a project. We established the project charter, scope statement, work breakdown structure, and created the activity list. This week, we will talk about one of the most important aspects of project management—building a project schedule. We'll use a tool called a network diagram. There are several ways to build a network diagram. In this course, we will use the Activity in Box (AIB) method.
Building a Network Diagram
Back to Top
Now that we know what needs to be done, we need to sequence all of the activities and establish a network diagram. With the concept of a network diagram, you will be able to determine: (1) a project's scheduled completion time, (2) the slack or float of project activities, and (3) the critical path of your project.
Depending on the size of the project, the network may be built in pieces or as a large group. Either way, the step-by-step process to build a project network is used.
Build a Project Network (or a Partial Network)
1. Brainstorm activities that are required to complete the work packages, recording those activities on Post-it notes (without regard to sequencing).
2. Sequence those activities. Determine:
· The order of activities
· Which activities can occur at the same time
· Which activities need dependencies
1. Mandatory: requires the completion of another task.
2. Discretionary: a best practice or convenience. However, the subsequent task can begin if the discretionary dependency is not completed.
3. External: from another project or process, such as permits.
4. Internal: dependencies within the control of the project team.
3. Put the notes on a wall using the above information.
4. Build a network using the notes.
Next, the activities are assigned to the people who will be doing the work. They build duration estimates for the activities. The most accurate estimates are built using actuals from previous, similar projects. Then, the activities can be loaded into an automated scheduling tool like Microsoft Project. At that point, you will be able to determine the project's scheduled completion time, the slack or float of project activities, and the critical path of your project.
Building an AIB Manually
Back to Top
The good thing about using a tool like Microsoft Project is that it makes it easy to build a network diagram. The bad thing about the tool is that it makes it so easy; project managers don't always understand what they are doing, and cannot see when they have made a mistake. They just plug in the activities and move on.
It's like adding—you should first do it manually, and then use a calculator. Every project manager should know how to build an AIB manually so that he or she really understands the.
Bragged Regression Tree Algorithm for Dynamic Distribution and Scheduling of ...Editor IJCATR
In the past few years, Grid computing came up as next generation computing platform which is a combination of
heterogeneous computing resources combined by a network across dynamic and geographically separated organizations. So, it
provides the perfect computing environment to solve large-scale computational demands. As the Grid computing demands are still
increasing from day to day due to rise in large number of complex jobs worldwide. So, the jobs may take much longer time to
complete due to poor distribution of batches or groups of jobs to inappropriate CPU’s. Therefore there is need to develop an efficient
dynamic job scheduling algorithm that would assign jobs to appropriate CPU’s dynamically. The main problem which dealt in the
paper is, how to distribute the jobs when the payload, importance, urgency, flow time etc. dynamically keeps on changing as the grid
expands or is flooded with number of job requests from different machines within the grid.
In this paper, we present a scheduling strategy which takes the advantage of decision tree algorithm to take dynamic decision
based on the current scenarios and which automatically incorporates factor analysis for considering the distribution of jobs.
GROUPING BASED JOB SCHEDULING ALGORITHM USING PRIORITY QUEUE AND HYBRID ALGOR...ijgca
Grid computing enlarge with computing platform which is collection of heterogeneous computing resources connected by a network across dynamic and geographically dispersed organization to form a distributed high performance computing infrastructure. Grid computing solves the complex computing
problems amongst multiple machines. Grid computing solves the large scale computational demands in a high performance computing environment. The main emphasis in the grid computing is given to the resource management and the job scheduler .The goal of the job scheduler is to maximize the resource utilization and minimize the processing time of the jobs. Existing approaches of Grid scheduling doesn’t give much emphasis on the performance of a Grid scheduler in processing time parameter. Schedulers allocate resources to the jobs to be executed using the First come First serve algorithm. In this paper, we have provided an optimize algorithm to queue of the scheduler using various scheduling methods like Shortest Job First, First in First out, Round robin. The job scheduling system is responsible to select best suitable machines in a grid for user jobs. The management and scheduling system generates job schedules for each machine in the grid by taking static restrictions and dynamic parameters of jobs and machines
into consideration. The main purpose of this paper is to develop an efficient job scheduling algorithm to maximize the resource utilization and minimize processing time of the jobs. Queues can be optimized by using various scheduling algorithms depending upon the performance criteria to be improved e.g. response
time, throughput. The work has been done in MATLAB using the parallel computing toolbox.
Preemptive Job Scheduling with Priorities and Starvation Avoidance CUM Throug...IDES Editor
This paper proposes a new scheduler to schedule
parallel jobs on Clusters that may be part of a Computational
Grid. This proposed policy proposes 3 Job Queues. In each
Cluster, the first Queue has some jobs which are from
Computational Grids. It means, it may be either bigger job or
part of the bigger job from the Computational Grids. The
second Queue has jobs which has low required execution time.
The third Queue has jobs which has high required execution
time. In first and second Queues, there is no chance of
starvation. But in third Queue, there is a chance of starvation.
So this proposed policy applied AGING technique to preempt
the jobs which has low priority. And the first Queue is fully
dedicated to execute a part of bigger jobs (Meta-Jobs) only. So
here we maintain three job Queues which are effectively
separate jobs according to their required execution time for
local Jobs, bigger job/part of bigger job(Meta-Job) from
Computational Grids. Here we preempt jobs by applying
AGING Technique. Initially 20% of total available resources
(processors) will be allocated to first Queue only. And 40% of
total available resources (processors) will be allocated to
second and third queues respectively. Whenever the third
queue is Empty then the proposed scheduler simply selects the
job which has least required execution time and executes it
immediately. In this way, the proposed scheduler increases the
throughput in clusters.
Scalable scheduling of updates in streaming data warehousesIRJET Journal
This document discusses scheduling updates in streaming data warehouses. It proposes a scheduling framework to handle complications like view hierarchies, data consistency, inability to preempt updates, heterogeneous update jobs from different data sources, and transient overload. It models the update problem as a scheduling problem where the objective is to minimize data staleness over time. It then presents several update scheduling algorithms and discusses how performance is affected by different factors based on simulation experiments.
Resource Optimization of Construction Project Using Primavera P6IOSRJMCE
Construction projects are unique in nature, having their own difficulties, uncertainties and risks, posing never-ending questions concerning the resources and costs. There is always a conflict between ‘how much it will cost?’ and ‘where to raise the finances from?’. The success of a project depends upon the efficiency with which the project management gets the work done by utilizing the planned resources of men, materials, machinery, money and time.. In large scale projects, preparing an accurate and workable plan is very difficult. Resources are required to carry out specific tasks in a project, but the availability of resources within a given firm is always limited. While preparing the schedule structure, the Project Manager might schedule certain tasks in parallel. In such cases it might be possible that the same resource is being used in both the parallel tasks, while its availability is limited. This paper emphasises how the Project Manager could resolve such conflicts by using Resource Balancing in modern softwares such as Primavera (P6) R8.3, to reduce laborious computations. In this paper, the Resource Balancing techniques namely smoothing & leveling have been investigated in detail. This paper uses a case study in order to portray how Resource Balancing could be done using Primavera p6 and its effects are on the duration and cost of the entire project.
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.
Distributed Feature Selection for Efficient Economic Big Data AnalysisIRJET Journal
The document proposes a new framework for efficiently analyzing large and high-dimensional economic big data. The framework combines methods for economic feature selection and econometric model construction to identify patterns in economic development from vast amounts of economic indicator data. It relies on three key aspects: 1) novel data pre-processing techniques to prepare high-quality economic data, 2) an innovative distributed feature identification solution to locate important economic indicators from multidimensional datasets, and 3) new econometric models to capture patterns of economic development. The framework is demonstrated on economic data collected over 30 years from over 300 towns in Dalian, China.
This document discusses Yahoo's use of the Capacity Scheduler in Hadoop YARN to manage job scheduling and service level agreements (SLAs). It provides an overview of how Capacity Scheduler works, including how it tracks resources, configures queues with guaranteed minimum capacities, and uses parameters like minimum user limits, capacity, and maximum capacity to allocate resources fairly while meeting SLAs. The document is presented by Sumeet Singh and Nathan Roberts of Yahoo to provide insight into how Capacity Scheduler is used at Yahoo to manage their large Hadoop clusters processing over a million jobs per day.
Project Priority MatrixProject NameProject Priority MatrixConstrainEnhanceAcceptScopeScheduleBudgetInstructions: Address the question of what is important to project success when Crashing is under consideration. Something has to give; either Scope, Schedule, or Budget. For each of Scope, Schedule, and Budget mark an X in only one of the three columns. Constrain means change is not allowed, Enhance means to improve if possible, and Accept means to allow change as necessary.
Risk ImpactProject NameRisk Impact MatrixConsequencesMajor Risk EventConsequenceLikelihoodImpact PotentialLowMediumHighA.LikelihoodHighB. MediumC. LowD. Instructions: See Chapter 7. These two charts address the four main risks to successful project conclusion.Instructions: Identify each major risk by its letter as to Likelihood of occurance and Consequence as a result.
Risk Response MatrixProject NameRisk Response MatrixMajor Risk EventWhat event would trigger the Risk?Risk Mitigation StrategyPerson ResponsibleA.B.C.D.Instructions: For each major risk to successful project completion, indicate how the problem will be addessed.
MS Project - Lesson #5 - Resource Workloads
Objectives
· View resource workloads
· Locate resource conflicts
· Use automatic leveling to resolve resource overallocations
· Manually resolve resource overallocations
When making resources assignments to tasks, MS Project tries to schedule the appropriate work for that resource; however conflicts can arise if a resource is scheduled to perform more work than the resource can accomplish. These conflicts can occur as a result of a single or multiple task assignment and are often a case of overallocation of the resource. (You can also underallocate a resource). The problem then becomes how to resolve those conflicts. With MS Project, some of these conflicts can be solved automatically or manually.
For this lab, we will be using the MS Project Lab, MyLab4_XXX (where XXX are your initials) from where we left off in Lab 4. Included with this lab is an Addendum, where you can quickly check your project information prior to starting this lab.
Viewing Resource Workloads
Viewing resource workloads helps to identify to what extent a resource is overallocated or underallocated. When a resource is overallocated, the resource text is highlighted in red and a leveling indicator is displayed.
To view the workloads:
1. Log onto Windows.
2. Open your completed file MyLab4_XXX.mpp. Check the addendum at the end of this lesson to make sure your beginning file is correct.
3. Save as MyLab5_XXX.mpp, where XXX are your initials
4. From the Task tab and the Resource Views group, select Resource Usage.
This view shows each resource, total assigned for the entire project, each task the resource is assigned and total hours for each task, and on the right, a time graph showing the detail of how the work is divided up. (You may need to expand the columns and move the time graph to see all details).
5. Noti ...
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
This document provides an overview of Hadoop and its ecosystem. It describes the key components of Hadoop including HDFS, MapReduce, YARN and various schedulers. It explains the architecture and functions of HDFS, MapReduce and YARN. It also summarizes the different schedulers in Hadoop including FIFO, Fair and Capacity schedulers.
The cloud environment offers an appropriate location for the implementation of huge range of scientific applications. However, in the existing workflows the major dispute is to assign the assets to the tasks in a well-organized way so, that it acquires less finishing time and load on every virtual machines will be impartial. To overcome this problem, GA_ MINMIN has been proposed that combines the features of GA and MINMIN scheduling algorithms. This algorithm is fundamentally a three-layer structure where GA is connected on the main level and hereditary calculation was performed for distributing belonging in an advanced way. At second level, the execution request of the assignments was resolved based on their size. This would be finished with the assistance of MIN-MIN. At third level, all the virtual machines have been running in parallel so that task response time will get decreased with more advanced outcomes. The proposed algorithm has been executed on the simulation environment.
How Does MS Project Works 6- Task Controlling FactorsSHAZEBALIKHAN1
MS Project is scheduling software. It takes multiple factors into account to schedule a task. The article explains all the inputs and their respective effect on the scheduling ability of the MS Project.
BIGDATA- Survey on Scheduling Methods in Hadoop MapReduce FrameworkMahantesh Angadi
This seminar presentation provides an overview of scheduling methods in the Hadoop MapReduce framework. It begins with motivations for using distributed computing for big data and introduces Hadoop and MapReduce. The presentation then surveys several proposed scheduling methods, including static methods like FIFO and adaptive methods like the Fair Scheduler. It summarizes five research papers on scheduling, discussing proposed approaches like optimizing job completion time and learning node capabilities for heterogeneous clusters. The presentation concludes that scheduling algorithms should improve data locality and use prediction to efficiently schedule jobs on heterogeneous Hadoop clusters.
Effective and Efficient Job Scheduling in Grid ComputingAditya Kokadwar
The integration of remote and diverse resources and the increasing computational needs of Grand Challenges problems combined with the faster growth of the internet and communication technologies leads to the development of global computational grids. Grid computing is a prevailing technology, which unites underutilized resources in order to support sharing of resources and services distributed across numerous administrative region. An efficient and effective scheduling system is essentially required in order to achieve the promising capacity of grids. The main goal of scheduling is to maximize the resource utilization and minimize processing time and cost of the jobs. In this research, the objective is to prioritize the jobs based on execution cost and then allocate the resources with minimum cost by merging it with conventional job grouping strategy to provide the solution for better and more efficient job scheduling which is beneficial to both user and resource broker. The proposed scheduling approach in grid computing employs a dynamic cost-based job scheduling algorithm for making an efficient mapping of a job to available resources in the grid. It also improves communication to computation ratio (CCR) and utilization of available resources by grouping the user jobs before resource allocation.
Similar to Novel Scheduling Algorithm in DFS9(1) (20)
Effective and Efficient Job Scheduling in Grid Computing
Novel Scheduling Algorithm in DFS9(1)
1. NOVEL SCHEDULING ALGORITHM IN
DFS
B.Sudheer Kumar
IT Department
ACE Engg College
Hyderabad,India.
sudheer.itdict@gmai
l.com
kota Ankita
IT Department
ACE Engg College
Hyderabad,India.
ankikota@gmail.co
m
Myaka Mounika
IT Department
ACE Engg College
Hyderabad,India.
m.mouniika@gmail.
com
Tigulla Sandhya
IT Department
ACE Engg College
Hyderabad,India.
tsandhyareddy890@
gmail.com
Y Sowjanya
IT Department
ACE Engg College
Hyderabad,India.
sowjanyayelmati@g
mail.com
Abstract— To manage big data environment scheduling has
become a necessity. In the present scenario it is very important to
use scheduling to manage the big data in any environment.
Scheduling is the method by which threads, processes or data flows
are given access to system resources. The need for a scheduling
algorithm arises from the requirement of most modern systems to
perform multitasking and multiplexing. In order to overcome the
problems in different schedulers we present the preemption and
priority based scheduling in Hadoop. This mechanism allows the
scheduler to make more efficient decisions about the jobs which
have high priority and provides the ability to preempt the job which
has low priority.
keywords: scheduler, priority, preemption, job weight,DFS
I. INTRODUCTION
In today’s technology, several scheduling algorithms have
been developed for Hadoop. Hadoop is fast developing tool. In
Hadoop job tracker initiates and co-ordinate the work of Task-
tracker. The Scheduler abides in the job tracker and allocates
the resources of Task-tracker to the running tasks.[8] Scheduler
comes when two or three jobs are participating. When two or
three job slots become free then scheduler chooses to decide,
which tasks is to allocate on those slots.
First Come First Serve (FCFS) and Processor Sharing (PS)
are most simple and mostly used scheduling algorithms. FIFO
and FAIR scheduler in Hadoop are inspired by those two
algorithms. FCFS approach schedule the jobs according to
their submission and delay time is more in this because long
jobs takes more time to complete by keeping small jobs on
hold and in PS resources and divided evenly and each active
jobs keeps progressing and each additional jobs delays the
completion of all other tasks. Facebook and Yahoo contributed
significant work in developing schedulers i.e. Fair Scheduler
and Capacity Scheduler respectively which subsequently
released to Hadoop Community. The Shortest Remaining
Processing Time (SRPT) prioritizes the jobs which has less
work to complete. The main problem in SRPT is starvation.
Larger jobs cannot be scheduled if smaller jobs are submitted
frequently.
Job aging is the solution for starvation problem, i.e. it
virtually decreases the size of job which are waiting in queue
so that all the jobs are processed evenly.
Size-based scheduling algorithm is also used in Hadoop. It
requires the size of the job before execution. But it is not
possible to know the size before execution of the job. So, it
estimates the job size roughly by using the characteristics of
job like number of tasks the job contains. After the first task is
executed total time is estimated and updated based on running
time. The estimation component has been designed in such a
way that response time is minimized rather than estimating the
accurate length of the job.
The problems solved by using Size-based Scheduling
algorithm are that, job size information is not necessary for the
proper functioning of scheduler. Starvation problem is solved
and job response time is distributed according to the favour of
size-based scheduling. It is simple to configure and allow
resource “pools” to be consolidated because workload diversity
is intrinsically accounted for by the size-based scheduling.
Dynamic proportional share scheduling in Hadoop: It is
one of the parallel tasks scheduler in Hadoop [3]. It allows the
userto adjust their spending over time to control their allocated
capacity. It allows the scheduler to take efficient decisions
about how to prioritize the user and jobs provide tools to
improve their allocations and requirements of their job.
Fig1.Elements of Hadoop cluster
[2]Namenode is the node which stores the file system
metadata i.e. which file maps to what block locations and
which are the blocks are stored on which datanode.The
namenode maintains two in-memory tables, one which maps
the blocks to datanodes (one block maps to 3 datanodes for a
replication value of 3) and a datanode to block number
mapping.
Data node is where the actual data resides. When the
datanode stores a block of information, it maintains a
2. checksum for it as well. The data nodes update the namenode
with the block information periodically and before updating
verify the checksums. If the checksum is incorrect for a
particular block i.e. there is a disk level corruption for that
block, it skips that block while reporting to the block
information to the namenode. In this way, namenode is aware
of the disk level corruption on that datanode and takes steps
accordingly.
II. DIFFERENT SCHEDULING STRATEGIES FOR
HADOOP DISTRIBUTED FILE SYSTEM
A. FIFO scheduler
FIFO stands for “First in first out”. This is the default
scheduler in Hadoop[5]. Original scheduling algorithm that
was integrated within the Job Tracker was called FIFO. In
FIFO scheduling, an oldest job is pulled first by the job tracker
from the work queue. This scheduler is not concerned with
priority or size of the job, but the approach was simple to
implement and efficient.
Example1: When client1 with priority and client 2 without any
priority try to compete for resources then FIFO scheduler
schedule themaccording to their submission.
Example2: When both client 1 and client 2 of no priority try to
compete for resources then FIFO scheduler schedule them in
first come first serve manner.
The main disadvantage of this scheduler is priority concept is
not considered.That is small jobs get stuck when large jobs are
under processing and size of the job is not considered.
Fig2: FIFO Scheduler
From the above figure (fig2) we can depict that FIFO
schedulerschedule the jobs according to their submission i.e in
the manner of first come first serve. Here 4 clients are
requesting for resource namely c1, c2, c3, c4 (kept in queue) as
shown in above figure. By following the policy of FIFO
scheduler c1 is submitted first to name node.
B. Fair scheduler
Fair scheduleris to assign resources to jobs such that they
can get equal share of the resources on average time. Jobs
which need less time are executed first thereby resulting the
jobs which need more time can find enough execution time on
CPU. [10]The implementation is based on creating a set of
pools. All these pools have equal shares by default, and they
can be assigned by people. Each user is assigned to a pool to
approach fairness. In this way, if one user submits more jobs,
he or she can receive the same share which are cluster
resources as all other users. The number of jobs active at one
time can also be constrained, if desired, to minimize
congestion and allow work to finish in a timely manner. This
scheduler was developed by “Facebook”.
ALGORITHM
When there is an available slot open, the scheduler will
allocate this slot to the job which has the largest job Deficit.
The system will update most of the information, including job
Deficit,jobWeight,minSlots,jobFairShare[1].
1. Calculate jobWeight: JobWeight is decided by the
priority Factor of the job by default, or it can also be
decided by the size and time of the job. In addition,
users can change weightAdjuster to adjust jobWeight.
2. Update jobWeight: Each running job updates its
jobWeight by multiplying poolWeight over
poolRunningJobsWeightSum.
3. Calculate deficit: set the mapDeficit and reduceDificit
to zero for each job.
4. Update minSlots: In each pool, the scheduler distributes
the available slots to jobs based on their jobWeight.
After that, it distributes open slots to the jobs that still
need resources. If there are still some open slots after
that, they will be shared with the other pools.
The detailed steps are given below: First of all set the
minMaps or minReduces of all the running jobs in this pool to
zero. Second, repeating the following steps until we find slots
remaining is zero: calculate jobinfo.minMaps or
jobinfo.minReduces; calculate minSlots by slots Left times
jobWeight over poolLowJobsWeightSum; adjust this number
according to the number of available slots in this pool; return
these slotsToGive as the minMaps or minReduces to the
associated job; change this number by minus slotsToGive. If in
this loop, slotsLeft stay unchanged, then it will share the
remaining slots to all jobs by sorting jobs by weight and
deficit, calculating minSlots, giving slotsToGive to jobs and
updating slotsLeft. If after all of this there are still open slots,
they will be shared with other pools.
5. UpdatejobFairShare: First, distribute available slots
by jobWeight. If minSlots is larger than jobFairShare,
it will meet minSlots first then update available slots,
repeating the steps above until all minSlots are equal
or smaller than jobFairShare. At last, all jobs share the
slots that are still open equally.
6. Updatedeficit: The jobDeficit will be updated by plus
the difference between job fair and running tasks times
time Delta. Actually, the mapping and reducing parts
will update deficit separately.
7. Allocate resources: Allocate it to the job that has the
largest deficit, when there is an available slot in the
system.
The advantage of fair scheduler is that it works well when
both small and large clusters are used by the same
3. organization with a limited number of workloads. Irrespective
of the shares assigned to pools, if the system is not loaded,
jobs receive the shares that would otherwise go unused.[9]
The scheduler implementation keeps track of the computation
time for each job in the system. Periodically, the scheduler
examines jobs to compute the difference between the
computation time the job received and the time it should have
received in an ideal scheduler. The result determines the
deficit for the task. The job of the scheduler is then to ensure
that the task with the highest deficit is scheduled next.
Fig3: FAIR SCHEDULER
From above figure we can depict that fair scheduler assigns
equal share of resources. Here 4 clients namely c1,c2,c3,c4
are requesting for resources(in queue).fair scheduler schedules
all the four clients to name node with equal amount of
share(by default 25%) as shown in fig3 .
Example 1: When client 1 with priority and client 2 with no
priority compete for resources then fair scheduler gives an
equal share to both the task and then schedule them.
Example 2: When both client1 and client2 of no priority
compete for resources then fair scheduler gives an equal share
to both the tasks and schedules them.
Disadvantage of fair scheduler is that it does not consider the
job weight of each node, resulting unbalanced performance in
each node.
C. Capacity scheduler
[7]Capacity scheduling is based on queues.Each queue has
its own assigned resources. It uses FIFO strategy. In order to
prevent the users to take more resources in one queue, this
scheduler can limit the resources for the jobs for each user.
While scheduling, if a queue does not use its allocated
capacity, the reserved capacity will be assigned to other
queues. Jobs with a higher priority can access the resources
faster than lower priority jobs. [6]We can configure the
capacity scheduler within multiple Hadoop configuration files.
This Capacity scheduler was developed by “Yahoo”. In
capacity scheduling, instead of pools, several queues are
created, each with a configurable number of map and reduce
slots.
Each queue is also assigned a guaranteed capacity.
ALGORITHM
When there are open slots in some taskTracker, the scheduler
will choose a queue, then choose a job in that queue, then
choose a task in the job and last give this slot to the task. The
detailed steps are described below[4].
1. Choose_queue:Sort all queues by
numSlotsOccupied/capacity, then deal with themone
by one until we find proper job.
2. Choose_job:Sort all jobs in the selected queue by
submitted time and job priority. Then the scheduler
takes jobs into consideration one by one, and at last find
proper jobs so that the user with that job does not reach
his/her limit of resource and there are enough memory
in that node where the Task Tracker stays for the tasks
in that job.
3. Choose a task: Call obtainNewMapTask() or
obtainNewReduceTask() in JobInProgress to choose a
task, based on the locality and resource situations.
The advantage of capacity scheduler is that when you're
running a large Hadoop cluster, with multiple clients and
different types and priorities of jobs, then the capacity
scheduler is the right choice to ensure guaranteed access with
the potential to reuse unused capacity and prioritize jobs within
the queues.
Example 1: When client1 with low priority is under
execution and client2 with high priority is competing for
resource then capacity scheduler does not preempt client1 until
its execution results in starvation of client2.
Example 2: When both client1 and client2 of no priority try
to compete for resources then capacity scheduler behaves as
FIFO scheduler and schedules them in first come first serve
manner.
Disadvantage of capacity scheduler is the most complex
scheduleramong all. Users need to know the systemwell to set
up configurations and choose proper queues well. The Hadoop
road map includes a desire to support preemption, but this
functionality has not yet been implemented.
4. III. COMPARISON TABLE
Different
Scheduling
Strategies for
Hadoop
Distributed File
system
Scheduling
methodology
Benefits of
Scheduler Limitations/drawbacks
Applications of
scheduler in
Distributed file
system
(when to use
each scheduler)
Behaviorof
scheduler with
priority tasks
Behavior of
scheduler with
no priority
tasks
FIFO/FCFS
SCHEDULER
FCFS approach
schedule the jobs
according to
their submission.
1. FIFO
schedulingis
simple to
implement.
2. Efficient.
1. Priority concept is not
considered (i.e. it is non-
preemptive). That is small
jobs get stuck when large job
is under processing and size
of the job is not considered.
2. It can hurt overall
throughput.
The FIFO
scheduler should
be chosen when
we have fewer
number of jobs
with no priority.
When client1 of
priority and
client2 without
prioritycompete
for resources
then FIFO
scheduler
schedule the
clients according
to their
submission.
When both
client1 and
client2 have no
priority then
FIFO scheduler
schedules both
the clients in
first come first
serve manner.
FAIR
SCHEDULER
Fair scheduling
is a method of
assigning
resources to jobs
such that all jobs
get, on average,
on an equal
share of
resources.
1. It allows
assigning
guaranteed
minimum
shares to
queues, which
is useful for
ensuring that
certain users,
groups or
production
applications
always get
sufficient
resources.
2. Less
complex
3.The fair
scheduler
works well
when both
small and
large clusters
are used by
same
organization
with a limited
number of
workloads
1. It does not consider the job
weight of each node, resulting
unbalanced performance in
each node.
2.It can work well only with
limited workload.
The fair
scheduler is
chosen in the
presence of
diverse jobs,
because it can
provide fast
response times
for small jobs
mixed with
larger jobs
When client1
with priority and
client2 with no
prioritycompete
for resources,
then fair
scheduler gives
an equal share to
both the task and
then schedules
them.
.
When client1
andclient2 of no
prioritycompete
for resources
then fair
scheduler gives
an equal share to
both the tasks
and schedule
them
CAPACITY
SCHEDULER
The Capacity
Scheduler is
designed to
allow sharing a
large cluster
while giving
each
organization
capacity
Guarantees.
Ensure
guaranteed
access with the
potential to
reuse unused
capacity and
prioritize jobs
within queues
over large
clusters.
The most complexamong
three schedulers. In case of large
Hadoop cluster,
with multiple
clients and
different types
andpriorities of
jobs, then the
capacity
scheduler is the
right choice to
ensure
guaranteed
access with the
potential to
reuse unused
capacity and
prioritize jobs
within
queues.(map
reduce)
When client1
with lowpriority
is under
execution and
client2 with high
priority is
competing for
resource then
capacity
scheduler does
not preempt
client1 until its
execution
resulting in
starvation of
client2.
When both
client1 and
client2 of no
priority try to
compete for
resources then
capacity
scheduler
behaves as FIFO
scheduler and
schedule them in
first come first
serve manner.
5. IV. NOVEL SCHEDULER
The proposed algorithm is used to schedule the requests
efficiently. In this algorithmwe are going to schedule the tasks
and prioritize them based on frequent requests sent. The
frequent number of times client sends the requests the highest
will be the probability of getting scheduled. This algorithm is
advantageous because of its simplicity and because it
minimizes the average waiting time. Starvation problem is
solved as jobs are not made to wait in the queue for long time.
All the jobs are scheduled based on below novel algorithm. In
this algorithm the weight of the job is calculated based on the
requests sent and the priority is assigned based on the job
weight to that of day’s of scheduling.
Using this scheduling algorithm we can overcome the
following drawback
1. The process which has no priority need not wait for long
time.
2. Both the priority and preemption are achieved.
NOVEL ALGORITHM
A key issue related to scheduling is, when to make
scheduling decisions. It turns out that there are a variety of
situations in which scheduling is done. In this algorithm “the
number of times requests sent by the client, from certain time
(the day the request is sent)to till date are monitored and based
on which, the clients are scheduled in the following way”
STEP 1: Calculate job weight (K)
The job weights of all clients are calculated based on number
of times, the requests are sent. Let the number of times the
requests sent is R then the,
job weight(K)=R
STEP 2: Calculate the priority (Pi)
The priority of job is calculated based on job weight of the
client till date of scheduling. Suppose the client’s job weight is
‘k’ for ‘n’ number of days then,
Pi= (job weight)K/(number of days)n
Where i=1 to n
If suppose two clients say client1 and client2 of priority p1
and p2 are competing for resource respectively then schedule
the jobs accordingly
STEP 3: Schedule the clients
1. If p1>p2 then the job sent by client1 is scheduled first
followed by client2 and if
2 If p1<p2 then the job sent by client2 is scheduled first
followed by client1 and if
3. If p1=p2 then jobs are scheduled based on FIFO scheduling
STEP 4: Preempt the jobs
The jobs of clients are preempted based on certain conditions:
1. If task completion is <50%
2. If two tasks say (client1 and client2) are competing for
resources then the task with ‘no’ priority is preempted.
3. If two clients having some priority are competing for
resource then go to step 3
Step 5: Repeat the steps 1 to 4 until all jobs are scheduled.
Example1: Initially when we have only 1 client requesting for
resources then that client will be scheduled.
Example2: When 3 clients namely client1,client2 and client3
compete for resources then first we need to calculate the job
weight using novel algorithm(as shown above in step1) which
is as follows
If Client1 sends request one time in a day i.e. client1 is a new
client, Client2 sends request five times in 10 days and client3
sends request ten times in 5days. therefore the job weight of
client1 is 1 and client 2 job weight is 5 and client3 job weight
is 10.
Then we need to calculate the priority(as shown above in
step2)
Client1(P1)=1/1=1
Client2(P2)=5/10=0.5
Client3(P3)=10/5=2
Therefore according to priority client3 will be scheduled first
followed by client1 and client2.
V. CONCLUSION
This paper has given an account of the present working
strategies and the drawbacks of the various scheduling
algorithms. This paper also gives the importance of the
efficient scheduling and the effective algorithm to overcome
the existing problems. The reasonable approach to tackle the
issue is the “Novel Scheduling”. This research has thrown up
many questions in need to implement the “Novel System” for
better and effective retrieval of data. In present strategy the
scheduling is based on the number of requests sent i.e. the
frequent requests made to that of total of number of days the
request is made. Therefore there is every chance to overcome
the drawbacks and schedules the jobs based on priority and
preempt the low priority jobs and this can be achieved by
Novel scheduling only.
VI. REFERENCES
[1] Hadoop Scheduler by Donghe :Fair Scheduler
[2] Scheduling in hadoop,An introduction to the pluggable scheduler
framework.M.Tim Jones
6. [3] Thomas SandholmandKevinLai .Dynamic Proportional share scheduling
in hadoop . Social Computing Lab, Hewlett-Packard Labs, Palo Alto, CA
94304, USA
[4] Hadoop Scheduler by Donghe :Capacity Scheduler
[5] Hadoop Scheduler by Donghe :FIFO Scheduler
[6] Bincy P Andrews, BinuA: Survey on JobSchedulers in Hadoop Cluster.
OSR Journal of Computer Engineering (IOSR-JCE)e-ISSN: 2278-0661, p-
ISSN: 2278-8727Volume15, Issue 1 (Sep. -Oct. 2013), PP 46-50
[7] Job Scheduling in Apache Hadoop by Amr Awadallah
[8] “Survey on Improved Scheduling in Hadoop MapReduce in Cloud
Environments” by b thirumala rao
[9] Vanderster, Daniel Colin; ResourceAllocation and Scheduling Strategies
an Computational Grids, Ph.D. Thesis, University of Victoria, 2008.
[10] White,Tom, Hadoop: The definite guide, O'Reilly Media, 2nd
edition,
2010.
AUTHORS PROFILE
Battula Sudheer Kumar did his M.Tech
in Software Engineering from
Jawaharlal Nehru Technological
University (JNTU), Hyderabad in 2012,
B.Tech from Jawaharlal Nehru
Technological University (JNTU),
Hyderabad in 2009.He is having 2 years of research
experience and presently working as an Assistant Professor of
IT department in ACE Engineering College, Hyderabad. His
areas of interest include Distributed Systems and Cloud
Computing.
Kota Ankita pursuing her graduation in
Information Technology from ACE Engineering
College, Ghatkesar and my research interests on
Distributed Computing and Cloud Computing.
Myaka Mounika Natasha pursuing her graduation
in Information Technology from ACE Engineering
College, Ghatkesar and my research interests on
Distributed Computing and Cloud Computing.
Tigullah Sandhya pursuing her graduation in
Information Technology from ACE Engineering
College, Ghatkesar and my research interests on
Distributed Computing and Cloud Computing.
Y. Sowjanya pursuing her graduation in
Information Technology from ACE
Engineering College, Ghatkesar and my
research interests on Distributed
Computing and Cloud Computing.