ABSTRACT:
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 paper proposes two classes of algorithms to minimize the makespan 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.
PROJECT OUTPUT VIDEO:
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18+ Hadoop Map Slots Configuration New eligible UK players only. Select Casino offer on sign-up and deposit. 4 deposits of Hadoop Map Slots Configuration £10, £20, £50, £100 matched with a bonus cash offer of same value (14 day expiry). 35x real money cash wagering (within 30 days) on eligible games before bonus cash is credited. Dynamic Job Ordering and Slot Configurations for MapReduce Workloads ABSTRACT: 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.
In Hadoop, map slots and reduce slots are separated and cannot be interchangeably used. Dynamic borrowing between them can improve slot utilization and system throughput 24. The map or reduce. In Hadoop, the JobTracker sets up TaskTrackers, where each TaskTracker assign slots to be either a map slot or a reduced slot. Slot assignments cannot be dynamically changed during the application process. Slot function reassignment by the TastTrackers need permission from the JobTracker, which consumes time.
EXISTING SYSTEM:
In Hadoop MRv1, it abstracts the cluster resources into slots (e.g., map slots and reduce slots). Due to varied slot demands for map and reduce tasks, different map/reduce slot configurations can also have significantly different performance and system utilization. Particularly, the importance and challenges of map/reduce slot configuration optimization are motivated.
The batch job ordering problem has been extensively studied in the high performance computing literature. Minimizing the makespan has been shown to be NP-hard, and a number of approximation and heuristic algorithms have been proposed. In addition, there has been work on bi-criteria optimization which aims to minimize makespan and total completion time simultaneously.
Moseley et al. present an offline 12-approximation algorithm for minimizing the total flow time of the jobs; this is the sum of the differences between the finishing and arrival times of all the jobs.
Verma et al. propose two algorithms for makes pan optimization. One is a greedy algorithm job ordering method based on Johnson's Rule.
DISADVANTAGES OF EXISTING SYSTEM:
The previous works all focused on the single-stage parallelism, where each job only has a single stage.
PROPOSED SYSTEM:
In this paper, we target at one subset of production MapReduce workloads that consist of a set of independent jobs (e.g., each of jobs processes distinct data sets with no dependency between each other) with different approaches.
For dependent jobs (i.e., MapReduce workflow), one MapReduce can only start only when its previous dependent jobs finish the computation subject to the input-output data dependency. In contrast, for independent jobs, there is an overlap computation between two jobs, i.e., when the current job completes its map-phase computation and starts its reduce-phase computation, the next job can begin to perform its map-phase computation in a pipeline processing mode by possessing the released map slots from its previous job.
Have a theoretical study on the Johnson's Rule-based heuristic algorithm for makespan, including its approximation ratio, upper bound and lower bound makespan.
Propose a bi-criteria heuristic algorithm to optimize makespan and total completion time simultaneously, observing that there is a tradeoff between makespan and total completion time. Moreover, we show that the optimized job order produced by the proposed orderings algorithms does not need to change (i.e., stable) in the face of server failures via theoretical analysis.
Propose slot configuration algorithms for makespan and total completion time. We also show that there is a proportional feature for them, which is very important and can be used to address the time efficiency problem of proposed enumeration algorithms for a large size of total slots.
Perform extensive experiments to validate the effectiveness of proposed algorithms and theoretical results.
ADVANTAGES OF PROPOSED SYSTEM:
We evaluate our algorithms using both testbed workloads and synthetic Facebook workloads.
Experiments show that, for the makespan, the job ordering optimization algorithm achieve an approximately 14-36 percent improvement for testbed workloads, and 10-20 percent improvement for Facebook workloads. In contrast, with the map/reduce slot configuration algorithm, there are about 50-60 percent improvement for testbed workloads and 54-80 percent makespan improvement for Facebook workloads;
Experiments show that, for the total completion time, there are nearly 5_ improvement with the bi-criteria job ordering algorithms and 4_ improvement with the bi-criteria map/reduce slot configuration algorithm, for Facebook workloads that contain many small-size jobs
MODULES:
- Job ordering module
Mapping module
Reducing module
Slot configuration module
MODULE DESCRIPTION:
JOB ORDERING MODULE:
The non- overlap computation constraint between map and reduce tasks of a MapReduce job, resulting in different resource utilizations for map/reduce slots under different job submission orders for batch jobs. The job ordering optimization for MapReduce workloads is important and challenging,
(i). There is a strong data dependency between the map tasks and reduce tasks of a job, i.e., reduce tasks can only perform after the map tasks,
(ii). map tasks have to be allocated with map slots and reduce tasks have to be allocated with reduce slots,
(iii). Both map slots and reduce slots are limited computing resources, configured by Hadoop administrator in advance.
MAPPING MODULE
The different job submission orders will result in different resource utilizations for map and reduce slots and in turn different performance (i.e., makespan) for batch jobs. The computation for each map task consists of sub-phases, namely, shuffle, mapping. The shuffle phase computation of reduce tasks can start earlier before all map tasks are completed, whereas other two sub-phases cannot. A Map job execution generally exhibits the multiple waves in its map phase. Only the first wave of tasks can overlap their data shuffling with map task computation.
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REDUCING MODULE:
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The computation for each reduce task consists of sub-phases, sort and aggregation. The shuffle phase computation of reduce tasks can start earlier before all map tasks are completed, whereas other two sub-phases cannot. It means that only sort and aggregation phases of reduce tasks cannot overlap with all map tasks. A MapReduce job execution generally exhibits the multiple waves in its map and reduce phase. Only the first wave of reduce tasks can overlap their data sorting with map task computation. For other remaining reduce tasks, they can only start sorting after all map tasks are completed.
SLOT CONFIGURATION MODULE:
The total number of map slots plus reduces slots for the cluster be taken. The influence of slot configuration to the overall performance by running jobs in all possible map/reduce slot configurations under an arbitrary job order. Then the optimal make-span are compared with the unoptimal configuration to show the performance.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
In Hadoop, map slots and reduce slots are separated and cannot be interchangeably used. Dynamic borrowing between them can improve slot utilization and system throughput 24. The map or reduce. In Hadoop, the JobTracker sets up TaskTrackers, where each TaskTracker assign slots to be either a map slot or a reduced slot. Slot assignments cannot be dynamically changed during the application process. Slot function reassignment by the TastTrackers need permission from the JobTracker, which consumes time.
EXISTING SYSTEM:
In Hadoop MRv1, it abstracts the cluster resources into slots (e.g., map slots and reduce slots). Due to varied slot demands for map and reduce tasks, different map/reduce slot configurations can also have significantly different performance and system utilization. Particularly, the importance and challenges of map/reduce slot configuration optimization are motivated.
The batch job ordering problem has been extensively studied in the high performance computing literature. Minimizing the makespan has been shown to be NP-hard, and a number of approximation and heuristic algorithms have been proposed. In addition, there has been work on bi-criteria optimization which aims to minimize makespan and total completion time simultaneously.
Moseley et al. present an offline 12-approximation algorithm for minimizing the total flow time of the jobs; this is the sum of the differences between the finishing and arrival times of all the jobs.
Verma et al. propose two algorithms for makes pan optimization. One is a greedy algorithm job ordering method based on Johnson's Rule.
DISADVANTAGES OF EXISTING SYSTEM:
The previous works all focused on the single-stage parallelism, where each job only has a single stage.
PROPOSED SYSTEM:
In this paper, we target at one subset of production MapReduce workloads that consist of a set of independent jobs (e.g., each of jobs processes distinct data sets with no dependency between each other) with different approaches.
For dependent jobs (i.e., MapReduce workflow), one MapReduce can only start only when its previous dependent jobs finish the computation subject to the input-output data dependency. In contrast, for independent jobs, there is an overlap computation between two jobs, i.e., when the current job completes its map-phase computation and starts its reduce-phase computation, the next job can begin to perform its map-phase computation in a pipeline processing mode by possessing the released map slots from its previous job.
Have a theoretical study on the Johnson's Rule-based heuristic algorithm for makespan, including its approximation ratio, upper bound and lower bound makespan.
Propose a bi-criteria heuristic algorithm to optimize makespan and total completion time simultaneously, observing that there is a tradeoff between makespan and total completion time. Moreover, we show that the optimized job order produced by the proposed orderings algorithms does not need to change (i.e., stable) in the face of server failures via theoretical analysis.
Propose slot configuration algorithms for makespan and total completion time. We also show that there is a proportional feature for them, which is very important and can be used to address the time efficiency problem of proposed enumeration algorithms for a large size of total slots.
Perform extensive experiments to validate the effectiveness of proposed algorithms and theoretical results.
ADVANTAGES OF PROPOSED SYSTEM:
We evaluate our algorithms using both testbed workloads and synthetic Facebook workloads.
Experiments show that, for the makespan, the job ordering optimization algorithm achieve an approximately 14-36 percent improvement for testbed workloads, and 10-20 percent improvement for Facebook workloads. In contrast, with the map/reduce slot configuration algorithm, there are about 50-60 percent improvement for testbed workloads and 54-80 percent makespan improvement for Facebook workloads;
Experiments show that, for the total completion time, there are nearly 5_ improvement with the bi-criteria job ordering algorithms and 4_ improvement with the bi-criteria map/reduce slot configuration algorithm, for Facebook workloads that contain many small-size jobs
MODULES:
- Job ordering module
Mapping module
Reducing module
Slot configuration module
MODULE DESCRIPTION:
JOB ORDERING MODULE:
The non- overlap computation constraint between map and reduce tasks of a MapReduce job, resulting in different resource utilizations for map/reduce slots under different job submission orders for batch jobs. The job ordering optimization for MapReduce workloads is important and challenging,
(i). There is a strong data dependency between the map tasks and reduce tasks of a job, i.e., reduce tasks can only perform after the map tasks,
(ii). map tasks have to be allocated with map slots and reduce tasks have to be allocated with reduce slots,
(iii). Both map slots and reduce slots are limited computing resources, configured by Hadoop administrator in advance.
MAPPING MODULE
The different job submission orders will result in different resource utilizations for map and reduce slots and in turn different performance (i.e., makespan) for batch jobs. The computation for each map task consists of sub-phases, namely, shuffle, mapping. The shuffle phase computation of reduce tasks can start earlier before all map tasks are completed, whereas other two sub-phases cannot. A Map job execution generally exhibits the multiple waves in its map phase. Only the first wave of tasks can overlap their data shuffling with map task computation.
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REDUCING MODULE:
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The computation for each reduce task consists of sub-phases, sort and aggregation. The shuffle phase computation of reduce tasks can start earlier before all map tasks are completed, whereas other two sub-phases cannot. It means that only sort and aggregation phases of reduce tasks cannot overlap with all map tasks. A MapReduce job execution generally exhibits the multiple waves in its map and reduce phase. Only the first wave of reduce tasks can overlap their data sorting with map task computation. For other remaining reduce tasks, they can only start sorting after all map tasks are completed.
SLOT CONFIGURATION MODULE:
The total number of map slots plus reduces slots for the cluster be taken. The influence of slot configuration to the overall performance by running jobs in all possible map/reduce slot configurations under an arbitrary job order. Then the optimal make-span are compared with the unoptimal configuration to show the performance.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
System : i3 Processor
Hard Disk : 500 GB.
Monitor : 15'' LED
Input Devices : Keyboard, Mouse
Ram : 4GB.
SOFTWARE REQUIREMENTS:
Operating system : Windows 7/UBUNTU.
Coding Language : Java 1.7 ,Hadoop 0.8.1
IDE : Eclipse
High limit mustang money slot wins 2018. Database : MYSQL
REFERENCE:
Shanjiang Tang, Bu-Sung Lee, and Bingsheng He, 'Dynamic Job Ordering and Slot Configurations for MapReduce Workloads', IEEE TRANSACTIONS ON SERVICES COMPUTING, VOL. 9, NO. 1, JANUARY/FEBRUARY 2016.
