A DAG based Task Scheduling Algorithms for Multiprocessor System - A Survey

Kaur, Gurjit

doi:10.14257/ijgdc.2016.9.9.10

Cited by 6 publications

(4 citation statements)

References 30 publications

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“…In [6], the author proposed a proportional share allocation system that allows users to bid higher in order to gain more resource shares. Even if this allocation system intends to minimize the total run time and cost, it does not guarantee the completion of the workload within a fixed budget; moreover, troubles can be incurred due to the continuous intervention of the broker to prefer the cheapest resources to complete the task within the deadline [7]. The multiprocessor executes the parallel task simultaneously to obtain quick results, process a massive amount of data, and solve a problem in the expected time.…”

Section: Related Workmentioning

confidence: 99%

A Cost-Optimized Data Parallel Task Scheduling with Deadline Constraints in Cloud

et al. 2022

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Large-scale distributed systems have the advantages of high processing speeds and large communication bandwidths over the network. The processing of huge real-world data within a time constraint becomes tricky, due to the complexity of data parallel task scheduling in a time constrained environment. This paper proposes data parallel task scheduling in cloud to address the minimization of cost and time constraints. By running concurrent executions of tasks on multi-core cloud resources, the number of parallel executions could be increased correspondingly, thereby, finishing the task within the deadline is possible. A mathematical model is developed here to minimize the operational cost of data parallel tasks by feasibly assigning a load to each virtual machine in the cloud data center. This work experiments with a machine learning model that is replicated on the multi-core cloud heterogeneous resources to execute different input data concurrently to accomplish distributive learning. The outcome of concurrent execution of data-intensive tasks on different parts of the input dataset gives better solutions in terms of processing the task by the deadline at optimized cost.

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Section: Related Workmentioning

confidence: 99%

A Cost-Optimized Data Parallel Task Scheduling with Deadline Constraints in Cloud

et al. 2022

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“…Zhang [14] proposed a load balancing task scheduling algorithm based on weighted random and feedback mechanisms to eliminate system bottlenecks and balance loads dynamically. Kaur [12] studied and explored several Directed Acyclic Graph (DAG) based task scheduling algorithms on a unified basis by using various scheduling parameters.…”

Section: Related Workmentioning

confidence: 99%

“…As hardware moves towards integrated memory controllers and higher bandwidth, selecting prefetchers properly will play a significant role in optimizing system performance. Recent works [11][12][13][14] on the topic of performance degradation in multi-core systems focused on contention for cache space and bus bandwidth when applications share the last level cache (LLC). It is well known that many factors contribute to performance degradation when threads share an LLC.…”

Section: Introductionmentioning

confidence: 99%

Performance Improvements by Deploying L2 Prefetchers with Helper Thread for Pointer-Chasing Applications

Huang¹

2018

IJPE

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Modern processor micro-architecture offers advanced prefetch mechanisms that are designed to effectively hide memory latency and improve application performance. However, pointer-chasing applications employing linked data structures expose a memory latency problem that is difficult to deal with by using hardware prefetchers. It is promising that helper threaded prefetching based on Chip Multiprocessor is an effective method for reducing the memory latency of accesses to linked data structures. In this paper, we first illustrated two L2 prefetchers on Chip Multiprocessor and two different helper threaded prefetching techniques for pointer-chasing applications. Then, we revealed the limitations of L2 prefetchers for pointer-intensive applications after applying two different threaded prefetching techniques. Finally, we optimized the deployment of L2 prefetchers with two different threaded prefetching techniques for pointer-chasing applications. The experimental results indicate that L2 prefetchers' effectiveness on helper threads depends on the memory access pattern of the targeted applications, and the optimized deployment of L2 prefetchers further improves the performance of pointer-intensive applications.

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“…Their algorithm gives improved results than Round-Robin and basic GA, but considered limited number of independent tasks. The steps of MGA algorithm is given as under:-3.1 Input DAG Workflow: The DAG [10] is input to the MGA algorithm at compile time. This DAG file contains the tasks, instruction size, name and dependency in XML form.…”

Section: Related Workmentioning

confidence: 99%

Modified Genetic Algorithm Based Solution for Task Scheduling in Cloud Computing Environment

Kaur¹,

Kaur²,

Kaur³

2018

INDJCSE

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Cloud computing is a growing technology that provides on demand shared pool of resources over the internet. Sharing of resources amongst the number of cloud users makes task scheduling a challenging issue. Task scheduling issue in many cases resolved by meta-heuristic approaches. This paper proposes a solution for task scheduling in a cloud computing environment based on the meta-heuristic, Genetic Algorithm. The proposed solution i.e. Modified Genetic algorithm (MGA) uses a hybrid solution based on Genetic Algorithm along with Predict Earliest Finish Time (PEFT) scheduling on Directed Acyclic Graph (DAG). Simulated results of the Modified Genetic Algorithm are compared with basic GA and with hybrid GA with HEFT (Heterogeneous Finish Time First) scheduling algorithms. Further, comparative analysis has been performed based on makespan, average processor utilization, processing cost metrics. It is observed that MGA gives optimal results in terms of processing cost and processor utilization for the unbounded number of processors.

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A DAG based Task Scheduling Algorithms for Multiprocessor System - A Survey

Abstract: The multiprocessor computing is composed of more than one central processing units (CPU)

Cited by 6 publications

References 30 publications

A Cost-Optimized Data Parallel Task Scheduling with Deadline Constraints in Cloud

A Cost-Optimized Data Parallel Task Scheduling with Deadline Constraints in Cloud

Performance Improvements by Deploying L2 Prefetchers with Helper Thread for Pointer-Chasing Applications

Modified Genetic Algorithm Based Solution for Task Scheduling in Cloud Computing Environment

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