A Task Duplication Based Optimal Scheduling Algorithm for Variable Execution Time Tasks

In this paper, the authors highlight the existence of close relations between the execution time, efficiency and number of communication rounds in a family of CGM-based parallel algorithms for the optimal binary search tree problem (OBST). In this case, these three parameters cannot be simultaneously improved. The family of CGM (Coarse Grained Multicomputer) algorithms they derive is based on Knuth's sequential solution running in time and space, where n is the size of the problem. These CGM algorithms use p processors, each with local memory. In general, the authors show that each algorithms runs in with communications rounds. is the granularity of their model, and is a parameter that depends on and . The special case of yields a load-balanced CGM-based parallel algorithm with communication rounds and execution steps. Alternately, if , they obtain another algorithm with better execution time, say , the absence of any load-balancing and communication rounds, i.e., not better than the first algorithm. The authors show that the granularity has a crucial role in the different techniques they use to partition the problem to solve and study the impact of each scheduling algorithm. To the best of their knowledge, this is the first unified method to derive a set of parameter-dependent CGM-based parallel algorithms for the OBST problem.

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An Algorithm for Task Scheduling in Heterogeneous Distributed Systems Using Task Duplication

International Journal of Grid and High Performance Computing ◽

10.4018/jghpc.2011010105 ◽

2011 ◽

Vol 3 (1) ◽

pp. 89-97 ◽

Cited By ~ 1

Author(s):

Amrit Agrawal ◽

Pranay Chaudhuri

Keyword(s):

Task Scheduling ◽

Execution Time ◽

High Speed ◽

Critical Path ◽

Task Assignment ◽

Directed Acyclic Graphs ◽

Parallel Tasks ◽

Schedule Length ◽

Task Duplication ◽

Heterogeneous Distributed Systems

Task scheduling in heterogeneous parallel and distributed computing environment is a challenging problem. Applications identified by parallel tasks can be represented by directed-acyclic graphs (DAGs). Scheduling refers to the assignment of these parallel tasks on a set of bounded heterogeneous processors connected by high speed networks. Since task assignment is an NP-complete problem, instead of finding an exact solution, scheduling algorithms are developed based on heuristics, with the primary goal of minimizing the overall execution time of the application or schedule length. In this paper, the overall execution time (schedule length) of the tasks is reduced using task duplication on top of the Critical-Path-On-a-Processor (CPOP) algorithm.

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Dynamic Job Scheduling Strategy Using Jobs Characteristics in Cloud Computing

Symmetry ◽

10.3390/sym12101638 ◽

2020 ◽

Vol 12 (10) ◽

pp. 1638 ◽

Cited By ~ 1

Author(s):

Mohammed A. Alsaih ◽

Rohaya Latip ◽

Azizol Abdullah ◽

Shamala K. Subramaniam ◽

Kamal Ali Alezabi

Keyword(s):

Execution Time ◽

Job Scheduling ◽

Scheduling Algorithm ◽

Job Characteristics ◽

Available Bandwidth ◽

Scheduling Strategy ◽

Cloud Users ◽

Performance Variations ◽

Available Resources

A crucial performance concern in distributed decentralized environments, like clouds, is how to guarantee that jobs complete their execution within the estimated completion times using the available resources’ bandwidth fairly and efficiently while considering the resource performance variations. Formerly, several models including reservation, migration, and replication heuristics have been implemented to solve this concern under a variety of scheduling techniques; however, they have some undetermined obstacles. This paper proposes a dynamic job scheduling model (DTSCA) that uses job characteristics to map them to resources with minimum execution time taking into account utilizing the available resources bandwidth fairly to satisfy the cloud users quality of service (QoS) requirements and utilize the providers’ resources efficiently. The scheduling algorithm makes use of job characteristics (length, expected execution time, expected bandwidth) with regards to available symmetrical and non-symmetrical resources characteristics (CPU, memory, and available bandwidth). This scheduling strategy is based on generating an expectation value for each job that is proportional to how these job’s characteristics are related to all other jobs in total. That should make their virtual machine choice closer to their expectation, thus fairer. It also builds a feedback method which deals with reallocation of failed jobs that do not meet the mapping criteria.

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