A Job Scheduling Strategy for Heterogeneous Multiprogrammed Systems

2004 ◽  
pp. 915-920
Author(s):  
Piyush Maheshwari
Keyword(s):  
Job Scheduling ◽  
Scheduling Strategy ◽  
Multiprogrammed Systems

scholarly journals Dynamic Job Scheduling Strategy Using Jobs Characteristics in Cloud Computing

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1638 ◽  
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.

The Effect of Real Workloads and Synthetic Workloads on the Performance of Job Scheduling for Non-Contiguous Allocation in 2D Mesh Multicomputers

2015 ◽  
Vol 6 (1) ◽  
pp. 53-68 ◽  
Author(s):  
Saad Bani-Mohammad
Keyword(s):  
System Performance ◽  
Job Scheduling ◽  
Allocation Strategy ◽  
Extensive Simulation ◽  
Scheduling Strategy ◽  
Need To Evaluate ◽  
Scheduling Strategies ◽  
Simulation Results ◽  
Allocation Algorithms ◽  
Workload Models

The performance of non-contiguous allocation has been traditionally carried out by means of simulations based on synthetic workloads, and also it can be significantly affected by the job scheduling strategy used for determining the order in which jobs are selected for execution. To validate the performance of the non-contiguous allocation algorithms, there has been a need to evaluate the algorithm's performance based on a real workload trace. In this paper, the performance of the well-known Greedy Available Busy List (GABL) non-contiguous allocation strategy for 2D mesh-connected multicomputers is revisited considering several important job scheduling strategies based on a real workload trace, and the results are compared to those obtained from using a synthetic workload. The scheduling strategies used are the First-Come-First-Served (FCFS), Out-of-Order (OO), and Window-Based job scheduling strategies. These strategies have been selected because they are common and they have been used in related works (Ababneh & Bani-Mohammad, 2011). Extensive simulation results based on synthetic and real workload models indicate that the Window-Based job scheduling strategy can improve both overall system performance and fairness (i.e., maximum job waiting delays) by adopting a large job scheduling window. Moreover, the relative performance merits of the scheduling strategies when a real workload trace is used are in general compatible with those obtained when a synthetic workload is used.

scholarly journals Intelligent Sensor-Cloud in Fog Computer: A Novel Hierarchical Data Job Scheduling Strategy

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5083 ◽  
Author(s):  
Sun ◽  
Li ◽  
Wei ◽  
Li ◽  
Min ◽  
...  
Keyword(s):  
Job Scheduling ◽  
Hierarchical Data ◽  
Intelligent Sensor ◽  
Distribution Strategy ◽  
Scheduling Strategy ◽  
Frame Sequence ◽  
Use Of Resources ◽  
Data Similarity ◽  
System Resource ◽  
Sensor Cloud

In the Fog Computer (FC), the process of data is prone to problems such as low data similarity and poor data tolerance. This paper proposes a hierarchical data job scheduling strategy Based on Intelligent Sensor-Cloud in Fog Computer (HDJS). HDJS dynamically adjusts the priority of the job to avoid job starvation and maximize the use of resources, uses the key frame to the resource occupied information, distributes the frame sequence to the unit, and then combines the intra frame distribution strategy to balance the load between the nodes. The experimental results show our proposed strategy may be possible to avoid the operation of hunger and resource fragmentation problems, make full use of the advantages of multi-core and multi-thread, improve system resource utilization, and shorten the execution time and response time.

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