Dynamic Job Scheduling Strategy Using Jobs Characteristics in Cloud Computing

Abstract

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.

Hybrid Cuckoo Search Algorithm for Scheduling in Cloud Computing

“…The proposed method minimizes the makespan however some more parameters need to be tested to find the performance of algorithm in multi-objective nature. Alsaih et al [6] developed a dynamic job scheduling model to map the jobs with resource based on available resources characteristics and also re-schedule the jobs that get allocated to VM. Proposed method minimizes the makespan, improved CPU utilization and bandwidth utilization.…”

“…However, there are methods of resource provisioning like leased and reserved, which are used by big organizations [5]. The additional benefits of cloud computing are scalability, availability, reliability, flexibility, and sustainability [6,7].…”

An Optimized Framework for Energy-Resource Allocation in a Cloud Environment based on the Whale Optimization Algorithm

“…These values are used when the system load the machines. For the proposed WOA algorithm, the XML file also includes Pmin and Pmax values for each server because our algorithm is not only scheduling tasks but also calculating energy consumption by that particular machine during the task allocation as per Equation (4): Power Consumed = Pmin + (Pmax − Pmin) * Utilization where Pmin is set as minimum power consumed by the server in its ideal state and Pmax is the maximum power consumed by the server in its utilization state [ 55 ] and these values are set in the configuration file. These values have been set as 200 and 400 as min and max values, respectively, because it has found that most servers consume near to 200 Watts of power when they are in an ideal state and if they contain a large number of resources then they can consume nearly 400 Watts of power [ 56 , 57 , 58 ].…”

DRALBA: Dynamic and Resource Aware Load Balanced Scheduling Approach for Cloud Computing

“…In such a scenario, the problem of resource assignments becomes more critical and challenging because it directly affects the performance of the Cloud system. Designing an efficient scheme for assigning resources to the workloads is known as scheduling strategy [11], face a lot of challenges. Scheduling is the process of decision making for mapping workloads (tasks) to Cloud computing resources to optimize some performance metrics like makespan, response time, resource utilization, and throughput [12]- [14].…”