Novel Approach to Task Scheduling and Load Balancing Using the Dominant Sequence Clustering and Mean Shift Clustering Algorithms

Abstract

Cloud computing (CC) is fast-growing and frequently adopted in information technology (IT) environments due to the benefits it offers. Task scheduling and load balancing are amongst the hot topics in the realm of CC. To overcome the shortcomings of the existing task scheduling and load balancing approaches, we propose a novel approach that uses dominant sequence clustering (DSC) for task scheduling and a weighted least connection (WLC) algorithm for load balancing. First, users’ tasks are clustered using the DSC algorithm, which represents user tasks as graph of one or more clusters. After task clustering, each task is ranked using Modified Heterogeneous Earliest Finish Time (MHEFT) algorithm. where the highest priority task is scheduled first. Afterwards, virtual machines (VM) are clustered using a mean shift clustering (MSC) algorithm using kernel functions. Load balancing is subsequently performed using a WLC algorithm, which distributes the load based on server weight and capacity as well as client connectivity to server. A highly weighted or least connected server is selected for task allocation, which in turn increases the response time. Finally, we evaluate the proposed architecture using metrics such as response time, makespan, resource utilization, and service reliability.

Task scheduling and resource allocation of seasonal requests of users in cloud using NMKA and CM-GA techniques

“…To increase the service quality along with the resource efficiency (Xiao and Wang, 2012) and to schedule the tasks to the virtual machine (VM) that are a core technology for CC, terminal server (TS) is done (Cho et al, 2015). The aim of VM scheduling having load balancing (LB) in CC is to allocate VMs to appropriate servers and balance resource utilization among the entire servers (Al-Rahayfeh et al, 2019).…”

Bi-objective scheduling algorithm for scientific workflows on cloud computing platform with makespan and monetary cost minimization approach

“…Highest Level First with Estimated Times(HLFET) [15] Modified Critical Path(MCP) [16] Dynamic Critical Path(DCP) [17] Dynamic Level Scheduling(DLS) [18] Heterogeneous Earliest Finish Time(HEFT) [19] Critical Path On a Processor(CPOP) [19] Longest Dynamic Critical Path(LDCP) [20] Cost-effective fault Tolerance (CEFT) [21] QL-HEFT [22] Clustering and Scheduling System II (CASS II) [23] Dominant Sequence Clustering(DSC) [24] Clustering heuristic [21], [22], [25] Critical Path Fast Duplication(CPFD) [26] Duplication Scheduling Heuristic(DSH) [12], [13], [27], [28] Task Duplication-based Scheduling(TDS) [29] HEFT Task Duplication(HEFT-TD) [30] Lookahead HEFT-TD [30] Genetic Algorithm (GA) [31]- [34] Simulated Annealing(SA) [35]- [38] Particle Swarm Optimization(PSO) [39]- [44] Ant Colony Optimization(ACO) [45]- [51] Artificial Bee Colony (ABC) [52] Cuckoo Search algorithm(CS) [53] Task-Scheduling Algorithms…”

“…This problem is formulated to a biobjective optimization problem with service cost and service time minimization viewpoints; this formulation is drawn in Eqs. (22)(23)(24)(25)(26). Note that the first and the second objectives have been elaborated in Eq.…”

A Predictive Priority-Based Dynamic Resource Provisioning Scheme With Load Balancing in Heterogeneous Cloud Computing

“…In this algorithm, the load balancing task scheduling finds the order in which the activity is completed. The next step is mapping the resources and tasks after that task is submitted for their completion to the cloud [26]. This proposed scheduling solved the load balancing problem.…”