• Department of Computer Science
  • 189

Academic Background
MS Computer Science (QoS-aware Task Scheduling in a Heterogeneous Cloud Computing Environment) University of Engineering and Technology, Taxila 2018
BS Computer SCience (Hybrid Authentication Techniques in Cloud-enabled applications deploying small Bussiness Organization) HITEC University, Taxila 2016
Ph.D. in Computer Science ( Multi-objective Task Scheduling in a Heterogenous Cloud Computing Environment ) University of Engineering and Technology, Taxila in progress
Lecturer NAMAL University, Mianwali 20-Sep-2022 - continue
Lecturer HITEC University, Taxila 09-Sep-2019 - 16-Sep-2022
Lecturer University of Lahore, Pakistan 01-Oct-2018 - 06-Sep-2019
Research Scholar University of Engineering and Technology (UET), Taxila 19-Sep-2016 - 24-Aug-2018
Honours and Awards
Gold Medalist Batch 2k16 Topper in MS Computer Science and received Gold Medal at the University of Engineering and Technology, Taxila Pakistan after scoring the highest CGPA of 3.75/4.0. 24-Aug-2018
Journal Publications
Parental Prioritization-Based Task Scheduling in Heterogeneous Systems 17-Jan-2019 Efficient task scheduling is important for achieving high performance in heterogeneous distributed computing systems. The main focus of this research is to build a task-scheduling algorithm for a heterogeneous environment. We proposed an algorithm named parental prioritization earliest finish time. It has two phases, task prioritization phase and processor assigning phase. In the tasks prioritization phase, tasks will schedule in the parental priority queue (PPQ) on the basis of downward rank and parental priority. Task prioritization is based on the directed acyclic graph. It can schedule the task of successor row before the current row if it has less communication cost. In the processor assigning phase, the processor will allocate to the scheduled tasks obtained from PPQ keeping the computation cost to a minimum. This proposed algorithm is compared with HEFT and CPOP algorithms through graphs generated from a random task graph generator and a set of tasks. The experimental results show that our proposed scheduling algorithm performs significantly better than other algorithms in terms of both cost and makespan of schedules.
Conference Publications
Flexible Genetic Algorithm Operators for Task Scheduling in Cloud Datacenters 29-Jan-2021 Cloud computing is the backbone of the modern information technology industry. Due to the increase in internet usage, social media, and smart phones, a large amount of data is producing. Cloud datacenters can provide resources to handle this data. If data is not properly handled, it can cause overhead on cloud servers and can increase operational costs. Genetic algorithm is used to solve scheduling problem efficiently, but they take a lot of time to find an optimal solution. In this paper, we proposed Flexible Genetic Algorithm Operators (FGAO) for Task Scheduling in Cloud Datacenters. This algorithm changes crossover and mutation operators according to the quality of scheduling solutions. Instead of giving a fixed stopping criteria algorithm uses flexible crossover and mutation operators as a stopping criterion. Experimental results show that the proposed FGAO algorithm reduces 40% execution time and 33% iterations as compared to the genetic algorithm.
  • Parallel and Distributed Systems
  • Cloud Computing
  • Operating Systems
  • Computer Networks
  • Internet Architecture and Protocols
  • Computing Fundamentals
  • Programming Fundamentals