Have a Question about JIIT?
Chat with VIDYA
+91-9999912862+91-9999962128
searchImportant Announcements:
Admissions Open for 2026.Apply NowAnother Opportunity : Open House (Parent Interaction) on 13th June 2026.Register NowCareer OpeningsApplyRound-1 of 10+2 Marks Based Counselling Scheduled for 03 June 2026. Instructions
Academic Experts
Dr. Kashav Ajmera

Biography

Dr. Kashav Ajmera is an Assistant Professor (Senior Grade) in the Department of Computer Science and Engineering at the Jaypee Institute of Information Technology (JIIT), Noida, where he has been serving since January 2015. He received his PhD in Computer Science and Engineering from JIIT in November 2023, with his doctoral research titled “Dynamic Virtual Machine Scheduling Algorithms for Energy Efficient Cloud Computing.” His academic and research expertise spans cloud computing, fog and edge computing, virtualization, green computing, optimization techniques, and emerging areas such as Edge AI and container-based technologies, including Docker and Kubernetes.


Dr. Ajmera’s research focuses on energy-efficient resource management in cloud data centers, secure and efficient task scheduling, and metaheuristic approaches for optimizing performance–energy trade-offs. He has published several peer-reviewed papers in reputed journals such as Cluster Computing, The Journal of Supercomputing, and The Computer Journal.

He is also a GATE-qualified academician and has contributed to national-level initiatives like the Community Cloud Pilot Project (IBCC) at IDRBT. Through his teaching, research, and interdisciplinary work, Dr. Ajmera continues to contribute toward sustainable and intelligent computing solutions for next-generation cloud and edge environments.

Research Highlights

Dr. Ajmera’s research is centered on optimizing cloud and distributed computing infrastructures with a particular focus on energy-efficient virtual machine scheduling and intelligent workload management. He has developed metaheuristic-based algorithms such as VMS-MCSA and SR-PSO that enhance performance, lower power consumption, and minimize SLA violations. These solutions integrate advanced optimization techniques with practical deployment strategies tailored for Infrastructure-as-a-Service (IaaS) environments.

His work extends beyond centralized cloud computing to encompass edge computing, fog computing, and edge AI, enabling low-latency, real-time decision-making close to the data source. By designing resource allocation models and orchestration mechanisms across cloud–fog–edge layers, he addresses challenges in latency reduction, scalability, and energy optimization. These methods are particularly relevant for emerging domains such as IoT, smart cities, healthcare monitoring, and industrial automation.

In addition, Dr. Ajmera investigates green computing models, container-based virtualization, and orchestration technologies including Docker, Docker Swarm, and Kubernetes to improve the deployment, scalability, and efficiency of cloud-native and edge-native applications. His research also explores sustainable workload distribution, migration overhead reduction, and high-availability service design for heterogeneous and large-scale computing environments.

Areas of Interest
  • Cloud computing
  • virtualization
  • optimization techniques
  • Docker-container
  • Kubernetes
  • Data Structures & Programming
  • Database System
  • Open Source Software
Publications

[1] K. Ajmera and T. K. Tewari, “VMS-MCSA: Virtual machine scheduling using modified clonal selection algorithm,” Cluster Computing, vol. 24, pp. 3531–3549, 2021.

[1] K. Ajmera and T. K. Tewari, “VMS-MCSA: Virtual machine scheduling using modified clonal selection algorithm,” Cluster Computing, vol. 24, pp. 3531–3549, 2021.

[3] K. Ajmera and T. K. Tewari, “Dynamic virtual machine scheduling using residual optimum power-efficiency in the cloud data center,” The Computer Journal, 2023.

[4] K. Ajmera and T. K. Tewari, “Energy-efficient virtual machine scheduling in IaaS cloud environment using energy-aware Green-particle swarm optimization,” International Journal of Information Technology, vol. 15, pp. 1927–1935, 2023.

[5] K. Ajmera and T. K. Tewari, “Greening the cloud through power-aware virtual machine allocation,” in Proc. 11th Int. Conf. Contemporary Computing (IC3), Noida, India, 2018, pp. 1–6.