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Performance Analysis of Scheduling Algorithms for Virtual Machines and Tasks in Cloud Computing: Cyber-Physical Security for Critical Infrastructure

Author(s) Padmaja Pulivarthy
Country United States
Abstract Cloud computing has become the cornerstone technology for the management of key infrastructure to offer efficient, elastic, and economic solutions to industries such as energy, healthcare, transportation, and telecommunication. Task and virtual machine (VM) scheduling is at the core of cloud computing systems, and it plays a crucial role in providing resource optimization, fault tolerance, and reliability. In cyber-physical systems (CPS), physical and computational systems are tightly integrated, and the task scheduling becomes even more complicated because of real-time response needs, high-security demands, and robustness against cyber attacks. The performance analysis of virtual machine and task scheduling algorithms, especially in cloud-based CPS for supporting critical infrastructure, is therefore a research area on the rise.
This work discusses the performance of some of the task scheduling algorithms applied in cloud computing environments, specifically in the context of their use in CPS and the security issues associated with such systems. The work begins by considering the inherent properties of cloud computing and CPS and the critical need for effective scheduling mechanisms in satisfying the distinct demands of such systems. It then offers a comprehensive examination of various scheduling algorithms, ranging from the classical and contemporary techniques like First-Come, First-Served (FCFS), Shortest Job First (SJF), Earliest Deadline First (EDF), and recent AI-based, machine learning-based algorithms. Performance of each algorithm is examined on the basis of resource utilization, load balancing, task ordering, and fault tolerance.
One of the key features covered in the paper is the incorporation of security-aware scheduling algorithms. In multi-tenant cloud environments, the confidentiality, integrity, and availability of tasks and data must be ensured. Security-aware scheduling is required to forestall attacks like data leakage, side-channel attacks, and denial-of-service (DoS) attacks, which could lead to crippling critical infrastructure operations. The paper also covers how the task scheduling must consider isolation policies, tenant trust levels, and vulnerability scans to make certain that there are minimized security risks
Furthermore, the paper discusses real-time scheduling issues in CPS, where timely task completion is critical for safety of operation. In the context of real-time scheduling techniques, including EDF and RMS, timely task completion is critical, particularly in systems like healthcare monitoring, autonomous cars, and power grid control. Fault tolerance and system reliability are of prime importance in scheduling, especially in scenarios where cloud-based resource failure would be catastrophic to physical systems. Like task replication, checkpointing, and live migration are techniques proposed for achieving redundancy and system resilience.
In the future, the paper foresees future trends in CPS scheduling and cloud computing to include edge computing convergence, AI optimization, and 5G networks. These will result in more responsive and adaptive scheduling algorithms, improved resource allocation, and better security in distributed real-time systems.
In summary, algorithmic performance of cloud-based CPS for critical infrastructure is still an issue of highest priority with wide-ranging implications towards operational efficacy and security. With AI, machine learning, edge computing, and 5G advancing deeper into the future, they will increasingly make scheduling techniques more efficient, reliable, and secure for mission-critical applications.
Field Engineering
Published In Volume 13, Issue 1, January-March 2022
Published On 2022-02-09
DOI https://doi.org/10.71097/IJSAT.v13.i1.6024
Short DOI https://doi.org/g9m7pq
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