Quantum-Inspired Optimization of Cloud Infrastructure for Reliability and Cost Efficiency (Published)
Quantum-inspired optimization emerges as a transformative paradigm for cloud infrastructure management, addressing the increasing complexity and multi-dimensional challenges faced by modern distributed systems. This article introduces a comprehensive framework that applies quantum computational principles to classical infrastructure, enabling more efficient navigation of complex solution landscapes without requiring quantum hardware. The framework targets critical operational areas including workload distribution, auto-scaling, resource allocation, and fault tolerance enhancement. By leveraging quantum-inspired algorithmic approaches such as quantum annealing simulations and hybrid methods, the solution demonstrates significant advantages over conventional optimization techniques, particularly as infrastructure complexity increases. The implementation incorporates seamless integration with existing cloud platforms through non-invasive APIs and abstraction layers. Experimental results reveal improved resource utilization, enhanced reliability during failure scenarios, substantial cost reductions, and favorable scalability characteristics. The quantum-inspired approach offers a practical pathway toward addressing the exponentially growing complexity of cloud infrastructure optimization while providing immediate benefits using classical computing resources and establishing foundations for future integration with quantum computing services.
Keywords: Cost Efficiency, Resource Allocation, cloud infrastructure, quantum-inspired optimization, reliability enhancement
Query Processing in the Cloud for Big Data Applications Benefits and Risks (Published)
The advent of cloud computing has transformed the landscape of big data processing, offering numerous benefits and presenting certain risks. This paper explores the domain of query processing in the cloud for big data applications, elucidating the advantages and challenges associated with this paradigm shift. Benefits: Scalability: Cloud platforms provide elastic resources, allowing big data applications to scale up or down based on demand. This scalability enables organizations to process vast amounts of data without significant upfront investments in hardware. Accessibility: Cloud-based query processing offers accessibility from anywhere, promoting remote work and collaboration, and facilitating data sharing and analysis among global teams. Risks: Data Security and Privacy: Storing and processing sensitive data in the cloud can pose security and privacy risks if not properly managed. Data breaches and unauthorized access are potential concerns. Data Transfer Costs: Transferring large volumes of data to and from the cloud can result in significant costs, particularly when dealing with extensive datasets. Vendor Lock-In: Adopting cloud services can lead to vendor lock-in, making it challenging to migrate to another provider or back to on-premises infrastructure. This paper delves into these benefits and risks in detail, providing insights into strategies for mitigating the associated challenges and making informed decisions when considering query processing in the cloud for big data applications. The balance between reaping the benefits of cloud scalability and managing the associated risks is crucial in the ever-evolving landscape of big data processing.
Keywords: Accessibility, Big Data, Cloud Computing, Cost Efficiency, Managed Services, Query processing, scalability