Efficient Data Structures and Algorithms for Cloud Computing Platforms

The extensive adoption of cloud computing platforms in storing and processing data have brought forth a new age of efficiency in the way data is stored, processed and managed, requiring new data structures and algorithms that aim to further improve efficiency in such working environments. The design, analysis, and optimization of data structures and algorithms for use in cloud computing platforms; in particular, the development of such solutions to the constraints and requirements of distributed, scalable, and high-availability systems. Cloud computing platforms require suitably efficient data structures and algorithms that scale to manage vast amounts of data while also optimizing for latency, fault-tolerance, and resource utilization. While the traditional data structures and algorithms remain inherently scalable, their efficiency degrades, hampering performance, in a distributed environment. In short, our work presents a study of advanced data structures (e.g., scalable Bloom filter, distributed hash table) and optimized graph structures built to serve in cloudbased applications. This paper also discusses algorithms for data partitioning, load balance, and parallel processing in order to improve the performance and reliability of cloud infrastructures.