Towards Cloud Computing

TL;DR: This research is based on finding better private cloud platform for educational societies based on their performance and how some educational societies are currently working on the cloud platform and why other schools, colleges, and universities must move toward cloud platform with proper data security assurance.

TL;DR: In this paper , a deadline-aware task scheduling algorithm for cloud environments based on the Firefly Optimization Algorithm (FOA) is proposed to achieve a higher level of efficiency in multiple parameters, including execution time, waiting time, resource utilization, the percentage of missed tasks, power consumption, and makespan.

TL;DR: A secure cloud-based robot control system using a blockchain system was developed that addresses the risks associated with cloud- based robot control systems, such as data tampering and unauthorized misuse, by leveraging the Proof of Work and hashing mechanisms.

TL;DR: This study explores cloud service composition using genetic algorithm and particle swarm optimization, enhancing service quality awareness in service-oriented computing, allowing companies to dynamically allocate computing resources based on demand.

TL;DR: A framework within which to locate existing and future research on cloud computing is provided and is supplemented with empirical evidence from interviews with cloud providers and cloud users that were undertaken between 2010 and 2012.

TL;DR: The vast stream of the state of the art in Everything as a Service (XaaS) is investigated and an integrated view of XaaS is explored to help propose approaches for migrating applications to the cloud and exposing them as services.

TL;DR: This paper presents a comparative analysis of open-source solutions that enable the provision of infrastructure resources as a service, including OpenNebula and OpenStack, which have attracted the attention both of the research community and of the industrial one.