/pdf/a-break-in-the-clouds-towards-a-cloud-definition-svovsw1r5a.pdf

A Break in the Clouds: Towards a Cloud Definition

Whale Optimization Algorithm (WOA) is an optimization algorithm developed by Mirjalili and Lewis in 2016. An overview of WOA is described in this paper, rooted from the bubble-net hunting strategy, besides an overview of WOA applications that are used to solve optimization problems in various categories. The best solution has been determined to make something as functional and effective as possible through the optimization process by minimizing or maximizing the parameters involved in the problems. Research and engineering attention have been paid to Meta-heuristics for purposes of decision-making given the growing complexity of models and the needs for quick decision making in the engineering. An updated review of research of WOA is provided in this paper for hybridization, improved, and variants. The categories included in the reviews are Engineering, Clustering, Classification, Robot Path, Image Processing, Networks, Task Scheduling, and other engineering applications. According to the reviewed literature, WOA is mostly used in the engineering area to solve optimization problems. Providing an overview and summarizing the review of WOA applications are the aims of this paper.

A comprehensive survey: Whale Optimization Algorithm and its applications

https://cyberleninka.org/article/n/468779.pdf

A review of metaheuristic scheduling techniques in cloud computing

A hybrid model of Internet of Things and cloud computing to manage big data in health services applications

A comprehensive survey for scheduling techniques in cloud computing

https://users.exa.unicen.edu.ar/~cmateos/files/journals/pacini2014-cee.pdf

Software Survey: Distributed job scheduling based on Swarm Intelligence: A survey

The Cloud Computing paradigm focuses on the provisioning of reliable and scalable infrastructures (Clouds) delivering execution and storage services. The paradigm, with its promise of virtually infinite resources, seems to suit well in solving resource greedy scientific computing problems. The goal of this work is to study private Clouds to execute scientific experiments coming from multiple users, i.e., our work focuses on the Infrastructure as a Service (IaaS) model where custom Virtual Machines (VM) are launched in appropriate hosts available in a Cloud. Then, correctly scheduling Cloud hosts is very important and it is necessary to develop efficient scheduling strategies to appropriately allocate VMs to physical resources. The job scheduling problem is however NP-complete, and therefore many heuristics have been developed. In this work, we describe and evaluate a Cloud scheduler based on Ant Colony Optimization (ACO). The main performance metrics to study are the number of serviced users by the Cloud and the total number of created VMs in online (non-batch) scheduling scenarios. Besides, the number of intra-Cloud network messages sent are evaluated. Simulated experiments performed using CloudSim and job data from real scientific problems show that our scheduler succeeds in balancing the studied metrics compared to schedulers based on Random assignment and Genetic Algorithms.

/pdf/balancing-throughput-and-response-time-in-online-scientific-5cg68octst.pdf

Balancing Throughput and Response Time in Online Scientific Clouds via Ant Colony Optimization

Balancing throughput and response time in online scientific Clouds via Ant Colony Optimization (SP2013/2013/00006)

https://users.exa.unicen.edu.ar/~cmateos/files/journals/epacini2013-aes.pdf

Elina Pacini

Papers

Software Survey: Distributed job scheduling based on Swarm Intelligence: A survey

Balancing Throughput and Response Time in Online Scientific Clouds via Ant Colony Optimization

Balancing throughput and response time in online scientific Clouds via Ant Colony Optimization (SP2013/2013/00006)

An ACO-inspired algorithm for minimizing weighted flowtime in cloud-based parameter sweep experiments

Reinforcement learning-based application Autoscaling in the Cloud: A survey