Cloud Computing and Virtualization

Mangalampalli, Sudheer; Sree, Pokkuluri Kiran; Swain, S. K.; Karri, Ganesh Reddy

doi:10.1002/9781119905233.ch2

Cited by 12 publications

(7 citation statements)

References 27 publications

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“…Therefore, we also need to calculate current running workload on physical machines in all considered 𝒊𝟏 physical machines in multiple cloud environments. It is calculated using below equation (2).…”

Section: 𝑙𝑜𝑎𝑑 𝑐𝑝𝑢 𝑖mentioning

confidence: 99%

“…This service gives a great relaxation to the users as they don't need to worry about setting the development environment and software licenses, patching of software and they can focus on development of the application by saving time and investment in infrastructure. Software as a service provides readymade software services provided to cloud users on demand based on the requirement [2], [3]. All these services are to be provided to cloud users around the world on demand based on user requirement i.e.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Objective Prioritized Task Scheduler Using Improved Asynchronous Advantage Actor Critic (a3c) Algorithm in Multi Cloud Environment

Mangalampalli,

Karri,

Mohanty

et al. 2024

IEEE Access

Self Cite

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Task scheduling is a crucial challenge in cloud computing paradigm as variety of tasks with different runtime processing capacities generated from various heterogeneous devices are coming up to cloud application console which effects system performance in terms of makespan, resource utilization, resource cost. Therefore, traditional scheduling algorithms may not adapt to this paradigm efficiently. Many existing authors developed various task schedulers by using metaheuristic approaches to solve Task scheduling problem(TSP) to get near optimal solutions but still TSP is a highly dynamic challenging scenario as it is a NP hard problem. To tackle this challenge, this paper introduces a multi objective prioritized task scheduler using improved asynchronous advantage actor critic(a3c) algorithm which uses priorities of tasks based on length of tasks, runtime processing capacities and priorities of VMs based on electricity unit cost using multi cloud environment. Scheduling process carried out in two stages. In the first stage, all incoming tasks, VM priorities are calculated at the task manager level and in the second stage, Priorities are fed to (MOPTSA3C) scheduler to generate scheduling decisions to map tasks effectively onto VMs by considering priorities and schedule tasks based on cost, resource utilization, makespan in the available multi cloud environment. Extensive simulations are conducted on Cloudsim toolkit by giving input trace different fabricated data distributions and real time worklogs of HPC2N, NASA datasets to the scheduler. For evaluating the efficacy of proposed MOPTSA3C, it compared against existing techniques i.e. DQN, A2C, MOABCQ. From the results, it is evident that proposed MOPTSA3C outperforms existing algorithms for makespan, resource utilization, resource cost, reliability.

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Section: 𝑙𝑜𝑎𝑑 𝑐𝑝𝑢 𝑖mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Objective Prioritized Task Scheduler Using Improved Asynchronous Advantage Actor Critic (a3c) Algorithm in Multi Cloud Environment

Mangalampalli,

Karri,

Mohanty

et al. 2024

IEEE Access

Self Cite

View full text Add to dashboard Cite

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“…Platform as a service which enables virtual development environment to the users so that users can develop cloud applications on top of cloud environment. Software as a service gives readymade application services to be given to users developed by cloud vendors [2] and all these services to be deployed using various models known as public, private, hybrid, community cloud deployment models [3]. All these services are given to users based on customer need and they are customized by specifying Service level agreements (SLA).…”

Section: Introductionmentioning

confidence: 99%

Reliability aware Task scheduler in Cloud Computing using improved AsynchronousAdvantage Actor critic(A3C) algorithm

Mangalampalli,

Karri,

Chakrabarti

et al. 2024

Preprint

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Task Scheduling problem (TSP) in cloud computing is a critical aspect as diversified tasks from heterogeneous resources comes to cloud console. Mapping these diversified tasks to suitable virtual machines is challenge for the cloud service provider(CSP) to employ an efficient algorithm to tackle TSP. Ineffective scheduling lead to increase in makespan, failures which impacts reliability on CSP. Many authors developed various task scheduling mechanisms to tackle parameters makespan, execution time, energy consumption but very few authors addressed Rate of failures, reliability but there is need to optimize scheduling process in Cloud paradigm as it is a dynamic scenario. In this paper, a reliability aware task scheduler is formulated which calculates task priorities at task manager level to effectively schedule tasks. All priorities are fed to scheduler which is integrated with a deep Reinforcement learning model A3C which improved by adding RCNN to accelerate learning capacity and to extract features accurately mapping tasks to VMs according to their priorities. Simulations are carried out on Cloudsim using fabricated data distributions, real time worklogs. We evaluated our proposed RTSIA3C with baseline algorithms DQN, A2C. Results revealed that RTSIA3C outperformed over baseline approaches by minimizing makespan, rate of failures while improving reliability.

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“…In these days wide variety of service models are available in the commercial vendor cloud platforms but majority of the services are falls under three categories i.e. Infrastructure -as-a-service where existing application running in on premises environment can be migrated and deployed in cloud platform with the support of virtual infrastructure provided by cloud vendor [2]. Platform-as-a-service where cloud user can develop their application on top of cloud environment by using various services and the necessary services can be readily available to users without installing any software to develop applications but user need to subscribe to the respective service and make use of it based on the subscription they chose for that service [3].…”

Section: Introductionmentioning

confidence: 99%

Multi Objective Prioritized Workflow Scheduling Using Deep Reinforcement Based Learning in Cloud Computing

Mangalampalli,

Hashmi,

Gupta

et al. 2024

IEEE Access

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Workflow Scheduling is a huge challenge in cloud paradigm as many number of workflows dynamically generated from various heterogeneous resources and task dependencies in each workflow varies from each other. Therefore, if a workflow with more number of dependencies is not scheduled onto an appropriate Virtual Machine i.e. with low processing capacity which leads to delay in executing workflows and it results in increase of makespan, cost, energy consumption. In order to effectively schedule complex workflows i.e. with more task dependencies, we propose a novel multi objective workflow scheduling algorithm using Deep reinforcement Learning. Initially, priorities of all workflows calculated based on their dependencies and then calculated priorities of VMs based on electricity cost at datacenters to map workflows onto precise VMs. These priorities are fed to scheduler which uses Deep Q-Network model to dynamically schedule tasks by considering both priorities of tasks and VMs. Extensive simulations carried out on workflowsim by considering realtime scientific workflows (Montage, cybershake, Epigenomics, LIGO). Our proposed MOPWSDRL compared against existing state of art approaches i.e. Heterogeneous Earliest First Deadline, Cat Swarm Optimization, Ant Colony Optimization. Results revealed that our proposed MOPDSWRL outperforms existing state of art algorithms by minimizing makespan, energy consumption.

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Cloud Computing and Virtualization

Cited by 12 publications

References 27 publications

Multi-Objective Prioritized Task Scheduler Using Improved Asynchronous Advantage Actor Critic (a3c) Algorithm in Multi Cloud Environment

Multi-Objective Prioritized Task Scheduler Using Improved Asynchronous Advantage Actor Critic (a3c) Algorithm in Multi Cloud Environment

Reliability aware Task scheduler in Cloud Computing using improved AsynchronousAdvantage Actor critic(A3C) algorithm

Multi Objective Prioritized Workflow Scheduling Using Deep Reinforcement Based Learning in Cloud Computing

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