scholarly journals Optimal allocation of virtual machines in multi-cloud environments with reserved and on-demand pricing

2017 ◽  
Vol 71 ◽  
pp. 129-144 ◽  
Author(s):  
José Luis Díaz ◽  
Joaquín Entrialgo ◽  
Manuel García ◽  
Javier García ◽  
Daniel Fernando García
Keyword(s):  
Optimal Allocation ◽  
Virtual Machines ◽  
On Demand ◽  
Cloud Environments ◽  
Multi Cloud

Scheduling Strategies for Business Process Applications in Cloud Environments

2013 ◽  
Vol 5 (4) ◽  
pp. 65-78 ◽  
Author(s):  
Kahina Bessai ◽  
Samir Youcef ◽  
Ammar Oulamara ◽  
Claude Godart ◽  
Selmin Nurcan
Keyword(s):  
Business Processes ◽  
Virtual Machines ◽  
Cloud Infrastructure ◽  
On Demand ◽  
Computing Paradigm ◽  
Elastic Computing ◽  
Cloud Environments ◽  
Scheduling Strategies ◽  
The Many ◽  
Distributed Cloud

The Cloud computing paradigm is adopted for its several advantages like reduction of cost incurred when using a set of resources. However, despite the many proven benefits of using a Cloud infrastructure to run business processes, it is still faced with a major problem that can compromise its success: the lack of guidance for choosing between multiple offerings. Moreover, when running business processes it is difficult to automate all tasks and several objectives often conflicting must be taken into account. For this, the authors propose a set of scheduling strategies for business processes in Cloud contexts. More precisely, the authors propose three bi-criteria complementary approaches for scheduling business processes on distributed Cloud resources while taking into account its elastic computing characteristic that allows users to allocate and release compute resources (virtual machines) on-demand and its business model based on pay as you go. Therefore, it is reasonable to assume that the number of virtual machine is infinite while the number of human resources is finite. Experiment results demonstrate that the proposed approaches present good performances.

scholarly journals Kumo: um serviço para portabilidade em multi-nuvens heterogêneas

2018 ◽  
Author(s):  
Marcus Rafael Xavier ◽  
Carlos André G. Ferraz ◽  
Ioram Schechtman Sette
Keyword(s):  
Virtual Machines ◽  
Cloud Provider ◽  
Daily Lives ◽  
Virtual Infrastructure ◽  
Diffi Cult ◽  
Cloud Environments ◽  
Lock In ◽  
Multi Cloud ◽  
Service Cloud

Cloud computing is maturing and becoming ubiquitous in people’s daily lives. As a result, cloud environments are providing moreand more services with better quality of service. Cloud customers, however, have suff ered from the vendor lock-in problem, in such a way that those who wish to migrate to another cloud provider require partial or total reimplementation of applications and virtual infrastructure. Moreover, the problem of heterogeneity among distinct cloud environments makes it diffi cult for the portability of resources between them. Therefore, this work focuses on the development of an ontology to handle multi-cloud heterogeneity, and thus, bring interoperability in the form of a service to perform the portability of virtual machines between diff erent providers.

SLO-Driven Monitoring and Adaptation of Multi-Cloud Service-Based Applications

2018 ◽  
pp. 43-65
Author(s):  
Chrysostomos Zeginis ◽  
Kyriakos Kritikos ◽  
Dimitris Plexousakis
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Cloud Service ◽  
Emerging Technology ◽  
Operational Costs ◽  
Cloud Environments ◽  
Multiple Clouds ◽  
Event Based ◽  
Multi Cloud

Cloud computing is an emerging technology that attracts the attention of an increasing number of organizations that are willing to virtualize their infrastructure. Towards this direction, enterprises can deploy their applications on different public virtual machines (VMs), in order to reduce their operational costs, as well as optimize the performance of the offered applications. In such heterogeneous multi-cloud environments, the need for monitoring the quality of (a) the acquired resources, (b) the services offered to the final users, and (c) the offered service-based processes, and adapting them has come into force. Current research approaches addressing these areas are limited and most of them target a subset of these offerings. This chapter aims at proposing a novel event-based approach for cross-layer monitoring and adaptation of service-based applications (SBAs) deployed on multiple clouds. The proposed approach is empirically evaluated based on an invoice management application.

Dynamic Programming Inspired Virtual Machine Instances Allocation in Cloud Computing

2017 ◽  
Vol 14 (1) ◽  
pp. 551-560 ◽  
Author(s):  
P Karthikeyan ◽  
M Chandrasekaran
Keyword(s):  
Cloud Computing ◽  
Dynamic Programming ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Cloud Provider ◽  
Revenue Generation ◽  
On Demand ◽  
Allocation Method ◽  
Cloud Environments ◽  
Time And Space Complexity

Cloud computing provides virtual machines instances to the user for performing various computational tasks on demand for a specific period of time. Considering the architecture and characteristics of the cloud environments, traditional virtual machine instances allocation algorithms cannot be applied to the cloud environment appropriately. In this paper, we propose Dynamic programming inspired virtual machine instances allocation algorithm which allocates virtual machine instances to the user based on demand. The aim of this algorithm is to maximize the cloud provider’s revenue. We have mainly focused on the total revenue generation of the cloud provider and percentage of user served rather than focusing on running time and space complexity of the virtual machine instances allocation problem. We evaluate the proposed mechanism by performing simulations. The experimental results show that the proposed Dynamic programming inspired virtual machine instances allocation method provided a higher revenue generation for the cloud provider than traditional fixed price and combinatorial auction greedy virtual machine instances allocation method.

scholarly journals GATECloud.net: a platform for large-scale, open-source text processing on the cloud

2013 ◽  
Vol 371 (1983) ◽  
pp. 20120071 ◽  
Author(s):  
Valentin Tablan ◽  
Ian Roberts ◽  
Hamish Cunningham ◽  
Kalina Bontcheva
Keyword(s):  
Cloud Computing ◽  
Language Processing ◽  
Large Scale ◽  
Virtual Machines ◽  
Cost Benefit Analysis ◽  
Text Processing ◽  
Cost Benefit ◽  
Data Intensive ◽  
On Demand ◽  
Usage Evaluation

Cloud computing is increasingly being regarded as a key enabler of the ‘democratization of science’, because on-demand, highly scalable cloud computing facilities enable researchers anywhere to carry out data-intensive experiments. In the context of natural language processing (NLP), algorithms tend to be complex, which makes their parallelization and deployment on cloud platforms a non-trivial task. This study presents a new, unique, cloud-based platform for large-scale NLP research—GATECloud. net. It enables researchers to carry out data-intensive NLP experiments by harnessing the vast, on-demand compute power of the Amazon cloud. Important infrastructural issues are dealt with by the platform, completely transparently for the researcher: load balancing, efficient data upload and storage, deployment on the virtual machines, security and fault tolerance. We also include a cost–benefit analysis and usage evaluation.

scholarly journals Security for Multi Cloud Using Server less Registering Approach

2021 ◽  
Vol 12 (3) ◽  
pp. 4801-4804
Author(s):  
Shravani Jasthi Et. al.
Keyword(s):  
Distributed Computing ◽  
Specific Area ◽  
Cloud Service ◽  
Service Access ◽  
Security Issue ◽  
Application Development ◽  
Security Enhancement ◽  
Execution Model ◽  
Cloud Environments ◽  
Multi Cloud

These days, in any application development, security for specific area has become crucial job in the service access environment. Since clients needs to utilize the unique services and resources in distributed computing environment. Here the security administrations and cloud portal frameworks have been highly advanced based on the client necessities. However cloud offers a lot of resources through the global service vendors and Multicloud technologies are rapidly in use, but still the cloud requires security enhancement. Applications become complex and have attacks when deployed on multiclouds .So it is very important factor to protect the data and resources from the hackers. In multiple cloud environments it is possible to control all the applications, user resources, secret information and other confidential user process level with the help of server less approach. The server less computing approach is a sort of Distributed computing execution model through which Cloud Service provider will allocate the resource to the client in a dynamic manner .This paper represents what is Multi cloud, advantages of Multicloud, Why Security issue with Multi cloud, How server less is different from monolith services and Security Approaches to multi cloud with server less computing.

Effective Utilization of Resources Through Optimal Allocation and Opportunistic Migration of Virtual Machines in Cloud Environment

2021 ◽  
Vol 11 (3) ◽  
pp. 72-91
Author(s):  
Priyanka H. ◽  
Mary Cherian
Keyword(s):  
Data Center ◽  
Execution Time ◽  
Data Centers ◽  
Optimal Allocation ◽  
Virtual Machines ◽  
Large Data ◽  
Migration Time ◽  
Resource Usage ◽  
Total Execution Time ◽  
Effective Utilization

Cloud computing has become more prominent, and it is used in large data centers. Distribution of well-organized resources (bandwidth, CPU, and memory) is the major problem in the data centers. The genetically enhanced shuffling frog leaping algorithm (GESFLA) framework is proposed to select the optimal virtual machines to schedule the tasks and allocate them in physical machines (PMs). The proposed GESFLA-based resource allocation technique is useful in minimizing the wastage of resource usage and also minimizes the power consumption of the data center. The proposed GESFL algorithm is compared with task-based particle swarm optimization (TBPSO) for efficiency. The experimental results show the excellence of GESFLA over TBPSO in terms of resource usage ratio, migration time, and total execution time. The proposed GESFLA framework reduces the energy consumption of data center up to 79%, migration time by 67%, and CPU utilization is improved by 9% for Planet Lab workload traces. For the random workload, the execution time is minimized by 71%, transfer time is reduced up to 99%, and the CPU consumption is improved by 17% when compared to TBPSO.

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