Clustering based virtual machines placement in distributed cloud computing

2017 ◽  
Vol 66 ◽  
pp. 1-10 ◽  
Author(s):  
Jiangtao Zhang ◽  
Xuan Wang ◽  
Hejiao Huang ◽  
Shi Chen
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Distributed Cloud

scholarly journals Heuristic-based Approach for Dynamic Consolidation of Software Licenses in Cloud Data Centers

2021 ◽  
Vol 13 (6) ◽  
pp. 1-12
Author(s):  
Leila Helali ◽  
◽  
Mohamed Nazih Omri
Keyword(s):  
Cloud Computing ◽  
Data Centers ◽  
Virtual Machines ◽  
Heuristic Algorithms ◽  
Energy Aware ◽  
Computing Systems ◽  
Cloud Data ◽  
Software Licenses ◽  
Cloud Data Centers ◽  
Distributed Cloud

Since its emergence, cloud computing has continued to evolve thanks to its ability to present computing as consumable services paid by use, and the possibilities of resource scaling that it offers according to client’s needs. Models and appropriate schemes for resource scaling through consolidation service have been considerably investigated,mainly, at the infrastructure level to optimize costs and energy consumption. Consolidation efforts at the SaaS level remain very restrained mostly when proprietary software are in hand. In order to fill this gap and provide software licenses elastically regarding the economic and energy-aware considerations in the context of distributed cloud computing systems, this work deals with dynamic software consolidation in commercial cloud data centers 𝑫𝑺𝟑𝑪. Our solution is based on heuristic algorithms and allows reallocating software licenses at runtime by determining the optimal amount of resources required for their execution and freed unused machines. Simulation results showed the efficiency of our solution in terms of energy by 68.85% savings and costs by 80.01% savings. It allowed to free up to 75% physical machines and 76.5% virtual machines and proved its scalability in terms of average execution time while varying the number of software and the number of licenses alternately.

Implementing Distributed, Self-Managing Computing Services Infrastructure using a Scalable, Parallel and Network-Centric Computing Model

2012 ◽  
pp. 57-78 ◽  
Author(s):  
Rao Mikkilineni ◽  
Giovanni Morana ◽  
Ian Seyler
Keyword(s):  
Cloud Computing ◽  
Network Architecture ◽  
Virtual Machines ◽  
Von Neumann ◽  
Accounting Performance ◽  
Computing Model ◽  
Computing Services ◽  
Many Core ◽  
Distributed Cloud ◽  
Process Level

This chapter introduces a new network-centric computing model using Distributed Intelligent Managed Element (DIME) network architecture (DNA). A parallel signaling network overlay over a network of self-managed von Neumann computing nodes is utilized to implement dynamic fault, configuration, accounting, performance, and security management of both the nodes and the network based on business priorities, workload variations and latency constraints. Two implementations of the new computing model are described which demonstrate the feasibility of the new computing model. One implementation provides service virtualization at the Linux process level and another provides virtualization of a core in a many-core processor. Both point to an alternative way to assure end-to-end transaction reliability, availability, performance, and security in distributed Cloud computing, reducing current complexity in configuring and managing virtual machines and making the implementation of Federation of Clouds simpler.

Distance Aware VM allocation process to minimize energy consumption in cloud computing

2019 ◽  
Vol 12 ◽  
Author(s):  
Gurpreet Singh ◽  
Manish Mahajan ◽  
Rajni Mohana
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Research Work ◽  
Service Level Agreement ◽  
Service Level ◽  
Physical Machine ◽  
Allocation Process

BACKGROUND: Cloud computing is considered as an on-demand service resource with the applications towards data center on pay per user basis. For allocating the resources appropriately for the satisfaction of user needs, an effective and reliable resource allocation method is required. Because of the enhanced user demand, the allocation of resources has now considered as a complex and challenging task when a physical machine is overloaded, Virtual Machines share its load by utilizing the physical machine resources. Previous studies lack in energy consumption and time management while keeping the Virtual Machine at the different server in turned on state. AIM AND OBJECTIVE: The main aim of this research work is to propose an effective resource allocation scheme for allocating the Virtual Machine from an ad hoc sub server with Virtual Machines. EXECUTION MODEL: The execution of the research has been carried out into two sections, initially, the location of Virtual Machines and Physical Machine with the server has been taken place and subsequently, the cross-validation of allocation is addressed. For the sorting of Virtual Machines, Modified Best Fit Decreasing algorithm is used and Multi-Machine Job Scheduling is used while the placement process of jobs to an appropriate host. Artificial Neural Network as a classifier, has allocated jobs to the hosts. Measures, viz. Service Level Agreement violation and energy consumption are considered and fruitful results have been obtained with a 37.7 of reduction in energy consumption and 15% improvement in Service Level Agreement violation.

Virtual Machines Scheduling Algorithm Based on Multi-Objective Optimization in Cloud Computing

2014 ◽  
Vol 1046 ◽  
pp. 508-511
Author(s):  
Jian Rong Zhu ◽  
Yi Zhuang ◽  
Jing Li ◽  
Wei Zhu
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Machine Scheduling ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Virtual Machine Scheduling ◽  
Set Up

How to reduce energy consumption while improving utility of datacenter is one of the key technologies in the cloud computing environment. In this paper, we use energy consumption and utility of data center as objective functions to set up a virtual machine scheduling model based on multi-objective optimization VMSA-MOP, and design a virtual machine scheduling algorithm based on NSGA-2 to solve the model. Experimental results show that compared with other virtual machine scheduling algorithms, our algorithm can obtain relatively optimal scheduling results.

Using Distributed Cloud Computing to Solve Resource-Intensive Tasks

2021 ◽  
Vol 12 (5) ◽  
pp. 233-254
Author(s):  
D. Yu. Bulgakov ◽  
Keyword(s):  
Cloud Computing ◽  
Point Of View ◽  
Free Software ◽  
Production Environment ◽  
Learning Models ◽  
Python Programming Language ◽  
Task Processing ◽  
Python Programming ◽  
Distributed Cloud ◽  
Machine Learning Models

A method for solving resource-intensive tasks that actively use the CPU, when the computing resources of one server become insufficient, is proposed. The need to solve this class of problems arises when using various machine learning models in a production environment, as well as in scientific research. Cloud computing allows you to organize distributed task processing on virtual servers that are easy to create, maintain, and replicate. An approach based on the use of free software implemented in the Python programming language is justified and proposed. The resulting solution is considered from the point of view of the theory of queuing. The effect of the proposed approach in solving problems of face recognition and analysis of biomedical signals is described.

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