scholarly journals MatrixDCN: a high performance network architecture for large-scale cloud data centers

2015 ◽  
Vol 16 (8) ◽  
pp. 942-959
Author(s):  
Yantao Sun ◽  
Min Chen ◽  
Limei Peng ◽  
Mohammad Mehedi Hassan ◽  
Abdulhameed Alelaiwi
Keyword(s):  
Network Architecture ◽  
High Performance ◽  
Large Scale ◽  
Data Centers ◽  
Cloud Data ◽  
Cloud Data Centers

scholarly journals Automated Clustering of Virtual Machines based on Correlation of Resource Usage

2012 ◽  
Vol 8 (4) ◽  
pp. 102 ◽  
Author(s):  
Claudia Canali ◽  
Riccardo Lancellotti
Keyword(s):  
Cloud Computing ◽  
High Performance ◽  
Large Scale ◽  
Virtual Machines ◽  
Resource Usage ◽  
Cloud Data ◽  
Multiple Resources ◽  
Computing Paradigm ◽  
Innovative Methodology ◽  
Cloud Data Centers

The recent growth in demand for modern applicationscombined with the shift to the Cloud computing paradigm have led to the establishment of large-scale cloud data centers. The increasing size of these infrastructures represents a major challenge in terms of monitoring and management of the system resources. Available solutions typically consider every Virtual Machine (VM) as a black box each with independent characteristics, and face scalability issues by reducing the number of monitored resource samples, considering in most cases only average CPU usage sampled at a coarse time granularity. We claim that scalability issues can be addressed by leveraging thesimilarity between VMs in terms of resource usage patterns.In this paper we propose an automated methodology to cluster VMs depending on the usage of multiple resources, both systemand network-related, assuming no knowledge of the services executed on them. This is an innovative methodology that exploits the correlation between the resource usage to cluster together similar VMs. We evaluate the methodology through a case study with data coming from an enterprise datacenter, and we show that high performance may be achieved in automatic VMs clustering. Furthermore, we estimate the reduction in the amount of data collected, thus showing that our proposal may simplify the monitoring requirements and help administrators totake decisions on the resource management of cloud computing datacenters.

scholarly journals Performance Evaluation and Implementation of Tasks in Virtual Machines

2019 ◽  
Vol 9 (1) ◽  
pp. 1524-1527
Keyword(s):  
Large Scale ◽  
Data Centers ◽  
Virtual Machines ◽  
Optimization Problems ◽  
Experimental Result ◽  
Cloud Data ◽  
Simulation Techniques ◽  
Cloud Data Centers ◽  
Application Codes ◽  
Cache Miss

In the present situation, it may be essential to build a simple data sharing environment to monitor and protect the unauthorized modification of data. In such case, mechanisms may be required to develop to focus on significant weakened networking with proper solutions. In some situations, block chain data management may be used considering the cloud environment. It is well understood that in virtual environment, allocating resources may have significant role towards evaluating the performance including utilization of resources linked to the data center. Accuracy towards allocation of virtual machines in cloud data centers may be more essential considering the optimization problems in cloud computing. In such cases, it may also be desirable to prioritize on virtual machines linked to cloud data centers. Consolidating the dynamic virtual machines may also permit the virtual server providers to optimize utilization of resources and to focus on energy consumption. In fact, tremendous rise in acquiring computational power driven by modern service applications may be linked towards establishment of large-scale virtualized data centers. Accordingly, the joint collaboration of smart connected devices with data analytics may also enable enormous applications towards different predictive maintenance systems. To obtain the near optimal as well as feasible results in this case, it may be desirable to simulate implementing the algorithms and focusing on application codes. Also, different approaches may also be needed to minimize development time and cost. In many cases, the experimental result proves that the simulation techniques may minimize the cache miss and improve the execution time. In this paper, it has been intended towards distribution of tasks along with implementation mechanisms linked to virtual machines.

scholarly journals Clustered Multicast Source Routing for Large-Scale Cloud Data Centers

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 12693-12705
Author(s):  
Jarallah Alqahtani ◽  
Hassan H. Sinky ◽  
Bechir Hamdaoui
Keyword(s):  
Large Scale ◽  
Data Centers ◽  
Cloud Data ◽  
Source Routing ◽  
Cloud Data Centers

scholarly journals EAMA: Efficient Adaptive Migration Algorithm for Cloud Data Centers (CDCs)

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 690
Author(s):  
Muhammad Ibrahim ◽  
Muhammad Imran ◽  
Faisal Jamil ◽  
Yun Jung Lee ◽  
Do-Hyeun Kim
Keyword(s):  
Energy Consumption ◽  
Resource Utilization ◽  
Large Scale ◽  
Data Centers ◽  
Cloud Services ◽  
Migration Strategy ◽  
Cloud Data ◽  
Sla Violation ◽  
Cloud Data Centers ◽  
Vm Consolidation

The rapid demand for Cloud services resulted in the establishment of large-scale Cloud Data Centers (CDCs), which ultimately consume a large amount of energy. An enormous amount of energy consumption eventually leads to high operating costs and carbon emissions. To reduce energy consumption with efficient resource utilization, various dynamic Virtual Machine (VM) consolidation approaches (i.e., Predictive Anti-Correlated Placement Algorithm (PACPA), Resource-Utilization-Aware Energy Efficient (RUAEE), Memory-bound Pre-copy Live Migration (MPLM), m Mixed migration strategy, Memory/disk operation aware Live VM Migration (MLLM), etc.) have been considered. Most of these techniques do aggressive VM consolidation that eventually results in performance degradation of CDCs in terms of resource utilization and energy consumption. In this paper, an Efficient Adaptive Migration Algorithm (EAMA) is proposed for effective migration and placement of VMs on the Physical Machines (PMs) dynamically. The proposed approach has two distinct features: first, selection of PM locations with optimum access delay where the VMs are required to be migrated, and second, reduces the number of VM migrations. Extensive simulation experiments have been conducted using the CloudSim toolkit. The results of the proposed approach are compared with the PACPA and RUAEE algorithms in terms of Service-Level Agreement (SLA) violation, resource utilization, number of hosts shut down, and energy consumption. Results show that proposed EAMA approach significantly reduces the number of migrations by 16% and 24%, SLA violation by 20% and 34%, and increases the resource utilization by 8% to 17% with increased number of hosts shut down from 10% to 13% as compared to the PACPA and RUAEE, respectively. Moreover, a 13% improvement in energy consumption has also been observed.

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