Virtual Machine Profiling for Analyzing Resource Usage of Applications

2018 ◽  
pp. 103-118 ◽  
Author(s):  
Xuesong Peng ◽  
Barbara Pernici ◽  
Monica Vitali
Keyword(s):  
Virtual Machine ◽  
Resource Usage

Three Misuse Patterns for Cloud Computing

2013 ◽  
pp. 36-53 ◽  
Author(s):  
Keiko Hashizume ◽  
Nobukazu Yoshioka ◽  
Eduardo B. Fernandez
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Point Of View ◽  
Resource Usage ◽  
Migration Process ◽  
Virtual Machine Migration ◽  
On Demand ◽  
Computing Model ◽  
Is Security

Cloud computing is a new computing model that allows providers to deliver services on demand by means of virtualization. One of the main concerns in cloud computing is security. In particular, the authors describe some attacks in the form of misuse patterns, where a misuse pattern describes how an attack is performed from the point of view of the attacker. Specially, they describe three misuse patterns: Resource Usage Monitoring Inference, Malicious Virtual Machine Creation, and Malicious Virtual Machine Migration Process.

scholarly journals An Efficient and Effective Framework to Track, Monitor, and Orchestrate Resource Usage in an Infrastructure as a Service

2019 ◽  
Vol 8 (3) ◽  
pp. 5067-5071 ◽  
Keyword(s):  
Principal Component Analysis ◽  
Virtual Machine ◽  
Service System ◽  
Principal Component ◽  
Component Analysis ◽  
Resource Usage ◽  
Infrastructure As A Service ◽  
Auto Scaling

The development of Infrastructure as a Service system brings new chances, which additionally goes with new difficulties in auto scaling, asset allotment, and security. A key test supporting these issues is the persistent following and checking of asset utilization in the framework. This paper, we will in general present ATOM, a proficient and compelling structure to naturally follow, screen, and coordinate asset use in a framework which is generally utilized in cloud foundation. We utilize novel following technique to constantly follow significant framework use measurement, and build up a Principal Component Analysis based way to deal with persistently screen and consequently discover oddities dependent on the approximated following outcomes. We tell the best way to powerfully set the following limit dependent on the identification results, and further, how to change following calculation to guarantee its optimality under unique remaining tasks at hand. We show the flexibility of ATOM over virtual machine (VM) bunching. In conclusion, when likely peculiarities are recognized, we will in general use thoughtfulness devices to perform memory legal sciences on VMs guided by examined comes about because of following and checking to distinguish malignant conduct inside a VM. We assess the presentation of our structure in a framework

scholarly journals Energy-Aware Virtual Machine Clustering for Consolidation in Multi-tenant IaaS Public Clouds

2019 ◽  
pp. 1123-1136
Author(s):  
Kenga Mosoti Derdus ◽  
Vincent Oteke Omwenga ◽  
Patrick Job Ogao
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Service Providers ◽  
Cloud Service ◽  
Resource Usage ◽  
Energy Aware ◽  
Allocation Policy ◽  
Cloud Data ◽  
Vm Allocation ◽  
Vm Consolidation

Cloud computing has gained a lot of interest from both small and big academic and commercial organizations because of its success in delivering service on a pay-as-you-go basis. Moreover, many users (organizations) can share server computing resources, which is made possible by virtualization. However, the amount of energy consumed by cloud data centres is a major concern. One of the major causes of energy wastage is the inefficient utilization of resources. For instance, in IaaS public clouds, users select Virtual Machine (VM) sizes set beforehand by the Cloud Service Providers (CSPs) without the knowledge of the kind of workloads to be executed in the VM. More often, the users overprovision the resources, which go to waste. Additionally, the CSPs do not have control over the types of applications that are executed and thus VM consolidation is performed blindly. There have been efforts to address the problem of energy consumption by efficient resource utilization through VM allocation and migration. However, these techniques lack collection and analysis of active real cloud traces from the IaaS cloud. This paper proposes an architecture for VM consolidation through VM profiling and analysis of VM resource usage and resource usage patterns, and a VM allocation policy. We have implemented our policy on CloudSim Plus cloud simulator and results show that it outperforms Worst Fit, Best Fit and First Fit VM allocation algorithms. Energy consumption is reduced through efficient consolidation that is informed by VM resource consumption.

scholarly journals Decision Making Framework for Decentralized Virtual Machine Placement in Cloud Computing

2018 ◽  
Vol 7 (2.7) ◽  
pp. 705
Author(s):  
Suresh B.Rathod ◽  
V Krishna Reddy
Keyword(s):  
Decision Making ◽  
Virtual Machine ◽  
Dynamic Environment ◽  
Resource Usage ◽  
Vm Placement ◽  
Resource Requirement ◽  
Network Bandwidth ◽  
Decentralized Decision Making ◽  
Decision Making Framework ◽  
Host Identification

In distributed cloud environment hosts are configured with Local Resource Monitors (LRM). This LRM monitors the underlying hosts’ resource usage, runs independently and balances the underling host’s load by migrating Virtual Machine (VM) instance. For the dynamic environment, each hosts has varying resource requirement, hosts load cannot remain constant. LRM at each host takes decision for VM migration considering static threshold on its own and other hosts current CPU utilization. This result in chances of getting selected same host for VM placement by multiple hosts to reduce resource usage of underlying hosts. The decision making at each server causes the problem of same host identification by multiple hosts during VM placement and consumes extra CPU power and network bandwidth consumption towards each server. This paper addresses the above said issue by proposing decentralized decision making framework for cloud considering hybrid Peer to Peer (P2P) network topology. Proposed solution results avoiding above said issues and balances the load across servers in DC.  

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