Managing Performance Overhead of Virtual Machines in Cloud Computing: A Survey, State of the Art, and Future Directions

2014 ◽  
Vol 102 (1) ◽  
pp. 11-31 ◽  
Author(s):  
Fei Xu ◽  
Fangming Liu ◽  
Hai Jin ◽  
Athanasios V. Vasilakos
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
State Of The Art ◽  
Future Directions

scholarly journals A Comparative Analysis of NSGA-II and NSGA-III for Autoscaling Parameter Sweep Experiments in the Cloud

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Virginia Yannibelli ◽  
Elina Pacini ◽  
David Monge ◽  
Cristian Mateos ◽  
Guillermo Rodriguez
Keyword(s):  
Cloud Computing ◽  
Optimization Algorithm ◽  
Virtual Machines ◽  
State Of The Art ◽  
Multiobjective Evolutionary Algorithms ◽  
Nsga Ii ◽  
Trade Off ◽  
Computing Paradigm ◽  
Virtual Infrastructure ◽  
And Storage

The Cloud Computing paradigm is focused on the provisioning of reliable and scalable virtual infrastructures that deliver execution and storage services. This paradigm is particularly suitable to solve resource-greedy scientific computing applications such as parameter sweep experiments (PSEs). Through the implementation of autoscalers, the virtual infrastructure can be scaled up and down by acquiring or terminating instances of virtual machines (VMs) at the time that application tasks are being scheduled. In this paper, we extend an existing study centered in a state-of-the-art autoscaler called multiobjective evolutionary autoscaler (MOEA). MOEA uses a multiobjective optimization algorithm to determine the set of possible virtual infrastructure settings. In this context, the performance of MOEA is greatly influenced by the underlying optimization algorithm used and its tuning. Therefore, we analyze two well-known multiobjective evolutionary algorithms (NSGA-II and NSGA-III) and how they impact on the performance of the MOEA autoscaler. Simulated experiments with three real-world PSEs show that MOEA gets significantly improved when using NSGA-III instead of NSGA-II due to the former provides a better exploitation versus exploration trade-off.

A comprehensive survey on container resource allocation approaches in cloud computing: State-of-the-art and research challenges

2022 ◽  
pp. 1-22
Author(s):  
Vhatkar Kapil Netaji ◽  
G.P. Bhole
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Virtual Machines ◽  
State Of The Art ◽  
Research Work ◽  
Research Papers ◽  
Virtualization Technology ◽  
Comprehensive Survey ◽  
Novel Algorithms ◽  
Allocation Algorithms

The allocation of resources in the cloud environment is efficient and vital, as it directly impacts versatility and operational expenses. Containers, like virtualization technology, are gaining popularity due to their low overhead when compared to traditional virtual machines and portability. The resource allocation methodologies in the containerized cloud are intended to dynamically or statically allocate the available pool of resources such as CPU, memory, disk, and so on to users. Despite the enormous popularity of containers in cloud computing, no systematic survey of container scheduling techniques exists. In this survey, an outline of the present works on resource allocation in the containerized cloud correlative is discussed. In this work, 64 research papers are reviewed for a better understanding of resource allocation, management, and scheduling. Further, to add extra worth to this research work, the performance of the collected papers is investigated in terms of various performance measures. Along with this, the weakness of the existing resource allocation algorithms is provided, which makes the researchers to investigate with novel algorithms or techniques.

Effects of Psychotherapy on Brain Activation Patterns in Anxiety Disorders

2016 ◽  
Vol 224 (2) ◽  
pp. 62-70 ◽  
Author(s):  
Thomas Straube
Keyword(s):  
Anxiety Disorders ◽  
Neuronal Plasticity ◽  
Functional Neuroimaging ◽  
State Of The Art ◽  
Methodological Issues ◽  
Future Directions ◽  
Activation Patterns ◽  
New Learning ◽  
Analytical Strategies ◽  
Sample Characteristics

Abstract. Psychotherapy is an effective treatment for most mental disorders, including anxiety disorders. Successful psychotherapy implies new learning experiences and therefore neural alterations. With the increasing availability of functional neuroimaging methods, it has become possible to investigate psychotherapeutically induced neuronal plasticity across the whole brain in controlled studies. However, the detectable effects strongly depend on neuroscientific methods, experimental paradigms, analytical strategies, and sample characteristics. This article summarizes the state of the art, discusses current theoretical and methodological issues, and suggests future directions of the research on the neurobiology of psychotherapy in anxiety disorders.

Targeting Cystathionine Beta-Synthase Misfolding in Homocystinuria by Small Ligands: State of the Art and Future Directions

2016 ◽  
Vol 17 (13) ◽  
pp. 1455-1470 ◽  
Author(s):  
Tomas Majtan ◽  
Angel L. Pey ◽  
June Ereño-Orbea ◽  
Luis Alfonso Martínez-Cruz ◽  
Jan P. Kraus
Keyword(s):  
State Of The Art ◽  
Future Directions ◽  
Cystathionine Beta Synthase ◽  
Small Ligands

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.

Sign in / Sign up

Export Citation Format

Share Document