scholarly journals IAAS Reactive Auto Scaling Performance Challenges

2019 ◽  
Vol 8 (10) ◽  
pp. 538-541
Keyword(s):  
Quality Of Service ◽  
Virtual Machines ◽  
Performance Estimation ◽  
After Effects ◽  
Cloud Application ◽  
Performance Challenges ◽  
Auto Scaling ◽  
Cloud Applications

The principle highlight of a cloud application is its versatility. Significant IaaS cloud administrations suppliers (CSP) utilize auto scaling on the dimension of virtual machines (VM). Other virtualization arrangements (for example compartments, units) can likewise scale. An application scales in light of progress in watched measurements, for example in CPU use. Every so often, cloud applications display the powerlessness to meet the Quality of Service (QoS) necessities during the scaling brought about by the reactivity of auto scaling arrangements. This paper gives the after effects of the auto scaling execution assessment for two-layered virtualization (VMs and units) directed in the open billows of AWS, Microsoft and Google utilizing the methodology and the Auto scaling Performance Estimation Tool created by the creators

scholarly journals Model-Based Cloud Service Deployment Optimisation Method For Minimisation of Application Service Operational Cost

2021 ◽  
Author(s):  
Ivana Stupar ◽  
Darko Huljenić
Keyword(s):  
Quality Of Service ◽  
Cloud Service ◽  
Operational Cost ◽  
Cloud Infrastructure ◽  
Application Service ◽  
Cloud Application ◽  
Wide Range ◽  
Service Deployment ◽  
Cloud Applications

Abstract Many of the currently existing solutions for cloud cost optimisation are aimed at cloud infrastructure providers, and they often deal only with specific types of application services, leaving the providers of cloud applications without the suitable cost optimization solution, especially concerning the wide range of different application types. In this paper, we present an approach that aims to provide an optimisation solution for the providers of applications hosted in the cloud environments, applicable at the early phase of a cloud application lifecycle and for a wide range of application services. The focus of this research is development of the method for identifying optimised service deployment option in available cloud environments based on the model of the service and its context, with the aim of minimising the operational cost of the cloud service, while fulfilling the requirements defined by the service level agreement. A cloud application context metamodel is proposed that includes parameters related to both the application service and the cloud infrastructure relevant for the cost and quality of service. By using the proposed optimisation method, the knowledge is gained about the effects that the cloud application context parameters have on the service cost and quality of service, which is then used to determine the optimised service deployment option. The service models are validated using cloud application services deployed in laboratory conditions, and the optimisation method is validated using the simulations based on proposed cloud application context metamodel. The experimental results based on two cloud application services demonstrate the ability of the proposed approach to provide relevant information about the impact of cloud application context parameters on service cost and quality of service, and use this information in the optimisation aimed at reducing service operational cost while preserving the acceptable service quality level. The results indicate the applicability and relevance of the proposed approach for cloud applications in the early service lifecycle phase since application providers can gain useful insights regarding service deployment decision without acquiring extensive datasets for the analysis.

Modeling And Analyzing Cloud Auto-Scaling Mechanism Using Stochastic Well-Formed Coloured Nets

2021 ◽  
Author(s):  
Mohamed M. Ould Deye ◽  
Mamadou Thiongane ◽  
Mbaye Sene
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Service Level ◽  
The State ◽  
Service Level Agreements ◽  
Auto Scaling

Auto-scaling is one of the most important features in Cloud computing. This feature promises cloud computing customers the ability to best adapt the capacity of their systems to the load they are facing while maintaining the Quality of Service (QoS). This adaptation will be done automatically by increasing or decreasing the amount of resources being leveraged against the workload’s resource demands. There are two types and several techniques of auto-scaling proposed in the literature. However, regardless the type or technique of auto-scaling used, over-provisioning or under-provisioning problem is often observed. In this paper, we model the auto-scaling mechanism with the Stochastic Well-formed coloured Nets (SWN). The simulation of the SWN model allows us to find the state of the system (the number of requests to be dispatched, the idle times of the started resources) from which the auto-scaling mechanism must be operated in order to minimize the amount of used resources without violating the service-level agreements (SLA).

Analytical Approach to Estimating Total Migration Time of Virtual Machines With Various Applications

2020 ◽  
Vol 11 (2) ◽  
pp. 58-75
Author(s):  
Andrew Toutov ◽  
Anatoly Vorozhtsov ◽  
Natalia Toutova
Keyword(s):  
Analytical Approach ◽  
Virtual Machines ◽  
Migration Time ◽  
Characteristic Functions ◽  
Live Migration ◽  
File Storage ◽  
Laguerre Series ◽  
Cloud Applications ◽  
Total Migration

Cloud applications and services such as social networks, file sharing services, and file storage have become increasingly popular among users in recent years. This leads to the enlargement of data centers, and an increase in the number of servers and virtual machines. In such systems, live migration is used to move virtual machines from one server to another, which affects the quality of service. Therefore, the problem of finding the total migration time is relevant. This article proposes analytical approach to obtaining analytical expression of the probability density of the total migration time based on the use of the apparatus of characteristic functions. The obtained expression is used to calculate characteristics of migration, taking into account the applications contributing the most randomness to the total migration time. To simplify the calculation of migration characteristics, the use of the Laguerre series can be recommended as giving more reliable results compared to Gram-Charlier series.

scholarly journals An Attribute-Oriented Task Scheduling Strategy for Improvement of Quality of Service in Cloud Computing

2021 ◽  
Author(s):  
Jianying Miao
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Cloud Environment ◽  
Simulation Environment ◽  
Attribute Matching ◽  
Resource Allocation Strategy ◽  
And Task

This thesis describes an innovative task scheduling and resource allocation strategy by using thresholds with attributes and amount (TAA) in order to improve the quality of service of cloud computing. In the strategy, attribute-oriented thresholds are set to decide on the acceptance of cloudlets (tasks), and the provisioning of accepted cloudlets on suitable resources represented by virtual machines (VMs,). Experiments are performed in a simulation environment created by Cloudsim that is modified for the experiments. Experimental results indicate that TAA can significantly improve attribute matching between cloudlets and VMs, with average execution time reduced by 30 to 50% compared to a typical non-filtering policy. Moreover, the tradeoff between acceptance rate and task delay, as well as between prioritized and non-prioritized cloudlets, may be adjusted as desired. The filtering type and range and the positioning of thresholds may also be adjusted so as to adapt to the dynamically changing cloud environment.

Application-Level Monitoring and SLA Violation Detection for Multi-Tenant Cloud Services

2015 ◽  
pp. 157-186 ◽  
Author(s):  
Vincent C. Emeakaroha ◽  
Marco A. S. Netto ◽  
Ivona Brandic ◽  
César A. F. De Rose
Keyword(s):  
Quality Of Service ◽  
Service Level ◽  
Cloud Services ◽  
Research Issue ◽  
Business Operations ◽  
Cloud Application ◽  
Key Factor ◽  
Monitoring Architecture ◽  
Level Monitoring

Keeping the quality of service defined by Service Level Agreements (SLAs) is a key factor to facilitate business operations of Cloud providers. SLA enforcement relies on resource and application monitoring—a topic that has been investigated by various Cloud-related projects. Application-level monitoring still represents an open research issue, especially for billing and accounting purposes. Such a monitoring is becoming fundamental, as Cloud services are multi-tenant, thus having users sharing the same resources. This chapter describes key challenges on application provisioning and SLA enforcement in Clouds, introduces a Cloud Application and SLA monitoring architecture, and proposes two methods for determining the frequency that applications needs to be monitored. The authors evaluate their architecture on a real Cloud testbed using applications that exhibit heterogeneous behaviors. The achieved results show that the architecture is low intrusive, able to monitor resources and applications, detect SLA violations, and automatically suggest effective measurement intervals for various workloads.

scholarly journals Uncertainty Aware Resource Provisioning Framework for Cloud Using Expected 3-SARSA Learning Agent: NSS and FNSS Based Approach

2019 ◽  
Vol 19 (3) ◽  
pp. 94-117
Author(s):  
K. Bhargavi ◽  
B. Sathish Babu
Keyword(s):  
Quality Of Service ◽  
Service Level Agreement ◽  
Service Level ◽  
Resource Provisioning ◽  
Soft Set ◽  
Learning Agent ◽  
Optimal Resource ◽  
Auto Scaling ◽  
Heterogeneous Workloads

Abstract Efficiently provisioning the resources in a large computing domain like cloud is challenging due to uncertainty in resource demands and computation ability of the cloud resources. Inefficient provisioning of the resources leads to several issues in terms of the drop in Quality of Service (QoS), violation of Service Level Agreement (SLA), over-provisioning of resources, under-provisioning of resources and so on. The main objective of the paper is to formulate optimal resource provisioning policies by efficiently handling the uncertainties in the jobs and resources with the application of Neutrosophic Soft-Set (NSS) and Fuzzy Neutrosophic Soft-Set (FNSS). The performance of the proposed work compared to the existing fuzzy auto scaling work achieves the throughput of 80% with the learning rate of 75% on homogeneous and heterogeneous workloads by considering the RUBiS, RUBBoS, and Olio benchmark applications.

scholarly journals An Approach to Modeling and Analyzing Reliability for Microservice-Oriented Cloud Applications

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Zheng Liu ◽  
Guisheng Fan ◽  
Huiqun Yu ◽  
Liqiong Chen
Keyword(s):  
High Performance ◽  
Fault Tolerant ◽  
Service Reliability ◽  
Architectural Style ◽  
Modeling And Analysis ◽  
User Request ◽  
Cloud Application ◽  
Cloud Applications ◽  
Microservice Architecture

Microservice architecture is a cloud-native architectural style, which has attracted extensive attention from the scientific research and industry communities to benefit independent development and deployment. However, due to the complexity of cloud-based platforms, the design of fault-tolerant strategies for microservice-oriented cloud applications becomes challenging. In order to improve the quality of service, it is essential to focus on the microservice with more criticality and maximize the reliability of the entire cloud application. This paper studies the modeling and analysis of service reliability in the cloud environment. Firstly, a formal description language is defined to model microservice, user request, and container accurately. Secondly, the reliability analysis is conducted to measure a critical microservice’s fluctuation and vibration attributes within a period, and the related properties of the constructed model are analyzed. Thirdly, a fault-tolerant strategy with redundancy operation has been proposed to optimize cloud application reliability. Finally, the effectiveness of the method is verified by experiments. The simulation results show that the algorithm obtains the maximum benefits and has high performance through several experiments.

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