scholarly journals Model-Based Extraction of Knowledge about the Effect of Cloud Application Context on Application Service Cost and Quality of Service

2019 ◽  
Vol 2019 ◽  
pp. 1-19
Author(s):  
Ivana Stupar ◽  
Darko Huljenić
Keyword(s):  
Machine Learning ◽  
Quality Of Service ◽  
Cloud Services ◽  
Service Cost ◽  
Model Based ◽  
Application Service ◽  
Service Placement ◽  
Cloud Application ◽  
Placement Decision

With the increased usage of cloud computing in production environments, both for scientific workflows and industrial applications, the focus of application providers shifts towards service cost optimisation. One of the ways to achieve minimised service execution cost is to optimise the placement of the service in the resource pool of the cloud data centres. An increasing number of research approaches is focusing on using machine learning algorithms to deal with dynamic cloud workloads by allocating resources to services in an adaptive way. Many of such solutions are intended for cloud infrastructure providers and deal only with specific types of cloud services. In this paper, we present a model-based approach aimed at the providers of applications hosted in the cloud, which is applicable in early phases of the service lifecycle and can be used for any cloud application service. Using several machine learning methods, we create models to predict cloud service cost and response times of two cloud applications. We also explore how to extract knowledge about the effect that the cloud application context has on both service cost and quality of service so that the gained knowledge can be used in the service placement decision process. The experimental results demonstrate the ability of providing relevant information about the impact of cloud application context parameters on service cost and quality of service. The results also indicate the relevance of our approach for applications in preproduction phase since application providers can gain useful insights regarding service placement decision without acquiring extensive training datasets.

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.

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.

Manufacturing service order allocation in the context of social manufacturing based on Stackelberg game

2018 ◽  
Vol 233 (8) ◽  
pp. 1890-1901
Author(s):  
Wei Guo ◽  
Pingyu Jiang
Keyword(s):  
Quality Of Service ◽  
Nash Equilibrium ◽  
Manufacturing Industry ◽  
Stackelberg Game ◽  
Game Model ◽  
Service Cost ◽  
Order Allocation ◽  
Multi Objective ◽  
Bird Swarm Algorithm

For adapting the socialization, individuation and servitization in manufacturing industry, a new manufacturing paradigm called social manufacturing has received a lot of attention. Social manufacturing can be seen as a network that enterprises with socialized resources self-organized into communities that provide personalized machining and service capabilities to customers. Since a community of social manufacturing has multiple enterprises and emphasizes on the importance of service, manufacturing service order allocation must be studied from the new perspective considering objectives on service cost and quality of service. The manufacturing service order allocation can be seen as a one-to-many game model with multi-objective. In this article, a Stackelberg game model is proposed to tackle the manufacturing service order allocation problem with considering the payoffs on cost and quality of service. Since this Stackelberg game can be mapped to a multi-objective bi-level programming, a modified multi-objective hierarchical Bird Swarm Algorithm is used to find the Nash equilibrium of the game. Finally, a case from a professional printing firm is analyzed to validate the proposed methodology and model. The objective of this research is to find the Nash equilibrium on the manufacturing service order allocation and provide strategies guidance for customer and small- and medium-sized enterprises with optimal service cost and lead time. According to the game process and Nash equilibrium, some rules are revealed, and they are useful for guiding practical production.

scholarly journals Toward Gender-Inclusive Coreference Resolution: An Analysis of Gender and Bias throughout the Machine Learning Lifecyle

2021 ◽  
pp. 1-47
Author(s):  
Yang Trista Cao ◽  
Hal Daumé
Keyword(s):  
Machine Learning ◽  
Quality Of Service ◽  
English Text ◽  
Coreference Resolution ◽  
Resolution System ◽  
Building Systems ◽  
Systematic Biases ◽  
Build Systems

Abstract Correctly resolving textual mentions of people fundamentally entails making inferences about those people. Such inferences raise the risk of systematic biases in coreference resolution systems, including biases that can harm binary and non-binary trans and cis stakeholders. To better understand such biases, we foreground nuanced conceptualizations of gender from sociology and sociolinguistics, and investigate where in the machine learning pipeline such biases can enter a coreference resolution system. We inspect many existing datasets for trans-exclusionary biases, and develop two new datasets for interrogating bias in both crowd annotations and in existing coreference resolution systems. Through these studies, conducted on English text, we confirm that without acknowledging and building systems that recognize the complexity of gender, we will build systems that fail for: quality of service, stereotyping, and over- or under-representation, especially for binary and non-binary trans users.

scholarly journals The VAO Transient Facility

2011 ◽  
Vol 7 (S285) ◽  
pp. 318-320
Author(s):  
Matthew J. Graham ◽  
S. G. Djorgovski ◽  
Andrew Drake ◽  
Ashish Mahabal ◽  
Roy Williams ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quality Of Service ◽  
Time Domain ◽  
Astronomical Observatory ◽  
Sky Surveys ◽  
Prior Art ◽  
Event Notification ◽  
The Time Domain ◽  
Time Critical

AbstractThe time-domain community wants robust and reliable tools to enable the production of, and subscription to, community-endorsed event notification packets (VOEvent). The Virtual Astronomical Observatory (VAO) Transient Facility (VTF) is being designed to be the premier brokering service for the community, both collecting and disseminating observations about time-critical astronomical transients but also supporting annotations and the application of intelligent machine-learning to those observations. Two types of activity associated with the facility can therefore be distinguished: core infrastructure, and user services. We review the prior art in both areas, and describe the planned capabilities of the VTF. In particular, we focus on scalability and quality-of-service issues required by the next generation of sky surveys such as LSST and SKA.

scholarly journals Fog Computing-Security Platform for IoT and Cloud in-Healthcare System

2019 ◽  
Vol 9 (2) ◽  
pp. 4419-4424
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Service Management ◽  
New Technology ◽  
Fog Computing ◽  
Cloud Services ◽  
Third Party ◽  
Selection System ◽  
Search Tool

The introduction of cloud computing has revolutionized business and technology. Cloud computing has merged technology and business creating an almost indistinguishable framework. Cloud computing has utilized various techniques that have been vital in reshaping the way computers are used in business, IT, and education. Cloud computing has replaced the distributed system of using computing resources to a centralized system where resources are easily shared between user and organizations located in different geographical locations. Traditionally the resources are usually stored and managed by a third-party, but the process is usually transparent to the user. The new technology led to the introduction of various user needs such as to search the cloud and associated databases. The development of a selection system used to search the cloud such as in the case of ELECTRE IS and Skyline; this research will develop a system that will be used to manage and determine the quality of service constraints of these new systems with regards to networked cloud computing. The method applied will mimic the various selection system in JAVA and evaluate the Quality of service for multiple cloud services. The FogTorch search tool will be used for quality service management of three cloud services.

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