Service level agreement management framework for utility-oriented computing platforms

2015 ◽  
Vol 71 (11) ◽  
pp. 4287-4303 ◽  
Author(s):  
Jemal Abawajy ◽  
Mohd Farhan Fudzee ◽  
Mohammad Mehedi Hassan ◽  
Majed Alrubaian
Keyword(s):  
Service Level Agreement ◽  
Service Level ◽  
Management Framework ◽  
Computing Platforms

Automating Network Management With Artificial Intelligence

2018 ◽  
pp. 113-145
Author(s):  
Imen Grida Ben Yahia ◽  
Jaafar Bendriss ◽  
Teodora Sandra Buda ◽  
Haytham Assem
Keyword(s):  
Artificial Intelligence ◽  
Network Management ◽  
Mobile Networks ◽  
Service Providers ◽  
Service Level Agreement ◽  
Service Level ◽  
Management Framework ◽  
Policy Based Management ◽  
Detailed Specification ◽  
Fast Adaptation

Artificial intelligence (AI) and in particular machine learning are seen as cornerstones to automate and rethink network management operations in the context of network softwarization (i.e., SDN, NFV, and Cloud). In this regard, operators and service providers target the creation of service offerings, the customization of network solutions, and the fast adaptation to rapidly changing market demands. This translates into requirements for increased flexibility, modularity, and scalability in network management operations. This chapter presents a detailed specification of a cognitive (AI-based) network management framework applicable for existing and future (software-defined) networks. The framework is built upon the combined state-of-the-art on autonomic, policy-based management and big data. It is exemplified with two detailed use cases: the urban mobility awareness for today's mobile networks and SLA (service level agreement) enforcement in the context of NFV and cloud.

scholarly journals An integrated three-tier trust management framework in mobile edge computing using fuzzy logic

2021 ◽  
Vol 7 ◽  
pp. e700
Author(s):  
Merrihan B.M. Mansour ◽  
Tamer Abdelkader ◽  
Mohamed Hashem ◽  
El-Sayed M. El-Horbaty
Keyword(s):  
Service Provider ◽  
Trust Management ◽  
Service Providers ◽  
Service Level Agreement ◽  
Cloud Service ◽  
Service Level ◽  
Edge Computing ◽  
Trust Value ◽  
Management Framework ◽  
Cloud Service Providers

Mobile edge computing (MEC) is introduced as part of edge computing paradigm, that exploit cloud computing resources, at a nearer premises to service users. Cloud service users often search for cloud service providers to meet their computational demands. Due to the lack of previous experience between cloud service providers and users, users hold several doubts related to their data security and privacy, job completion and processing performance efficiency of service providers. This paper presents an integrated three-tier trust management framework that evaluates cloud service providers in three main domains: Tier I, which evaluates service provider compliance to the agreed upon service level agreement; Tier II, which computes the processing performance of a service provider based on its number of successful processes; and Tier III, which measures the violations committed by a service provider, per computational interval, during its processing in the MEC network. The three-tier evaluation is performed during Phase I computation. In Phase II, a service provider total trust value and status are gained through the integration of the three tiers using the developed overall trust fuzzy inference system (FIS). The simulation results of Phase I show the service provider trust value in terms of service level agreement compliance, processing performance and measurement of violations independently. This disseminates service provider’s points of failure, which enables a service provider to enhance its future performance for the evaluated domains. The Phase II results show the overall trust value and status per service provider after integrating the three tiers using overall trust FIS. The proposed model is distinguished among other models by evaluating different parameters for a service provider.

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.

scholarly journals Internet of Things for Mental Health: Open Issues in Data Acquisition, Self-Organization, Service Level Agreement, and Identity Management

2021 ◽  
Vol 18 (3) ◽  
pp. 1327
Author(s):  
Leonardo J. Gutierrez ◽  
Kashif Rabbani ◽  
Oluwashina Joseph Ajayi ◽  
Samson Kahsay Gebresilassie ◽  
Joseph Rafferty ◽  
...  
Keyword(s):  
Mental Health ◽  
Internet Of Things ◽  
Data Acquisition ◽  
Identity Management ◽  
Service Level Agreement ◽  
Service Level ◽  
Self Organization ◽  
Future Research ◽  
Study Results ◽  
Open Issues

The increase of mental illness cases around the world can be described as an urgent and serious global health threat. Around 500 million people suffer from mental disorders, among which depression, schizophrenia, and dementia are the most prevalent. Revolutionary technological paradigms such as the Internet of Things (IoT) provide us with new capabilities to detect, assess, and care for patients early. This paper comprehensively survey works done at the intersection between IoT and mental health disorders. We evaluate multiple computational platforms, methods and devices, as well as study results and potential open issues for the effective use of IoT systems in mental health. We particularly elaborate on relevant open challenges in the use of existing IoT solutions for mental health care, which can be relevant given the potential impairments in some mental health patients such as data acquisition issues, lack of self-organization of devices and service level agreement, and security, privacy and consent issues, among others. We aim at opening the conversation for future research in this rather emerging area by outlining possible new paths based on the results and conclusions of this work.

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