Autonomous service level agreement negotiation for service composition provision

2007 ◽  
Vol 23 (6) ◽  
pp. 748-759 ◽  
Author(s):  
Jun Yan ◽  
Ryszard Kowalczyk ◽  
Jian Lin ◽  
Mohan B. Chhetri ◽  
Suk Keong Goh ◽  
...  
Keyword(s):  
Service Composition ◽  
Service Level Agreement ◽  
Service Level ◽  
Autonomous Service

scholarly journals SLA based cloud service composition using genetic algorithm

2016 ◽  
Vol 2 (2) ◽  
pp. 77 ◽  
Author(s):  
N Sasikaladevi
Keyword(s):  
Business Process ◽  
Service Composition ◽  
Service Level Agreement ◽  
Cloud Service ◽  
Service Level ◽  
Convergence Time ◽  
Composite Service ◽  
Composition Problem ◽  
Utility Rate ◽  
Cloud Service Composition

Cloud computing tends to provide high quality on-demand services to the users. Numerous services are evolving today. Functionally similar services are having different non-functional properties such as reliability, availability, accessibility, response time and cost. A single service is inadequate for constructing the business process. Such business process is modeled as composite service. Composite service consists of several atomic services connected by workflow patterns. Selecting services for service composition with the constraints specified in Service Level Agreement is the NP-hard problem. Such a cloud service composition problem is modeled in this paper. Genetic based cloud service composition algorithm (GCSC) is proposed. Proposed algorithm is compared with the existing genetic based cloud service composition algorithm based on average utility rate and convergence time. It is proved that the proposed algorithm provides better performance as compared to the existing cloud service composition algorithm.

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.

Sign in / Sign up

Export Citation Format

Share Document