Agent-based Cloud service composition

Abstract

Service composition in multi-Cloud environments must coordinate self-interested participants, automate service selection, (re)configure distributed services, and deal with incomplete information about Cloud providers and their services. This work proposes an agent-based approach to compose services in multi-Cloud environments for different types of Cloud services: one-time virtualized services, e.g., processing a rendering job, persistent virtualized services, e.g., infrastructure-as-a-service scenarios, vertical services, e.g., integrating homogenous services, and horizontal services, e.g., integrating heterogeneous services. Agents are endowed with a semi-recursive contract net protocol and service capability tables (information catalogs about Cloud participants) to compose services based on consumer requirements. Empirical results obtained from an agent-based testbed show that agents in this work can: successfully compose services to satisfy service requirements, autonomously select services based on dynamic fees, effectively cope with constantly changing consumers' service needs that trigger updates, and compose services in multiple Clouds even with incomplete information about Cloud participants.

Enhanced Adaptive Learning Mechanism for Cloud Selection

“…It contains the reward for the giant, but it does not bear extra-provisioning for needed function [2]. Review between operators includes the operation setting of the administrations for the web technology in the area of agent-based and also from the field of the context-oriented concept is the major problem in [3].Expert-based administration setting has given a faithful execution the worldview to the web structure benefits which is programmed.The distribution of planning algorithm technique had established in [4].Arrangement of administration in the area of the multi-cloud situation must classify personal charm representatives, mechanical benefit selection and reconsider spreader administrations and classification with separated administration [5].A design that objectifies the implemented concept which uses BPEL for finding the invite task of a web need is designed and finished in as a investigate for searching at our implemented algorithm and other gauge web need selection steps [6].…”

“…Work [24] highlights reliability, throughput, latency, the response time for some dataset like lung cancer, balance scale, care valuation, out digits, Turkey student evaluation, and diabetic data.This paper deals with the selection of the cloud dynamically using the adaptive learning method (mechanism). It contains only three strategies for adaptive learning to optimize the cloud services for each cloud broker [25]. To increase the reliability in case of calculation, this paper uses matrix multiplication.…”

“…This work focuses on solving distributed task problems. To help the user to select cloud, based on their favoured cost cloud selection service is performed [1][2][3][4][5][6]. Selection [7][8][9][10][11][12][13][14][15][16][17] made based on metrics like availability, throughput, scalability, fault tolerance, resilience, and elasticity.…”

Towards Semantic-Based Cloud Application Management

“…The self-organizing agent-based cloud service composition method in [Gutierrez-Garcia et al 2013] exploits distributed problem solving techniques, by also relying on the contract-net protocol, and is able to produce vertical, horizontal, one-time and persistent service compositions. Both SaaS and IaaS type of services are handled.…”

Ontology-enhanced agent-based cloud service discovery

“…Software agents involving brokering agents, information agents, and negotiation agents can be used to bolster the resource management system because of their adaptability and scalability with high-level abstraction for modelling of complex software systems (Raman et al, 1998). One possible approach is to employ self-organising agents for automating and coordinating resource management with promising highly dynamic behaviours (Gutierrez-Garcia and Sim, 2013;Sim, 2006Sim, , 2009Sim, -1, 2010Sim, -2, 2012Sim, , 2013Sim, -1, 2013Sim, -2, 2015. In addition, the diversity of the resources is indispensable in heterogeneous systems like the cloud.…”