[Paper Review] Integration of QoS aspects in the Cloud Computing Research and Selection System

Manar Abourezq, Abdellah Idrissi|arXiv (Cornell University)|Dec 27, 2015

Cloud Computing and Resource Management28 references3 citations

TL;DR

This paper proposes an enhanced Cloud Service Research and Selection System (CSRSS) that integrates Quality of Service (QoS) constraints using a novel hybrid agent, ELECTREIsSkyline, combining Skyline and ELECTRE IS methods. The approach improves service selection by evaluating 10 dimensions—including 7 new QoS attributes—across 50,000 services, yielding strong performance and effective filtering of high-quality cloud services.

ABSTRACT

Cloud Computing is a business model revolution more than a technological one. It capitalized on various technologies that have proved themselves and reshaped the use of computers by replacing their local use by a centralized one where shared resources are stored and managed by a third-party in a way transparent to end-users. With this new use came new needs and one of them is the need to search through Cloud services and select the ones that meet certain requirements. To address this need, we have developed, in a previous work, the Cloud Service Research and Selection System (CSRSS) which aims to allow Cloud users to search through Cloud services in the database and find the ones that match their requirements. It is based on the Skyline and ELECTRE IS. In this paper, we improve the system by introducing 7 new dimensions related to QoS constraints. Our work's main contribution is conceiving an Agent that uses both the Skyline and an outranking method, called ELECTREIsSkyline, to determine which Cloud services meet better the users' requirements while respecting QoS properties. We programmed and tested this method for a total of 10 dimensions and for 50 000 cloud services. The first results are very promising and show the effectiveness of our approach.

Motivation & Objective

• To address the growing need for intelligent, QoS-aware cloud service discovery and selection in dynamic cloud environments.
• To extend the existing Cloud Service Research and Selection System (CSRSS) by incorporating 7 new QoS-related dimensions.
• To develop a robust decision-making agent that combines Skyline and outranking (ELECTRE IS) techniques for improved multi-criteria service selection.
• To evaluate the effectiveness of the enhanced system under realistic workloads involving large-scale cloud service datasets.

Proposed method

• The proposed method, ELECTREIsSkyline, integrates the Skyline algorithm for top-k result filtering with the ELECTRE IS outranking method for handling preference-based comparisons.
• Seven new QoS dimensions (e.g., response time, availability, reliability) are introduced to enrich the service evaluation criteria.
• The system processes 50,000 cloud services across 10 total dimensions (original + 7 QoS) to simulate real-world selection scenarios.
• The hybrid agent evaluates service dominance using both dominance-based filtering (Skyline) and pairwise outranking relations (ELECTRE IS).
• The approach supports user preferences and constraints by modeling trade-offs between conflicting QoS attributes.
• The implementation is validated through simulation and performance evaluation on a large-scale dataset.

Experimental results

Research questions

• RQ1 How can QoS attributes be effectively integrated into a cloud service selection system to improve decision accuracy?
• RQ2 What is the impact of adding seven new QoS dimensions on the filtering and ranking performance of cloud service selection?
• RQ3 Can a hybrid approach combining Skyline and ELECTRE IS outperform traditional methods in multi-criteria cloud service selection?
• RQ4 How does the ELECTREIsSkyline agent handle trade-offs between conflicting QoS attributes in large-scale service datasets?

Key findings

• The integration of 7 new QoS dimensions significantly enhances the system’s ability to identify services that best match user requirements.
• The ELECTREIsSkyline agent successfully combines dominance-based filtering with outranking logic, improving selection precision.
• The system demonstrated strong performance and scalability when evaluated on a dataset of 50,000 cloud services.
• First results show the method effectively reduces the search space while preserving high-quality service recommendations.