The increasing tendency of network service users to use cloud computing encourages web service vendors to supply services that have different functional and nonfunctional (quality of service) features and provide them in a service pool. Based on supply and demand rules and because of the exuberant growth of the services that are offered, cloud service brokers face tough competition against each other in providing quality of service enhancements. Such competition leads to a difficult and complicated process to provide simple service selection and composition in supplying composite services in the cloud, which should be considered an NP-hard problem. How to select appropriate services from the service pool, overcome composition restrictions, determine the importance of different quality of service parameters, focus on the dynamic characteristics of the problem, and address rapid changes in the properties of the services and network appear to be among the most important issues that must be investigated and addressed. In this paper, utilizing a systematic literature review, important questions that can be raised about the research performed in addressing the above-mentioned problem have been extracted and put forth. Then, by dividing the research into four main groups based on the problem-solving approaches and identifying the investigated quality of service parameters, intended objectives, and developing environments, beneficial results and statistics are obtained that can contribute to future research.

http://romisatriawahono.net/lecture/rm/survey/network%20security/Jula%20-%20Cloud%20Computing%20-%202014.pdf

Review: Cloud computing service composition: A systematic literature review

Cloud service selection

In order to select and rank the best services in a cloud computing environment, the end-to-end quality of service (QoS) values of cloud services have to be computed. For a new SaaS provider, the deployment of its software application in the cloud is a challenging job. It has to find a hosting service (IaaS) that hosts its service. The primary goal of the SaaS provider is to make its service at the top of the ranked list of cloud services returned to end users through satisfying their QoS requirements. In this paper, we propose a mechanism to map the users' QoS requirements of cloud services to the right QoS specifications of SaaS then map them to best IaaS service that offers the optimal QoS guarantees. Then together SaaS and IaaS services can provide the best service offer to end users. As a result of the mapping, the end-to-end QoS values can be calculated. We propose a set of rules to perform the mapping process. We hierarchically model the QoS specifications of cloud services using the Analytic Hierarchy Process (AHP) method. The AHP based model helps to facilitate the mapping process across the cloud layers, and to rank the candidate cloud services for end users. We use a case study to illustrate and validate our solution approach.

An End-to-End QoS Mapping Approach for Cloud Service Selection

Towards an efficient and a more accurate web service selection using MCDM methods

A systematic literature review on QoS-aware service composition and selection in cloud environment

Data center power consumption is among the largest commodity expenditures for many organizations. Reduction of power used in cloud data centres with heterogeneous physical resources can be achieved through Virtual-Machine (VM) consolidation which reduces the number of Physical Machines (PMs) used, subject to Quality of Service (QoS) constraints. This paper provides an in-depth survey of the most recent techniques and algorithms used in proactive dynamic VM consolidation focused on energy consumption. We present a general framework that can be used on multiple phases of a complete consolidation process.

/pdf/proactive-dynamic-virtual-machine-consolidation-for-energy-54g32am6q5.pdf

Proactive dynamic virtual-machine consolidation for energy conservation in cloud data centres

Since selecting a web service based on Quality of Services (QoS) is essentially a Multi-Criteria Decision Making (MCDM) problem, various MCDM models would be suitable for implementing the selection systems. A few of the MCDM approaches have been explored in previous research works. In this paper, we propose to use an enhanced PROMETHEE model for QoS-based web service selection. Many selection algorithms assume the independency between the QoS criteria, which is not very accurate. Thus, our first enhancement is to take into account the QoS interdependency by using the Analytical Network Process (ANP) to calculate the weight/priority associated with each criterion. User's QoS requirement is not considered in the original PROMETHEE model. As a consequence, during the process of finding an optimal service, when tradeoff decisions are involved, we may end up with a service which optimizes the overall QoS criteria, however, does not satisfy the user request. To overcome this insufficiency, our second enhancement is to check the outranking flows of each service with respect to the request in the ranking step, so that we know how well a service satisfies the user requirement. A case study is presented to explain the detailed selection process.

An Enhanced PROMETHEE Model for QoS-Based Web Service Selection

In a cloud-based service selection system, for a given request, there could be a large number of software services matching the functional requirements. The selection should then be done based on their QoS values. Since in a cloud environment, a software service might need collaboration from other types of cloud services (e.g., A software service delivered through an infrastructure service) to offer a complete solution to an end user, the selection system should have a way to measure the QoS values of the whole solution, instead of QoS of software services alone. This kind of end-to-end QoS values of cloud-based software solutions may or may not be available in recorded history logs. In this paper, we propose a model for predicting end-to-end QoS values of cloud-based software solutions composed of services from multiple cloud layers. It relies on the internal features of services and end users such as locations, configurations, functionality, and user profiles to calculate service similarity and then predict QoS values. The experiments demonstrate the accuracy of our approach. We also studied the impact of the proposed internal features on QoS prediction accuracy.

End-to-End QoS Prediction of Vertical Service Composition in the Cloud

The rapid growth of published cloud services in the Internet makes the service selection and recommendation a challenging task for both users and service providers. In cloud environments, software re services collaborate with other complementary services to provide complete solutions to end users. The service selection is performed based on QoS requirements submitted by end users. Software providers alone cannot guarantee users' QoS requirements. These requirements must be end-to-end, representing all collaborating services in a cloud solution. In this paper, we propose a prediction model to compute end-to-end QoS values for vertically composed services which are composed of three types of cloud services: software (SaaS), infrastructure (IaaS) and data (DaaS) services. These values can be used during the service selection and recommendation process. Our model exploits historical QoS values and cloud service and user information to predict unknown end-to-end QoS values of composite services. The experiments demonstrate that our proposed model outperforms other prediction models in terms of the prediction accuracy. We also study the impact of different parameters on the prediction results. In the experiments, we used real cloud services' QoS data collected using our developed QoS monitoring and collecting system.

Raed Karim

Papers

An End-to-End QoS Mapping Approach for Cloud Service Selection

Proactive dynamic virtual-machine consolidation for energy conservation in cloud data centres

An Enhanced PROMETHEE Model for QoS-Based Web Service Selection

End-to-End QoS Prediction of Vertical Service Composition in the Cloud

Incorporating service and user information and latent features to predict QoS for selecting and recommending cloud service compositions