Sahar Arshad

Bio: Sahar Arshad is an academic researcher from University of the Sciences. The author has contributed to research in topics: Cloud testing & Provisioning. The author has an hindex of 1, co-authored 2 publications receiving 15 citations.

A survey of Cloud computing variable pricing models

Abstract: Cloud computing has grasped the attention of scientific community and business industry towards the provisioning of computing resources as utility and software as a service over a network. Profitability and revenue maximization are the most important goals for any cloud service provider which can be employed through different pricing models. Historically, cloud providers were following the fixed pricing strategies while provisioning their services to the clients. However, these approaches have their own shortcomings resulting in resource wastage, lack of fairness and user satisfaction. With expansion of cloud users in market every day, provisioning of fair resource allocation with service differentiation and efficient pricing model is demand seeking, resulting in a shift from static to dynamic pricing models. In this paper, we discuss and investigate different pricing scheme with respect to their advantages, limitations and possible future directions. The study will open a way for vendors to seek new research directions in dynamic pricing schemes.

BROKER BASED QoS CENTRIC RESOURCE PROVISIONING FRAMEWORK WITH FINANCIAL OPTIONS

Abstract: Cloud computing has grasped the attention of scientific community and business industry towards the provisioning of computing resources as a utility and software as a service over a network. Access to software or hardware resources are seamless to cloud consumers in a way that provider of the service could be anywhere in the world by providing services in nebulous cloud. Most of the research studies have focused on the benefits associated with single cloud provider by provisioning and managing resources at provider’s end. This has led to the problems associated with pricing and quality of the services that cloud provider guarantees in SLA to their consumers. To tackle this issue, financial options theory in cloud computing economics has been introduced in the recent past where options can be bought in advance so that these may be utilized when required. However, QoS factors, that are vital for a cloud provider in order to accommodate cloud consumer trust, are not yet been addressed. We propose a federated cloud resource provisioning framework based on financial options theory to address the issue of job denial during peaks by leasing spare capacity from other cloud providers as well as satisfying cost and QoS requirements. We assess cloud providers based on the services they provide and best-fit selection is made while purchasing options to meet service level objectives. Also the issue of VM placement across multiple clouds is addressed to provide a resource sharing platform where the objectives of avoiding resource rejection and efficient service level agreement management are incorporated in the proposed framework. Using CloudSim simulator, various workloads were used to test our proposed framework. Results indicate that our broker based cloud federation framework provides a win-win scenario for both cloud providers and consumers by avoiding job denial and achieving better SLA management and resource utilization.

A Survey of Profit Optimization Techniques for Cloud Providers

Abstract: As the demand for computing resources grows, cloud computing becomes more and more popular as a pay-as-you-go model, in which the computing resources and services are provided to cloud users efficiently. For cloud providers, the typical goal is to maximize their profits. However, maximizing profits in a highly competitive cloud market is a huge challenge for cloud providers. In this article, a survey of profit optimization techniques is proposed to increase cloud provider profitability through service quality improvement, service pricing, energy consumption reduction, and virtual network function (VNF) deployment. The strategy of improving user service quality is discussed first, followed by the pricing strategy for cloud resources to maximize revenue. Then, this article summarizes the techniques for cloud data centers to reduce server power consumption. Finally, various heuristic algorithms for VNF deployment in the cloud are further described to reduce the cost of cloud providers while maintaining performance. We classify research works based on components of profit and methods used to demonstrate similarities and differences in these studies. We hope this survey will provide researchers with insights into cloud profit optimization techniques.

Real-Time Dynamic Pricing for Revenue Management with Reusable Resources, Advance Reservation, and Deterministic Service Time Requirements

Abstract: In “Real-Time Dynamic Pricing for Revenue Management with Reusable Resources, Advance Reservation, and Deterministic Service Time Requirements,” Lei and Jasin consider a fundamental dynamic pricing...

CM Cloud Simulator: A Cost Model Simulator Module for Cloudsim

Abstract: The vast cloud computing environment holds out good prospects for researchers in the computing technology field. However, with several Cloud providers offering different pricing models, the evaluation and modeling of Cloud environments and applications are getting harder because there is a lack of tools for this task. We propose the CM Cloud Simulator to fill this gap since it provides a comprehensive and dynamic simulation of applications with various deployment configurations and incurs the cost it would require when implemented in a Cloud Provider, according to the cost model of any service provider. The CM Cloud Simulator also provides custom-built cost models through the XML file.

An Online Continuous Progressive Second Price Auction for Electric Vehicle Charging

Abstract: In this paper, we address the issue of the energy trading in the scenario of electric vehicles (EVs) charging in the smart grid. The EVs energy trading problems have attracted growing attention with the popularity of EVs. As the traditional first-reserve-first-serve scheme in the energy trading market impairs the benefits of both buyers and seller, we consider an auction scheme, called progressive second price (PSP), which has been proved to be an efficient way to conduct resource allocation in the trading market. Compared with other auction schemes, the PSP scheme can achieve both incentive compatibility and Nash equilibrium, which are important properties for the market. Nonetheless, the PSP auction scheme is not designed for online auction and it cannot guarantee that the seller can provide an enough number of charging piles to satisfy the demand of winners. To tackle these issues, in this paper we propose a novel online continuous PSP-based auction scheme, which is capable of not only achieving the property of online energy trading but also guaranteeing that the number of winners is limited to be no more than the number of charging piles. Further, we prove that our auction scheme achieves incentive compatibility and Nash equilibrium. The extensive experimental results demonstrate that our auction scheme achieves good performance with respect to social welfare, the seller satisfaction ratio, the buyer satisfaction ratio, as well as computation overhead.

Software System Migration to Cloud-Native Architectures for SME-Sized Software Vendors

Abstract: Independent software vendors (ISVs) are often faced with the need to migrate their software products as software-as-a-service (SaaS) solutions to the cloud. We document and evaluate four case studies by considering various factors that the respective companies need to consider in a cloud migration process. We look at migration project as a software re-engineering activity, involving project planning, cloud architecture design and architecture transformation. Specifically for software vendors, a cloud migration opens opportunities such as the possibility of modernising their software through re-engineering their product architecture. However, small and mid-size enterprises(SMEs)often do not have the required cloud expertise to plan and implement a cloud migration.