Cloudbus Toolkit for Market-Oriented Cloud Computing

This keynote paper: (1) presents the 21st century vision of computing and identifies various IT paradigms promising to deliver computing as a utility; (2) defines the architecture for creating market-oriented Clouds and computing atmosphere by leveraging technologies such as virtual machines; (3) provides thoughts on market-based resource management strategies that encompass both customer-driven service management and computational risk management to sustain SLA-oriented resource allocation; (4) presents the work carried out as part of our new Cloud Computing initiative, called Cloudbus: (i) Aneka, a Platform as a Service software system containing SDK (Software Development Kit) for construction of Cloud applications and deployment on private or public Clouds, in addition to supporting market-oriented resource management; (ii) internetworking of Clouds for dynamic creation of federated computing environments for scaling of elastic applications; (iii) creation of 3rd party Cloud brokering services for building content delivery networks and e-Science applications and their deployment on capabilities of IaaS providers such as Amazon along with Grid mashups; (iv) CloudSim supporting modelling and simulation of Clouds for performance studies; (v) Energy Efficient Resource Allocation Mechanisms and Techniques for creation and management of Green Clouds; and (vi) pathways for future research.Comment: 21 pages, 6 figures, 2 tables, Conference pape

Partitioning workflow applications over federated clouds to meet non-functional requirements

PhD ThesisWith cloud computing, users can acquire computer resources when they need them on a pay-as-you-go business model. Because of this, many applications are now being deployed in the cloud, and there are many di erent cloud providers worldwide. Importantly, all these various infrastructure providers o er services with di erent levels of quality. For example, cloud data centres are governed by the privacy and security policies of the country where the centre is located, while many organisations have created their own internal \private cloud" to meet security needs. With all this varieties and uncertainties, application developers who decide to host their system in the cloud face the issue of which cloud to choose to get the best operational conditions in terms of price, reliability and security. And the decision becomes even more complicated if their application consists of a number of distributed components, each with slightly di erent requirements. Rather than trying to identify the single best cloud for an application, this thesis considers an alternative approach, that is, combining di erent clouds to meet users' non-functional requirements. Cloud federation o ers the ability to distribute a single application across two or more clouds, so that the application can bene t from the advantages of each one of them. The key challenge for this approach is how to nd the distribution (or deployment) of application components, which can yield the greatest bene ts. In this thesis, we tackle this problem and propose a set of algorithms, and a framework, to partition a work ow-based application over federated clouds in order to exploit the strengths of each cloud. The speci c goal is to split a distributed application structured as a work ow such that the security and reliability requirements of each component are met, whilst the overall cost of execution is minimised. To achieve this, we propose and evaluate a cloud broker for partitioning a work ow application over federated clouds. The broker integrates with the e-Science Central cloud platform to automatically deploy a work ow over public and private clouds. We developed a deployment planning algorithm to partition a large work ow appli- - i - cation across federated clouds so as to meet security requirements and minimise the monetary cost. A more generic framework is then proposed to model, quantify and guide the partitioning and deployment of work ows over federated clouds. This framework considers the situation where changes in cloud availability (including cloud failure) arise during work ow execution

Cloud Asset Pricing Tree (CAPT) Elastic Economic Model for Cloud Service Providers

Cloud providers are incorporating novel techniques to cope with prospective aspects of trading like resource allocation over future demands and its pricing elasticity that was not foreseen before. To leverage the pricing elasticity of upcoming demand and supply, we employ financial option theory (future contracts) as a mechanism to alleviate the risk in resource allocation over future demands. This study introduces a novel Cloud Asset Pricing Tree (CAPT) model that finds the optimal premium price of the Cloud federation options efficiently. Providers will benefit by this model to make decisions when to buy options in advance and when to exercise them to achieve more economies of scale. The CAPT model adapts its structure to address the price elasticity concerns and makes the demand, price inelastic and the supply, price elastic. Our empirical evidences suggest that using the CAPT model, exploits the Cloud market potential as an opportunity for more resource utilization and future capacity planning.

Dynamic Datasets and Market Environments for Financial Reinforcement Learning

The financial market is a particularly challenging playground for deep reinforcement learning due to its unique feature of dynamic datasets. Building high-quality market environments for training financial reinforcement learning (FinRL) agents is difficult due to major factors such as the low signal-to-noise ratio of financial data, survivorship bias of historical data, and model overfitting. In this paper, we present FinRL-Meta, a data-centric and openly accessible library that processes dynamic datasets from real-world markets into gym-style market environments and has been actively maintained by the AI4Finance community. First, following a DataOps paradigm, we provide hundreds of market environments through an automatic data curation pipeline. Second, we provide homegrown examples and reproduce popular research papers as stepping stones for users to design new trading strategies. We also deploy the library on cloud platforms so that users can visualize their own results and assess the relative performance via community-wise competitions. Third, we provide dozens of Jupyter/Python demos organized into a curriculum and a documentation website to serve the rapidly growing community. The open-source codes for the data curation pipeline are available at https://github.com/AI4Finance-Foundation/FinRL-MetaComment: 49 pages, 15 figures. arXiv admin note: substantial text overlap with arXiv:2211.0310

A new revenue maximization model using customized plans in cloud service allocation (Applied on a real company case study)

Cloud computing is emerging as a promising field offering a variety of computing services to end users. These services are offered at different prices using various pricing schemes and techniques. End users will favor the service provider offering the best quality with the lowest price. Therefore, applying a fair pricing model will attract more customers and achieve higher revenues for service providers. This work focuses on a novel dynamic pricing model which is able to satisfy advance users requirements based on normal fixed price model. This paper considers many factors that affect pricing and user satisfaction, such as fairness, QoS, SLA, and more, by highlighting their importance in recent markets and propose a flexible model which tries to utilize all resources to the highest capacity and offers low prices for underutilized resources. The simulated results shows the appropriateness of dynamic pricing for sharing of computing resources, where providers want to have more customers as a managerial decision and even more income in total.Keywords: Cloud Computing; Digital Pricing; Dynamic Pricin

Business Integration as a Service

This paper presents Business Integration as a Service (BIaS) which enables connections between services operating in the Cloud. BIaS integrates different services and business activities to achieve a streamline process. We illustrate this integration using two services; Return on Investment (ROI) Measurement as a Service (RMaaS) and Risk Analysis as a Service (RAaaS) in two case studies at the University of Southampton and Vodafone/Apple. The University of Southampton case study demonstrates the cost-savings and the risk analysis achieved, so two services can work as a single service. The Vodafone/Apple case study illustrates statistical analysis and 3D Visualisation of expected revenue and associated risk. These two cases confirm the benefits of BIaS adoption, including cost reduction and improvements in efficiency and risk analysis. Implementation of BIaS in other organisations is also discussed. Important data arising from the integration of RMaaS and RAaaS are useful for management of University of Southampton and potential and current investors for Vodafone/Apple

FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning

Finance is a particularly difficult playground for deep reinforcement learning. However, establishing high-quality market environments and benchmarks for financial reinforcement learning is challenging due to three major factors, namely, low signal-to-noise ratio of financial data, survivorship bias of historical data, and model overfitting in the backtesting stage. In this paper, we present an openly accessible FinRL-Meta library that has been actively maintained by the AI4Finance community. First, following a DataOps paradigm, we will provide hundreds of market environments through an automatic pipeline that collects dynamic datasets from real-world markets and processes them into gym-style market environments. Second, we reproduce popular papers as stepping stones for users to design new trading strategies. We also deploy the library on cloud platforms so that users can visualize their own results and assess the relative performance via community-wise competitions. Third, FinRL-Meta provides tens of Jupyter/Python demos organized into a curriculum and a documentation website to serve the rapidly growing community. FinRL-Meta is available at: https://github.com/AI4Finance-Foundation/FinRL-MetaComment: NeurIPS 2022 Datasets and Benchmarks. 36th Conference on Neural Information Processing Systems Datasets and Benchmarks Trac

Measuring the Business Value of Cloud Computing

The importance of demonstrating the value achieved from IT investments is long established in the Computer Science (CS) and Information Systems (IS) literature. However, emerging technologies such as the ever-changing complex area of cloud computing present new challenges and opportunities for demonstrating how IT investments lead to business value. Recent reviews of extant literature highlights the need for multi-disciplinary research. This research should explore and further develops the conceptualization of value in cloud computing research. In addition, there is a need for research which investigates how IT value manifests itself across the chain of service provision and in inter-organizational scenarios. This open access book will review the state of the art from an IS, Computer Science and Accounting perspective, will introduce and discuss the main techniques for measuring business value for cloud computing in a variety of scenarios, and illustrate these with mini-case studies

Measuring the Business Value of Cloud Computing

The importance of demonstrating the value achieved from IT investments is long established in the Computer Science (CS) and Information Systems (IS) literature. However, emerging technologies such as the ever-changing complex area of cloud computing present new challenges and opportunities for demonstrating how IT investments lead to business value. Recent reviews of extant literature highlights the need for multi-disciplinary research. This research should explore and further develops the conceptualization of value in cloud computing research. In addition, there is a need for research which investigates how IT value manifests itself across the chain of service provision and in inter-organizational scenarios. This open access book will review the state of the art from an IS, Computer Science and Accounting perspective, will introduce and discuss the main techniques for measuring business value for cloud computing in a variety of scenarios, and illustrate these with mini-case studies