Maximizing the Profit of Cloud Broker with Priority Aware Pricing

A practical problem facing Infrastructure-as-a-Service (IaaS) cloud users is how to minimize their costs by choosing different pricing options based on their own demands. Recently, cloud brokerage service is introduced to tackle this problem. But due to the perishability of cloud resources, there still exists a large amount of idle resource waste during the reservation period of reserved instances. This idle resource waste problem is challenging cloud broker when buying reserved instances to accommodate users' job requests. To solve this challenge, we find that cloud users always have low priority jobs (e.g., non latency-sensitive jobs) which can be delayed to utilize these idle resources. With considering the priority of jobs, two problems need to be solved. First, how can cloud broker leverage jobs' priorities to reserve resources for profit maximization? Second, how to fairly price users' job requests with different priorities when previous studies either adopt pricing schemes from IaaS clouds or just ignore the pricing issue. To solve these problems, we first design a fair and priority aware pricing scheme, PriorityPricing, for the broker which charges users with different prices based on priorities. Then we propose three dynamic algorithms for the broker to make resource reservations with the objective of maximizing its profit. Experiments show that the broker's profit can be increased up to 2.5× than that without considering priority for offline algorithm, and 3.7× for online algorithm

A simulation analysis of different allocation and pricing policies for cloud computing service providers

The cloud computing is regarded as a paradigm shift in nowadays IT world. As services of cloud computing behave like perishable products, revenue management techniques can be applied to increase cloud service provider's total revenue. In this thesis, we develop various methods for pricing and capacity allocation. We consider three types of instances; subscription, on-demand and spot instances. We introduce three allocation and pricing policies and propose 8 models based on their combinations. First, we establish a queuing mechanism for on-demand instances which are rejected initially by the cloud with a price incentive. Second, we consider an auction based model for spot instances and introduce two types of threshold policies where it is constant or dependent on the remaining capacity. Finally, the criterion for spot instances selection is based on expected revenue or bid of that customer. We simulate these models on several datasets and evaluate the models with di erent capacities. The results we obtain indicate the sensitivity of revenue based on the policies we propose over the studied datasets

Cost Optimization in Cloud Computing

In recent years, cloud computing has increased in popularity from both industry and academic perspectives. One of the key features of the success of cloud computing is the low initial capital expenditure needed compared to the cost of planning and purchasing physical machines. However, owners of large and complex cloud infrastructures may incur high operating costs. In order to reduce operating costs and allow elasticity, cloud providers offer two types of computing resources: on-demand instances and reserved instances. On-demand instances are paid only when utilized and they are useful to satisfy a fluctuating demand. Conversely, reserved instances are paid for a certain time period and are independent of usage. Since reserved instances require more commitment from users, they are cheaper than on-demand instances. However, in order to be cost-effective compared to on-demand instances, they have to be extensively utilized. This thesis focuses on cost optimization of cloud resources by balancing on-demand and reserved instances. The challenge is to find an optimal resource allocation under uncertainty. In order to solve the problem, this study introduces a theoretical model based on Inventory Theory and a heuristic-based implementation for reserved instances optimization. The inventory theory model provides a theoretical framework for cost optimization. In addition, the model describes a mathematical method to solve the optimization problem. The heuristic-based implementation analyzes the cloud infrastructure of a company and proposes a purchase plan of reserved instances. The implemented system validates the theoretical finding. In order to evaluate the proposed approaches, this work describes a set of experiments, using simulations and data from an industry case. The experiments demonstrate the effectiveness of the reserved instances optimizer and the validity of the theoretical model

Evaluating the profitability of the MediaWiki application under different cloud distribution scenarios

Cloud computing has gained popularity over the last years, causing a significant increase of available cloud offerings among providers. Therefore, this wide spectrum of options has led to an increment of possibilities for distributing applications in the cloud, by means of selecting specialized services to host each application component. Nevertheless, it also implies the need of finding the optimal solution depending on its purpose, usually based on future economical profitability. Nowadays, instead of considering an application as a whole when deploying it in the cloud, e.g. deploying whole application stack in a virtual machine, investigations focus on how to distribute the application components in heterogeneous cloud environments. Consequently, users have an even higher range of options and should carefully choose good decision criterion, going further than only considering the direct cost for the needed cloud instances. Some challenges are deriving a revenue model - as they tend to be application specific - and customizing the evaluation of different migration configurations of a real application with authentic data metrics. In this sense, this document uses utility analysis as it includes a non-directly countable element, preferences, and allows basing the decision on a trade-off taking into account other aspects which have an influence on the final performance such as users satisfaction or cloud instance availability under different deployment topologies. Therefore, the evaluation and comparison of different selected cloud offerings is possible and helps throughout the decision. This thesis presents an overview of state-of-the-art revenue models used nowadays on web applications and afterwards specifies the study and aims to apply the utility concept to evaluate a current application, MediaWiki, based on real data. Results show that this approach is more complex and differs from the one considering only the monetary expenses, pursuing a better balance between the possible business-technology conflict

Efficient Resource Allocation for Throughput Maximization in Next-Generation Networks

Software-Defined Networking (SDN) and Network Function Virtualization (NFV) have emerged as the foundation of the next-generation network architecture by introducing great flexibility and network automation capabilities, including automatic response to faults and load changes and programmatic provision of network resources and connections. It has been envisioned that the SDN- and NFV-based next-generation network architecture will play a critical role in providing network services to users, where the desired network services, including data transfer and policy enforcement, are fulfilled by allocating network resources using virtualization technologies. However, the disparity between ever-growing user demands and scarce network resources makes resource allocation exceptionally central to the performance of a network service, because only by effectively allocating these scarce resources can a network service provider satisfy users and maximize the gain from running the service. In this thesis, we study efficient resource allocation for network throughput maximization in next-generation networks, while meeting user resource demands and Quality of Service (QoS) requirements, subject to network resource capacities. This however poses great challenges, namely, (1) how to maximize network throughput, considering that both SDN-enabled switches and links are capacitated, (2) how to maximize the network throughput while taking into account network function and QoS requirements of users, (3) how to dynamically scale and readjust resource allocation for user requests, and (4) how to provision a network service that can satisfy user reliability requirements. To address these challenges, we provide a thorough study of network throughput maximization problems in the context of the next-generation network architecture, by formulating the problems as optimizations problems and developing novel optimization frameworks and algorithms for the problems. Specifically, this thesis makes the following contributions. Firstly, we consider dynamic user request admissions where user requests arrive one by one and the knowledge of future request arrivals is not given as a priori. We develop a novel cost model that accurately captures the usage costs of network resources and propose online algorithms with provable performance guarantees. Secondly, we study the problem of realizing user requests with network function requirements, with the objective of maximizing network throughput, while meeting user QoS requirements, subject to resource capacity constraints. For this problem, we develop two algorithms that strive for the trade-off between the accuracy/quality of a solution and the running time of obtaining the solution. Thirdly, we investigate maximization of network throughput by dynamically scaling network resources while minimizing the overall operational cost of a network. We propose a unified framework for two types of resource scaling {--} vertical scaling and horizontal scaling. Through non-trivial reductions of the problem of concern into several classic problems, we propose an algorithm that has been empirically demonstrated to deliver near-optimal solutions. Fourthly, we deal with the problem of reliability-aware provisioning of network resources for users, with the aim of maximizing network throughput. We devise an approximation algorithm with a logarithmic approximation ratio for the general case of this problem. We also develop constant-factor approximation and exact algorithm for two special cases of the problem, respectively. The formulated problem is a generalization of several classic optimization problems. Finally, in addition to extensive theoretical analyses, we also evaluate the performance of proposed algorithms empirically through experimental simulations based on real and synthetic datasets. Experimental results show that the proposed algorithms significantly outperform existing algorithms

Fault Tolerant Placement of Stateful VNFs and Dynamic Fault Recovery in Cloud Networks

Traditional network functions such as firewalls are implemented in costly dedicated hardware. By decoupling network functions from physical devices, network function virtualization enables virtual network functions (VNF) to run in virtual machines (VMs). However, VNFs are vulnerable to various faults such as software and hardware failures. To enhance VNF fault tolerance, the deployment of backup VNFs in stand-by VM instances is necessary. In case of stateful VNFs, stand-by instances require constant state updates from active instances during its operation. This will guarantee a correct and seamless handover from failed instances to stand-by instances after failures. Nevertheless, such state updates to stand-by instances could consume significant network bandwidth resources and lead to potential admission failures for VNF requests. In this paper, we study the fault-tolerant VNF placement problem with the optimization objective of admitting as many requests as possible. In particular, the VNF placement of active/stand-by instances, the request routing paths to active instances, and state transfer paths to stand-by instances are jointly considered. We devise an efficient heuristic algorithm to solve this problem. For the fault tolerance problem without computing or bandwidth constraints, we also propose two bicriteria approximation algorithms with performance guarantees for a special case of the problem. Given the placement locations of VNFs, some of them may go faulty. We thus consider the dynamic fault recovery problem, for which we propose an approximation algorithm that dynamically switches traffic processing from faulty VNFs to normal ones. Simulations with realistic settings show that our algorithms can significantly improve the request admission rate compared to conventional approaches

Improving Resource Efficiency in Cloud Computing

Customers inside the cloud computing market are heterogeneous in several aspects, e.g., willingness to pay and performance requirement. By taking advantage of trade-offs created by these heterogeneities, the service provider can realize a more efficient system. This thesis is concerned with methods to improve the utilization of cloud infrastructure resources, and with the role of pricing in realizing those improvements and leveraging heterogeneity. Towards improving utilization, we explore methods to optimize network usage through traffic engineering. Particularly, we introduce a novel optimization framework to decrease the bandwidth required by inter-data center networks through traffic scheduling and shaping, and then propose algorithms to improve network utilization based on the analytical results derived from the optimization. When considering pricing, we focus on elucidating conditions under which providing a mix of services can increase a service provider\u27s revenue. Specifically, we characterize the conditions under which providing a ``delayed\u27\u27 service can result in a higher revenue for the service provider, and then offer guidelines for both users and providers

Pricing of reusable resources under ambiguous distributions of demand and service time with emerging applications

Monopolistic pricing models for revenue management are widely used in practice to set prices of multiple products with uncertain demand arrivals. The literature often assumes deterministic time of serving each demand and that the distribution of uncertainty is fully known. In this paper, we consider a new class of revenue management problems inspired by emerging applications such as cloud computing and city parking, where we dynamically determine prices for multiple products sharing limited resource and aim to maximize the expected revenue over a finite horizon. Random demand of each product arrives in each period, modeled by a function of the arrival time, product type, and price. Unlike the traditional monopolistic pricing, here each demand stays in the system for uncertain time. Both demand and service time follow ambiguous distributions, and we formulate robust deterministic approximation models to construct efficient heuristic fixed-price pricing policies. We conduct numerical studies by testing cloud computing service pricing instances based on data published by the Amazon Web Services (AWS) and demonstrate the efficacy of our approach for managing revenue and risk under various distributions of demand and service time

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC