Isabel Amigo

Federation and Revenue Sharing in Cloud Computing Environment

In Cloud computing, resources such as CPU, RAM, and disk etc. are provided as a service via the Internet. Elasticity is a key feature of Cloud Computing. It aims to enabling a Cloud Service Provider (CSP) to negotiate the possibility to borrow external resources from other CSPs when its own facilities are not able to satisfy a client request. Reciprocally, a CSP may sell some of its unused resources to another CSP in case of under load. Today, Cloud Federation is a key approach considered for Cloud elasticity. In the context of the Easi-Clouds European ITEA 2 research project, we aim to develop Pricing-as-a-Service (PraaS) suited to the Federated Cloud environment. The aim of this paper is twofold. First, we provide a state of the art of the pricing and revenue sharing models in federated environment. We discuss the specifications of Cloud Federation as well as its different drivers and barriers. Three types of pricing strategies (on-demand, spot and reserved) are presented. In the second part, we present the problem of revenue sharing in the federation with some properties we are willing to fulfill. We propose and evaluate our revenue sharing model suited to the Federated environment with numerical analysis via simulation. We compare our approach to the proportional share and the Shapley value method. Finally, we provide and analyze the results of our simulations with our conclusion and perspectives.

Network bandwidth allocation with end-to-end QoS constraints and revenue sharing in multi-domain federations

Internet is evolving, traffic continues to grow, new revenue sources are sought by Network and Service Providers. Value added services with real time characteristics are likely to be common currency in the near future. Quality of Service (QoS) could allow Application/Service Providers (APs) to offer better services to the end users. At the same time, all actors claim for a fair distribution of revenues. Inspired by this scenario, we propose a complete framework for selling interdomain quality assured services, and subsequently distributing revenues, in an Autonomous System (AS) association context. We state the problem as a network utility maximization problem with QoS constraints and show that a distributed solution can be carried out. In order to fairly share the resulting revenue we study concepts from coalitional game theory and propose a solution based on the Shapley value and statistics on the revenues. Simulations of the whole proposal are shown.

On the problem of revenue sharing in multi-domain federations

Autonomous System alliances or federations are envisioned to emerge in the near future as a means of selling end-to-end quality assured services through interdomain networks. This collaborative paradigm mainly responds to the ever increasing Internet traffic volumes that requires assured quality, and constitutes a new business opportunity for Network Service Providers (NSPs). However, current Internet business rules are not likely to satisfy all involved partners in this emerging scenario. How the revenue is shared among NSPs must be agreed in advance, and should enforce economical incentives to join an alliance and remain in it, so that the alliance remains stable. In this paper, we work on the scenario of such federations, where service selling is formulated as a Network Utility Maximization (NUM) problem. In this context, we formally formulate the properties the revenue sharing (RS) method should fulfill and argue why the existing methods are not suitable. Finally, we propose a family of solutions to the RS problem such that the economical stability and efficiency of the alliance in the long term is guaranteed. The proposed method is based on solving a series of Optimization Problems and considering statistics on the incomes.

SDN-based Overlay Networks for QoS-aware Routing

We propose an SDN-based architecture for distant points interconnection through an overlay network. The main goal of the overlay network is to provide with resilient and high-performance interconnection between its nodes, without the need of changing nor handling Internet routers. Our proposed architecture benefits from the advantages inherited from SDN such as simplicity of management and flow-level Traffic Engineering capabilities. In particular, our approach allows to build the overlay network without any tunneling technology, which promises to provide a gain in terms of performance, and to ease deployment and management. In addition, we address one of the challenges of SDN, by discussing a possible approach for active monitoring in the proposed SDN-overlay architecture.

MONTE: an implementation of an MPLS online traffic engineering tool

Multiservice networks require careful mapping of traffic in order to provide quality of service. Applying offline Traffic Engineering techniques leads to a better usage of resources and allows to assure some degree of quality of service. Even with those techniques applied, as network and traffic conditions change dynamically, the initial quality could be reduced. When addressing this problem, online Traffic Engineering has a major role. In MONTE project a solution for addressing this problem in Multiprotocol Label Switching networks was proposed and implemented in software. Such solution involves network discovering and monitoring, congestion detection, a corrective algorithm, and a mechanism for signalling changes in the network. The entire solution was conceived to work in real time and vendor independent. This paper explains the details of the solution and its implementation. Results validating the correct operation of the tool are also shown. This results were obtained through tests in a live network.

Joint Minimization of Monitoring Cost and Delay in Overlay Networks: Optimal Policies with a Markovian Approach

Continuous monitoring of network resources enables to make more-informed resource allocation decisions but incurs overheads. We investigate the trade-off between monitoring costs and benefits of accurate state information for a routing problem. In our approach link delays are modeled by Markov chains or hidden Markov models. The current delay information on a link can be obtained by actively monitoring this link at a fixed cost. At each time slot, the decision maker chooses to monitor a subset of links with the objective of minimizing a linear combination of long-run average delay and monitoring costs. This decision problem is modeled as a Markov decision process whose solution is computed numerically. In addition, in simple settings we prove that immediate monitoring cost and delay minimization leads to a threshold policy on a filter which sums up information from past measurements. The lightweight method as well as the optimal policy are tested on several use-cases. We demonstrate on an overlay of 30 nodes of RIPE Atlas that we obtain delay values close to the performance of the always best path with an extremely low monitoring effort when delays between nodes are modeled with hierarchical Dirichlet process hidden Markov models.

Online electric vehicle recharge scheduling under different e-mobility operator's pricing models

Electric Vehicles (EV) appear as a clean transportation technology with clear environmental advantages and potential benefits for the grid, being a key element of future modern smart cities. However, autonomy of the vehicle and lack of recharging stations are barriers that need to be overcome in order to make the service reliable and broadly accepted. To tackle this problem, French GreenFeed project is working to define and implement an interoperable and universal architecture to allow EV-recharge roaming. In this work, we consider such architecture and focus on issues faced by the e-mobility operator (EMO) and identified by the GreenFeed project. The EMO, a key actor in the architecture, maintains contracts with EV users to allow EV recharge at any geographical place (recharge roaming) through agreements with charging infrastructure operators. The interest of the EMO is to schedule the recharge demands and fulfil the contracts while maximizing its revenue. We analyse different online EV recharge scheduling under different pricing models, agreed between the EV users and the EMO, to study their impact on the EMOs revenues. In this analysis, we assume recharges arriving at random times and requesting a certain amount of energy during a fixed time period. We simulate a scenario with real day-ahead hourly electricity prices over two years and different scheduling polices to illustrate the feasibility of online recharge scheduling.

Game theoretic approaches for revenue sharing in federated cloud

Cloud federation is a new paradigm in Cloud Computing. It helps the Cloud Service Providers (CSPs) to overcome the limitation of their own resources to serve their clients. The CSPs can negotiate the possibility to borrow external resources from each other when their own facilities are not able to satisfy their client requests. Reciprocally, the CSPs may sell some of their unused resources to another CSPs in case of under load. The aim of this paper is to propose and analyse multiple revenue sharing models in a federated environment in the context of the Easi-Clouds, a European ITEA 2 research project. We discuss the specifications and models of Cloud Federation. We present the problem of revenue sharing in the federation and propose a set of properties the solution should fulfil. We propose and evaluate our revenue sharing models suited to the Federated environment with numerical analysis via simulation. We compare the proposed revenue sharing mechanisms and classify them based on different scenarios. Simple revenue sharing models can be adapted in specific cases. However, Game theoretical approaches can be more suitable in some scenarios, with the drawback of being more complex to implement.

Maximum delay computation under traffic matrix uncertainty and its application to interdomain path selection

One of the most important problems when deploying interdomain path selection with quality of service requirements is being able to rely the computations on metrics that hold for a long period of time. Our proposal for achieving such assurance is to compute bounds on the metrics, taking into account the uncertainty on the traffic demands. In particular, we will explore the computation of the maximum end-to-end delay of traversing a domain considering that the traffic is unknown but bounded. Since this provides a robust quality of service value for traversing the Autonomous System (AS), without revealing confidential information, we claim that the bound can be safely conceived as a metric to be announced by each AS to the entities performing the path selection, in the process of interdomain path selection. We show how the maximum delay value is obtained for an interdomain bandwidth demand and we propose an exact method for solving the optimization problem. Simulations with real data are also presented.