Mario Macías

A genetic model for pricing in cloud computing markets

Cloud Computing markets arise as an efficient way to allocate resources for the execution of tasks and services within a set of geographically dispersed providers from different organisations. Client applications and service providers meet in a market and negotiate for the sales of services by means of the signature of a Service Level Agreement. Depending on the status of the demand, the provider is able to offer higher or lower prices for maximising its profit. It is difficult to establish a profitable pricing function in competitive markets, because there are several factors that can influence in the prices. This paper deals with the problem of offering competitive prices in the negotiation of services in Cloud Computing markets. A Genetic Algorithms approach is proposed, in which a naive pricing function evolves to a pricing function that offers suitable prices in function of the system status. Its results are compared with other pricing strategies, demonstrating its validity.

Rule-based SLA management for revenue maximisation in Cloud Computing Markets

This paper introduces several Business Rules for maximising the revenue of Providers in Cloud Computing Markets. These rules apply in both negotiation and execution time, and enforce the achievement of Business-Level Objectives by establishing a bidirectional data flow between market and resource layers. The experiments demonstrate that the revenue is maximized by using both resource data when negotiating, and economic information when managing the resources.

Resource-level QoS metric for CPU-based guarantees in cloud providers

Success of Cloud computing requires that both customers and providers can be confident that signed Service Level Agreements (SLA) are supporting their respective business activities to their best extent. Currently used SLAs fail in providing such confidence, especially when providers outsource resources to other providers. These resource providers typically support very simple metrics, or metrics that hinder an efficient exploitation of their resources. In this paper, we propose a resource-level metric for specifying finegrain guarantees on CPU performance. This metric allows resource providers to allocate dynamically their resources among the running services depending on their demand. This is accomplished by incorporating the customers CPU usage in the metric definition, but avoiding fake SLA violations when the customers task does not use all its allocated resources. As demonstrated in our evaluation, which has been conducted in a virtualized provider where we have implemented the needed infrastructure for using our metric, our solution presents fewer SLA violations than other CPU-related metrics.

Using resource-level information into nonadditive negotiation models for cloud Market environments

Markets arise as an efficient way of organising resources in Cloud Computing scenarios. In Cloud Computing Markets, Brokers that represent both Clients and Service Providers meet in a Market and negotiate for the sales of resources or services. This paper defends the idea that efficient negotiations require of the usage of resource-level information for increasing the accuracy of negotiated Service Level Agreements and facilitating the achievement of both performance and business goals. A negotiation model based on the maximisation of nonadditive utility functions that considers multiple objectives is defined, and its validity is demonstrated in the experiments.

Economically enhanced resource management for internet service utilities

As competition on global markets increases the vision of utility computing gains more and more interest. To attract more providers it is crucial to improve the performance in commercialization of resources. This makes it necessary to not only base components on technical aspects, but also to include economical aspects in their design. This work presents an framework for an Economically Enhanced Resource Manager (EERM) which features enhancements to technical resource management like dynamic pricing and client classification. The introduced approach is evaluated considering various economic design criteria and example scenarios. Our preliminary results, e.g. an increase in achieved revenue from 77% to 92% of the theoretic maximum in our first scenario, show that our approach is very promising.

Supporting CPU-based guarantees in cloud SLAs via resource-level QoS metrics

Success of Cloud computing requires that both customers and providers can be confident that signed Service Level Agreements (SLA) are supporting their respective business activities to their best extent. Currently used SLAs fail in providing such confidence, especially when providers outsource resources to other providers. These resource providers typically support very simple metrics like availability, or metrics that hinder an efficient exploitation of their resources. In this paper, we propose a resource-level metric for specifying fine-grain guarantees on CPU performance. This metric allows resource providers to allocate dynamically their resources among running services depending on their demand. This is accomplished by incorporating the customers CPU usage in the metric definition, but avoiding fake SLA violations when the customers task does not use all its allocated resources. We have conducted the evaluation in a virtualized provider where we have implemented the needed infrastructure for using our metric. As demonstrated in our evaluation, our solution presents fewer SLA violations than other CPU-related metrics while maintaining the Quality of Service.

Toward business-driven risk management for Cloud computing

The Cloud computing paradigm is offering an innovative and promising vision concerning the Information and Communications Technology (ICT). Notwithstanding, the use of Cloud resources, which usually are external assets to their consumers, implies risk issues that must be taken into account.

Client Classification Policies for SLA Enforcement in Shared Cloud Datacenters

In Utility Computing business model, the owners of the computing resources negotiate with their potential clients to sell computing power. The terms of the Quality of Service (QoS) and the economic conditions are established in a Service-Level Agreement (SLA). There are many scenarios in which the agreed QoS cannot be provided because of errors in the service provisioning or failures in the system. Since providers have usually different types of clients, according to their relationship with the provider or by the fee that they pay, it is important to minimize the impact of the SLA violations in preferential clients. This paper proposes a set of policies to provide better QoS to preferential clients in such situations. The criterion to classify clients is established according to the relationship between client and provider (external user, internal or another privileged relationship) and the QoS that the client purchases (cheap contracts or extra QoS by paying an extra fee). Most of the policies use key features of virtualization: Selective Violation of the SLAs, Dynamic Scaling of the Allocated Resources, and Runtime Migration of Tasks. The validity of the policies is demonstrated through exhaustive experiments.

Enforcing service level agreements using an economically enhanced resource manager

Traditional resource management has had as its main objective the optimisation of throughput, based on parameters such as CPU, memory, and network bandwidth. With the appearance of Grid Markets, new variables that determine economic expenditure, benefit and opportunity must be taken into account. The SORMA project aims to allow resource owners and consumers to exploit market mechanisms to sell and buy resources across the Grid. SORMA’s motivation is to achieve efficient resource utilisation by maximising revenue for resource providers, and minimising the cost of resource consumption within a market environment. An overriding factor in Grid markets is the need to ensure that desired Quality of Service levels meet the expectations of market participants. This paper explains the proposed use of an Economically Enhanced Resource Manager (EERM) for resource provisioning based on economic models. In particular, this paper describes techniques used by the EERM to support revenue maximisation across multiple Service Level Agreements.

SLA negotiation and enforcement policies for revenue maximization and client classification in cloud providers

In Cloud Computing markets, owners of computing resources negotiate with their potential clients to sell computing power. The terms of the Quality of Service (QoS) to be provided as well as the economic conditions are established in a Service-Level Agreement (SLA). The common objective of a Cloud provider is to maximize its economic profit. However, there are situations in which providers must differentiate the SLAs with respect to the type of client that is willing to access the resources or the agreed QoS, e.g. when the hardware resources are shared between users of the company that own the resources and external users.This article proposes two sets of policies to manage SLAs with respect to the business objectives of a Cloud provider: Revenue Maximization or classification of clients. The criterion to classify clients is established according to the relationship between client and provider (external user, internal or another privileged relationship) and the QoS that the client purchases (cheap contracts or extra QoS by paying an extra fee). The validity of the policies is demonstrated through exhaustive experiments. Providing a set of policies for maximizing BLOs achievement of a Cloud provider.Modeling of client classification as a complementary BLO to Revenue Maximization.Our rules use state-of-the-art cloud technologies: VM migration, elasticity, etc.Development of a fine-grain simulator for exhaustive validation of the model.