Jordi Guitart

Economic model of a Cloud provider operating in a federated Cloud

Resource provisioning in Cloud providers is a challenge because of the high variability of load over time. On the one hand, the providers can serve most of the requests owning only a restricted amount of resources, but this forces to reject customers during peak hours. On the other hand, valley hours incur in under-utilization of the resources, which forces the providers to increase their prices to be profitable. Federation overcomes these limitations and allows providers to dynamically outsource resources to others in response to demand variations. Furthermore, it allows providers with underused resources to rent them to other providers. Both techniques make the provider getting more profit when used adequately. Federation of Cloud providers requires having a clear understanding of the consequences of each decision. In this paper, we present a characterization of providers operating in a federated Cloud which helps to choose the most convenient decision depending on the environment conditions. These include when to outsource to other providers, rent free resources to other providers (i.e., insourcing), or turn off unused nodes to save power. We characterize these decisions as a function of several parameters and implement a federated provider that uses this characterization to exploit federation. Finally, we evaluate the profitability of using these techniques using the data from a real provider.

Energy-Aware Scheduling in Virtualized Datacenters

The reduction of energy consumption in large-scale datacenters is being accomplished through an extensive use of virtualization, which enables the consolidation of multiple workloads in a smaller number of machines. Nevertheless, virtualization also incurs some additional overheads (e.g. virtual machine creation and migration) that can influence what is the best consolidated configuration, and thus, they must be taken into account. In this paper, we present a dynamic job scheduling policy for power-aware resource allocation in a virtualized datacenter. Our policy tries to consolidate workloads from separate machines into a smaller number of nodes, while fulfilling the amount of hardware resources needed to preserve the quality of service of each job. This allows turning off the spare servers, thus reducing the overall datacenter power consumption. As a novelty, this policy incorporates all the virtualization overheads in the decision process. In addition, our policy is prepared to consider other important parameters for a datacenter, such as reliability or dynamic SLA enforcement, in a synergistic way with power consumption. The introduced policy is evaluated comparing it against common policies in a simulated environment that accurately models HPC jobs execution in a virtualized datacenter including power consumption modeling and obtains a power consumption reduction of 15% with respect to typical policies.

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.

A service framework for energy-aware monitoring and VM management in Clouds

The monitoring of QoS parameters in Services Computing as well as in Clouds has been a functionality provided by all contemporary systems. As the optimization of energy consumption becomes a major concern for system designers and administrators, it can be considered as another QoS metric to be monitored. In this paper, we present a service framework that allows us to monitor the energy consumption of a Cloud infrastructure, calculate its energy efficiency, and evaluate the gathered data in order to put in place an effective virtual machine (VM) management. In that context, a simulation scenario of an eco-driven VM placement policy resulted in a 14% improvement of the infrastructures energy efficiency. In total, the proposed approaches and implementations have been validated against a testbed, producing very promising results regarding the prospect of energy efficiency as an important quality factor in Clouds.

Prediction of Job Resource Requirements for Deadline Schedulers to Manage High-Level SLAs on the Cloud

For a non IT expert to use services in the Cloud is more natural to negotiate the QoS with the provider in terms of service-level metrics --e.g. job deadlines-- instead of resource-level metrics --e.g. CPU MHz. However, current infrastructures only support resource-level metrics --e.g. CPU share and memory allocation-- and there is not a well-known mechanism to translate from service-level metrics to resource-level metrics. Moreover, the lack of precise information regarding the requirements of the services leads to an inefficient resource allocation --usually, providers allocate whole resources to prevent SLA violations. According to this, we propose a novel mechanism to overcome this translation problem using an online prediction system which includes a fast analytical predictor and an adaptive machine learning based predictor. We also show how a deadline scheduler could use these predictions to help providers to make the most of their resources. Our evaluation shows: i) that fast algorithms are able to make predictions with an 11% and 17% of relative error for the CPU and memory respectively; ii) the potential of using accurate predictions in the scheduling compared to simple yet well-known schedulers.

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.

Session-based adaptive overload control for secure dynamic Web applications

As dynamic Web content and security capabilities are becoming popular in current Web sites, the performance demand on application servers that host the sites is increasing, leading sometimes these servers to overload. As a result, response times may grow to unacceptable levels and the server may saturate or even crash. In this paper we present a session-based adaptive overload control mechanism based on SSL (secure socket layer) connections differentiation and admission control. The SSL connections differentiation is a key factor because the cost of establishing a new SSL connection is much greater than establishing a resumed SSL connection (it reuses an existing SSL session on server). Considering this big difference, we have implemented an admission control algorithm that prioritizes the resumed SSL connections to maximize performance on session-based environments and limits dynamically the number of new SSL connections accepted depending on the available resources and the current number of connections in the system to avoid server overload. In order to allow the differentiation of resumed SSL connections from new SSL connections we propose a possible extension of the Java Secure Sockets Extension (JSSE) API. Our evaluation on Tomcat server demonstrates the benefit of our proposal for preventing server overload.

Checkpoint-based fault-tolerant infrastructure for virtualized service providers

Crash and omission failures are common in service providers: a disk can break down or a link can fail anytime. In addition, the probability of a node failure increases with the number of nodes. Apart from reducing the providers computation power and jeopardizing the fulfillment of his contracts, this can also lead to computation time wasting when the crash occurs before finishing the task execution. In order to avoid this problem, efficient checkpoint infrastructures are required, especially in virtualized environments where these infrastructures must deal with huge virtual machine images. This paper proposes a smart checkpoint infrastructure for virtualized service providers. It uses Another Union File System to differentiate read-only from read-write parts in the virtual machine image. In this way, read-only parts can be checkpointed only once, while the rest of checkpoints must only save the modifications in read-write parts, thus reducing the time needed to make a checkpoint. The checkpoints are stored in a Hadoop Distributed File System. This allows resuming a task execution faster after a node crash and increasing the fault tolerance of the system, since checkpoints are distributed and replicated in all the nodes of the provider. This paper presents a running implementation of this infrastructure and its evaluation, demonstrating that it is an effective way to make faster checkpoints with low interference on task execution and efficient task recovery after a node failure.

Characterizing secure dynamic Web applications scalability

Security in the access to Web contents and the interaction with Web sites is becoming one of the most important issues in Internet. Servers need to provide certain levels of security so that the user feels comfortable when running the applications that provide the services he/she requires. HTTP over SSL is the most used solution, providing mutual authentication between the two interacting parts. The SSL protocol does not introduce complexity in Web applications but increases the computational demand on the server, reducing its capacity to serve large number of clients and increasing the time to serve them. In order to compensate the degradation in the quality of service, the server needs to be upgraded with additional resources, mainly processors and memory. In this paper we analyze the scalability of servers that run secure dynamic Web applications. We analyze how the server behaves when it is stressed with different number of clients and how the quality of service is degraded. We perform a detailed analysis of the server behavior and analyze the impact of adding more processors to the system that runs the server. The analysis is done using a fine-grained analysis framework that considers all levels in the application server execution (i.e. application, server, JVM and OS kernel). The RUBiS auction site benchmark is used to stress a Tomcat application server running on a commodity 4-way multiprocessor Intel platform with Linux.