Enabling global experiments with interactive reconfiguration and steering by multiple users

In global scientific experiments with collaborative scenarios involving multinational teams there are big challenges related to data access, namely data movements are precluded to other regions or Clouds due to the constraints on latency costs, data privacy and data ownership. Furthermore, each site is processing local data sets using specialized algorithms and producing intermediate results that are helpful as inputs to applications running on remote sites. This paper shows how to model such collaborative scenarios as a scientific workflow implemented with AWARD (Autonomic Workflow Activities Reconfigurable and Dynamic), a decentralized framework offering a feasible solution to run the workflow activities on distributed data centers in different regions without the need of large data movements. The AWARD workflow activities are independently monitored and dynamically reconfigured and steering by different users, namely by hot-swapping the algorithms to enhance the computation results or by changing the workflow structure to support feedback dependencies where an activity receives feedback output from a successor activity. A real implementation of one practical scenario and its execution on multiple data centers of the Amazon Cloud is presented including experimental results with steering by multiple users.info:eu-repo/semantics/publishedVersio

An Elasticity-aware Governance Platform for Cloud Service Delivery

In cloud service provisioning scenarios with a changing demand from consumers, it is appealing for cloud providers to leverage only a limited amount of the virtualized resources required to provide the service. However, it is not easy to determine how much resources are required to satisfy consumers expectations in terms of Quality of Service (QoS). Some existing frameworks provide mechanisms to adapt the required cloud resources in the service delivery, also called an elastic service, but only for consumers with the same QoS expectations. The problem arises when the service provider must deal with several consumers, each demanding a different QoS for the service. In such an scenario, cloud resources provisioning must deal with trade-offs between different QoS, while fulfilling these QoS, within the same service deployment. In this paper we propose an elasticity-aware governance platform for cloud service delivery that reacts to the dynamic service load introduced by consumers demand. Such a reaction consists of provisioning the required amount of cloud resources to satisfy the different QoS that is offered to the consumers by means of several service level agreements. The proposed platform aims to keep under control the QoS experienced by multiple service consumers while maintaining a controlled cost.Junta de Andalucía P12--TIC--1867Ministerio de Economía y Competitividad TIN2012-32273Agencia Estatal de Investigación TIN2014-53986-RED

Optimizing Data Placement for Cost Effective and High Available Multi-Cloud Storage

With the advent of big data age, data volume has been changed from trillionbyte to petabyte with incredible speed. Owing to the fact that cloud storage offers the vision of a virtually infinite pool of storage resources, data can be stored and accessed with high scalability and availability. But a single cloud-based data storage has risks like vendor lock-in, privacy leakage, and unavailability. Multi-cloud storage can mitigate these risks with geographically located cloud storage providers. In this storage scheme, one important challenge is how to place a user's data cost-effectively with high availability. In this paper, an architecture for multi-cloud storage is presented. Next, a multi-objective optimization problem is defined to minimize total cost and maximize data availability simultaneously, which can be solved by an approach based on the non-dominated sorting genetic algorithm II (NSGA-II) and obtain a set of non-dominated solutions called the Pareto-optimal set. Then, a method is proposed which is based on the entropy method to determine the most suitable solution for users who cannot choose one from the Pareto-optimal set directly. Finally, the performance of the proposed algorithm is validated by extensive experiments based on real-world multiple cloud storage scenarios

Brokering Algorithms for Optimizing the Availability and Cost of Cloud Storage Services

Abstract—In recent years, cloud storage providers have gained popularity for personal and organizational data, and provided highly reliable, scalable and flexible resources to cloud users. Although cloud providers bring advantages to their users, most cloud providers suffer outages from time-to-time. Therefore, relying on a single cloud storage services threatens service availability of cloud users. We believe that using multi-cloud broker is a plausible solution to remove single point of failure and to achieve very high availability. Since highly reliable cloud storage services impose enormous cost to the user, and also as the size of data objects in the cloud storage reaches magnitude of exabyte, optimal selection among a set of cloud storage providers is a crucial decision for users. To solve this problem, we propose an algorithm that determines the minimum replication cost of objects such that the expected availability for users is guaranteed. We also propose an algorithm to optimally select data centers for striped objects such that the expected availability under a given budget is maximized. Simulation experiments are conducted to evaluate our algorithms, using failure probability and storage cost taken from real cloud storage providers