A modeling language for mathematical programming
Management Science
HPCC '08 Proceedings of the 2008 10th IEEE International Conference on High Performance Computing and Communications
Future Generation Computer Systems
Accounting and Billing for Federated Cloud Infrastructures
GCC '09 Proceedings of the 2009 Eighth International Conference on Grid and Cooperative Computing
Virtual Infrastructure Management in Private and Hybrid Clouds
IEEE Internet Computing
Reducing Costs of Spot Instances via Checkpointing in the Amazon Elastic Compute Cloud
CLOUD '10 Proceedings of the 2010 IEEE 3rd International Conference on Cloud Computing
The reservoir model and architecture for open federated cloud computing
IBM Journal of Research and Development
Hybrid Computing-Where HPC meets grid and Cloud Computing
Future Generation Computer Systems
An elasticity model for High Throughput Computing clusters
Journal of Parallel and Distributed Computing
Multicloud Deployment of Computing Clusters for Loosely Coupled MTC Applications
IEEE Transactions on Parallel and Distributed Systems
Elastic management of web server clusters on distributed virtual infrastructures
Concurrency and Computation: Practice & Experience
Cloud brokering mechanisms for optimized placement of virtual machines across multiple providers
Future Generation Computer Systems
OPTIMIS: A holistic approach to cloud service provisioning
Future Generation Computer Systems
Online cost-efficient scheduling of deadline-constrained workloads on hybrid clouds
Future Generation Computer Systems
Multi-Cloud: expectations and current approaches
Proceedings of the 2013 international workshop on Multi-cloud applications and federated clouds
Cost-effective cloud HPC resource provisioning by building semi-elastic virtual clusters
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
Solidifying the foundations of the cloud for the next generation Software Engineering
Journal of Systems and Software
An approximate ϵ-constraint method for a multi-objective job scheduling in the cloud
Future Generation Computer Systems
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The current cloud market, constituted by many different public cloud providers, is highly fragmented in terms of interfaces, pricing schemes, virtual machine offers and value-added features. In this context, a cloud broker can provide intermediation and aggregation capabilities to enable users to deploy their virtual infrastructures across multiple clouds. However, most current cloud brokers do not provide advanced service management capabilities to make automatic decisions, based on optimization algorithms, about how to select the optimal cloud to deploy a service, how to distribute optimally the different components of a service among different clouds, or even when to move a given service component from a cloud to another to satisfy some optimization criteria. In this paper we present a modular broker architecture that can work with different scheduling strategies for optimal deployment of virtual services across multiple clouds, based on different optimization criteria (e.g. cost optimization or performance optimization), different user constraints (e.g. budget, performance, instance types, placement, reallocation or load balancing constraints), and different environmental conditions (i.e., static vs. dynamic conditions, regarding instance prices, instance types, service workload, etc.). To probe the benefits of this broker, we analyse the deployment of different clustered services (an HPC cluster and a Web server cluster) on a multi-cloud environment under different conditions, constraints, and optimization criteria.