Papers by Michele Ciavotta
NCA 2014 External Reviewers
CLOUD 2014 External Reviewers
A Model-Driven DevOps Framework for QoS-Aware Cloud Applications
2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), 2015
Palladio Optimization Suite: QoS optimization for component-based Cloud applications
Proceedings of the 9th EAI International Conference on Performance Evaluation Methodologies and Tools, 2016
A Joint Benchmark-Analytic Approach For Design-Time Assessment of Multi-Cloud Applications
Procedia Computer Science, 2015
SPACE4Cloud: a DevOps environment for multi-cloud applications
Proceedings of the 1st International Workshop on Quality-Aware DevOps - QUDOS 2015, 2015
This short paper gathers the initial results obtained after including the consideration of additi... more This short paper gathers the initial results obtained after including the consideration of additional resources, like for example, tools, fixings or man power, into flowshop scheduling problems. The literature on scheduling assumes that the only limiting resource is the machines. However, in real life, personnel operating the plant could be scarcer than the machines themselves. More in details , we consider a single resource with a non-negative and integer consumption from all the tasks in a flowshop. The optimization criterion is the minimization of the maximum completion time or makespan. An important consideration is that resource constrained flowshop problems can be easily transformed into the well-known resource constrained project scheduling problem or RCPSP. Therefore, a natural question is to see how good metaheuristics for the RCPSP behave for the considered resource constrained scheduling setting studied in this paper. Initial results show that simple adaptations of existi...
ACM SIGMETRICS Performance Evaluation Review, 2015
We are entering a Big Data world. Many sectors of our economy are now guided by data-driven decis... more We are entering a Big Data world. Many sectors of our economy are now guided by data-driven decision processes. Big Data and business intelligence applications are facilitated by the MapReduce programming model while, at infrastructural layer, cloud computing provides flexible and cost effective solutions for allocating on demand large clusters. Capacity allocation in such systems is a key challenge to provide performance for MapReduce jobs and minimize cloud resource costs. The contribution of this paper is twofold: (i) we provide new upper and lower bounds for MapReduce job execution time in shared Hadoop clusters, (ii) we formulate a linear programming model able to minimize cloud resources costs and job rejection penalties for the execution of jobs of multiple classes with (soft) deadline guarantees. Simulation results show how the execution time of MapReduce jobs falls within 14% of our upper bound on average. Moreover, numerical analyses demonstrate that our method is able to determine the global optimal solution of the linear problem for systems including up to 1,000 user classes in less than 0.5 seconds.
NCA 2014 External Reviewers
Additional Reviewers MASCOTS 2014
Run-time adaptation policies
CLOUD 2014 External Reviewers
Journal of Internet Services and Applications, 2014
Recent years have seen the massive migration of enterprise applications to the cloud. One of the ... more Recent years have seen the massive migration of enterprise applications to the cloud. One of the challenges posed by cloud applications is Quality-of-Service (QoS) management, which is the problem of allocating resources to the application to guarantee a service level along dimensions such as performance, availability and reliability. This paper aims at supporting research in this area by providing a survey of the state of the art of QoS modeling approaches suitable for cloud systems. We also review and classify their early application to some decision-making problems arising in cloud QoS management.
IEEE Transactions on Services Computing, 2000
In recent years the evolution and the widespread adoption of virtualization, service-oriented arc... more In recent years the evolution and the widespread adoption of virtualization, service-oriented architectures, autonomic, and utility computing have converged letting a new paradigm to emerge: The Cloud Computing. Clouds allow the on-demand delivering of software, hardware, and data as services. Currently the Cloud offer is becoming day by day wider since all the major IT Companies and Service providers, like Microsoft, Google, Amazon, HP, IBM, and VMWare have started providing solutions involving this new technological paradigm.
Cloud Computing is assuming a relevant role in the world of web applications and web services. Cl... more Cloud Computing is assuming a relevant role in the world of web applications and web services. Cloud technologies allow to build dynamic systems which are able to adapt their performance to workload fluctuations delegating to the Cloud Provider the intensive tasks of management and maintenance of the cloud infrastructure. Which is the best provider for our application? The application will guarantee the required service level objectives (SLOs)? Those are relevant issues that call for a tool able to carry on cost and performance analysis of the system before its actual development.
The permutation flowshop scheduling problem has been thoroughly studied in recent decades, both f... more The permutation flowshop scheduling problem has been thoroughly studied in recent decades, both from single objective as well as from multi-objective perspectives. To the best of our knowledge, little has been done regarding the multi-objective flowshop with Pareto approach when sequence dependent setup times are considered. As setup times and multi-criteria problems are important in industry, we must focus on this area. We propose a simple, yet powerful algorithm for the sequence dependent setup times flowshop problem with several criteria. The presented method is referred to as Restarted Iterated Pareto Greedy or RIPG and is compared against the best performing approaches from the relevant literature. Comprehensive computational and statistical analyses are carried out in order to demonstrate that the proposed RIPG method clearly outperforms all other algorithms and, as a consequence, it is a state-ofart method for this important and practical scheduling problem.
Minimising general setup costs in a two-stage production system
ABSTRACT This paper addresses a problem arising in the coordination between two consecutive stage... more ABSTRACT This paper addresses a problem arising in the coordination between two consecutive stages of a production system. Production is organised in batches of identical jobs. Each job is characterised by two distinct attributes, and all jobs sharing the same attributes are processed together as a single batch. Due to the structural and organisational characteristics of the production system, the two stages have to process the same batch sequence. When two consecutive batches with different attributes are processed, at least one stage must pay a setup, in order to reconfigure its own devices. Each stage incurs a setup cost that is a general non-decreasing function of the number of its own setups, and the problem consists of finding a batch sequence minimising the total setup costs of the production system. We present an original solution approach for the considered problem that is shown to be very effective using an extensive experimental campaign.
A Receding Horizon Approach for the Runtime Management of IaaS Cloud Systems
Long-term Auto-Scaling Algorithm for Multi-Cloud IaaS Systems
Un algoritmo eficaz para el problema del taller de flujo multiobjetivo con tiempos de cambio dependientes de la secuencia
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Papers by Michele Ciavotta