Mircea Moca | Babes-Bolyai University (original) (raw)

Papers by Mircea Moca

IFIP Advances in Information and Communication Technology, 2015

2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity), 2015

International conference KNOWLEDGE-BASED ORGANIZATION, 2015

About 80% of the nancial market investors fail, the main reason for this being their poor invest... more About 80% of the nancial market investors fail, the main
reason for this being their poor investment decisions. Without advanced
nancial analysis tools and the knowledge to interpret the analysis, the
investors can easily make irrational investment decisions. Moreover, in-
vestors are challenged by the dynamism of the market and a relatively
large number of indicators that must be computed. In this paper we
propose E-Fast, an innovative approach for on-line technical analysis for
helping small investors to obtain a greater eciency on the market by
increasing their knowledge. The E-Fast technical analysis platform proto-
type relies on High Performance Computing (HPC), allowing to rapidly
develop and extensively validate the most sophisticated nance analysis
algorithms. In this work, we aim at demonstrating that the E-Fast im-
plementation, based on the CloudPower HPC infrastructure, is able to
provide small investors a realistic, low-cost and secure service that would
otherwise be available only to the large nancial institutions.We describe
the architecture of our system and provide design insights. We present
the results obtained with a real service implementation based on the
Exponential Moving Average computational method, using CloudPower
and Grid5000 for the computations' acceleration. We also elaborate a set
of interesting challenges emerging from this work, as next steps towards
high performance technical analysis for small investors.

Assembling and simultaneously using different types of distributed computing infrastructures (DCI... more Assembling and simultaneously using different types of distributed computing infrastructures (DCI) like
Grids and Clouds is an increasingly common situation. Because infrastructures are characterized by
different attributes such as price, performance, trust, and greenness, the task scheduling problem becomes
more complex and challenging. In this paper we present the design for a fault-tolerant and trust-aware
scheduler, which allows to execute Bag-of-Tasks applications on elastic and hybrid DCI, following userdefined
scheduling strategies. Our approach, named Promethee scheduler, combines a pull-based scheduler
with multi-criteria Promethee decision making algorithm. Because multi-criteria scheduling leads to the
multiplication of the possible scheduling strategies, we propose SOFT, a methodology that allows to find
the optimal scheduling strategies given a set of application requirements. The validation of this method is
performed with a simulator that fully implements the Promethee scheduler and recreates an hybrid DCI
environment including Internet Desktop Grid, Cloud and Best Effort Grid based on real failure traces. A
set of experiments shows that the Promethee scheduler is able to maximize user satisfaction expressed
accordingly to three distinct criteria: price, expected completion time and trust, while maximizing
the infrastructure useful employment from the resources owner point of view. Finally, we present an
optimization which bounds the computation time of the Promethee algorithm, making realistic the
possible integration of the scheduler to a wide range of resource management software.

2012 IEEE 8th International Conference on E-Science, 2012

2010 International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 2010

Lecture Notes in Computer Science, 2013

Future Generation Computer Systems, 2015

Efficiently scheduling tasks in hybrid Distributed Computing Infrastructures (DCI) is a challengi... more Efficiently scheduling tasks in hybrid Distributed Computing Infrastructures (DCI) is a challenging pursue because the scheduler must deal with a set of parameters that simultaneously characterize the tasks and the hosts originating from different types of infrastructure. In this paper we propose a scheduling method for hybrid DCIs, based on advanced multi-criteria decision methods. The scheduling decisions are made using pairwise comparisons of the tasks for a set of criteria like expected completion time and price charged for computation. The results are obtained with an XtremWeb-like pull-based scheduler simulator using real failure traces from [1] for a combination of three types of infrastructure. We also show how such a scheduler should be configured to enhance user satisfaction regardless their profiles, while maintaining good values for makespan and cost. We validate our approach with a statistical analysis on empirical data and show that our proposed scheduling method improves performance by 12-17% compared to other scheduling methods. Experimenting on large time-series and using realistic scheduling scenarios lead us to conclude about time consistency results of the method.

ieeexplore.ieee.org

Scheduling tasks in distributed computing infrastructures (DCIs) is challenging mainly because th... more Scheduling tasks in distributed computing infrastructures (DCIs) is challenging mainly because the scheduler is facing a number of more or less dependent parameters that characterize the hosts coming from a particular computing environment and the tasks. In this paper we introduce a multicriteria scheduling method for DCIs, aiming a better matching between hosts, and tasks waiting in a priority queue at a pull-based scheduler. The novelty of the approach consists in employing the Promethee [1] decision aid for selecting tasks. In the aim of computing preference relationships (priorities) among tasks, this approach performs pairwise comparisons of values that characterize tasks. The method exhibits interesting advantages, such as allowing the user to choose the values for the computation of the priorities, like the expected completion time (ECT) and cost. The approach is also very flexible, allowing through a set of parameters the specification of particular scheduling policies. To validate this method we built an XtrebWeb-like simulator, which is capable of running on real traces. We experiment on internet desktop grid (IDG), cloud and best effort grid (BEG), with various workloads. The results show that the Prometheebased scheduling method obtains good performance especially on IDG when certain fractions of the tasks fail. We also prove that multi-criteria scheduling using Promethee performs better than single-criterion scheduling, improving both makespan and cost. Also, a simple definition of ECT is the most efficient in terms of makespan. In this work we also explain the challenges of using Promethee for scheduling in DCIs.

IEEE Computer Society, Jan 1, 2010

MapReduce is an emerging programming model for dataintensive application proposed by Google, whic... more MapReduce is an emerging programming model for dataintensive application proposed by Google, which has attracted a lot of attention recently. MapReduce borrows ideas from functional programming, where programmer defines Map and Reduce tasks to process large set of distributed data. In this paper we propose an implementation of the MapReduce programming model. We present the architecture of the prototype based on BitDew, a middleware for large scale data management on Desktop Grid. We describe the set of features which makes our approach suitable for large scale and loosely connected Internet Desktop Grid: massive fault tolerance, replica management, barriers-free execution, latency-hiding optimisation as well as distributed result checking. We also present performance evaluation of the prototype both against micro-benchmarks and real MapReduce application. The scalability test shows that we achieve linear speedup on the classic WordCount benchmark. Several scenarios involving lagger hosts and host crashes demonstrate that the prototype is able to cope with an experimental context similar to real-world Internet.

Student Session

The Student Session, organised for students by students, is designed to encourage student interac... more The Student Session, organised for students by students, is designed to encourage student interaction and feedback from the community. By providing the students with a conference-like setup, both in the presentation and in the review process, students have the opportunity to prepare their own submission, go through the selection process and present their work to each other and their interests to their fellow students as well as internationally leading experts in the agent field, both from the theoretical and the practical sector.

Parallel and Distributed …, Jan 1, 2011

MapReduce is a promising approach to support data-intensive applications on Volunteer Computing S... more MapReduce is a promising approach to support data-intensive applications on Volunteer Computing Systems. Existent middleware like BitDew allows running MapReduce applications in a Desktop Grid environment. If the Desktop Grid is deployed in the Internet under the Volunteer Computing paradigm, it harnesses untrustable, volatile and heterogeneous resources and the results produced by MapReduce applications can be subject of sabotage. However, the implementation of large-scale MapReduce presents significant challenges with respect to the state of the art in Desktop Grid. A key issue is the design of the result certification, an operation needed to verify that malicious volunteers do not tamper with the results of computations. Because the volume of data produced and processed is so large that cannot be sent back to the server, the result certification cannot be centralized as it is currently implemented in Desktop Grid systems. In this paper we present a distributed result checker based on the Majority Voting method. We evaluate the efficiency of our approach using a model for characterizing errors and sabotage in the MapReduce paradigm. With this model, we can compute the aggregated probability with which a MapReduce implementation produces an erroneous result. The challenge is to capture the aggregated probability for the entire system, composed from probabilities resulted from the two phases of computation: Map and Reduce. We provide a detailed analysis on the performance of the result verification method and also discuss the generated overhead of managing security. We also give guidelines about how the result verification phase should be configured, given a MapReduce application.