Upon a Message-Oriented Trading API

In this paper, we introduce the premises for a trading system application-programming interface (API) based on a message-oriented middleware (MOM), and present the results of our research regarding the design and the implementation of a simulation-trading system employing a service-oriented architecture (SOA) and messaging. Our research has been conducted with the aim of creating a simulation-trading platform, within the academic environment, that will provide both the foundation for future experiments with trading systems architectures, components, APIs, and the framework for research on trading strategies, trading algorithm design, and equity markets analysis tools. Mathematics Subject Classification: 68M14 (distributed systems).Trading System API, Straight-Through Processing, Distributed Computing, Service-Oriented Architecture (SOA), Message-Oriented Middleware (MOM), Java Message Service (JMS), OpenMQ

Modeling cloud resources using machine learning

Cloud computing is a new Internet infrastructure paradigm where management optimization has become a challenge to be solved, as all current management systems are human-driven or ad-hoc automatic systems that must be tuned manually by experts. Management of cloud resources require accurate information about all the elements involved (host machines, resources, offered services, and clients), and some of this information can only be obtained a posteriori. Here we present the cloud and part of its architecture as a new scenario where data mining and machine learning can be applied to discover information and improve its management thanks to modeling and prediction. As a novel case of study we show in this work the modeling of basic cloud resources using machine learning, predicting resource requirements from context information like amount of load and clients, and also predicting the quality of service from resource planning, in order to feed cloud schedulers. Further, this work is an important part of our ongoing research program, where accurate models and predictors are essential to optimize cloud management autonomic systems.Postprint (published version

An Architectural Model of Rights Management Framework for Information Systems

The demands towards the contemporary information systems are constantly increasing. In a dynamic business environment an organization has to be prepared for sudden growth, shrinking or other type of reorganization. Such change would bring the need of adaptation of the information system, servicing the company. The association of access rights to parts of the system with users, groups of users, user roles etc. is of great importance to defining the different activities in the company and the restrictions of the access rights for each employee, according to his status. The mechanisms for access rights management in a system are taken in account during the system design. In most cases they are build in the system. This paper offers an approach in user rights framework development that is applicable in information systems. This work presents a reusable extendable mechanism that can be integrated in information systems

On Communication Protocols that Compute Almost Privately

A traditionally desired goal when designing auction mechanisms is incentive compatibility, i.e., ensuring that bidders fare best by truthfully reporting their preferences. A complementary goal, which has, thus far, received significantly less attention, is to preserve privacy, i.e., to ensure that bidders reveal no more information than necessary. We further investigate and generalize the approximate privacy model for two-party communication recently introduced by Feigenbaum et al.[8]. We explore the privacy properties of a natural class of communication protocols that we refer to as "dissection protocols". Dissection protocols include, among others, the bisection auction in [9,10] and the bisection protocol for the millionaires problem in [8]. Informally, in a dissection protocol the communicating parties are restricted to answering simple questions of the form "Is your input between the values \alpha and \beta (under a predefined order over the possible inputs)?". We prove that for a large class of functions, called tiling functions, which include the 2nd-price Vickrey auction, there always exists a dissection protocol that provides a constant average-case privacy approximation ratio for uniform or "almost uniform" probability distributions over inputs. To establish this result we present an interesting connection between the approximate privacy framework and basic concepts in computational geometry. We show that such a good privacy approximation ratio for tiling functions does not, in general, exist in the worst case. We also discuss extensions of the basic setup to more than two parties and to non-tiling functions, and provide calculations of privacy approximation ratios for two functions of interest.Comment: to appear in Theoretical Computer Science (series A

Fixation for Distributed Clustering Processes

We study a discrete-time resource flow in $Z^d$, where wealthier vertices attract the resources of their less rich neighbors. For any translation-invariant probability distribution of initial resource quantities, we prove that the flow at each vertex terminates after finitely many steps. This answers (a generalized version of) a question posed by van den Berg and Meester in 1991. The proof uses the mass-transport principle and extends to other graphs

Deadline Scheduling for Aperiodic Tasks in inter-Cloud Environments: a new approach to resource management

This is a copy of the author 's final draft version of an article published in the journal Journal of supercomputing. The final publication is available at Springer via http://dx.doi.org/10.1007/s11227-014-1285-8In the big data era, the speed of analytical processing is influenced by the storage and retrieval capabilities to handle large amounts of data. While the distributed crunching applications themselves can yield useful information, the analysts face difficult challenges: they need to predict how much data to process and where, such that to get an optimum data crunching cost, while also respect deadlines and service level agreements within a limited budget. In today's data centers, data processing on demand and data transfers requests coming from distributed applications are usually expressed as aperiodic tasks. In this paper, we challenge the problem of tasks scheduling with deadline constraints of aperiodic tasks within inter-Cloud environments. In massively multithreaded computing systems that deal with data-intensive applications, Hadoop and BaTs tasks arrive periodically, which challenges traditional scheduling approaches previously proposed for supercomputing. Here, we consider the deadline as the main constraint, and propose a method to estimate the number of resources needed to schedule a set of aperiodic tasks, considering both execution and data transfers costs. Starting from classical scheduling techniques, and considering asynchronous tasks handling, we analyze the possibility of decoupling task arriving from task creation, scheduling and execution, sets of actions that can be put into a peer-to-peer relation over a network or over a client-server architecture in the Cloud. Based on a mathematical model, and using different simulation scenarios, we prove the following statements: (1) multiple source of independent aperiodic tasks can be considered similar to a single one; (2) with respect to the global deadline, the tasks migration between different regional centers is the appropriate solution when the number of estimated resources exceed a data center capacity; and (3) in a heterogeneous data center, we need a higher number of resources for the same request in order to respect the deadline constraints. We believe such results will benefit researchers and practitioners alike, who are interested in optimizing the resource management in data centers according to novel challenges coming from next-generation big data applications.Peer ReviewedPostprint (author's final draft

A variant of the multi-agent rendezvous problem

The classical multi-agent rendezvous problem asks for a deterministic algorithm by which $n$ points scattered in a plane can move about at constant speed and merge at a single point, assuming each point can use only the locations of the others it sees when making decisions and that the visibility graph as a whole is connected. In time complexity analyses of such algorithms, only the number of rounds of computation required are usually considered, not the amount of computation done per round. In this paper, we consider $\Omega(n^2 \log n)$ points distributed independently and uniformly at random in a disc of radius $n$ and, assuming each point can not only see but also, in principle, communicate with others within unit distance, seek a randomised merging algorithm which asymptotically almost surely (a.a.s.) runs in time O(n), in other words in time linear in the radius of the disc rather than in the number of points. Under a precise set of assumptions concerning the communication capabilities of neighboring points, we describe an algorithm which a.a.s. runs in time O(n) provided the number of points is $o(n^3)$. Several questions are posed for future work.Comment: 18 pages, 3 figures. None of the authors has any previous experience in this area of research (multi-agent systems), hence we welcome any feedback from specialist