Resource Brokering in Grid Computing

Grid Computing has emerged in the academia and evolved towards the bases of what is currently known as Cloud Computing and Internet of Things (IoT). The vast collection of resources that provide the nature for Grid Computing environment is very complex; multiple administrative domains control access and set policies to the shared computing resources. It is a decentralized environment with geographically distributed computing and storage resources, where each computing resource can be modeled as an autonomous computing entity, yet collectively can work together. This is a class of Cooperative Distributed Systems (CDS). We extend this by applying characteristic of open environments to create a foundation for the next generation of computing platform where entities are free to join a computing environment to provide capabilities and take part as a collective in solving complex problems beyond the capability of a single entity. This thesis is focused on modeling “Computing” as a collective performance of individual autonomous fundamental computing elements interconnected in a “Grid” open environment structure. Each computing element is a node in the Grid. All nodes are interconnected through the “Grid” edges. Resource allocation is done at the edges of the “Grid” where the connected nodes are simply used to perform computation. The analysis put forward in this thesis identifies Grid Computing as a form of computing that occurs at the resource level. The proposed solution, coupled with advancements in technology and evolution of new computing paradigms, sets a new direction for grid computing research. The approach here is a leap forward with the well-defined set of requirements and specifications based on open issues with the focus on autonomy, adaptability and interdependency. The proposed approach examines current model for Grid Protocol Architecture and proposes an extension that addresses the open issues in the diverged set of solutions that have been created

Adaptive Quality of Service Control in Distributed Real-Time Embedded Systems

An increasing number of distributed real-time embedded systems face the critical challenge of providing Quality of Service (QoS) guarantees in open and unpredictable environments. For example, such systems often need to enforce CPU utilization bounds on multiple processors in order to avoid overload and meet end-to-end dead-lines, even when task execution times deviate significantly from their estimated values or change dynamically at run-time. This dissertation presents an adaptive QoS control framework which includes a set of control design methodologies to provide robust QoS assurance for systems at different scales. To demonstrate its effectiveness, we have applied the framework to the end-to-end CPU utilization control problem for a common class of distributed real-time embedded systems with end-to-end tasks. We formulate the utilization control problem as a constrained multi-input-multi-output control model. We then present a centralized control algorithm for small or medium size systems, and a decentralized control algorithm for large-scale systems. Both algorithms are designed systematically based on model predictive control theory to dynamically enforce desired utilizations. We also introduce novel task allocation algorithms to ensure that the system is controllable and feasible for utilization control. Furthermore, we integrate our control algorithms with fault-tolerance mechanisms as an effective way to develop robust middleware systems, which maintain both system reliability and real-time performance even when the system is in face of malicious external resource contentions and permanent processor failures. Both control analysis and extensive experiments demonstrate that our control algorithms and middleware systems can achieve robust utilization guarantees. The control framework has also been successfully applied to other distributed real-time applications such as end-to-end delay control in real-time image transmission. Our results show that adaptive QoS control middleware is a step towards self-managing, self-healing and self-tuning distributed computing platform

A temporal logic for HD-automata

Due to the broad diffusion of every kind of network and mobile device in the last few years, computing is rapidly evolving towards what is now called "global computing". With this term we refer to a new field of research and development in computer science, with innovative features with respects to standard development processes and software architectures: computing is distributed and mobile, programs are heterogeneous, systems are open-ended, computational entities are autonomous. To completely meet these goals in software production, work is required from the theoretical point of view, both to develop new models of computation that satisfy the given requirements, and to learn how to reason about the behavior, and check properties, of such systems. In this thesis, we will concentrate on a particular operational model called history dependent automata, an enriched version of labeled transition systems that can represent name generation and name passing, and is particularly adapt to model in a compact way finite state, concurrent mobile systems. We develop a temporal logic for pi calculus and HD-automata, provide proofs of adequacy, soundness and completeness, and describe the model checking algorithm. Case studies are provided which reveal the expressive power of the logic

Black-Box Parallelization for Machine Learning

The landscape of machine learning applications is changing rapidly: large centralized datasets are replaced by high volume, high velocity data streams generated by a vast number of geographically distributed, loosely connected devices, such as mobile phones, smart sensors, autonomous vehicles or industrial machines. Current learning approaches centralize the data and process it in parallel in a cluster or computing center. This has three major disadvantages: (i) it does not scale well with the number of data-generating devices since their growth exceeds that of computing centers, (ii) the communication costs for centralizing the data are prohibitive in many applications, and (iii) it requires sharing potentially privacy-sensitive data. Pushing computation towards the data-generating devices alleviates these problems and allows to employ their otherwise unused computing power. However, current parallel learning approaches are designed for tightly integrated systems with low latency and high bandwidth, not for loosely connected distributed devices. Therefore, I propose a new paradigm for parallelization that treats the learning algorithm as a black box, training local models on distributed devices and aggregating them into a single strong one. Since this requires only exchanging models instead of actual data, the approach is highly scalable, communication-efficient, and privacy-preserving. Following this paradigm, this thesis develops black-box parallelizations for two broad classes of learning algorithms. One approach can be applied to incremental learning algorithms, i.e., those that improve a model in iterations. Based on the utility of aggregations it schedules communication dynamically, adapting it to the hardness of the learning problem. In practice, this leads to a reduction in communication by orders of magnitude. It is analyzed for (i) online learning, in particular in the context of in-stream learning, which allows to guarantee optimal regret and for (ii) batch learning based on empirical risk minimization where optimal convergence can be guaranteed. The other approach is applicable to non-incremental algorithms as well. It uses a novel aggregation method based on the Radon point that allows to achieve provably high model quality with only a single aggregation. This is achieved in polylogarithmic runtime on quasi-polynomially many processors. This relates parallel machine learning to Nick's class of parallel decision problems and is a step towards answering a fundamental open problem about the abilities and limitations of efficient parallel learning algorithms. An empirical study on real distributed systems confirms the potential of the approaches in realistic application scenarios

Implementing MAS agreement processes based on consensus networks

[EN] Consensus is a negotiation process where agents need to agree upon certain quantities of interest. The theoretical framework for solving consensus problems in dynamic networks of agents was formally introduced by Olfati-Saber and Murray, and is based on algebraic graph theory, matrix theory and control theory. Consensus problems are usually simulated using mathematical frameworks. However, implementation using multi-agent system platforms is a very difficult task due to problems such as synchronization, distributed finalization, and monitorization among others. The aim of this paper is to propose a protocol for the consensus agreement process in MAS in order to check the correctness of the algorithm and validate the protocol. © Springer International Publishing Switzerland 2013.This work is supported by ww and PROMETEO/2008/051 projects of the Spanish government, CONSOLIDER-INGENIO 2010 under grant CSD2007-00022, TIN2012-36586-C03-01 and PAID-06-11-2084.Palomares Chust, A.; Carrascosa Casamayor, C.; Rebollo Pedruelo, M.; Gómez, Y. (2013). Implementing MAS agreement processes based on consensus networks. Distributed Computing and Artificial Intelligence. 217:553-560. https://doi.org/10.1007/978-3-319-00551-5_66S553560217Argente, E.: et al: An Abstract Architecture for Virtual Organizations: The THOMAS approach. Knowledge and Information Systems 29(2), 379–403 (2011)Búrdalo, L.: et al: TRAMMAS: A tracing model for multiagent systems. Eng. Appl. Artif. Intel. 24(7), 1110–1119 (2011)Fogués, R.L., et al.: Towards Dynamic Agent Interaction Support in Open Multiagent Systems. In: Proc. of the 13th CCIA, vol. 220, pp. 89–98. IOS Press (2010)Luck, M., et al.: Agent technology: Computing as interaction (a roadmap for agent based computing). Eng. Appl. Artif. Intel. (2005)Mailler, R., Lesser, V.: Solving distributed constraint optimization problems using cooperative mediation. In: AAMAS 2004, pp. 438–445 (2004)Olfati-Saber, R., Fax, J.A., Murray, R.M.: Consensus and cooperation in networked multi-agent systems. Proceedings of the IEEE 95(1), 215–233 (2007)Pujol-Gonzalez, M.: Multi-agent coordination: Dcops and beyond. In: Proc. of IJCAI, pp. 2838–2839 (2011)Such, J.: et al: Magentix2: A privacy-enhancing agent platform. Eng. Appl. Artif. Intel. 26(1), 96–109 (2013)Vinyals, M., et al.: Constructing a unifying theory of dynamic programming dcop algorithms via the generalized distributive law. Autonomous Agents and Multi-Agent Systems 22, 439–464 (2011

FedLesScan: Mitigating Stragglers in Serverless Federated Learning

Federated Learning (FL) is a machine learning paradigm that enables the training of a shared global model across distributed clients while keeping the training data local. While most prior work on designing systems for FL has focused on using stateful always running components, recent work has shown that components in an FL system can greatly benefit from the usage of serverless computing and Function-as-a-Service technologies. To this end, distributed training of models with serverless FL systems can be more resource-efficient and cheaper than conventional FL systems. However, serverless FL systems still suffer from the presence of stragglers, i.e., slow clients due to their resource and statistical heterogeneity. While several strategies have been proposed for mitigating stragglers in FL, most methodologies do not account for the particular characteristics of serverless environments, i.e., cold-starts, performance variations, and the ephemeral stateless nature of the function instances. Towards this, we propose FedLesScan, a novel clustering-based semi-asynchronous training strategy, specifically tailored for serverless FL. FedLesScan dynamically adapts to the behaviour of clients and minimizes the effect of stragglers on the overall system. We implement our strategy by extending an open-source serverless FL system called FedLess. Moreover, we comprehensively evaluate our strategy using the 2nd generation Google Cloud Functions with four datasets and varying percentages of stragglers. Results from our experiments show that compared to other approaches FedLesScan reduces training time and cost by an average of 8% and 20% respectively while utilizing clients better with an average increase in the effective update ratio of 17.75%.Comment: IEEE BigData 202

Performance enhancement of a GIS-based facility location problem using desktop grid infrastructure

This paper presents the integration of desktop grid infrastructure with GIS technologies, by proposing a parallel resolution method in a generic distributed environment. A case study focused on a discrete facility location problem, in the biomass area, exemplifies the high amount of computing resources (CPU, memory, HDD) required to solve the spatial problem. A comprehensive analysis is undertaken in order to analyse the behaviour of the grid-enabled GIS system. This analysis, consisting of a set of the experiments on the case study, concludes that the desktop grid infrastructure is able to use a commercial GIS system to solve the spatial problem achieving high speedup and computational resource utilization. Particularly, the results of the experiments showed an increase in speedup of fourteen times using sixteen computers and a computational efficiency greater than 87 % compared with the sequential procedure.This work has been developed under the support of the program Formacion de Personal Investigador, grants number BFPI/2009/103 and BES-2007-17019, from the Conselleria d'Educacio of the Generalitat Valenciana and the Spanish Ministry of Science and Technology.García García, A.; Perpiñá Castillo, C.; Alfonso Laguna, CD.; Hernández García, V. (2013). Performance enhancement of a GIS-based facility location problem using desktop grid infrastructure. Earth Science Informatics. 6(4):199-207. https://doi.org/10.1007/s12145-013-0119-1S19920764Anderson D (2004) Boinc: a system for public-resource computing and storage. Proceedings of the 5th IEEE/ACM International Workshop on Grid Computing. IEEE Computer Society, Washington DC, pp 4–10Available scripts webpage: http://personales.upv.es/angarg12/Campos I et al (2012) Modelling of a watershed: a distributed parallel application in a grid framework. Comput Informat 27(2):285–296Church RL (2002) Geographical information systems and location science. Comput Oper Res 29:541–562Clarke KC (1986) Advances in geographic information systems, computers. Environ Urban Syst 10:175–184Dowers S, Gittings BM, Mineter MJ (2000) Towards a framework for high-performance geocomputation: handling vector-topology within a distributed service environment. Comput Environ Urban Syst 24:471–486Geograma SL (2009). Teleatlas. http://www.geograma.com . Accessed September 2009GRASS Development Team (2012) GRASS GIS. http://grass.osgeo.org/Hoekstra AG, Sloot PMA (2005) Introducing grid speedup: a scalability metric for parallel applications on the grid, EGC 2005, LNCS 3470, pp. 245–254Hu Y et al. (2004) Feasibility study of geo-spatial analysis using grid computing. Computational Science-ICCS. Springer Berlin Heidelberg, 956–963Huang Z et al (2009) Geobarn: a practical grid geospatial database system. Adv Electr Comput Eng 9:7–11Huang F et al (2011) Explorations of the implementation of a parallel IDW interpolation algorithm in a Linux cluster-based parallel GIS. Comput Geosci 37:426–434Laure E et al (2006) Programming the grid with gLite. CMST 12(1):33–45Li WJ et al (2005) The Design and Implementation of GIS Grid Services. In: Zhuge H, Fox G (eds) Grid and Cooperative Computing. Vol. 3795 of Lecture Notes in Computer Science 10. Springer, Berlin, pp 220–225National Geographic Institute (2010) BCN25: numerical cartographic database. http://www.ign.es/ign/main/index.do . Accessed April 2010Open Geospatial Consortium, Inc (2012) Open GIS Specification Model, http://www.opengeospatial.org/Openshaw S, Turton I (1996) A parallel Kohonen algorithm for the classification of large spatial datasets. Comput Geosci 22:1019–1026Perpiñá C, Alfonso D, Pérez-Navarro A (2007) BIODER project: biomass distributed energy resources assessment and logistic strategies for sitting biomass plants in the Valencia province (Spain), 17th European Biomass Conference and Exhibition Proceedings, Hamburg, Germany, pp. 387–393Perpiñá C et al (2008) Methodology based on Geographic Information Systems for biomass logistics and transport optimization. Renew Energ 34:555–565Shen Z et al (2007) Distributed computing model for processing remotely sensed images based on grid computing. Inf Sci 177:504–518Spanish Ministry of Agriculture, fisheries and food (2009). http://www.magrama.gob.es/es/ . Accessed March 2009Spanish Ministry of Environment (2008). http://www.magrama.gob.es/es/ . Accessed May 2008University of California. List of BOINC projects. http://boinc.berkeley.edu/projects.phpXiao N, Fu W (2003) SDPG: Spatial data processing grid. J Comput Sci Technol 18:523–53

Engineering security into distributed systems: a survey of methodologies

Rapid technological advances in recent years have precipitated a general shift towards software distribution as a central computing paradigm. This has been accompanied by a corresponding increase in the dangers of security breaches, often causing security attributes to become an inhibiting factor for use and adoption. Despite the acknowledged importance of security, especially in the context of open and collaborative environments, there is a growing gap in the survey literature relating to systematic approaches (methodologies) for engineering secure distributed systems. In this paper, we attempt to fill the aforementioned gap by surveying and critically analyzing the state-of-the-art in security methodologies based on some form of abstract modeling (i.e. model-based methodologies) for, or applicable to, distributed systems. Our detailed reviews can be seen as a step towards increasing awareness and appreciation of a range of methodologies, allowing researchers and industry stakeholders to gain a comprehensive view of the field and make informed decisions. Following the comprehensive survey we propose a number of criteria reflecting the characteristics security methodologies should possess to be adopted in real-life industry scenarios, and evaluate each methodology accordingly. Our results highlight a number of areas for improvement, help to qualify adoption risks, and indicate future research directions.Anton V. Uzunov, Eduardo B. Fernandez, Katrina Falkne

Next Generation Cloud Computing: New Trends and Research Directions

The landscape of cloud computing has significantly changed over the last decade. Not only have more providers and service offerings crowded the space, but also cloud infrastructure that was traditionally limited to single provider data centers is now evolving. In this paper, we firstly discuss the changing cloud infrastructure and consider the use of infrastructure from multiple providers and the benefit of decentralising computing away from data centers. These trends have resulted in the need for a variety of new computing architectures that will be offered by future cloud infrastructure. These architectures are anticipated to impact areas, such as connecting people and devices, data-intensive computing, the service space and self-learning systems. Finally, we lay out a roadmap of challenges that will need to be addressed for realising the potential of next generation cloud systems.Comment: Accepted to Future Generation Computer Systems, 07 September 201