Improving Real-Time Data Dissemination Performance by Multi Path Data Scheduling in Data Grids

The performance of data grids for data intensive, real-time applications is highly dependent on the data dissemination algorithm employed in the system. Motivated by this fact, this study first formally defines the real-time splittable data dissemination problem (RTS/DDP) where data transfer requests can be routed over multiple paths to maximize the number of data transfers to be completed before their deadlines. Since RTS/DDP is proved to be NP-hard, four different heuristic algorithms, namely kSP/ESMP, kSP/BSMP, kDP/ESMP, and kDP/BSMP are proposed. The performance of these heuristic algorithms is analyzed through an extensive set of data grid system simulation scenarios. The simulation results reveal that a performance increase up to 8 % as compared to a very competitive single path data dissemination algorithm is possible

Strategies for Increased Energy Awareness in Cloud Federations

This chapter first identifies three scenarios that current energy aware cloud solutions cannot handle as isolated IaaS, but their federative efforts offer opportunities to be explored. These scenarios are centered around: (i) multi-datacenter cloud operator, (ii) commercial cloud federations, (iii) academic cloud federations. Based on these scenarios, we identify energy-aware scheduling policies to be applied in the management solutions of cloud federations. Among others, these policies should consider the behavior of independent administrative domains, the frequently contradicting goals of the participating clouds and federation wide energy consumption

Deploying and Evaluating OF@TEIN Access Center and Its Feasibility for Access Federation

For the emerging software-defined infrastructure, to be orchestrated from so-called logically centralized DevOps Tower, the shared accessibility of distributed playground resources and the timely interaction among operators and developers are highly required. In this paper, by taking OF@TEIN SDN-Cloud playground as a target environment, we discuss an access center effort to address the above requirements. In providing the developer presence via the proposed access center, the inherent heterogeneity of internationally dispersed OF@TEIN resources is setting a unique challenge to cope with the broad spectrum of link bandwidths and round-trip delays. The access capability of deployed access center is experimentally verified against a wide range of access network conditions, which would be extended for futuristic access federation with appropriate identity management and resources abstraction for multiple developers and operators

Towards intelligent distributed computing : cell-oriented computing

Distributed computing systems are of huge importance in a number of recently established and future functions in computer science. For example, they are vital to banking applications, communication of electronic systems, air traffic control, manufacturing automation, biomedical operation works, space monitoring systems and robotics information systems. As the nature of computing comes to be increasingly directed towards intelligence and autonomy, intelligent computations will be the key for all future applications. Intelligent distributed computing will become the base for the growth of an innovative generation of intelligent distributed systems. Nowadays, research centres require the development of architectures of intelligent and collaborated systems; these systems must be capable of solving problems by themselves to save processing time and reduce costs. Building an intelligent style of distributed computing that controls the whole distributed system requires communications that must be based on a completely consistent system. The model of the ideal system to be adopted in building an intelligent distributed computing structure is the human body system, specifically the body’s cells. As an artificial and virtual simulation of the high degree of intelligence that controls the body’s cells, this chapter proposes a Cell-Oriented Computing model as a solution to accomplish the desired Intelligent Distributed Computing system

Discovering Piecewise Linear Models of Grid Workload

International audienceDespite extensive research focused on enabling QoS for grid users through economic and intelligent resource provisioning, no consensus has emerged on the most promising strategies. On top of intrinsically challenging problems, the complexity and size of data has so far drastically limited the number of comparative experiments. An alternative to experimenting on real, large, and complex data, is to look for well-founded and parsimonious representations. This study is based on exhaustive information about the gLite-monitored jobs from the EGEE grid, representative of a significant fraction of e-science computing activity in Europe. Our main contributions are twofold. First we found that workload models for this grid can consistently be discovered from the real data, and that limiting the range of models to piecewise linear time series models is sufficiently powerful. Second, we present a bootstrapping strategy for building more robust models from the limited samples at hand

Discovering Linear Models of Grid Workload

Despite extensive research focused on enabling QoS for grid users through economic and intelligent resource provisioning, no consensus has emerged on the most promising strategies. On top of intrinsically challenging problems, the complexity and size of data has so far drastically limited the number of comparative experiments. An alternative to experimenting on real, large, and complex data, is to look for well-founded and parsimonious representations. The goal of this paper is to answer a set of preliminary questions, which may help steering the design of those along feasible paths: is it possible to exhibit consistent models of the grid workload? If such models do exist, which classes of models are more appropriate, considering both simplicity and descriptive power? How can we actually discover such models? And finally, how can we assess the quality of these models on a statistically rigorous basis? Our main contributions are twofold. First we found that grid workload models can consistently be discovered from the real data, and that limiting the range of models to piecewise linear time series models is sufficiently powerful. Second, we presents a bootstrapping strategy for building more robust models from the limited samples at hand. This study is based on exhaustive information representative of a significant fraction of e-science computing activity in Europe

User centric community clouds

With the evolution in cloud technologies, users are becoming acquainted with seamless service provision. Nevertheless, clouds are not a user centric technology, and users become completely dependent on service providers. We propose a novel concept for clouds, where users self-organize to create their clouds. We present such an architecture for user-centric clouds, which relies on self-managed clouds based on doctrine and on identity management concepts

Network Powered by Computing: Next Generation of Computational Infrastructure

This paper is an extended version of my talk on the MoNeTec-2022. It gives a detailed presentation of the concept Network Powered by Computing. The main differences from the previously published one are that the functional architecture of the NPC is presented, the main problems on the way to its implementation are formulated, the mathematical statements of the problems of control and management of the resources in the NPC environment by methods of multi-agent optimization are given, the existence of a solution to these problems is justified, and the relationship between the problem of control in such an infrastructure and the Barabási-Albert model is shown. An example of the predicting execution time of services in the NPC environment is given

Smartcells : a Bio-Cloud theory towards intelligent cloud computing system

Cloud computing is the future of web technologies and the goal for all web companies as well. It reinforces some old concepts of building highly scalable Internet architectures and introduces some new concepts that entirely change the way applications are built and deployed. In the recent years, some technology companies adopted the cloud computing strategy. This adoption took place when these companies have predicted that cloud computing will be the solutions of Web problems such as availability. However, organizations find it almost impossible to launch the cloud idea without adopting previous approaches like that of Service-Oriented approach. As a result of this dependency, web service problems are transferred into the cloud. Indeed, the current cloud’s availability is too expensive due to service replication, some cloud services face performance problem, a majority of these services is weak regarding security, and cloud services are randomly discovered while it is difficult to precisely select the best ones in addition to being spontaneously fabricated in an ocean of services. Moreover, it is impossible to validate cloud services especially before runtime. Finally, according to the W3C standards, cloud services are not yet internationalized. Indeed, the predicted web is a smart service model while it lacks intelligence and autonomy. This is why the adoption of service-oriented model was not an ideal decision. In order to minimize the consequences of cloud problems and achieve more benefits, each cloud company builds its own cloud platform. Currently, cloud vendors are facing a big problem that can be summarized by the “Cloud Platform Battle”. The budget of this battle will cost about billions of dollars due to the absence of an agreement to reach a standard cloud platform. Why intelligent collaboration is not applied between distributed clouds to achieve better Cloud Computing results? The appropriate approach is to restructure the cloud model basis to recover its issues. Multiple intelligent techniques may be used to develop advanced intelligent Cloud systems. Classical examples of distributed intelligent systems include: human body, social insect colonies, flocks of vertebrates, multi-agent systems, transportation systems, multi-robot systems, and wireless sensor networks. However, the intelligent system that could be imitated is the human body system, in which billions of body cells work together to achieve accurate results. Inspired by Bio-Informatics strategy that benefits from technologies to solve biological facts (like our genes), this thesis research proposes a novel Bio-Cloud strategy which imitates biological facts (like brain and genes) in solving the Cloud Computing issues. Based on Bio-Cloud strategy, I have developed through this thesis project the “SmartCells” framework as a smart solution for Cloud problems. SmartCells framework covers: 1) Cloud problems which are inherited from the service paradigm (like issues of service reusability, security, etc.); 2) The intelligence insufficiency problem in Cloud Computing systems. SmartCells depends on collaborations between smart components (Cells) that take advantage of the variety of already built web service components to produce an intelligent Cloud system. Le « Cloud Computing » est certes le futur des technologies du web. Il renforce certains vieux concepts de construction d’architectures internet hautement évolutifs, et introduit de nouveaux concepts qui changent complètement la façon dont les applications sont développées et déployées. Au cours des dernières années, certaines entreprises technologiques ont adopté la stratégie du Cloud Computing. Cette adoption a eu lieu lorsque ces entreprises ont prédit que le Cloud Computing sera les solutions des plusieurs problèmes Web tels que la disponibilité. Toutefois, les organisations pensent qu'il est presque impossible de lancer l'idée du « Cloud » sans adopter les concepts et les normes antérieures comme celle du paradigme orienté service (Service-Oriented Paradigm). En raison de cette dépendance, les problèmes de l'approche orientée service et services web sont transférés au Cloud. En effet, la disponibilité du Cloud actuel s’avère trop chère à cause de la reproduction de services, certains services Cloud sont confrontés à des problèmes de performances, une majorité des services Cloud est faible en matière de sécurité, et ces services sont découverts d’une façon aléatoire, il est difficile de choisir le meilleur d’entre eux ainsi qu’ils sont composés d’un groupe de services web dans un monde de services. Egalement, il est impossible de valider les services Cloud en particulier, avant le temps d’exécution. Finalement, selon les normes du W3C, les services Cloud ne sont pas encore internationalisés. En effet, le web comme prévu, est un modèle de service intelligent bien qu’il manque d’intelligence et d’autonomie. Ainsi, l'adoption d'un modèle axé sur le service n’était pas une décision idéale. Afin de minimiser les conséquences des problèmes du Cloud et réaliser plus de profits, certaines entreprises de Cloud développent leurs propres plateformes de Cloud Computing. Actuellement, les fournisseurs du Cloud font face à un grand problème qui peut se résumer par la « Bataille de la plateforme Cloud ». Le budget de cette bataille coûte des milliards de dollars en l’absence d’un accord pour accéder à une plateforme Cloud standard. Pourquoi une collaboration intelligente n’est pas mise en place entre les nuages distribués pour obtenir de meilleurs résultats ? L’approche appropriée est de restructurer le modèle de cloud afin de couvrir ses problèmes. Des techniques intelligentes multiples peuvent être utilisées pour développer des systèmes Cloud intelligents avancés. Parmi les exemples classiques de systèmes intelligents distribués se trouvent : le corps humain, les colonies d’insectes sociaux, les troupeaux de vertébrés, les systèmes multi-agents, les systèmes de transport, les systèmes multi-robots, et les réseaux de capteurs sans fils. Toutefois, le système intelligent qui pourrait être imité est le système du corps humain dans lequel vivent des milliards de cellules du corps et travaillent ensemble pour atteindre des résultats précis. En s’inspirant de la stratégie Bio-Informatique qui bénéficie de technologies pour résoudre des faits biologiques (comme les gènes). Cette thèse propose une nouvelle stratégie Bio-Cloud qui imite des faits biologiques (comme le cerveau et les gènes) pour résoudre les problèmes du Cloud Computing mentionnés ci-haut. Ainsi, en me basant sur la stratégie Bio-Cloud, j’ai développé au cours de cette thèse la théorie « SmartCells » conçue comme une proposition (approche) cherchant à résoudre les problèmes du Cloud Computing. Cette approche couvre : 1) les problèmes hérités du paradigme services (comme les questions de réutilisation de services, les questions de sécurité, etc.); 2) le problème d’insuffisance d’intelligence dans les systèmes du Cloud Computing. SmartCells se base sur la collaboration entre les composants intelligents (les Cellules) qui profitent de la variété des composants des services web déjà construits afin de produire un système de Cloud intelligent