Unified Theory of Relativistic Identification of Information in a Systems Age: Proposed Convergence of Unique Identification with Syntax and Semantics through Internet Protocol version 6

Unique identification of objects are helpful to the decision making process in many domains. Decisions, however, are often based on information that takes into account multiple factors. Physical objects and their unique identification may be one of many factors. In real-world scenarios, increasingly decisions are based on collective information gathered from multiple sources (or systems) and then combined to a higher level domain that may trigger a decision or action. Currently, we do not have a globally unique mechanism to identify information derived from data originating from objects and processes. Unique identification of information, hence, is an open question. In addition, information, to be of value, must be related to the context of the process. In general, contextual information is of greater relevance in the decision making process or in decision support systems. In this working paper, I shall refer to such information as decisionable information. The suggestion here is to utilize the vast potential of internet protocol version six (IPv6) to uniquely identify not only objects and processes but also relationships (semantics) and interfaces (sensors). Convergence of identification of diverse entities using the globally agreed structure of IPv6 offers the potential to identify 3.4x10[subscript 38] instances based on the fact that the 128-bit IPv6 structure can support 3.4x10[subscript 38] unique addresses. It is not necessary that all instances must be connected to the internet or routed or transmitted simply because an IP addressing scheme is suggested. This is a means for identification that will be globally unique and offers the potential to be connected or routed via the internet. In this working paper, scenarios offer [1] new revenue potential from data routing (P2P traffic track and trace) for telecommunication industries, [2] potential for use in healthcare and biomedical community, [3] scope of use in the semantic web structure by transitioning URIs used in RDF, [4] applications involving thousands of mobile ad hoc sensors (MANET) that demand dynamic adaptive auto-reconfiguration. This paper presents a confluence of ideas

Topology-aware GPU scheduling for learning workloads in cloud environments

Recent advances in hardware, such as systems with multiple GPUs and their availability in the cloud, are enabling deep learning in various domains including health care, autonomous vehicles, and Internet of Things. Multi-GPU systems exhibit complex connectivity among GPUs and between GPUs and CPUs. Workload schedulers must consider hardware topology and workload communication requirements in order to allocate CPU and GPU resources for optimal execution time and improved utilization in shared cloud environments. This paper presents a new topology-aware workload placement strategy to schedule deep learning jobs on multi-GPU systems. The placement strategy is evaluated with a prototype on a Power8 machine with Tesla P100 cards, showing speedups of up to ≈1.30x compared to state-of-the-art strategies; the proposed algorithm achieves this result by allocating GPUs that satisfy workload requirements while preventing interference. Additionally, a large-scale simulation shows that the proposed strategy provides higher resource utilization and performance in cloud systems.This project is supported by the IBM/BSC Technology Center for Supercomputing collaboration agreement. It has also received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 639595). It is also partially supported by the Ministry of Economy of Spain under contract TIN2015-65316-P and Generalitat de Catalunya under contract 2014SGR1051, by the ICREA Academia program, and by the BSC-CNS Severo Ochoa program (SEV-2015-0493). We thank our IBM Research colleagues Alaa Youssef and Asser Tantawi for the valuable discussions. We also thank SC17 committee member Blair Bethwaite of Monash University for his constructive feedback on the earlier drafts of this paper.Peer ReviewedPostprint (published version

M-Grid : A distributed framework for multidimensional indexing and querying of location based big data

The widespread use of mobile devices and the real time availability of user-location information is facilitating the development of new personalized, location-based applications and services (LBSs). Such applications require multi-attribute query processing, handling of high access scalability, support for millions of users, real time querying capability and analysis of large volumes of data. Cloud computing aided a new generation of distributed databases commonly known as key-value stores. Key-value stores were designed to extract value from very large volumes of data while being highly available, fault-tolerant and scalable, hence providing much needed features to support LBSs. However complex queries on multidimensional data cannot be processed efficiently as they do not provide means to access multiple attributes. In this thesis we present MGrid, a unifying indexing framework which enables key-value stores to support multidimensional queries. We organize a set of nodes in a P-Grid overlay network which provides fault-tolerance and efficient query processing. We use Hilbert Space Filling Curve based linearization technique which preserves the data locality to efficiently manage multi-dimensional data in a key-value store. We propose algorithms to dynamically process range and k nearest neighbor (kNN) queries on linearized values. This removes the overhead of maintaining a separate index table. Our approach is completely independent from the underlying storage layer and can be implemented on any cloud infrastructure. Experiments on Amazon EC2 show that MGrid achieves a performance improvement of three orders of magnitude in comparison to MapReduce and four times to that of MDHBase scheme --Abstract, pages iii-iv

Designing and Handling Failure issues in a Structured Overlay Network Based Grid

Grid computing is the computing paradigm that is concerned with coordinated resource sharing and problem solving in dynamic, autonomous multi-institutional virtual organizations. Data exchange and service allocation between virtual organizations are challenging problems in the field of Grid computing, due to the decentralization of Grid systems. The resource management in a Grid system ensures efficiency and usability. The required efficiency and usability of Grid systems can be achieved by building a decentralized multi-virtual Grid system. In this thesis we present a decentralized multi-virtual resource management framework in which the system is divided into virtual organizations, each controlled by a broker. An overlay network of brokers is responsible for global resource management and managing the allocation of services. We address two main issues for both local and global resource management: 1) decentralized allocation of tasks to suitable nodes to achieve both local and global load balancing; and 2) handling of both regular and broker failures. Experimental results verify that the system achieves dependable performance with various loads of services and broker failures

An SLA-based resource virtualization approach for on-demand service provision

Cloud computing is a newly emerged research infrastructure that builds on the latest achievements of diverse research areas, such as Grid computing, Service-oriented computing, business processes and virtualization. In this paper we present an architecture for SLA-based resource virtualization that provides an extensive solution for executing user applications in Clouds. This work represents the first attempt to combine SLA-based resource negotiations with virtualized resources in terms of on-demand service provision resulting in a holistic virtualization approach. The architecture description focuses on three topics: agreement negotiation, service brokering and deployment using virtualization. The contribution is also demonstrated with a real-world case study

FreeCore : Un substrat d'indexation des filtres de Bloom fragmentés pour la recherche par mots clés

National audienceLe support efficace de la recherche par mots clés est essentiel pour une bonne exploitation des réseaux de stockage pair-à-pair structurés. Un nombre important de solutions existent dans la littérature, toutefois elles sont confrontées aux problèmes de performance inhérents au schéma d'indexation mis en oeuvre. Ce papier présente FreeCore, un substrat d'indexation de filtres de Bloomfragmentés et de recherche par mots clés. Les contributions de ce travail sont au nombre de trois. La réalisation d'un système de stockage offrant une interface qui permet d'associer une description à chaque contenu. La clé de stockage d'un contenu est déterminée à partir du filtre de Bloom de sa description. Cette facilité permet de ramener la recherche par mots clés au problème de recherche des clés de stockage qui matchent un filtre de Bloom. En second lieu, la construction d'un index distribué dont le coût de maintenance est indépendant du nombre de mots clés fournis lors d'une publication. Enfin, une méthode de recherche d'information à base de mots clés dont le coût est indépendant du nombre de mots clés fournis. Les propriétés découlant des choix opérés et les résultats des évaluations font de FreeCore une brique de base pour des applications et systèmes désirant un support efficace de la recherche par mots clés

Partial Replica Location And Selection For Spatial Datasets

As the size of scientific datasets continues to grow, we will not be able to store enormous datasets on a single grid node, but must distribute them across many grid nodes. The implementation of partial or incomplete replicas, which represent only a subset of a larger dataset, has been an active topic of research. Partial Spatial Replicas extend this functionality to spatial data, allowing us to distribute a spatial dataset in pieces over several locations. We investigate solutions to the partial spatial replica selection problems. First, we describe and develop two designs for an Spatial Replica Location Service (SRLS), which must return the set of replicas that intersect with a query region. Integrating a relational database, a spatial data structure and grid computing software, we build a scalable solution that works well even for several million replicas. In our SRLS, we have improved performance by designing a R-tree structure in the backend database, and by aggregating several queries into one larger query, which reduces overhead. We also use the Morton Space-filling Curve during R-tree construction, which improves spatial locality. In addition, we describe R-tree Prefetching(RTP), which effectively utilizes the modern multi-processor architecture. Second, we present and implement a fast replica selection algorithm in which a set of partial replicas is chosen from a set of candidates so that retrieval performance is maximized. Using an R-tree based heuristic algorithm, we achieve O(n log n) complexity for this NP-complete problem. We describe a model for disk access performance that takes filesystem prefetching into account and is sufficiently accurate for spatial replica selection. Making a few simplifying assumptions, we present a fast replica selection algorithm for partial spatial replicas. The algorithm uses a greedy approach that attempts to maximize performance by choosing a collection of replica subsets that allow fast data retrieval by a client machine. Experiments show that the performance of the solution found by our algorithm is on average always at least 91% and 93.4% of the performance of the optimal solution in 4-node and 8-node tests respectively