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Title
Abstract
Introduction
Design Principles
Evaluation and Simulation Results
Simulation Results
Related Work
Conclusion
References
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Computer Science
Distributed Systems

HARD: Hybrid Adaptive Resource Discovery for Jungle Computing

Profile image of Rui AguiarRui Aguiar

2017, Journal of Network and Computer Applications

https://doi.org/10.1016/J.JNCA.2017.04.014
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37 pages

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Abstract

In recent years, Jungle Computing has emerged as a distributed computing paradigm based on simultaneous combination of various hierarchical and distributed computing environments which are composed by large number of heterogeneous resources. In such a computing environment, the resources and the underlying computation and communication infrastructures are highly-hierarchical and heterogeneous. This creates a lot of difficulty and complexity for finding the proper resources in a precise way in order to run a particular job on the system efficiently. This paper proposes Hybrid Adaptive Resource Discovery (HARD), a novel efficient and highly scalable resource-discovery approach which is built upon a virtual hierarchical overlay based on self-organization and self-adaptation of processing resources in the system, where the computing resources are organized into distributed hierarchies according to a proposed hierarchical multi-layered resource description model. The proposed approach supports distributed query processing within and across hierarchical layers by deploying various distributed resource discovery services and functionalities in the system which are implemented using different adapted algorithms and mechanisms in each level of hierarchy. The proposed approach addresses the requirements for resource discovery in Jungle Computing environments such as high-hierarchy, high-heterogeneity, high-scalability and dynamicity. Simulation results show significant scalability and efficiency of the proposed approach over highly heterogeneous, hierarchical and dynamic computing environments.

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Wireless Personal Communications, 2021

The introduction of jungle computing raises many issues regarding its feasibility because of the distributed heterogeneous highly dynamic resources. Difference in resource representation in different platform poses the first hurdle. Discovering and allocating appropriate resources in jungle without much delay is a challenging task. The paper proposes a three layer distributed architecture that handles jobs separately and in parallel. Dedicated hardware devices that are connected to each other handle resource discovery and allocation. A uniform resource description framework helps reducing the latency for resource discovery. Additionally, the data migration delay is reduced by carefully considering the location of user, resource and dynamicity of the resource. The proposed method achieved better performance that the existing methods.

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Resource discovery for distributed computing systems: A comprehensive survey
Javad Zarrin

Journal of Parallel and Distributed Computing, 2018

Large-scale distributed computing environments provide a vast amount of heterogeneous computing resources from different sources for resource sharing and distributed computing. Discovering appropriate resources in such environments is a challenge which involves several different subjects. In this paper, we provide an investigation on the current state of resource discovery protocols, mechanisms, and platforms for large-scale distributed environments, focusing on the design aspects. We classify all related aspects, general steps, and requirements to construct a novel resource discovery solution in three categories consisting of structures, methods, and issues. Accordingly, we review the literature, analyzing various aspects for each category.

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Resource Discovery for Arbitrary Scale Systems
Javad Zarrin

2017

Large scale distributed computing technologies such as Cloud, Grid, Cluster and HPC supercomputers are progressing along with the revolutionary emergence of many-core designs (e.g. GPU, CPUs on single die, supercomputers on chip, etc.) and significant advances in networking and interconnect solutions. In future, computing nodes with thousands of cores may be connected together to form a single transparent computing unit which hides from applications the complexity and distributed nature of these many core systems. In order to efficiently benefit from all the potential resources in such large scale many-core-enabled computing environments, resource discovery is the vital building block to maximally exploit the capabilities of all distributed heterogeneous resources through precisely recognizing and locating those resources in the system. The efficient and scalable resource discovery is challenging for such future systems where the resources and the underlying computation and communication infrastructures are highly-dynamic, highly-hierarchical and highly-heterogeneous. In this thesis, we investigate the problem of resource discovery with respect to the general requirements of arbitrary scale future many-core-enabled computing environments. The main contribution of this thesis is to propose Hybrid Adaptive Resource Discovery (HARD), a novel efficient and highly scalable resource-discovery approach which is built upon a virtual hierarchical overlay based on self-organization and self-adaptation of processing resources in the system, where the computing resources are organized into distributed hierarchies according to a proposed hierarchical multi-layered resource description model. Operationally, at each layer, it consists of a peer-to-peer architecture of modules that, by interacting with each other, provide a global view of the resource availability in a large, dynamic and heterogeneous distributed environment. The proposed resource discovery model provides the adaptability and flexibility to perform complex querying by supporting a set of significant querying features (such as multi-dimensional, range and aggregate querying) while supporting exact and partial matching, both for static and dynamic object contents. The simulation shows that HARD can be applied to arbitrary scales of dynamicity, both in terms of complexity and of scale, positioning this proposal as a proper architecture for future many-core systems. We also contributed to propose a novel resource management scheme for future systems which efficiently can utilize distributed resources in a fully decentralized fashion. Moreover, leveraging discovery components (RR-RPs) enables our resource management platform to dynamically find and allocate available resources that guarantee the QoS parameters on demand. List of Acronyms v List of Figures ix List of Tables xv List of Algorithms xvii iii iv List of Acronyms ACO Ant Colony Optimization. AI Arithmetic Intelligence. ALU Arithmetic Logic Unit. AN Aggregate-Node. ANN Artificial Neuron Network. ASL Average Search Length. ATNoG Advanced Telecommunications and Networks Group. AVL Georgy Adelson-Velsky and Evgenii Landis. BCA Bee Colony Algorithm. BF Bloom Filter. BFS Breadth First Search. BOINC Berkeley Open Infrastructure for Network Computing. BRW2 a 2-layered hybrid broadcast and full random-walk based discovery. Cell BE Cell Broadband Engine. CP Client Proxy. CPU Central Processing Unit. DAML DARPA's Agent Markup Language. DFS Depth First Search. DHT Distributed Hash Table. DIS DHT Information Service. DNS Domain Name System. DOS Distributed Operation System. DoS Denial of Service. DPT Distributed Probability Table. ElCore Elastic Core. FALKON Fast and LightWeight Task Execution Framework. FMCS Future ManyCore Systems. FPGA Field Programmable Gate Array. FRW2 a 2-layered hybrid DHT and full random-walk based discovery.

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Resource discovery support for time-critical adaptive applications
Carlo Bertolli

Proceedings of the …, 2010

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Resource and application-aware resource discovery in computing environments
mohammad norouzi

The Journal of Supercomputing, 2014

Efficient resource discovery plays a vital role in the effective management of resources and applications in heterogeneous computing environments. Therefore, the knowledge of applications' behavior and resources' usage pattern improves resource discovery decisions. This knowledge can be provided for the resource discovery mechanism by cooperating with the load balancing mechanism. In this paper, we formulate their cooperation by considering some parameters that represent applications' behavior and resources' usage patterns and extract the relation between them to introduce a formula using mathematical methods. Further, the resource discovery mechanism uses the formula to predict resources' load before assigning them new processes and thus it prevents resource overloading which happens frequently in computing environments.

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Related topics

  • Computer Science
  • Distributed Computing
  • Grid Computing
  • High Performance Computing
  • Distributed Systems
  • Parallel & Distributed Computing
  • Resource Discovery
  • Distributed Operating Systems

    • Cited by

    RDAD: An Efficient Distributed Multi-Layered Resource Handler in Jungle Computing
    Jeraldin Auxillia

    Wireless Personal Communications, 2021

    The introduction of jungle computing raises many issues regarding its feasibility because of the distributed heterogeneous highly dynamic resources. Difference in resource representation in different platform poses the first hurdle. Discovering and allocating appropriate resources in jungle without much delay is a challenging task. The paper proposes a three layer distributed architecture that handles jobs separately and in parallel. Dedicated hardware devices that are connected to each other handle resource discovery and allocation. A uniform resource description framework helps reducing the latency for resource discovery. Additionally, the data migration delay is reduced by carefully considering the location of user, resource and dynamicity of the resource. The proposed method achieved better performance that the existing methods.

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