AC-RDVT: Acyclic Resource Distance Vector Routing Tables for Dynamic Grid Resource Discovery

Since the objective of grid is sharing the numerous and heterogeneous resources, resource discovery is a challenging issue. Recently appeared, Ontosum, is a resource discovery method based on semantically linked organizations and a routing algorithm Resource Distance Vector (RDV), has been presented to forward resource discovery queries into the clusters. Although this framework is efficient for large-scale grids and nodes are clustered automatically based on semantic attributes to constitute a semantically linked overlay network, but the dynamic behavior of grid isn’t considered. In this method, deceptive information is stored in RDV tables (RDVT) which cause some problems in routing process. In this paper, a method is proposed to improve the dynamism of RDV routing algorithm, so the consistency with grid environments is increased. The developed algorithm is assessed by investigating the success probability, number of hops and routing time of resource discovery.DOI:http://dx.doi.org/10.11591/ijece.v3i1.183

A performance study of routing protocols for mobile grid environment

Integration of mobile wireless consumer devices into the Grid initially seems unlikely due to limitation such as CPU performance,small secondary storage, heightened battery consumption sensitivity and unreliable low-bandwidth communication. The current grid architecture and algorithm also do not take into account the mobile computing environment since mobile devices have not been seriously considered as valid computing resources or interfaces in grid communities. This paper presents the results of simulation done in identifying a suitable ad hoc routing protocol that can be used for the target grid application in mobile environment. The simulation comparing three ad hoc routing protocols named DSDV, DSR and AODV

Small-world networks, distributed hash tables and the e-resource discovery problem

Resource discovery is one of the most important underpinning problems behind producing a scalable, robust and efficient global infrastructure for e-Science. A number of approaches to the resource discovery and management problem have been made in various computational grid environments and prototypes over the last decade. Computational resources and services in modern grid and cloud environments can be modelled as an overlay network superposed on the physical network structure of the Internet and World Wide Web. We discuss some of the main approaches to resource discovery in the context of the general properties of such an overlay network. We present some performance data and predicted properties based on algorithmic approaches such as distributed hash table resource discovery and management. We describe a prototype system and use its model to explore some of the known key graph aspects of the global resource overlay network - including small-world and scale-free properties

Radio Link Enabler for Context-aware D2D Communication in Reuse Mode

Device-to-Device (D2D) communication is considered as one of the key technologies for the fifth generation wireless communication system (5G) due to certain benefits provided, e.g. traffic offload and low end-to-end latency. A D2D link can reuse resource of a cellular user for its own transmission, while mutual interference in between these two links is introduced. In this paper, we propose a smart radio resource management (RRM) algorithm which enables D2D communication to reuse cellular resource, by taking into account of context information. Besides, signaling schemes with high efficiency are also given in this work to enable the proposed RRM algorithm. Simulation results demonstrate the performance improvement of the proposed scheme in terms of the overall cell capacity

Fuzzy C-Mean And Genetic Algorithms Based Scheduling For Independent Jobs In Computational Grid

The concept of Grid computing is becoming the most important research area in the high performance computing. Under this concept, the jobs scheduling in Grid computing has more complicated problems to discover a diversity of available resources, select the appropriate applications and map to suitable resources. However, the major problem is the optimal job scheduling, which Grid nodes need to allocate the appropriate resources for each job. In this paper, we combine Fuzzy C-Mean and Genetic Algorithms which are popular algorithms, the Grid can be used for scheduling. Our model presents the method of the jobs classifications based mainly on Fuzzy C-Mean algorithm and mapping the jobs to the appropriate resources based mainly on Genetic algorithm. In the experiments, we used the workload historical information and put it into our simulator. We get the better result when compared to the traditional algorithms for scheduling policies. Finally, the paper also discusses approach of the jobs classifications and the optimization engine in Grid scheduling