Enabling object storage via shims for grid middleware

The Object Store model has quickly become the basis of most commercially successful mass storage infrastructure, backing so-called "Cloud" storage such as Amazon S3, but also underlying the implementation of most parallel distributed storage systems. Many of the assumptions in Object Store design are similar, but not identical, to concepts in the design of Grid Storage Elements, although the requirement for "POSIX-like" filesystem structures on top of SEs makes the disjunction seem larger. As modern Object Stores provide many features that most Grid SEs do not (block level striping, parallel access, automatic file repair, etc.), it is of interest to see how easily we can provide interfaces to typical Object Stores via plugins and shims for Grid tools, and how well experiments can adapt their data models to them. We present evaluation of, and first-deployment experiences with, (for example) Xrootd-Ceph interfaces for direct object-store access, as part of an initiative within GridPP[1] hosted at RAL. Additionally, we discuss the tradeoffs and experience of developing plugins for the currently-popular Ceph parallel distributed filesystem for the GFAL2 access layer, at Glasgow

Analyse the Performance of Mobile Peer to Peer Network using Ant Colony Optimization

A mobile peer-to-peer computer network is the one in which each computer in the network can act as a client or server for the other computers in the network. The communication process among the nodes in the mobile peer to peer network requires more no of messages. Due to this large number of messages passing, propose an interconnection structure called distributed Spanning Tree (DST) and it improves the efficiency of the mobile peer to peer network. The proposed method improves the data availability and consistency across the entire network and also reduces the data latency and the required number of message passes for any specific application in the network. Further to enhance the effectiveness of the proposed system, the DST network is optimized with the Ant Colony Optimization method. It gives the optimal solution of the DST method and increased availability, enhanced consistency and scalability of the network. The simulation results shows that reduces the number of message sent for any specific application and average delay and increases the packet delivery ratio in the network

Anticipatory models of load balancing in cloud computing

University of Technology Sydney. Faculty of Engineering and Information Technology.Cloud Computing is a recent arrival to the world of IT infrastructure. The concept allows companies to maximise utilisation of their potentials and consequently boost their performance. One of the main benefits of Cloud Computing is the significant increase in efficiency of executing business plans. Additionally, Cloud Computing provides large-scale applications with powerful computing power across global locations. Yet Cloud users are able to share their data easily by using replication methodologies. Cloud Computing structure has been developed based on a multi-tenancy concept. Therefore, availability and efficiency of the resources are important factors in the Cloud architecture. However, as the numbers of users are increasing rapidly, the load will have a significant impact on performance and operation of the Cloud systems. Accordingly, optimised load balancing algorithms that can manage the Cloud load in a time- and cost-efficient manner are required. Much research in recent years has been dedicated to optimising load balancing in Cloud Computing. This optimisation is demonstrated through a balanced network of interacting resources. The goal of this network is to minimise the wait time and maximise utilisation of the throughput. This thesis provides a set of solutions which mitigate the problem of load balancing in the Cloud. The dissertation investigates a novel class of heuristic scheduling algorithms that improves load balancing in workflow scheduling applications. Furthermore, it proposes a new anticipatory replication methodology with the objective of improving data availability to enhance the load balancing between the Cloud sites. In summary, this research innovation implicates the design of optimised load balancing algorithms that consider the magnitude and direction of the load in workflow applications. Furthermore, by architecting the anticipatory replication algorithm, it minimises the numbers of the replicas and enhances the effective network usage in Cloud-based systems

Web Service Composition Framework using Petrinet and Web Service Data Cache in MANET

A Mobile Ad Hoc Network (MANET) is characterized by multi-hop wireless links and frequent node mobility. Every neighboring node in the MANET is likely to have similar task and interests, several nodes might need to access the similar web service at different times. So, by caching the repeatedly accessed web service data within MANET, it is possible to reduce the cost of accessing the same service details from the UDDI and also from the external providers. Composition of web services leads to a better alternative as, at times a candidate web service may not completely serve the need of the customer. An effective Data Cache Mechanism (DCM) has been proposed in [6] using the Distributed Spanning Tree (DST) as a communication structure in Mobile network to improve scalability and lessen network overload. As an enhancement, Ant Colony Optimization (ACO) technique has been applied on DST to cope with the fragile nature of the MANET and to improve the network fault tolerance [1]. In these perspectives, an efficient Web Service Cache Mechanism (WSCM) can be modeled to improve the performance of the web service operations in MANET. In this paper, a fine grained theoretical model has been formulated to assess the various performance factors such as Cooperative Cache and Mobility Handoff. In addition to these, the performance improvement of WSCM using DST and ACO optimized DST techniques in MANET has been proved experimentally using Precision and Data Reliability of the system using appropriate simulation