Exploring the E-science Knowledge Base through co-citation analysis

E-Science is the “science of this age”; it is realized through collaborative scientific enquiry which requires utilization of non-trivial amounts of computing resources and massive data sets. In this paper we explore the e-Science knowledge base through co-citation analysis of extant literature. Our objective is to use the knowledge domain visualization software CiteSpace to identifying the turning point articles and authors. In other words, our analysis is not solely based on tabulating the frequency of co-cited articles and authors, but the identification of landmark articles and authors irrespective of their co-citation count. The dataset for this analysis is downloaded from the ISI Web of Science and includes approx. 1000 articles. It is expected that this paper will be an important source of reference for academics and researchers working in the area of e-Science and its three technology enablers - grid computing, desktop grids and cloud computing

Big data and virtual communities: methodological issues

Virtual communities represent today en emergent phenomenon through which users get together to create ideas, to obtain help from one another, or just to casually engage in discussions. Their increasing popularity as well as their utility as a source of business value and marketing strategies justify the necessity of defi ning some specifi c methodologies for analyzing them. The aim of this paper is providing new insights into virtual communities from a methodological viewpoint, highlighting the main trends and challenge

Meta-scheduling Issues in Interoperable HPCs, Grids and Clouds

Over the last years, interoperability among resources has been emerged as one of the most challenging research topics. However, the commonality of the complexity of the architectures (e.g., heterogeneity) and the targets that each computational paradigm including HPC, grids and clouds aims to achieve (e.g., flexibility) remain the same. This is to efficiently orchestrate resources in a distributed computing fashion by bridging the gap among local and remote participants. Initially, this is closely related with the scheduling concept which is one of the most important issues for designing a cooperative resource management system, especially in large scale settings such as in grids and clouds. Within this context, meta-scheduling offers additional functionalities in the area of interoperable resource management, this is because of its great agility to handle sudden variations and dynamic situations in user demands. Accordingly, the case of inter-infrastructures, including InterCloud, entitle that the decentralised meta-scheduling scheme overcome issues like consolidated administration management, bottleneck and local information exposition. In this work, we detail the fundamental issues for developing an effective interoperable meta-scheduler for e-infrastructures in general and InterCloud in particular. Finally, we describe a simulation and experimental configuration based on real grid workload traces to demonstrate the interoperable setting as well as provide experimental results as part of a strategic plan for integrating future meta-schedulers

The Contemporary Review of Notable Cloud Resource Scheduling Strategies

Cloud computing has become a revolutionary development that has changed the dynamics of business for the organizations and in IT infrastructure management. While in one dimension, it has improved the scope of access, reliability, performance and operational efficiency, in the other dimension, it has created a paradigm shift in the way IT systems are managed in an organizational environment. However, with the increasing demand for cloud based solutions, there is significant need for improving the operational efficiency of the systems and cloud based services that are offered to the customers. As cloud based solutions offer finite pool of virtualized on-demand resources, there is imperative need for the service providers to focus on effective and optimal resource scheduling systems that could support them in offering reliable and timely service, workload balancing, optimal power efficiency and performance excellence. There are numerous models of resource scheduling algorithms that has been proposed in the earlier studies, and in this study the focus is upon reviewing varied range of resource scheduling algorithms that could support in improving the process efficiency. In this manuscript, the focus is upon evaluating various methods that could be adapted in terms of improving the resource scheduling solutions

An Inter-Cloud Meta-Scheduling (ICMS) simulation framework: architecture and evaluation

Inter-cloud is an approach that facilitates scalable resource provisioning across multiple cloud infrastructures. In this paper, we focus on the performance optimization of Infrastructure as a Service (IaaS) using the meta-scheduling paradigm to achieve an improved job scheduling across multiple clouds. We propose a novel inter-cloud job scheduling framework and implement policies to optimize performance of participating clouds. The framework, named as Inter-Cloud Meta-Scheduling (ICMS), is based on a novel message exchange mechanism to allow optimization of job scheduling metrics. The resulting system offers improved flexibility, robustness and decentralization. We implemented a toolkit named “Simulating the Inter-Cloud” (SimIC) to perform the design and implementation of different inter-cloud entities and policies in the ICMS framework. An experimental analysis is produced for job executions in inter-cloud and a performance is presented for a number of parameters such as job execution, makespan, and turnaround times. The results highlight that the overall performance of individual clouds for selected parameters and configuration is improved when these are brought together under the proposed ICMS framework

An Energy-Efficient, Time-Constrained Scheduling Scheme in local mobile cloud

Mobile devices have limited resource, such as computation performance and battery life. Mobile cloud computing is gaining popularity as a solution to overcome these resource limitations by sending heavy computation to resourceful servers and receiving the results from these servers. Local mobile clouds comprised of nearby mobile devices are proposed as a better solution to support real-time applications. Since network bandwidth and computational resource is shared among all the mobile devices, a scheduling scheme is needed to ensure that multiple mobile devices can efficiently offload tasks to local mobile clouds, satisfying the tasks\u27 time constraint while keeping low-energy consumption. Two critical challenges need to be solved: (1) estimation of the energy consumption and completion time for tasks to be scheduled, (2) schedule the tasks from multiple source nodes to an appropriate device to accomplish the computation and receive the results. In this thesis, the adaptive probabilistic task scheduler for local mobile clouds is proposed. The scheduler relies on periodic network messages to discover neighboring computation and network resources. It first estimates the completion time and energy consumption at each potential processing node. Next, it schedules the current task to the proper processing node in a probabilistic way and adaptively adjusts its time margin to improve performance under the unpredictable network condition. Comparing with other existing scheduling schemes, the experimental results confirm that the proposed scheduler achieves highest task completion rate and the lowest average energy per successful task. In addition, the proposed scheduler is able to accommodate different types of tasks and network scenarios

Co-citation analysis of literature in e-science and e-infrastructures

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this recordAdvances in computer networking, storage technologies and high-performance computing are helping global communities of researchers to address increasingly ambitious problems in Science collaboratively. EScience is the “science of this age”; it is realized through collaborative scientific enquiry which requires the utilization of non-trivial amounts of computing resources and massive data sets. Core to this is the integrated set of technologies collectively known as e-Infrastructures. In this paper, we explore the e-Science and the eInfrastructure knowledge base through co-citation analysis of existing literature. The dataset for this analysis is downloaded from the ISI Web of Science and includes over 12,000 articles. We identify prominent articles, authors and articles with citation bursts. The detection of research clusters and the underlying seminal papers provide further insights. Our analysis is an important source of reference for academics, researchers and students starting research in this field

Exploring decentralized dynamic scheduling for grids and clouds using the community-aware scheduling algorithm

Job scheduling strategies have been studied for decades in a variety of scenarios. Due to the new characteristics of the emerging computational systems, such as the grid and cloud, metascheduling turns out to be an important scheduling pattern because it is responsible for orchestrating resources managed by independent local schedulers and bridges the gap between participating nodes. Equally, to overcome issues such as bottleneck, single point failure, and impractical unique administrative management, which are normally led by conventional centralized or hierarchical schemes, the decentralized scheduling scheme is emerging as a promising approach because of its capability with regards to scalability and flexibility. In this work, we introduce a decentralized dynamic scheduling approach entitled the community- aware scheduling algorithm (CASA). The CASA is a two-phase scheduling solution comprised of a set of heuristic sub-algorithms to achieve optimized scheduling performance over the scope of overall grid or cloud, instead of individual participating nodes. The extensive experimental evaluation with a real grid workload trace dataset shows that, when compared to the centralized scheduling scheme with BestFit as the metascheduling policy, the use of CASA can lead to a 30%–61% better average job slowdown, and a 68%–86% shorter average job waiting time in a decentralized scheduling manner without requiring detailed real-time processing information from participating nodes.Swiss Hasler Foundation, Marie Curie Knowledge Transfe