MIN-COST WITH DELAY SCHEDULING FOR LARGE SCALE CLOUD-BASED WORKFLOW APPLICATIONS PLATFORM

Cloud computing is a promising solution to provide the resource scalability dynamically. In order to support large scale workflow applications, we present Nuts-LSWAP which is implementation for Cloud workflow. Then, a novel Min-cost with delay scheduling algorithm is presented in this paper. We also focuses on the global scheduling including genetic evolution method and other scheduling methods (sequence and greedy) to evaluate and decrease the execution cost. Finally, three primary experiments divided into two parts. One parts of experiment demonstrate the global mapping algorithm effectively, and the second parts compare execution of a large scale workflow instances with or without delay scheduling. It is primarily proved the Nuts-LSWAP is efficient platform for building Cloud workflow environment

EDGeS: a bridge between desktop grids and service grids

Desktop grids and service grids widely used by their different users communities as efficient solutions for making full use of computing power and achieving loads balances across Intranet or Internet. Nevertheless,little work has been done to combine these two grids technologies together to establish a seamless and vast grid resources pool. In this paper we will present a new European FP7 infrastructure project:EDGeS (enabling desktop grids for e-science), which aim to build technological bridges to facilitate interoperability between desktop grid and service grid. We give also a taxonomy of existing grid systems: desktop grids such as BONIC and XtremWeb, service grids such as EGEE. Then we describe furtherly our solution for identifying translation technologies for porting applications between desktop grids and service grids, and vice versa. There are three themes in our solution, which discuss actual popular bridging technologies, user access issues, and distributed data issues about deployment and application development

Remote sensing big data computing: challenges and opportunities

As we have entered an era of high resolution earth observation, the RS data are undergoing an explosive growth. The proliferation of data also give rise to the increasing complexity of RS data, like the diversity and higher dimensionality characteristic of the data. RS data are regarded as RS ‘‘Big Data’’. Fortunately, we are witness the coming technological leapfrogging. In this paper, we give a brief overview on the Big Data and data-intensive problems, including the analysis of RS Big Data, Big Data challenges, current techniques and works for processing RS Big Data

Cloud computing resource scheduling and a survey of its evolutionary approaches

A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon

Regionally distributed architecture for dynamic e-learning environment (RDADeLE)

e-Learning is becoming an influential role as an economic method and a flexible mode of study in the institutions of higher education today which has a presence in an increasing number of college and university courses. e-Learning as system of systems is a dynamic and scalable environment. Within this environment, e-learning is still searching for a permanent, comfortable and serviceable position that is to be controlled, managed, flexible, accessible and continually up-to-date with the wider university structure. As most academic and business institutions and training centres around the world have adopted the e-learning concept and technology in order to create, deliver and manage their learning materials through the web, it has become the focus of investigation. However, management, monitoring and collaboration between these institutions and centres are limited. Existing technologies such as grid, web services and agents are promising better results. In this research a new architecture has been developed and adopted to make the e-learning environment more dynamic and scalable by dividing it into regional data grids which are managed and monitored by agents. Multi-agent technology has been applied to integrate each regional data grid with others in order to produce an architecture which is more scalable, reliable, and efficient. The result we refer to as Regionally Distributed Architecture for Dynamic e-Learning Environment (RDADeLE). Our RDADeLE architecture is an agent-based grid environment which is composed of components such as learners, staff, nodes, regional grids, grid services and Learning Objects (LOs). These components are built and organised as a multi-agent system (MAS) using the Java Agent Development (JADE) platform. The main role of the agents in our architecture is to control and monitor grid components in order to build an adaptable, extensible, and flexible grid-based e-learning system. Two techniques have been developed and adopted in the architecture to build LOs' information and grid services. The first technique is the XML-based Registries Technique (XRT). In this technique LOs' information is built using XML registries to be discovered by the learners. The registries are written in Dublin Core Metadata Initiative (DCMI) format. The second technique is the Registered-based Services Technique (RST). In this technique the services are grid services which are built using agents. The services are registered with the Directory Facilitator (DF) of a JADE platform in order to be discovered by all other components. All components of the RDADeLE system, including grid service, are built as a multi-agent system (MAS). Each regional grid in the first technique has only its own registry, whereas in the second technique the grid services of all regional grids have to be registered with the DF. We have evaluated the RDADeLE system guided by both techniques by building a simulation of the prototype. The prototype has a main interface which consists of the name of the system (RDADeLE) and a specification table which includes Number of Regional Grids, Number of Nodes, Maximum Number of Learners connected to each node, and Number of Grid Services to be filled by the administrator of the RDADeLE system in order to create the prototype. Using the RST technique shows that the RDADeLE system can be built with more regional grids with less memory consumption. Moreover, using the RST technique shows that more grid services can be registered in the RDADeLE system with a lower average search time and the search performance is increased compared with the XRT technique. Finally, using one or both techniques, the XRT or the RST, in the prototype does not affect the reliability of the RDADeLE system.Royal Commission for Jubail and Yanbu - Directorate General For Jubail Project Kingdom of Saudi Arabi

A Survey on Load Balancing Algorithms for VM Placement in Cloud Computing

The emergence of cloud computing based on virtualization technologies brings huge opportunities to host virtual resource at low cost without the need of owning any infrastructure. Virtualization technologies enable users to acquire, configure and be charged on pay-per-use basis. However, Cloud data centers mostly comprise heterogeneous commodity servers hosting multiple virtual machines (VMs) with potential various specifications and fluctuating resource usages, which may cause imbalanced resource utilization within servers that may lead to performance degradation and service level agreements (SLAs) violations. To achieve efficient scheduling, these challenges should be addressed and solved by using load balancing strategies, which have been proved to be NP-hard problem. From multiple perspectives, this work identifies the challenges and analyzes existing algorithms for allocating VMs to PMs in infrastructure Clouds, especially focuses on load balancing. A detailed classification targeting load balancing algorithms for VM placement in cloud data centers is investigated and the surveyed algorithms are classified according to the classification. The goal of this paper is to provide a comprehensive and comparative understanding of existing literature and aid researchers by providing an insight for potential future enhancements.Comment: 22 Pages, 4 Figures, 4 Tables, in pres

A hybrid genetic algorithm with mapreduce technique for cloud computing energy efficiency

Computer clouds generally comprise large power-consuming data centers as they are designed to support the elasticity and scalability required by customers. However, while cloud computing reduces energy consumption for customers, it is an issue for providers who have to deal with increasing demand and performance expectations. This creates the need for mechanisms to improve the energy-efficiency of cloud computing data centers while maintaining desired levels of performance. This research seeks to formulate a hybrid algorithm based on Genetic algorithm and MapReduce algorithm techniques to further promote energy efficiency in the cloud computing platform. The function of the MapReduce algorithm is to optimize scheduling performance which is one of the more efficient techniques for handling large data in servers. Genetic algorithm is effective in optimally measuring the value of operations and allows for the minimization of energy efficiency where it includes the formula for single optimization energy efficiency. A series of simulations were developed to evaluate the effectiveness of the proposed algorithm. The evaluation results show the amount of Information Technology equipment power required for Power Usage Effectiveness values to optimize energy usage where the performance of the proposed algorithm is 6% better than the previous genetic algorithm and 5% better than Hadoop MapReduce scheduling on low load conditions. On the other hand, the proposed algorithm improved energy efficiency in comparison with the previous work

A Survey of Security and Privacy Challenges in Cloud Computing: Solutions and Future Directions

While cloud computing is gaining popularity, diverse security and privacy issues are emerging that hinder the rapid adoption of this new computing paradigm. And the development of defensive solutions is lagging behind. To ensure a secure and trustworthy cloud environment it is essential to identify the limitations of existing solutions and envision directions for future research. In this paper, we have surveyed critical security and privacy challenges in cloud computing, categorized diverse existing solutions, compared their strengths and limitations, and envisioned future research directions