The eucalyptus open-source cloud-computing system

Lecture Notes in Computer Science, 2014

In this position paper, we describe the current status and plans for a Swiss National Research Infrastructure. Swiss academic and research institutions are very autonomous. While being loosely coupled, they do not rely on any centralized management entities. A coordinated national research infrastructure can only be established by federating the local resources of the individual institutions. We discuss current efforts and business models for a federated infrastructure.

Cloud Computing for Data-Intensive Applications, 2014

Cloud computing is an emerging computing paradigm that can offer unprecedented scalability and resources on demand, and is gaining significant adoption in the science community. At the same time, scientific workflow management systems provide essential support and functionality to scientific computing, such as management of data and task dependencies, job scheduling and execution, provenance tracking, fault tolerance. Migrating scientific workflow management systems from traditional Grid computing environments into the Cloud would enable a much broader user base to conduct their scientific research with ever increasing data scale and analysis complexity. This paper presents our experience in integrating the Swift scientific workflow management system with the OpenNebula Cloud platform, which supports workflow specification and submission, on-demand virtual cluster provisioning, high-throughput task scheduling and execution, and efficient and scalable resource management in the Cloud. We set up a series of experiments to demonstrate the capability of our integration and use a MODIS image processing workflow as a showcase of the implementation.

Journal of Grid Computing, 2013

The rapid advancements in recent years of high-throughput technologies in the life sciences are facilitating the generation and storage of huge amount of data in different databases. Despite significant developments in computing capacity and performance, an analysis of these large-scale data in a search for biomedical relevant patterns remains a challenging task. Scientific workflow applications are deemed to support data-mining in more complex scenarios that include many data sources and computational tools, as commonly found in bioinformatics. A scientific workflow application is a holistic unit that defines, executes, and manages scientific applications using different software tools. Existing workflow applications are process-or data-rather than resource-oriented. Thus, they lack efficient computational resource management capabilities, such as those provided by Cloud computing environments. Insufficient computational resources disrupt the execution of workflow applications, wasting time and money. To address this issue, advanced resource monitoring and management strategies are required to determine the resource consumption behaviours of workflow applications to enable a dynamical allocation and deallocation of resources. In this paper, we present a novel Cloud management infrastructure consisting of resource level-, application level monitoring techniques, and a knowledge management strategy to manage computational resources for supporting workflow application executions in order to guarantee their performance goals and their successful completion. We present the design description of these techniques, demonstrate how they can be applied to scientific workflow applications, and present detailed evaluation results as a proof of concept.

Earth Science Informatics, 2010

Tremendous volumes of data have been captured, archived and analyzed. Sensors, algorithms and processing systems for transforming and analyzing the data are evolving over time. Web Portals and Services can create transient data sets on-demand. Data are transferred from organization to organization with additional transformations at every stage. Provenance in this context refers to the source of data and a record of the process that led to its current state. It encompasses the documentation of a variety of artifacts related to particular data. Provenance is important for understanding and using scientific datasets, and critical for independent confirmation of scientific results. Managing provenance throughout scientific data processing has gained interest lately and there are a variety of approaches. Large scale scientific datasets consisting of thousands to millions of individual data files and processes offer particular challenges. This paper uses Communicated by: Thomas Narock C. Tilmes (B)

Lecture Notes in Computer Science, 2011

Low upfront costs, rapid deployment of infrastructure and flexible management of resources has resulted in the quick adoption of cloud computing. Nowadays, different types of applications in areas such as enterprise web, virtual labs and high-performance computing are already being deployed in private and public clouds. However, one of the remaining challenges is how to allow users to specify Quality of Service (QoS) requirements for composite groups of virtual machines and enforce them effectively across the deployed resources. In this paper, we propose an Infrastructure as a Service resource manager capable of allocating Distributed Ensembles of Virtual Appliances (DEVAs) in the Cloud. DEVAs are groups of virtual machines and their network connectivities instantiated on heterogeneous shared resources with QoS specifications for individual entities as well as their connections. We discuss the different stages in their lifecycle: declaration, scheduling, provisioning and dynamic management, and show how this approach can be used to maintain QoS for complex deployments of virtual resources.

2012

NSF's GENI program seeks to enable experiments that run within virtual network topologies built-to-order from testbed infrastructure offered by multiple providers (domains). GENI is often viewed as a testbed integration effort, but behind it is an ambitious vision for multi-domain infrastructure-as-a-service (IaaS). This paper presents ExoGENI, a new GENI testbed that links GENI to key advances in virtual infrastructure services outside of GENI: cloud computing systems and network circuit fabrics. ExoGENI orchestrates a federation of independent cloud and circuit providers through their native IaaS interfaces, and links them to other GENI tools and resources.

Proceedings of the 29th Annual ACM Symposium on Applied Computing - SAC '14, 2014

Cloud applications consist of a set of interconnected software elements distributed over several virtual machines, themselves hosted on remote physical servers. Most existing solutions for deploying such applications require human intervention to configure parts of the system, do not respect functional dependencies among elements that must be respected when starting them, and do not handle virtual machine failures that can occur when deploying an application. This paper presents a self-deployment protocol that was designed to automatically configure a set of software elements to be deployed on different virtual machines. This protocol works in a decentralized way, i.e., there is no need for a centralized server. It also starts the software elements in a certain order, respecting important architectural invariants. This protocol supports virtual machine and network failures, and always succeeds in deploying an application when faced with a finite number of failures. Designing such highly parallel management protocols is difficult, therefore formal modeling techniques and verification tools were used for validation purposes. The protocol was implemented in Java and was used to deploy industrial applications.

Social Sciences

The aim of the study was to map the scholarly community interested in research on cloud computing and to identify thematic boundaries of the field. The methodology of research profiling, representing bibliometric descriptive studies, was applied to achieve the aim of the study. Using research profiling for mapping the cloud computing field can be considered as an innovation. Although the research profiling methodology has been widely used across various subject areas, including Computer Science, Social Sciences, Engineering, Arts and Humanities, Business, Management and Accounting, and Psychology, thus far neither Scopus nor Web of Science indexed publications including the conjunction of phrases “cloud computing” and “research profiling” in their titles, keywords and abstracts. The previous important scientometric study of the research output in the field was published by Heilig and Voß in 2014. Taking into account a very dynamic growth of the field, all this indicates the research...

ACM SIGMOD Record, 2010

Data warehouses are the core of decision support systems, which nowadays are used by all kind of enterprises in the entire world. Although many studies have been conducted on the need of decision support systems (DSSs) for small businesses, most of them adopt existing solutions and approaches, which are appropriate for large-scaled enterprises, but are inadequate for small and middle-sized enterprises. Small enterprises require cheap, lightweight architectures and tools (hardware and software) providing online data analysis. In order to ensure these features, we review web-based business intelligence approaches. For real-time analysis, the traditional OLAP architecture is cumbersome and storage-costly; therefore, we also review in-memory processing. Consequently, this paper discusses the existing approaches and tools working in main memory and/or with web interfaces (including freeware tools), relevant for small and middle-sized enterprises in decision making.

Journal of Cloud Computing

Cloud computing evolved from the concept of utility computing, which is defined as the provision of computational and storage resources as a metered service. Another key characteristic of cloud computing is multitenancy, which enables resource and cost sharing among a large pool of users. Characteristics such as multitenancy and elasticity perfectly fit the requirements of modern data-intensive research and scientific endeavors. In parallel, as science relies on the analysis of very large data sets, data management and processing must be performed in a scalable and automated way. Workflows have emerged as a way to formalize and structure data analysis, thus becoming an increasingly popular paradigm for scientists to handle complex scientific processes. One of the key enablers of this conjunction of cloud computing and scientific workflows is resource management. However, several issues related to data-intensive loads, complex infrastructures such as hybrid and multicloud environments to support large-scale execution of workflows, performance fluctuations, and reliability, pose as challenges to truly position clouds as viable high-performance infrastructures for scientific computing. This paper presents a survey on cloud resource management that provides an extensive study of the field. A taxonomy is proposed to analyze the selected works and the analysis ultimately leads to the definition of gaps and future challenges to be addressed by research and development.

Concurrency and Computation: Practice and Experience, 2011

In this paper we present the case study of an application of a parallel simulation optimization deployed on a private Cloud. The compute-intensive application uses a Master/Worker model, supporting communication over both Java RMI and Globus Grid Services between the nodes. The Master deploys Workers over a Eucalyptus Cloud using the Nimbus Context Broker for just-in-time configuration and runtime Worker discovery. The computational performance of the Workers under different communication mechanisms and deployment scenarios is presented in an attempt to evaluate the use of Virtual Machines in a Cloud as a tool to achieve application scaling. The deployment of this particular application was crafted to support on-the-fly addition of working nodes. The case study suggests a deployment pattern that shapes some requirements and considerations of a scalable Globus-driven Platform as a Service Cloud.

Journal of Cloud Computing, 2015

The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

Annals of Telecommunications, 2016

Access control systems are often seen as the most effective tool to address the security challenges faced by cloud computing. Most of the proposed approaches are designed for specific application domains or service models. The goal of this paper is to propose a generic access control system for the cloud that is applicable to the different cloud service models. We rely on Kerberos as well as access control lists and authorization tickets for the implementation of access control and no replay. We use CloudSim to evaluate our proposal and show that it has an acceptable overhead. We also show that the architecture's elasticity has no significant impact on the access time. To prove its feasibility, we implemented the proposed solution over an Openstack cloud platform integrated within Kerberos.

International Journal of Intelligent Information Technologies, 2011

Cloud Computing provides dynamic leasing of server capabilities as a scalable, virtualized service to end users. The discussed work focuses on Infrastructure as a Service (IaaS) model where custom Virtual Machines (VM) are launched in appropriate servers available in a data-center. The context of the environment is a large scale, heterogeneous and dynamic resource pool. Nonlinear variation in the availability of processing elements, memory size, storage capacity, and bandwidth causes resource dynamics apart from the sporadic nature of workload. The major challenge is to map a set of VM instances onto a set of servers from a dynamic resource pool so the total incremental power drawn upon the mapping is minimal and does not compromise the performance objectives. This paper proposes a novel Self Adaptive Particle Swarm Optimization (SAPSO) algorithm to solve the intractable nature of the above challenge. The proposed approach promptly detects and efficiently tracks the changing optimum...

Advances in Intelligent Systems and Computing, 2019

The paper presents an overview of existing workload management systems and diagnoses difficulties in their use. Such systems require a complex configuration, and the process of ordering tasks to be carried out is burdened with many restrictions. A new solution is presented which supports the use of massive distributed computations. The work presents the process of designing, implementing and testing the workload management Shapp library, based on the HTCondor system. It implements a convenient application interface in the form of a dynamically linked library, which extends the capabilities of existing applications with a convenient mechanism allowing the use of massive distributed processing. Recursive computations in tree-like structure are possible using Shapp library.

Communications in Computer and Information Science, 2016

In many researches they have created one environment with multiple clouds for scalable capabilities. The demand for scalable resources in some applications has been rapidly increased. By understanding the mutual dependencies between resources, utilization of users, user preferences a collaborative cloud computing has been introduced, It can be achieve emphasized efficient management of resources and user satisfaction between distributed resources in Collaborative cloud computing. These which do collaboratively supported by many organizations (Google, Microsoft, Amazon). Collaborative cloud computing (CCC) is next logical step in evolution of cloud and service grid technologies. Here mainly discussing about how resource selection done in collaborative cloud, resource selection based on user preferences, which mostly user preferred resources. This environment is to point out the suitable resources, ability and capability of resources, user requirements, and availability of resources. So that users can choose credible cloud platform.

2019

In this paper, we present CSPOT, a distributed runtime system implementing a functions-as-service (FaaS) programming model for the "Internet of Things" (IoT). With FaaS, developers express arbitrary computations as simple functions that are automatically invoked and managed by a cloud platform in response to events. We extend this FaaS model so that it is suitable for use in all tiers of scale for IoT-sensors, edge devices, and cloud-to facilitate robust, portable, and low-latency IoT application development and deployment. To enable this, we combine the use of Linux containers and namespaces for isolation and portability, an append-only object store for robust persistence, and a causal event log for triggering functions and tracking event dependencies. We present the design and implementation of CSPOT, detail its abstractions and APIs, and overview examples of its use. We empirically evaluate the performance of CSPOT using different devices and applications and find that it implements function invocation with significantly lower latency than other FaaS offerings, while providing portability across tiers and similar data durability characteristics. CCS CONCEPTS • Applied computing → Event-driven architectures; • Computing methodologies → Distributed computing methodologies; • Computer systems organization → Distributed architectures;

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 2018

A shift from the doctor-centric model to a patient-centric model is required to face the challenges of the healthcare sector. The vision of patient-centric model can be materialized integrating ubiquitous healthcare and the notion of personalization in services. Cloud computing can be the underlying technology for ubiquitous healthcare. The use of profiles enables the personalization in healthcare services and the use of profile management systems facilitates the deployment of these services. In this paper, we propose a profile management system in ubiquitous healthcare cloud computing environment. The proposed system exploits the cloud computing technology and the smart card technology to increase the efficiency and the quality of the provided healthcare services in the context of the patient-centric model. Furthermore, we propose generic healthcare profile structures corresponding to the main classes of the participating entities in a ubiquitous healthcare cloud computing environment.

IGI Global eBooks, 2019

IGI Global eBooks, 2021

In current era, the trend of cloud computing is increasing with every passing day due to one of its dominant service i.e. Infrastructure as a service (IAAS), which virtualizes the hardware by creating multiple instances of VMs on single physical machine. Virtualizing the hardware leads to the improvement of resource utilization but it also makes the system over utilized with inefficient performance. Therefore, these VMs need to be migrated to another physical machine using VM consolidation process in order to reduce the amount of host machines and to improve the performance of system. Thus, the idea of placing the virtual machines on some other hosts leads to the proposal of many new algorithms of VM placement. However, the reduced set of physical machines needs the lesser amount of power consumption therefore; in current work the authors have presented a decision making VM placement system based on genetic algorithm and compared it with three predefined VM placement techniques based on classical bin packing. This analysis contributes to better understand the effects of the placement strategies over the overall performance of cloud environment and how the use of genetic algorithm delivers the better results for VM placement than classical bin packing algorithms.

Springer eBooks, 2016

NSF's GENI program seeks to enable experiments that run within virtual network topologies built-toorder from testbed infrastructure offered by multiple providers (domains). GENI is often viewed as a network testbed integration effort, but behind it is an ambitious vision for multi-domain infrastructure-asa-service (IaaS). This paper presents ExoGENI, a new GENI testbed that links GENI to two advances in virtual infrastructure services outside of GENI: open cloud computing (OpenStack) and dynamic circuit fabrics. ExoGENI orchestrates a federation of independent cloud sites and circuit providers through their native IaaS interfaces, and links them to other GENI tools and resources. The ExoGENI deployment consists of cloud site "racks" on host campuses within the US, linked with national research networks and other circuit networks through programmable exchange points. The ExoGENI sites and control software are enabled for software-defined networking using OpenFlow. ExoGENI offers a powerful unified hosting platform for deeply networked, multi-domain, multi-site cloud applications. We intend that ExoGENI will seed a larger, evolving platform linking other third-party cloud sites, transport networks, and other infrastructure services, and that it will enable real-world deployment of innovative distributed services and new visions of a Future Internet.