IaaS Level Research Articles

An Overview of Cloud Computing Solutions in Library Services

This article intends to provide a comprehensive overview of the current applications of cloud computing services in libraries. This study investigates the ways in which cloud-based information systems and services can administer library records in a cloud computing environment and support the evolving needs of library patrons. The authors endeavor to rectify the identified deficiency, advantages, and prospects associated with the integration of cloud storage at the Infrastructure as a Service (IaaS) level with the remaining two tiers of cloud computing Platform as a Service (PaaS) and Software as a Service (SaaS).

Smart City IoT System Network Level Routing Analysis and Blockchain Security Based Implementation.

This paper demonstrates, network-level performance analysis and implementation of smart city Internet of Things (IoT) system with Infrastructure as a Service (IaaS) level cloud computing architecture. The smart city IoT network topology performance is analyzed at the simulation level using the NS3 simulator by extracting most of the performance-deciding parameters. The performance-enhanced smart city topology is practically implemented in IaaS level architecture. The intended smart city IoT system can monitor the principal parameters like video surveillance with a thermal camera (to identify the virus-like COVID-19 infected people), transport, water quality, solar radiation, sound pollution, air quality (O3, NO2, CO, Particles), parking zones, iconic places, E-suggestions, PRO information over low power wide area network in 61.88 km × 61.88 km range. Primarily we have addressed the IoT network-level routing and quality of service (QoS) challenges and implementation level security challenges. The simulation level network topology analysis is performed to improve the routing and QoS. Blockchain technology-based decentralization is adopted to enrich the IoT system performance in terms of security.

Intelligent and compliant dynamic software license consolidation in cloud environment

Based on the virtualization technology and pushed by the softwarization paradigm and the actual demand for services and resources, commercial cloud data centers know an unprecedented expansion. The systematic presence of software and the services generated enabled the development of the already dense application expenses. This causes, not only a cost explosion, especially when proprietary solutions protected by licenses are in hand, but also, represents a critical need in terms of software asset and resource management at the SaaS level. In addition to these costs, inefficient resource utilization, and the resulting energy represent an important part of the operational expenditure of data centers and are still a hot topic despite the consolidation initiatives put in place. The main objective of the consolidation service is to maximize resource exploitation while minimizing energy consumption and costs, among others. Even so, we have noticed that the reported literature doesn’t treat license management in the cloud environment as a whole, especially, from the resource management perspective and the overwhelming majority of the consolidation work focuses on resource optimization at the IaaS level. Therefore, we propose a reinforcement learning-based scheme that allows efficient use of resources and optimizes costs, energy consumption, and resource wastage, while remaining compliant. The experimental results show that our intelligent consolidator outperforms the baseline approaches according to the evaluation metrics used regardless of the resource heterogeneity and the data center dimensionality.

BlastFunction: A Full-stack Framework Bringing FPGA Hardware Acceleration to Cloud-native Applications

“Cloud-native” is the umbrella adjective describing the standard approach for developing applications that exploit cloud infrastructures’ scalability and elasticity at their best. As the application complexity and user-bases grow, designing for performance becomes a first-class engineering concern. As an answer to these needs, heterogeneous computing platforms gained widespread attention as powerful tools to continue meeting SLAs for compute-intensive cloud-native workloads. We propose BlastFunction, an FPGA-as-a-Service full-stack framework to ease FPGAs’ adoption for cloud-native workloads, integrating with the vast spectrum of fundamental cloud models. At the IaaS level, BlastFunction time-shares FPGA-based accelerators to provide multi-tenant access to accelerated resources without any code rewriting. At the PaaS level, BlastFunction accelerates functionalities leveraging the serverless model and scales functions proactively, depending on the workload’s performance. Further lowering the FPGAs’ adoption barrier, an accelerators’ registry hosts accelerated functions ready to be used within cloud-native applications, bringing the simplicity of a SaaS-like approach to the developers. After an extensive experimental campaign against state-of-the-art cloud scenarios, we show how BlastFunction leads to higher performance metrics (utilization and throughput) against native execution, with minimal latency and overhead differences. Moreover, the scaling scheme we propose outperforms the main serverless autoscaling algorithms in workload performance and scaling operation amount.

Cloud-based Network Virtualization in IoT with OpenStack

In Cloud computing deployments, specifically in the Infrastructure-as-a-Service (IaaS) model, networking is one of the core enabling facilities provided for the users. The IaaS approach ensures significant flexibility and manageability, since the networking resources and topologies are entirely under users’ control. In this context, considerable efforts have been devoted to promoting the Cloud paradigm as a suitable solution for managing IoT environments. Deep and genuine integration between the two ecosystems, Cloud and IoT, may only be attainable at the IaaS level. In light of extending the IoT domain capabilities’ with Cloud-based mechanisms akin to the IaaS Cloud model, network virtualization is a fundamental enabler of infrastructure-oriented IoT deployments. Indeed, an IoT deployment without networking resilience and adaptability makes it unsuitable to meet user-level demands and services’ requirements. Such a limitation makes the IoT-based services adopted in very specific and statically defined scenarios, thus leading to limited plurality and diversity of use cases. This article presents a Cloud-based approach for network virtualization in an IoT context using the de-facto standard IaaS middleware, OpenStack, and its networking subsystem, Neutron. OpenStack is being extended to enable the instantiation of virtual/overlay networks between Cloud-based instances (e.g., virtual machines, containers, and bare metal servers) and/or geographically distributed IoT nodes deployed at the network edge.

Revisiting Cloud Migration: Strategies and Methods

Cloud computing attracts users with its advantage of unlimited resource supply where resources can be elastically expanded on demand and balanced-load at the same time. This means that the application in the cloud environment should run in the way of elastic expansion cluster. At present, most of the elastic expansion and load balancing technologies provided by IaaS level are oriented to virtual machines (VM), with little consideration of application level, which does not fundamentally meet the needs of users. Based on this, starting from the motivation of cloud migration, we first analyse the cloud migration technology from three aspects: migration object, migration means and migration objectives, then explore the cloud migration criteria and four cloud migration strategies at the application level in the cloud environment, and point out that cloud migration should be optimized and maintained in terms of elastic expansion, load balancing, security, etc.

Бізнес-моделі хмарного надання ІТ-послуг

Бізнес-моделі хмарного надання ІТ-послуг

Detection of Malware Attacks on Virtual Machines for a Self-Heal Approach in Cloud Computing using VM Snapshots

Cloud Computing strives to be dynamic as a service oriented architecture. The services in the SoA are rendered in terms of private, public and in many other commercial domain aspects. These services should be secured and thus are very vital to the cloud infrastructure. In order, to secure and maintain resilience in the cloud, it not only has to have the ability to identify the known threats but also to new challenges that target the infrastructure of a cloud. In this paper, we introduce and discuss a detection method of malwares from the VM logs and corresponding VM snapshots are classified into attacked and non-attacked VM snapshots. As snapshots are always taken to be a backup in the backup servers, especially during the night hours, this approach could reduce the overhead of the backup server with a self-healing capability of the VMs in the local cloud infrastructure. A machine learning approach at the hypervisor level is projected, the features being gathered from the API calls of VM instances in the IaaS level of cloud service. Our proposed scheme can have a high detection accuracy of about 93% while having the capability to classify and detect different types of malwares with respect to the VM snapshots. Finally the paper exhibits an algorithm using snapshots to detect and thus to self-heal using the monitoring components of a particular VM instances applied to cloud scenarios. The self-healing approach with machine learning algorithms can determine new threats with some prior knowledge of its functionality.

Trust Management and Monitoring at an IaaS Level of Cloud Computing

Now a days Cloud Computing is as a major utility, same as electricity and water bills where we pay as we go with the consumption of the said resources, as organization usages cloud computing services and pays as per their usage of the resources. In this research paper, the authors aspect are towards at what trust is and how trust value can be maintained to create its trust level among the end users and the cloud service provider with the help of on demand monitoring mechanism and invoking the monitoring techniques only when the trust fluctuates to maintain its dignity towards the trust shown by the end use .In monitoring the present status of the resources are analyzed and based upon the same the new instances will be created to satisfy the need of the end users to maintain the trust level.

Chronicles of Assaults at Cloud Computing and Its Influence at an IaaS

Cloud Computing provides several services and the attacker always tries to breach the security at an Infrastructure as a Service (IaaS) level. Cloud is a hub of resources like network, storage, server, memory and any user uses these hubs of resources at anywhere and anytime by subscription system. Distributed Denial-of-Service (DDoS) attacks on IaaS which target resources and this is major concern about the cloud system Providers. In this research at an initial level Intrusion Detection System (IDS) technique is been deployed to detect the DDoS attack and to overcome the problem of DDoS attack, Reinforcement Learning (RL) algorithm has been used to train our system for detection of or any anomaly in the network. In this research paper, we have discussed procedures to deal with such types of problems or scenarios.

Inspection of Trust Based Cloud Using Security and Capacity Management at an IaaS Level

Cloud Computing is an example of the distributed system where the end user has to connect to the services given by the cloud which is maintained by the cloud service provider (CSP). The user has to have certain trust upon the cloud as finally, the end user has to migrate the jobs into the cloud of some third party, as the on-premises data or sources are to be kept across the globe, the CSP have to maintain the trust level so that the end user can opt for the services given by the certain trusted Cloud. Ultimately there will be various elements of levels happening at the CSP side to maintain the trust level, like the safety features for security has to be identified, federation related or Virtual Machine migration techniques status has to be always monitored to maintain and avoid certain uncertainty which will affect the trust level of the cloud, which can lead to the compromised situation in between the end user and CSP, as a result the trust value will decrease, In this paper we are proposing a techniques where the security features and conditions for load balancing monitoring technique with proactive actions will be analyzed to maintain the specified trust level.

TOSCA-based orchestration of complex clusters at the IaaS level

This paper describes the adoption and extension of the TOSCA standard by the INDIGO-DataCloud project for the definition and deployment of complex computing clusters together with the required support in both OpenStack and OpenNebula, carried out in close collaboration with industry partners such as IBM. Two examples of these clusters are described in this paper, the definition of an elastic computing cluster to support the Galaxy bioinformatics application where the nodes are dynamically added and removed from the cluster to adapt to the workload, and the definition of an scalable Apache Mesos cluster for the execution of batch jobs and support for long-running services. The coupling of TOSCA with Ansible Roles to perform automated installation has resulted in the definition of high-level, deterministic templates to provision complex computing clusters across different Cloud sites.

StopGap: elastic VMs to enhance server consolidation

SummaryVirtualized cloud infrastructures (also known as IaaS platforms) generally rely on a server consolidation system to pack virtual machines (VMs) on as few servers as possible. However, an important limitation of consolidation is not addressed by such systems. Because the managed VMs may be of various sizes (small, medium, large, etc.), VM packing may be obstructed when VMs do not fit available spaces. This phenomenon leaves servers with a set of unused resources (‘holes’). It is similar to memory fragmentation, a well‐known problem in operating system domain. In this paper, we propose a solution which consists in resizing VMs so that they can fit with holes. This operation leads to the management of what we call elastic VMs and requires cooperation between the application level and the IaaS level, because it impacts management at both levels. To this end, we propose a new resource negotiation and allocation model in the IaaS, called HRNM. We demonstrate HRNM's applicability through the implementation of a prototype compatible with two main IaaS managers (OpenStack and OpenNebula). By performing thorough experiments with SPECvirt_sc2010 (a reference benchmark for server consolidation), we show that the impact of HRNM on customer's application is negligible. Finally, using Google data center traces, we show an improvement of about 62.5% for the traditional consolidation engines. Copyright © 2017 John Wiley & Sons, Ltd.

Building an open source cloud environment with auto-scaling resources for executing bioinformatics and biomedical workflows

Fast and simple access to computing resources for in-silico experiments and processing of large amounts of data have become crucial factors for researchers in the bioinformatics and biomedical domains. Cloud computing offers the possibility of accessing computing resources for a flexible amount of time and with varying requirements. We discuss how a full cloud stack ranging from Infrastructure-as-a-Service (IaaS) via Platform-as-a-Service (PaaS) to Software-as-a-Service (SaaS) can be built on open source technologies including the integration of high-throughput data transfers in all service types. Based on the scaling capabilities provided by the tools used on the IaaS level we devise a strategy to build a dynamically scaling PaaS offering for building and running workflows using Galaxy. Finally, applications — available as SaaS offerings — for two distinct use cases from the bioinformatics and biomedical domains are presented to demonstrate the feasibility and performance of our solution.

Towards Monitoring-as-a-service for Scientific Computing Cloud applications using the ElasticSearch ecosystem

The INFN computing centre in Torino hosts a private Cloud, which is managed with the OpenNebula cloud controller. The infrastructure offers Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) services to different scientific computing applications. The main stakeholders of the facility are a grid Tier-2 site for the ALICE collaboration at LHC, an interactive analysis facility for the same experiment and a grid Tier-2 site for the BESIII collaboration, plus an increasing number of other small tenants. The dynamic allocation of resources to tenants is partially automated. This feature requires detailed monitoring and accounting of the resource usage. We set up a monitoring framework to inspect the site activities both in terms of IaaS and applications running on the hosted virtual instances. For this purpose we used the ElasticSearch, Logstash and Kibana (ELK) stack. The infrastructure relies on a MySQL database back-end for data preservation and to ensure flexibility to choose a different monitoring solution if needed. The heterogeneous accounting information is transferred from the database to the ElasticSearch engine via a custom Logstash plugin. Each use-case is indexed separately in ElasticSearch and we setup a set of Kibana dashboards with pre-defined queries in order to monitor the relevant information in each case. For the IaaS metering, we developed sensors for the OpenNebula API. The IaaS level information gathered through the API is sent to the MySQL database through an ad-hoc developed RESTful web service. Moreover, we have developed a billing system for our private Cloud, which relies on the RabbitMQ message queue for asynchronous communication to the database and on the ELK stack for its graphical interface. The Italian Grid accounting framework is also migrating to a similar set-up. Concerning the application level, we used the Root plugin TProofMonSenderSQL to collect accounting data from the interactive analysis facility. The BESIII virtual instances used to be monitored with Zabbix, as a proof of concept we also retrieve the information contained in the Zabbix database. In this way we have achieved a uniform monitoring interface for both the IaaS and the scientific applications, mostly leveraging off-the-shelf tools. At present, we are working to define a model for monitoring-as-a-service, based on the tools described above, which the Cloud tenants can easily configure to suit their specific needs.

Monitoring of IaaS and scientific applications on the Cloud using the Elasticsearch ecosystem

The private Cloud at the Torino INFN computing centre offers IaaS services to different scientific computing applications. The infrastructure is managed with the OpenNebula cloud controller. The main stakeholders of the facility are a grid Tier-2 site for the ALICE collaboration at LHC, an interactive analysis facility for the same experiment and a grid Tier-2 site for the BES-III collaboration, plus an increasing number of other small tenants. Besides keeping track of the usage, the automation of dynamic allocation of resources to tenants requires detailed monitoring and accounting of the resource usage. As a first investigation towards this, we set up a monitoring system to inspect the site activities both in terms of IaaS and applications running on the hosted virtual instances. For this purpose we used the Elasticsearch, Logstash and Kibana stack. In the current implementation, the heterogeneous accounting information is fed to different MySQL databases and sent to Elasticsearch via a custom Logstash plugin. For the IaaS metering, we developed sensors for the OpenNebula API. The IaaS level information gathered through the API is sent to the MySQL database through an ad-hoc developed RESTful web service, which is also used for other accounting purposes. Concerning the application level, we used the Root plugin TProofMonSenderSQL to collect accounting data from the interactive analysis facility. The BES-III virtual instances used to be monitored with Zabbix, as a proof of concept we also retrieve the information contained in the Zabbix database. Each of these three cases is indexed separately in Elasticsearch. We are now starting to consider dismissing the intermediate level provided by the SQL database and evaluating a NoSQL option as a unique central database for all the monitoring information. We setup a set of Kibana dashboards with pre-defined queries in order to monitor the relevant information in each case. In this way we have achieved a uniform monitoring interface for both the IaaS and the scientific applications, mostly leveraging off-the-shelf tools.

Addressing the interoperability in cloud: the vendor agent

Cloud computing has made the use of IT services more accessible and cheap. Resources are easily configurable and accessible using networked client devices and are characterised by particular agility and efficiency of use that allows to scale dynamically and on demand. However, owing to the current lack of standards and to the heterogeneity of technologies, users have little flexibility in choosing from alternative providers simultaneously in order to optimise resources at different levels so being affected by the vendor lock-in problem. In this paper, we address the problem of interoperability at IaaS level by proposing an agent abstraction, the vendor agent that is a wrapper to a specific provider technology, and its implementation to support two well-known cloud platforms: Open Nebula and Amazon. This abstraction represents a key driver towards interoperability giving users the opportunity to choose and use services provided by many different cloud vendors.

A case study on IVR applications' provisioning as cloud computing services

Interactive Voice Response (IVR) applications (e.g. automated attendant, automated survey) have become ubiquitous. However, very few, if any, IVR applications are provisioned today as cloud computing services despite all the potential benefits. Cloud computing is an emerging multi-faceted paradigm, whose main facets are Infrastructure as a Service-IaaS, Platform as a Service-PaaS, and Software as a Service-SaaS. This paper proposes a case study on IVR applications' provisioning as cloud computing services. It identifies the IVR substrates (e.g. key detector, voice recorder and dialog manager) on which the IVR applications rely and proposes at the IaaS level an IVR substrate virtualization infrastructure. Substrates' virtualization enables a more efficient resource usage, a key benefit inherent to cloud computing. The article also proposes at the PaaS level an accompanying platform to develop and manage the IVR applications which use the identified virtualized substrates. A few typical IVR applications are also developed and managed to illustrate the SaaS facet of cloud computing. The implementation is described, along with the prototype and the performance measurements.

IaaS cloud computing services for libraries: cloud storage and virtual machines

PurposeThe purpose of this article is to provide an overview of current uses of cloud computing (CC) services in libraries, address a gap identified in integrating cloud storage in IaaS level, and show how to use EC2 tools for easy backup and resource monitoring.Design/methodology/approachThe article begins a literature review of CC uses in libraries, organized at the SaaS, PaaS and IaaS levels. The author presents his experience of integrating cloud storage services S3 and GCS. In addition, he also shows how to use virtual machine EC2 tools for backup and monitoring resources.FindingsThe article describes a case study of integrating cloud storage using S3 and GCS. S3 can be integrated with any program whether the program runs on cloud or locally, while GCS is only good for applications running on GAE. The limitation of the current GCS approach makes it hard to use for a stand‐alone cloud storage. The author also discusses virtual machines using EC2 and its related tools for backup, increase storage, and monitoring service. These services make system administration easier as compared to the traditional approach.Research limitations/implicationsThe article presents current CC uses in libraries at the SaaS, PaaS, and IaaS levels. CC services are changing quickly. For example, Google has stated that its APIs are experimental. Readers should be aware of this.Practical implicationsThe author shows his experience of integrating cloud storage services. Readers can understand the similarities and differences between S3 and GCS. In addition, readers can learn the advantages and concerns associated with implementing cloud computing. Readers are encouraged to consider questions such as content, skills, costs, and security.Originality/valueThere are many uses of CC services in libraries. However, gaps are identified: in IaaS cloud storage, a few libraries used Amazon S3 and Microsoft Azure, but none explored using Google Cloud Storage (GCS); none provided implementation details, difficulties, and comparisons of S3 and GCS; and a few articles have briefly discussed implementations on Amazon EC2, but have not provided specific details about upgrade and backup. This article addresses those gaps.

The top ten cloud-security practices in next-generation networking

Next-generation networking is moving out of the LAN and adopting cloud computing increasingly. Virtual machines and clouds mask the hardware infrastructure from the LAN administrator as well. Cloud architecture will be ubiquitous and in the future clouds will drop the unnecessary distinction of platform as a service, leaving a slightly larger infrastructure as a service (IaaS) level and an application level (SaaS). Provisioning costs will get lower but the costs of data breaches will continue to climb. This paper explores the security ramifications of cloud-adoption by reviewing the literature and presenting a collection of best-practices for security in the cloud.