InterCloud: Utility-Oriented Federation of Cloud Computing Environments for Scaling of Application Services

Cloud computing providers have setup several data centers at different geographical locations over the Internet in order to optimally serve needs of their customers around the world. However, existing systems do not support mechanisms and policies for dynamically coordinating load distribution among different Cloud-based data centers in order to determine optimal location for hosting application services to achieve reasonable QoS levels. Further, the Cloud computing providers are unable to predict geographic distribution of users consuming their services, hence the load coordination must happen automatically, and distribution of services must change in response to changes in the load. To counter this problem, we advocate creation of federated Cloud computing environment (InterCloud) that facilitates just-in-time, opportunistic, and scalable provisioning of application services, consistently achieving QoS targets under variable workload, resource and network conditions. The overall goal is to create a computing environment that supports dynamic expansion or contraction of capabilities (VMs, services, storage, and database) for handling sudden variations in service demands. This paper presents vision, challenges, and architectural elements of InterCloud for utility-oriented federation of Cloud computing environments. The proposed InterCloud environment supports scaling of applications across multiple vendor clouds. We have validated our approach by conducting a set of rigorous performance evaluation study using the CloudSim toolkit. The results demonstrate that federated Cloud computing model has immense potential as it offers significant performance gains as regards to response time and cost saving under dynamic workload scenarios.Comment: 20 pages, 4 figures, 3 tables, conference pape

Cloudbus Toolkit for Market-Oriented Cloud Computing

This keynote paper: (1) presents the 21st century vision of computing and identifies various IT paradigms promising to deliver computing as a utility; (2) defines the architecture for creating market-oriented Clouds and computing atmosphere by leveraging technologies such as virtual machines; (3) provides thoughts on market-based resource management strategies that encompass both customer-driven service management and computational risk management to sustain SLA-oriented resource allocation; (4) presents the work carried out as part of our new Cloud Computing initiative, called Cloudbus: (i) Aneka, a Platform as a Service software system containing SDK (Software Development Kit) for construction of Cloud applications and deployment on private or public Clouds, in addition to supporting market-oriented resource management; (ii) internetworking of Clouds for dynamic creation of federated computing environments for scaling of elastic applications; (iii) creation of 3rd party Cloud brokering services for building content delivery networks and e-Science applications and their deployment on capabilities of IaaS providers such as Amazon along with Grid mashups; (iv) CloudSim supporting modelling and simulation of Clouds for performance studies; (v) Energy Efficient Resource Allocation Mechanisms and Techniques for creation and management of Green Clouds; and (vi) pathways for future research.Comment: 21 pages, 6 figures, 2 tables, Conference pape

Energy-Aware Lease Scheduling in Virtualized Data Centers

Energy efficiency has become an important measurement of scheduling algorithms in virtualized data centers. One of the challenges of energy-efficient scheduling algorithms, however, is the trade-off between minimizing energy consumption and satisfying quality of service (e.g. performance, resource availability on time for reservation requests). We consider resource needs in the context of virtualized data centers of a private cloud system, which provides resource leases in terms of virtual machines (VMs) for user applications. In this paper, we propose heuristics for scheduling VMs that address the above challenge. On performance evaluation, simulated results have shown a significant reduction on total energy consumption of our proposed algorithms compared with an existing First-Come-First-Serve (FCFS) scheduling algorithm with the same fulfillment of performance requirements. We also discuss the improvement of energy saving when additionally using migration policies to the above mentioned algorithms.Comment: 10 pages, 2 figures, Proceedings of the Fifth International Conference on High Performance Scientific Computing, March 5-9, 2012, Hanoi, Vietna

HPC Cloud for Scientific and Business Applications: Taxonomy, Vision, and Research Challenges

High Performance Computing (HPC) clouds are becoming an alternative to on-premise clusters for executing scientific applications and business analytics services. Most research efforts in HPC cloud aim to understand the cost-benefit of moving resource-intensive applications from on-premise environments to public cloud platforms. Industry trends show hybrid environments are the natural path to get the best of the on-premise and cloud resources---steady (and sensitive) workloads can run on on-premise resources and peak demand can leverage remote resources in a pay-as-you-go manner. Nevertheless, there are plenty of questions to be answered in HPC cloud, which range from how to extract the best performance of an unknown underlying platform to what services are essential to make its usage easier. Moreover, the discussion on the right pricing and contractual models to fit small and large users is relevant for the sustainability of HPC clouds. This paper brings a survey and taxonomy of efforts in HPC cloud and a vision on what we believe is ahead of us, including a set of research challenges that, once tackled, can help advance businesses and scientific discoveries. This becomes particularly relevant due to the fast increasing wave of new HPC applications coming from big data and artificial intelligence.Comment: 29 pages, 5 figures, Published in ACM Computing Surveys (CSUR

EPOBF: Energy Efficient Allocation of Virtual Machines in High Performance Computing Cloud

Cloud computing has become more popular in provision of computing resources under virtual machine (VM) abstraction for high performance computing (HPC) users to run their applications. A HPC cloud is such cloud computing environment. One of challenges of energy efficient resource allocation for VMs in HPC cloud is tradeoff between minimizing total energy consumption of physical machines (PMs) and satisfying Quality of Service (e.g. performance). On one hand, cloud providers want to maximize their profit by reducing the power cost (e.g. using the smallest number of running PMs). On the other hand, cloud customers (users) want highest performance for their applications. In this paper, we focus on the scenario that scheduler does not know global information about user jobs and user applications in the future. Users will request shortterm resources at fixed start times and non interrupted durations. We then propose a new allocation heuristic (named Energy-aware and Performance per watt oriented Bestfit (EPOBF)) that uses metric of performance per watt to choose which most energy-efficient PM for mapping each VM (e.g. maximum of MIPS per Watt). Using information from Feitelson's Parallel Workload Archive to model HPC jobs, we compare the proposed EPOBF to state of the art heuristics on heterogeneous PMs (each PM has multicore CPU). Simulations show that the EPOBF can reduce significant total energy consumption in comparison with state of the art allocation heuristics.Comment: 10 pages, in Procedings of International Conference on Advanced Computing and Applications, Journal of Science and Technology, Vietnamese Academy of Science and Technology, ISSN 0866-708X, Vol. 51, No. 4B, 201

An inter-cloud architecture for future internet infrastructures

In latest years, the concept of interconnecting clouds to allow common service coordination has gained significant attention mainly because of the increasing utilization of cloud resources from Internet users. An efficient common management between different clouds is essential benefit, like boundless elasticity and scalability. Yet, issues related with different standards led to interoperability problems. For this reason, the definition of the open cloud-computing interface defines a set of open community-lead specifications along with a flexible API to build cloud systems. Today, there are cloud systems like OpenStack, OpenNebula, Amazon Web Services and VMWare VCloud that expose APIs for inter-cloud communication. In this work we aim to explore an inter-cloud model by creating a new cloud platform service to act as a mediator among OpenStack, FI-WARE datacenter resource management and Amazon Web Service cloud architectures, therefore to orchestrate communication of various cloud environments. The model is based on the FI-WARE and will be offered as a reusable enabler with an open specification to allow interoperable service coordination

Vertical and horizontal elasticity for dynamic virtual machine reconfiguration

Today, cloud computing applications are rapidly constructed by services belonging to different cloud providers and service owners. This work presents the inter-cloud elasticity framework, which focuses on cloud load balancing based on dynamic virtual machine reconfiguration when variations on load or on user requests volume are observed. We design a dynamic reconfiguration system, called inter-cloud load balancer (ICLB), that allows scaling up or down the virtual resources (thus providing automatized elasticity), by eliminating service downtimes and communication failures. It includes an inter-cloud load balancer for distributing incoming user HTTP traffic across multiple instances of inter-cloud applications and services and we perform dynamic reconfiguration of resources according to the real time requirements. The experimental analysis includes different topologies by showing how real-time traffic variation (using real world workloads) affects resource utilization and by achieving better resource usage in inter-cloud

iFaaSBus: A Security and Privacy based Lightweight Framework for Serverless Computing using IoT and Machine Learning

As data of COVID-19 patients is increasing, the new framework is required to secure the data collected from various Internet of Things (IoT) devices and predict the trend of disease to reduce its spreading. This article proposes a security and privacy-based lightweight framework called iFaaSBus, which uses the concept of IoT, Machine Learning (ML), and Function as a Service (FaaS) or serverless computing to diagnose the COVID-19 disease and manages resources automatically to enable dynamic scalability. iFaaSBus offers OAuth-2.0 Authorization protocol-based privacy and JSON Web Token & Transport Layer Socket (TLS) protocol-based security to secure the patient's health data. iFaaSBus outperforms in terms of response time compared to non-serverless computing while responding to up to 1100 concurrent requests. Further, the performance of various ML models is evaluated based on accuracy, precision, recall, F-score, and AUC values and the K-Nearest Neighbour model gives the highest accuracy rate of 97.51 %

Dynamic management of virtual infrastructures

The final publication is available at Springer via http://dx.doi.org/10.1007/s10723-014-9296-5Cloud infrastructures are becoming an appropriate solution to address the computational needs of scientific applications. However, the use of public or on-premises Infrastructure as a Service (IaaS) clouds requires users to have non-trivial system administration skills. Resource provisioning systems provide facilities to choose the most suitable Virtual Machine Images (VMI) and basic configuration of multiple instances and subnetworks. Other tasks such as the configuration of cluster services, computational frameworks or specific applications are not trivial on the cloud, and normally users have to manually select the VMI that best fits, including undesired additional services and software packages. This paper presents a set of components that ease the access and the usability of IaaS clouds by automating the VMI selection, deployment, configuration, software installation, monitoring and update of Virtual Appliances. It supports APIs from a large number of virtual platforms, making user applications cloud-agnostic. In addition it integrates a contextualization system to enable the installation and configuration of all the user required applications providing the user with a fully functional infrastructure. Therefore, golden VMIs and configuration recipes can be easily reused across different deployments. Moreover, the contextualization agent included in the framework supports horizontal (increase/decrease the number of resources) and vertical (increase/decrease resources within a running Virtual Machine) by properly reconfiguring the software installed, considering the configuration of the multiple resources running. This paves the way for automatic virtual infrastructure deployment, customization and elastic modification at runtime for IaaS clouds.The authors would like to thank to thank the financial support received from the Ministerio de Economia y Competitividad for the project CodeCloud (TIN2010-17804).Caballer Fernández, M.; Blanquer Espert, I.; Moltó, G.; Alfonso Laguna, CD. (2015). Dynamic management of virtual infrastructures. Journal of Grid Computing. 13(1):53-70. https://doi.org/10.1007/s10723-014-9296-5S5370131de Alfonso, C., Caballer, M., Alvarruiz, F., Molto, G., Hernández, V.: Infrastructure deployment over the cloud. In: 2011 IEEE 3rd International Conference on Cloud Computing Technology and Science, pp. 517–521. IEEE. (2011). doi: 10.1109/CloudCom.2011.77Alvarruiz, F., De Alfonso, C., Caballer, M., Hernández, V.: An energy manager for high performance computer clusters. In: 2012 IEEE 10th International Symposium on Parallel and Distributed Processing with Applications, pp. 231–238. (2012). doi: 10.1109/ISPA.2012.38Amazon Web Services: AWS CloudFormation. (2013). http://aws.amazon.com/es/cloudformation/Apache: Whirr (2013). http://whirr.apache.org/Blanquer, I., Brasche, G., Lezzi, D.: Requirements of scientific applications in cloud offerings. In: Proceedings of the 2012 6th Iberian Grid Infrastructure Conference, IBERGRID ’12, pp. 173–182 (2012)Bresnahan, J., Freeman, T., LaBissoniere, D., Keahey, K.: Managing appliance launches in infrastructure clouds. In: Proceedings of the 2011 TeraGrid Conference: Extreme Digital Discovery, TG ’11, pp. 12:1–12:7. ACM, New York (2011). doi: 10.1145/2016741.2016755Buyya, R., Ranjan, R., Calheiros, R.N.: InterCloud: utility-oriented federation of cloud computing environments for scaling of application services. Algoritm. Archit. Parallel Process. 6081, 20 (2010)Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Futur. Gener. Comput. Syst. 25(6), 599–616 (2009). doi: 10.1016/j.future.2008.12.001Caballer, M., De Alfonso, C., Alvarruiz, F., Moltó, G.: EC3: elastic cloud computing cluster. J. Comput. Syst. Sci. (2013). doi: 10.1016/j.jcss.2013.06.005Caballer, M., García, A., Moltó, G., de Alfonso, C.: Towards SLA-driven management of cloud infrastructures to elastically execute scientific applications. In: 6th Iberian Grid Infrastructure Conference (IberGrid), pp. 207–218 (2012)Carrión, J.V., Moltó, G., De Alfonso, C., Caballer, M., Hernández, V.: A generic catalog and repository service for virtual machine images. In: 2nd International ICST Conference on Cloud Computing CloudComp 2010 (2010)Cuomo, A., Modica, G., Distefano, S., Puliafito, A., Rak, M., Tomarchio, O., Venticinque, S., Villano, U.: An SLA-based broker for cloud infrastructures. J. Grid Comput 11(1), 1–25 (2012). doi: 10.1007/s10723-012-9241-4DeHaan, M.: Ansible. http://ansible.cc/ (2013)Distributed Management Task Force, Inc: Open Virtualization Format (OVF) (2010). http://dmtf.org/sites/default/files/standards/documents/DSP0243_1.1.0.pdfDistributed Management Task Force, Inc: Cloud Infrastructure Management Interface (CIMI) Model and REST Interface over HTTP Specification (2012). http://dmtf.org/sites/default/files/standards/documents/DSP0263_1.0.1.pdfEGI.eu: Seeking new horizons: EGI’s role for 2020. Tech. rep. (2012). https://documents.egi.eu/public/RetrieveFile?docid=1098&version=4&filename=EGI-1098-D230-final.pdfElmroth, E., Tordsson, J., Hernández, F.: Self-management challenges for multi-cloud architectures. Towards a service-based internet. Lect. Notes Comput. Sci. 6994, 38–49 (2011)HashiCorp: Vagrant (2013). http://www.vagrantup.com/Jacob, A.: Infrastructure in the cloud era. In: Proceedings of the 2009 International OReilly Conference Velocity (2009)Juve, G., Deelman, E.: Automating application deployment in infrastructure clouds. In: Proceedings of the 2011 IEEE 3rd International Conference on Cloud Computing Technology and Science, CLOUDCOM ’11, pp. 658–665. IEEE Computer Society, Washington DC (2011). doi: 10.1109/CloudCom.2011.102Keahey, K., Freeman, T.: Contextualization: providing one-click virtual clusters. In: 4th IEEE International Conference on eScience, pp. 301–308 (2008)Keahey, K., Freeman, T.: Architecting a large-scale elastic environment: recontextualization and adaptive cloud services for scientific computing (2012)Kecskemeti, G., Kertesz, A., Marosi, A., Kacsuk, P.: Interoperable resource management for establishing federated clouds. In: Achieving Federated and SelfManageable Cloud Infrastructures Theory and Practice, pp. 18–35 (2012). doi: 10.4018/978-1-4666-1631-8.ch002Kertesz, A., Kecskemeti, G., Oriol, M., Kotcauer, P., Acs, S., Rodríguez, M., Mercè, O., Marosi, A.C., Marco, J., Franch, X.: Enhancing federated cloud management with an integrated service monitoring approach. J. Grid Comput. 11(4), 699–720 (2013). doi: 10.1007/s10723-013-9269-0Loutas, N., Kamateri, E., Bosi, F., Tarabanis, K.: Cloud computing interoperability: the state of play. 2011 IEEE 3rd International Conference on Cloud Computing Technology and Science, pp. 752–757 (2011). doi: 10.1109/CloudCom.2011.116Marshall, P., Keahey, K., Freeman, T.: Elastic site: using clouds to elastically extend site resources. In: Proceedings of the 2010 IEEE/ACM 10th International Conference on Cluster, Cloud and Grid Computing, CCGRID ’10, pp. 43–52. IEEE Computer Society, Washington DC (2010). doi: 10.1109/CCGRID.2010.80Massie, M.L., Chun, B.N., Culler, D.E.: The ganglia distributed monitoring system: design, implementation, and experience. Parallel Comput. 30(5-6), 817–840 (2004)Mell, P., Grance, T.: The NIST definition of cloud computing. NIST Special Publication 800-145 (Final). Tech. rep. (2011). http://csrc.nist.gov/publications/nistpubs/800-145/SP800-145.pdfMoltó, G., Caballer, M., Romero, E., Alfonso, C.D.: Elastic memory management of virtualized infrastructures for applications with dynamic memory requirements. In: Proceedings of the International Conference on Computational Science ICCS 2013, pp. 159–168. Elsevier (2013). doi: 10.1016/j.procs.2013.05.179Morfeo: Claudia (2013). http://claudia.morfeo-project.org/wiki/index.php/Main_PageOASIS: Topology and Orchestration Specification for Cloud Applications Version 1.0 (2013). http://docs.oasis-open.org/tosca/TOSCA/v1.0/TOSCA-v1.0.htmlOCCI working group within the Open Grid Forum: Open Cloud Computing Interface Infrastructure (2011). http://ogf.org/documents/GFD.184.pdfOpscode: Chef (2013). http://www.opscode.com/chef/Pawluk, P., Simmons, B., Smit, M., Litoiu, M., Mankovski, S.: Introducing STRATOS: a cloud broker service. In: 2012 IEEE 5th International Conference on Cloud Computing, pp. 891–898 (2012). doi: 10.1109/CLOUD.2012.24Puppet Labs: IT Automation Software for System Administrators (2013). http://www.puppetlabs.com/Redl, C., Breskovic, I., Brandic, I., Dustdar, S.: Automatic SLA matching and provider selection in grid and cloud computing markets. In: Proceedings of the 2012 ACM/IEEE 13th International Conference on Grid Computing, GRID ’12, pp. 85–94. IEEE Computer Society, Washington (2012). doi: 10.1109/Grid.2012.18Rodero-Merino, L., Vaquero, L.M., Gil, V., Galán, F., Fontán, J., Montero, R.S., Llorente, I.M.: From infrastructure delivery to service management in clouds. Futur. Gener. Comput. Syst. 26(8), 1226–1240 (2010). doi: 10.1016/j.future.2010.02.013StratusLab: Claudia Platform (2013). http://stratuslab.eu/doku.php/claudiaSundareswaran, S., Squicciarini, A., Lin, D.: A brokerage-based approach for cloud service selection. In: Proceedings of the 2012 IEEE 5th International Conference on Cloud Computing, CLOUD ’12, pp. 558–565 (2012). doi: 10.1109/CLOUD.2012.119Telefónica Investigación y Desarrollo S.A. Unipersonal.: Telefónicas TCloud API Specification. (2010). http://www.tid.es/files/doc/apis/TCloud_API_Spec_v0.9.pdfYangui, S., Marshall, I.J., Laisne, J.P., Tata, S.: CompatibleOne: The open source cloud broker. J. Grid Comput. (2013). doi: 10.1007/s10723-013-9285-