Relevant bibliographies by topics / Hybrid cloud orchestration

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Hybrid cloud orchestration'

Author: Grafiati
Published: 7 June 2025
Last updated: 17 July 2025

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Journal articles on the topic "Hybrid cloud orchestration"

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Voruganti, Kiran Kumar. "Orchestrating Multi-Cloud Environments for Enhanced Flexibility and Resilience." Journal of Technology and Systems 6, no. 2 (2024): 9–25. http://dx.doi.org/10.47941/jts.1810.

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Purpose: This paper examines the essential role of multi-cloud orchestration in navigating the complexities of the contemporary cloud computing landscape, aimed at optimizing the deployment and management of cloud resources across diverse environments.
 Methodology: Utilizing a systematic review of scholarly articles, industry reports, and case studies, including the Flexera 2021 State of the Cloud Report and insights from Gartner, alongside academic contributions from researchers like Jamshidi et al. and Garg et al., this study delves into the strategies and tools facilitating effective multi-cloud orchestration.
 Findings: The research highlights multi-cloud orchestration as a critical enabler for enhancing operational efficiency, resilience, and cost-effectiveness in cloud deployments. It emphasizes the strategic benefits of orchestrating a heterogeneous mix of cloud services, including public, private, and hybrid clouds, to meet the intricate demands of modern applications. The study underscores the importance of advanced orchestration tools in ensuring seamless operations, security, and compliance across multi-cloud architectures.
 Unique contributor to theory, policy and practice: By following the principles outlined in this paper, organizations can leverage multi-cloud orchestration to unlock the full potential of their cloud investments and achieve a well-orchestrated symphony of success.
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Natta, Prasanna Kumar. "AI-Powered Cloud Orchestration: Automating Multi-Cloud & Hybrid Cloud Workloads." European Journal of Computer Science and Information Technology 13, no. 8 (2025): 138–47. https://doi.org/10.37745/ejcsit.2013/vol13n8138147.

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AI-powered cloud orchestration revolutionizes how enterprises manage and optimize their multi-cloud and hybrid cloud environments. Integrating artificial intelligence into cloud management addresses complexity, manual intervention, and reactive problem-solving challenges that plague traditional orchestration methods. By implementing intelligent algorithms for resource allocation, workload balancing, predictive scaling, security enhancement, and self-healing capabilities, organizations can transform their cloud operations from manually-defined workflows to autonomous systems capable of continuous optimization. These advanced orchestration technologies enable dynamic resource distribution based on usage patterns and forecasted demand while simultaneously identifying cost-saving opportunities through workload consolidation and intelligent scheduling. Security frameworks are significantly strengthened through anomaly detection, predictive threat intelligence, and adaptive access control policies that evolve with changing organizational needs. Perhaps most transformative is the ability of self-healing infrastructure to automatically detect, diagnose, and remediate issues before they cause service disruptions, dramatically reducing the operational burden on technical teams and allowing them to focus on innovation rather than troubleshooting. This technological shift represents a fundamental evolution in cloud management, offering enterprises unprecedented efficiency, reliability, and cost optimization across their distributed computing environments.
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Bhanuprakash, Madupati. "Kubernetes for Multi-Cloud and Hybrid Cloud: Orchestration, Scaling, and Security Challenges." Journal of Scientific and Engineering Research 10, no. 6 (2023): 290–97. https://doi.org/10.5281/zenodo.14050147.

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Kubernetes enables organizations to manage applications globally, from multi-cloud to hybrid clouds. This paper presents challenges and solutions for workload orchestration, scaling, and security in such infrastructures. Running workloads across different cloud providers enables one to escape vendor lock-in and improve service availability. However, it leads to more complexity in terms of management and security. This paper will analyze the tendency of Kubernetes to orchestrate across multiple clouds in which it operates in a Multi-Cloud environment. It covers important solutions for scaling & managing cross-cloud workloads and securing applications in a multi-cloud or hybrid-cloud environment. Security discussions in the paper include managing identity and access across multiple cloud providers and supporting zero-trust security models. We then examine optimal Kubernetes operations across multi-cloud and hybrid cloud infrastructures, considering solutions like Cluster Federation, service mesh and encryption methods.
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Dmitry, Vasilenko and Mahesh Kurapati. "DYNAMIC TENANT PROVISIONING AND SERVICE ORCHESTRATION IN HYBRID CLOUD." International Journal on Cloud Computing: Services and Architecture (IJCCSA) 9, no. 2/3 (2022): 1. https://doi.org/10.5281/zenodo.7476019.

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The advent of container orchestration and cloud computing, as well as associated security and compliance complexities, make it challenging for the enterprises to develop robust, secure, manageable and extendable architectures which would be applicable to the public and private cloud. The main challenges stem from the fact that on-premises, private cloud and third-party, public cloud services often have seemingly different and sometimes conflicting requirements to tenant provisioning, service deployment, security and compliance and that can lead to rather different architectures which still have a lot of commonalities but evolve independently. Understanding and bridging the functionality gaps between such architectures is highly desirable in terms of common approaches, API/SPI as well as maintainability and extendibility. The authors discuss and propose common architectural approaches to the dynamic tenant provisioning and service orchestration in public, private and hybrid clouds focusing on deployment, security, compliance, scalability and extendibility of stateful Kubernetes runtimes.
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Dmitry, Vasilenko, and Kurapati Mahesh. "Dynamic Tenant Provisioning and Service Orchestration in Hybrid Cloud." International Journal on Cloud Computing: Services and Architecture (IJCCSA) 9, no. 2/3 (2019): 1–10. https://doi.org/10.5281/zenodo.3483491.

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The advent of container orchestration and cloud computing, as well as associated security and compliance complexities, make it challenging for the enterprises to develop robust, secure, manageable and extendable architectures which would be applicable to the public and private cloud. The main challenges stem from the fact that on-premises, private cloud and third-party, public cloud services often have seemingly different and sometimes conflicting requirements to tenant provisioning, service deployment, security and compliance and that can lead to rather different architectures which still have a lot of commonalities but evolve independently. Understanding and bridging the functionality gaps between such architectures is highly desirable in terms of common approaches, API/SPI as well as maintainability and extendibility. The authors discuss and propose common architectural approaches to the dynamic tenant provisioning and service orchestration in public, private and hybrid clouds focusing on deployment, security, compliance, scalability and extendibility of stateful Kubernetes runtimes.
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Dathwal, Prashant. "Frameworks for implementing AI-driven cloud orchestration." American Journal of Engineering and Technology 07, no. 06 (2025): 81–87. https://doi.org/10.37547/tajet/volume07issue06-08.

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This article presents an analysis of frameworks designed for AI-driven orchestration of cloud resources, focusing on contemporary methods and architectural models aimed at improving the efficiency, adaptability, and energy performance of cloud computing environments. The study includes a comprehensive review of applied machine learning techniques, deep learning, reinforcement learning algorithms, evolutionary algorithms, and hybrid approaches used for workload prediction, resource allocation optimization, and autonomous decision-making. The paper identifies key integration challenges, computational overhead, issues of interpretability and security, and outlines development prospects through the implementation of Explainable AI and standardized modular architectures. The findings demonstrate the potential of the proposed approaches for practical implementation in dynamic cloud infrastructures. The insights provided in this article will be of interest to researchers and professionals working in the fields of distributed computing, cloud technologies, and artificial intelligence, as it analyzes modern frameworks designed to build efficient coordination systems within hybrid computing environments. Moreover, the material will be useful for specialists and academics seeking to integrate cutting-edge technological solutions into corporate and research projects, enabling optimized data processing and enhanced adaptability of information systems in an era of continuous digital transformation.
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Pasupuleti, Murali Krishna. "Container Orchestration in Multi-Cloud Environments: A Performance Evaluation." International Journal of Academic and Industrial Research Innovations(IJAIRI) 05, no. 06 (2025): 327–40. https://doi.org/10.62311/nesx/rphcrcscrcec2.

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The emergence of multi-cloud strategies has significantly transformed how enterprises deploy and manage applications, particularly through the use of container orchestration platforms like Kubernetes. This study investigates the performance efficiency of container orchestration in multi-cloud environments by evaluating key parameters such as deployment time, resource utilization, latency, scalability, and fault tolerance. A comparative analysis is conducted using Google Kubernetes Engine (GKE), Amazon Elastic Kubernetes Service (EKS), and Azure Kubernetes Service (AKS), supported by statistical regression and predictive modeling. Results reveal varying degrees of orchestration performance across platforms, with hybrid orchestration strategies offering promising trade-offs between latency and cost-efficiency. The findings provide insights into optimal workload distribution and orchestration techniques for robust cloud-native deployments. Keywords Container Orchestration, Multi-Cloud, Kubernetes, Performance Evaluation, Resource Utilization, Regression Analysis, Predictive Modeling
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Srikanth Gurram. "Cross-domain integration for hybrid cloud management: Innovations and future directions." World Journal of Advanced Engineering Technology and Sciences 15, no. 1 (2025): 1755–61. https://doi.org/10.30574/wjaets.2025.15.1.0405.

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Cross-domain integration for hybrid cloud management presents a significant paradigm shift in how organizations orchestrate resources, secure data, and maintain governance across heterogeneous environments. This article explores the transformative impact of emerging technologies that enable seamless integration across public cloud providers, private clouds, and on-premise infrastructure. Integrating artificial intelligence into orchestration platforms has revolutionized workload placement optimization and resource allocation in hybrid environments. At the same time, Zero Trust security frameworks have redefined authentication paradigms to address the unique challenges of distributed cloud architectures. Kubernetes Federation has emerged as a critical enabler for consistent container orchestration across domain boundaries, with service mesh technologies providing essential networking and observability capabilities. Adopting policy-as-code frameworks represents the final component in establishing truly unified governance across hybrid environments. Together, these technological innovations create a foundation for organizations to leverage the advantages of multiple cloud environments while maintaining operational cohesion, security integrity, and compliance assurance. The convergence of these technologies enables unprecedented levels of automation, consistency, and resilience in hybrid cloud operations, transforming what was once a complex integration challenge into a strategic advantage for organizations seeking to maximize flexibility while minimizing management complexity.
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Nikhil, Bhagat. "Optimizing Performance, Cost-Efficiency, and Flexibility through Hybrid Multi-Cloud Architectures." Journal of Scientific and Engineering Research 11, no. 4 (2024): 372–79. https://doi.org/10.5281/zenodo.14273093.

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Cloud Computing is the foundation of every modern company that is scalable, adaptable and economical. Hybrid multi-cloud environments, which combine private clouds, public clouds, and multiple cloud providers, represent the next generation for scaling cloud infrastructures. Hybrid cloud architecture lets organizations reap the security and control benefits of a private cloud while also taking advantage of the scalability and cost efficiency of a public cloud. Meanwhile, multi-cloud models avoid vendor lock-in, provide risk mitigation, and enable organizations to choose the best options from multiple providers. Hybrid and multi-cloud solutions together offer an integrated cloud architecture that maximizes usage, performance, and resilience. The paper delves into the advantages of hybrid and multi-cloud environments, including agility, cost efficiency and increased security. The paper also touches on organizational design considerations such as workload assignment, interoperability, security, and vendor selection. The paper provides guidelines for implementing hybrid multi-cloud environments where orchestration tools and automation play a vital role to facilitate the operations. Even though Hybrid multi-cloud architectures provide greater flexibility, they must be strategically designed, implemented and managed. By modernizing these environments, businesses can enhance performance, profitability, and agility, better preparing them to thrive in today’s competitive market.
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Katta, Tejaswi Bharadwaj. "AI-Enhanced Orchestration in Hybrid Cloud Enterprise Integration: Transforming Enterprise Data Flows." European Journal of Computer Science and Information Technology 13, no. 9 (2025): 92–103. https://doi.org/10.37745/ejcsit.2013/vol13n992103.

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Hybrid cloud enterprise integration presents a formidable challenge as organizations strive to harmonize legacy systems with modern, cloud-native applications. This article investigates the potential of AI-enhanced orchestration to dynamically manage integration workflows across such heterogeneous environments. By embedding artificial intelligence within orchestration platforms, enterprises can achieve real-time optimization of data flows, resource allocation, and security compliance, transforming static integration approaches into adaptive, self-healing systems. The article focuses on three key dimensions: dynamic resource allocation, real-time data flow management, and enhanced security monitoring. Traditional orchestration frameworks often struggle to react to fluctuating workloads and unpredictable network conditions. In contrast, AI algorithms analyze historical and real-time operational metrics to predict bottlenecks and proactively adjust resources across serverless functions, containerized microservices, and legacy infrastructures. AI-enhanced orchestration also improves fault tolerance by continuously monitoring integration pipelines, detecting anomalies, and initiating automated recovery processes. Various implementation approaches are examined, including augmenting existing platforms, leveraging cloud-native frameworks, and developing custom AI integration layers, along with challenges organizations face in the adoption and potential future directions of this transformative technology.
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Dissertations / Theses on the topic "Hybrid cloud orchestration"

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Jendi, Khaled. "Evaluation and Improvement of Application Deployment in Hybrid Edge Cloud Environment : Using OpenStack, Kubernetes, and Spinnaker." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-275714.

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Traditional mechanisms of deployment of deferent applications can be costly in terms of time and resources, especially when the application requires a specific environment to run upon and has a different kind of dependencies so to set up such an application, it would need an expert to find out all required dependencies. In addition, it is difficult to deploy applications with efficient usage of resources available in the distributed environment of the cloud. Deploying different projects on the same resources is a challenge. To solve this problem, we evaluated different deployment mechanisms using heterogeneous infrastructure-as-a-service (IaaS) called OpenStack and Microsoft Azure. we also used platform-as-a-service called Kubernetes. Finally, to automate and auto integrate deployments, we used Spinnaker as the continuous delivery framework. The goal of this thesis work is to evaluate and improve different deployment mechanisms in terms of edge cloud performance. Performance depends on achieving efficient usage of cloud resources, reducing latency, scalability, replication and rolling upgrade, load balancing between data nodes, high availability and measuring zero- downtime for deployed applications. These problems are solved basically by designing and deploying infrastructure and platform in which Kubernetes (PaaS) is deployed on top of OpenStack (IaaS). In addition, the usage of Docker containers rather than regular virtual machines (containers orchestration) will have a huge impact. The conclusion of the report would demonstrate and discuss the results along with various test cases regarding the usage of different methods of deployment, and the presentation of the deployment process. It includes also suggestions to develop more reliable and secure deployment in the future when having heterogeneous container orchestration infrastructure.<br>Traditionella mekanismer för utplacering av deferentapplikationer kan vara kostsamma när det gäller tid och resurser, särskilt när applikationen kräver en specifik miljö att löpa på och har en annan typ av beroende, så att en sådan applikation upprättas, skulle det behöva en expert att hitta ut alla nödvändiga beroenden. Dessutom är det svårt att distribuera applikationer med effektiv användning av resurser tillgängliga i molnens distribuerade i Edge Cloud Computing. Att distribuera olika projekt på samma resurser är en utmaning. För att lösa detta problem skulle jag utvärdera olika implementeringsmekanismer genom att använda heterogen infrastruktur-as-a-service (IaaS) som heter OpenStack och Microsoft Azure. Jag skulle också använda plattform-som-en-tjänst som heter Kubernetes. För att automatisera och automatiskt integrera implementeringar skulle jag använda Spinnaker som kontinuerlig leveransram. Målet med detta avhandlingsarbete är att utvärdera och förbättra olika implementeringsmekanismer när det gäller Edge Cloud prestanda. Prestanda beror på att du uppnår effektiv användning av Cloud resurser, reducerar latens, skalbarhet, replikering och rullningsuppgradering, lastbalansering mellan datodenoder, hög tillgänglighet och mätning av nollstanntid för distribuerade applikationer. Dessa problem löses i grunden genom att designa och distribuera infrastruktur och plattform där Kubernetes (PaaS) används på toppen av OpenStack (IaaS). Dessutom kommer användningen av Docker- behållare istället för vanliga virtuella maskiner (behållare orkestration) att ha en stor inverkan. Slutsatsen av rapporten skulle visa och diskutera resultaten tillsammans med olika testfall angående användningen av olika metoder för implementering och presentationen av installationsprocessen. Det innehåller också förslag på att utveckla mer tillförlitlig och säker implementering i framtiden när den har heterogen behållareorkesteringsinfrastruktur.
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Book chapters on the topic "Hybrid cloud orchestration"

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Diaz, Francesco, and Roberto Freato. "Working with SQL Server on Hybrid Cloud and Azure IaaS." In Cloud Data Design, Orchestration, and Management Using Microsoft Azure. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3615-4_2.

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Ravindra, Pushkara, Aakash Khochare, Siva Prakash Reddy, Sarthak Sharma, Prateeksha Varshney, and Yogesh Simmhan. "$$\mathbb {ECHO}$$ : An Adaptive Orchestration Platform for Hybrid Dataflows across Cloud and Edge." In Service-Oriented Computing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69035-3_28.

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Vassilev, Vassil, Sylvia Ilieva, Iva Krasteva, Irena Pavlova, Dessisslava Petrova-Antonova, and Wiktor Sowinski-Mydlarz. "AI-Based Hybrid Data Platforms." In Data Spaces. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98636-0_8.

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AbstractThe current digital transformation of many businesses and the exponential growth of digital data are two of the key factors of digital revolution. For the successful meeting of high expectations, the data platforms need to employ the recent theoretical, technological, and methodological advances in contemporary computing and data science and engineering. This chapter presents an approach to address these challenges by combining logical methods for knowledge processing and machine learning methods for data analysis into a hybrid AI-based framework. It is applicable to a wide range of problems that involve both synchronous operations and asynchronous events in different domains. The framework is a foundation for building the GATE Data Platform, which aims at the application of Big Data technologies in civil and government services, industry, and healthcare. The platform implementation will utilize several recent distributed technologies such as Internet of Things, cloud, and edge computing and will integrate them into a multilevel service-oriented architecture that supports services along the entire data value chain, while the service orchestration guarantees a high degree of interoperability, reusability, and automation. The platform is designed to be compliant with the open-source software, but its open architecture supports also mixing with commercial components and tools.
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"CLOUD COMPUTING AND EDGE TECHNOLOGIES." In Intelligent Shields: Artificial Intelligence and Machine Learning for Cybersecurity. Iterative International Publishers (IIP), Selfypage Developers Pvt Ltd., 2025. https://doi.org/10.58532/nbennurasai2.

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This chapter examines the transformative landscape of cloud computing and edge technologies that form the backbone of modern digital infrastructure. It introduces fundamental service models (IaaS, PaaS, SaaS) and deployment approaches (public, private, hybrid), explaining how organizations leverage these paradigms for operational efficiency. The discussion extends to distributed systems architecture, serverless computing, and container orchestration technologies that enable scalable application deployment. Special attention is given to edge computing‘s emerging role in processing data closer to its source, reducing latency and enabling real-time analytics for IoT applications [1]. Netflix‘s migration to cloud infrastructure serves as an illustrative case study of successful cloudnative implementation. The chapter also addresses critical considerations of security, cost optimization, and environmental sustainability that organizations must navigate in adopting cloud and edge computing solutions. Through this comprehensive overview, readers gain insight into how these technologies are reshaping enterprise IT infrastructure.
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Shubneet, Mr, Anushka Raj Yadav, Partha Chanda, Arnab Das, and Atahar Shihab. "BIG DATA TECHNOLOGIES AND CLOUD COMPUTING FOR DATA SCIENCE ANALYTICS." In Artificial Intelligence Technology in Healthcare: Security and Privacy Issues. Iterative International Publishers (IIP), Selfypage Developers Pvt Ltd., 2025. https://doi.org/10.58532/nbennuraith8.

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Big Data refers to extremely large, complex datasets that challenge traditional data processing systems, characterized by the four Vs: Volume (sheer data size), Velocity (rapid data generation/ingestion), Variety (diverse formats like structured, unstructured, and semi-structured data), and Veracity (data quality and reliability). Managing these datasets poses significant challenges in storage, distributed processing, and scalability, necessitating specialized tools such as Hadoop’s HDFS for distributed storage, MapReduce for batch processing, and Spark for in-memory analytics. Modern solutions leverage distributed computing frameworks and NoSQL databases (e.g., MongoDB, Cassandra) to handle heterogeneity and scale. Cloud platforms like AWS and Azure further address these challenges through elastic resources and managed services (e.g., AWS EMR, Azure HDInsight), enabling efficient data pipeline orchestration. However, organizations must still navigate trade-offs between consistency, avail- ability, and partition tolerance (CAP theorem) in distributed systems. Emerging advancements in real-time stream processing (e.g., Apache Flink) and hybrid cloud architectures continue to reshape Big Data ecosystems, driving innovation in sectors from healthcare to finance. [1, 2]
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Vankayalapati, Ravi Kumar. "Automation and orchestration in hybrid clouds." In Deep Science Publishing. Deep Science Publishing, 2025. https://doi.org/10.70593/978-81-984306-5-6_9.

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Conference papers on the topic "Hybrid cloud orchestration"

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Sitaram, Dinkar, Sudheendra Harwalkar, N. Ashwin, and S. K. Ajmal. "Secure Orchestration Based Federation in Hybrid Cloud Environments." In 2015 International Conference on Information Technology (ICIT). IEEE, 2015. http://dx.doi.org/10.1109/icit.2015.35.

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Sitaram, Dinkar, Sudheendra Harwalkar, Chetna Sureka, et al. "Orchestration Based Hybrid or Multi Clouds and Interoperability Standardization." In 2018 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM). IEEE, 2018. http://dx.doi.org/10.1109/ccem.2018.00018.

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Wang, Long, Harigovind V. Ramasamy, Alexei Karve, and Richard E. Harper. "Providing Resiliency to Orchestration and Automation Engines in Hybrid Cloud." In 2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshop (DSN-W). IEEE, 2017. http://dx.doi.org/10.1109/dsn-w.2017.35.

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Sangle, Rajiv, Tuhin Khare, Padmanabha V. Seshadri, and Yogesh Simmhan. "Comparing the Orchestration of Quantum Applications on Hybrid Clouds." In 2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW). IEEE, 2023. http://dx.doi.org/10.1109/ccgridw59191.2023.00069.

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Asthana, Neeraj, Tom Chefalas, Alexei Karve, Alla Segal, Mahika Dubey, and Sai Zeng. "A declarative approach for service enablement on hybrid cloud orchestration engines." In NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium. IEEE, 2018. http://dx.doi.org/10.1109/noms.2018.8406175.

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Sun, Ruiqi, Jie Yang, Zhan Gao, and Zhiqiang He. "A Visualized Framework of Automatic Orchestration Engine Supporting Hybrid Cloud Resources." In 2014 IEEE 11th International Conference on e-Business Engineering (ICEBE). IEEE, 2014. http://dx.doi.org/10.1109/icebe.2014.14.

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Khochare, Aakash, Tuhin Khare, Varad Kulkarni, and Yogesh Simmhan. "XFaaS: Cross-platform Orchestration of FaaS Workflows on Hybrid Clouds." In 2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing (CCGrid). IEEE, 2023. http://dx.doi.org/10.1109/ccgrid57682.2023.00053.

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Elisseev, Vadim, Robert Manson-Sawko, Carlos Pena-Monferrer, et al. "Multiscale scientific workflows on high-performance hybrid cloud." In 2022 IEEE/ACM 4th International Workshop on Containers and New Orchestration Paradigms for Isolated Environments in HPC (CANOPIE-HPC). IEEE, 2022. http://dx.doi.org/10.1109/canopie-hpc56864.2022.00006.

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Pham, Linh Manh, Alain Tchana, Didier Donsez, Noel de Palma, Vincent Zurczak, and Pierre-Yves Gibello. "Roboconf: A Hybrid Cloud Orchestrator to Deploy Complex Applications." In 2015 IEEE 8th International Conference on Cloud Computing (CLOUD). IEEE, 2015. http://dx.doi.org/10.1109/cloud.2015.56.

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Senna, Carlos R., Luis G. C. Russi, and Edmundo R. M. Madeira. "An Architecture for Orchestrating Hadoop Applications in Hybrid Cloud." In 2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid). IEEE, 2014. http://dx.doi.org/10.1109/ccgrid.2014.46.

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