492 research outputs found

    Cost-Effective Resource Allocation and Throughput Maximization in Mobile Cloudlets and Distributed Clouds

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    With the advance in communication networks and the use explosion of mobile devices, distributed clouds consisting of many small and medium datacenters in geographical locations and cloudlets defined as "mini" datacenters are envisioned as the next-generation cloud computing platform. In particular, distributed clouds enable disaster-resilient and scalable services by scaling the services into multiple datacenters, while cloudlets allow pervasive and continuous services with low access delay by further enabling mobile users to access the services within their proximity. To realize the promises provided by distributed clouds and mobile cloudlets, it is urgently to optimize various system performance of distributed clouds and cloudlets, such as system throughput and operational cost by developing efficient solutions. In this thesis, we aim to devise novel solutions to maximize the system throughput of mobile cloudlets, and minimize the operational costs of distributed clouds, while meeting the resource capacity constraints and users' resource demands. This however poses great challenges, that is, (1) how to maximize the system throughput of a mobile cloudlet, considering that a mobile cloudlet has limited resources to serve energy-constrained mobile devices, (2) how to efficiently and effectively manage and evaluate big data in distributed clouds, and (3) how to efficiently allocate the resources of a distributed cloud to meet the resource demands of various users. Existing studies mainly focused on implementing systems and lacked systematic optimization methods to optimize the performance of distributed clouds and mobile cloudlets. Novel techniques and approaches for performance optimization of distributed clouds and mobile cloudlets are desperately needed. To address these challenges, this thesis makes the following contributions. We firstly study online request admissions in a cloudlet with the aim of maximizing the system throughput, assuming that future user requests are not known in advance. We propose a novel admission cost model to accurately model dynamic resource consumption, and devise efficient algorithms for online request admissions. We secondly study a novel collaboration- and fairness-aware big data management problem in a distributed cloud to maximize the system throughput, while minimizing the operational cost of service providers, subject to resource capacities and users' fairness constraints, for which, we propose a novel optimization framework and devise a fast yet scalable approximation algorithm with an approximation ratio. We thirdly investigate online query evaluation for big data analysis in a distributed cloud to maximize the query acceptance ratio, while minimizing the query evaluation cost. For this problem, we propose a novel metric to model the costs of different resource consumptions in datacenters, and devise efficient online algorithms under both unsplittable and splittable source data assumptions. We fourthly address the problem of community-aware data placement of online social networks into a distributed cloud, with the aim of minimizing the operational cost of the cloud service provider, and devise a fast yet scalable algorithm for the problem, by leveraging the close community concept that considers both user read rates and update rates. We also deal with social network evolutions, by developing a dynamic evaluation algorithm for the problem. We finally evaluate the performance of all proposed algorithms in this thesis through experimental simulations, using real and/or synthetic datasets. Simulation results show that the proposed algorithms significantly outperform existing algorithms

    How to Place Your Apps in the Fog -- State of the Art and Open Challenges

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    Fog computing aims at extending the Cloud towards the IoT so to achieve improved QoS and to empower latency-sensitive and bandwidth-hungry applications. The Fog calls for novel models and algorithms to distribute multi-service applications in such a way that data processing occurs wherever it is best-placed, based on both functional and non-functional requirements. This survey reviews the existing methodologies to solve the application placement problem in the Fog, while pursuing three main objectives. First, it offers a comprehensive overview on the currently employed algorithms, on the availability of open-source prototypes, and on the size of test use cases. Second, it classifies the literature based on the application and Fog infrastructure characteristics that are captured by available models, with a focus on the considered constraints and the optimised metrics. Finally, it identifies some open challenges in application placement in the Fog

    Ranking Cloud Computing Criteria in Developing Electronic Communications Services Using MCDM

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    The main purpose of this study was ranking cloud computing criteria in developing electronic communications services using multiple-criteria decision analysis methodology (MCDM). This correlational research was applied in terms of purpose. Statistical population of the study included ICT experts of the steel industry in Yazd province (industrial experts), among which, 312 individuals were selected using purposeful nonprobability (judgmental) sampling method. Considering the conducted investigations and critically reviewing the related books and articles, the variables, criteria, and scales were identified using cloud computing technology. To analyze the data, MCDM and fuzzy logic calculations were utilized in Expert Choice software. According to the results and considering fuzzy calculations related to the capabilities of cloud computing in developing electronic communications services, the most important criteria in the "IT management in steel industries" cluster having (A) network code was "communicating with steel industries` costumers" having (AB) network code and fuzzy network weight equal to 0.096; the most important criteria in "cloud computing capabilities" cluster having (B) network code were "reducing steel industries` costs" having (BA) network code and fuzzy network weight equal to 0.191; and "providing rapid services to steel industries` costumers" having (BB) network code and fuzzy network weight equal to 0.120. on the other hand, the most important criteria in "developing electronic communications services" cluster having (C) network code was "storing the data in electronic communications services" having (CD) network code and fuzzy network weight equal to 0.123, since based on fuzzy logic calculation, they had the highest fuzzy rank in Matlab programming environment regarding cloud computing capabilities in developing electronic communications services

    Ranking Cloud Computing Criteria in Developing Electronic Communications Services Using MCDM

    Get PDF
    The main purpose of this study was ranking cloud computing criteria in developing electronic communications services using multiple-criteria decision analysis methodology (MCDM). This correlational research was applied in terms of purpose. Statistical population of the study included ICT experts of the steel industry in Yazd province (industrial experts), among which, 312 individuals were selected using purposeful nonprobability (judgmental) sampling method. Considering the conducted investigations and critically reviewing the related books and articles, the variables, criteria, and scales were identified using cloud computing technology. To analyze the data, MCDM and fuzzy logic calculations were utilized in Expert Choice software. According to the results and considering fuzzy calculations related to the capabilities of cloud computing in developing electronic communications services, the most important criteria in the "IT management in steel industries" cluster having (A) network code was "communicating with steel industries` costumers" having (AB) network code and fuzzy network weight equal to 0.096; the most important criteria in "cloud computing capabilities" cluster having (B) network code were "reducing steel industries` costs" having (BA) network code and fuzzy network weight equal to 0.191; and "providing rapid services to steel industries` costumers" having (BB) network code and fuzzy network weight equal to 0.120. on the other hand, the most important criteria in "developing electronic communications services" cluster having (C) network code was "storing the data in electronic communications services" having (CD) network code and fuzzy network weight equal to 0.123, since based on fuzzy logic calculation, they had the highest fuzzy rank in Matlab programming environment regarding cloud computing capabilities in developing electronic communications services

    Management And Security Of Multi-Cloud Applications

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    Single cloud management platform technology has reached maturity and is quite successful in information technology applications. Enterprises and application service providers are increasingly adopting a multi-cloud strategy to reduce the risk of cloud service provider lock-in and cloud blackouts and, at the same time, get the benefits like competitive pricing, the flexibility of resource provisioning and better points of presence. Another class of applications that are getting cloud service providers increasingly interested in is the carriers\u27 virtualized network services. However, virtualized carrier services require high levels of availability and performance and impose stringent requirements on cloud services. They necessitate the use of multi-cloud management and innovative techniques for placement and performance management. We consider two classes of distributed applications – the virtual network services and the next generation of healthcare – that would benefit immensely from deployment over multiple clouds. This thesis deals with the design and development of new processes and algorithms to enable these classes of applications. We have evolved a method for optimization of multi-cloud platforms that will pave the way for obtaining optimized placement for both classes of services. The approach that we have followed for placement itself is predictive cost optimized latency controlled virtual resource placement for both types of applications. To improve the availability of virtual network services, we have made innovative use of the machine and deep learning for developing a framework for fault detection and localization. Finally, to secure patient data flowing through the wide expanse of sensors, cloud hierarchy, virtualized network, and visualization domain, we have evolved hierarchical autoencoder models for data in motion between the IoT domain and the multi-cloud domain and within the multi-cloud hierarchy

    Leveraging Cloud-based NFV and SDN Platform Towards Quality-Driven Next-Generation Mobile Networks

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    Network virtualization has become a key approach for Network Service Providers (NSPs) to mitigate the challenge of the continually increasing demands for network services. Tightly coupled with their software components, legacy network devices are difficult to upgrade or modify to meet the dynamically changing end-user needs. To virtualize their infrastructure and mitigate those challenges, NSPs have started to adopt Software Defined Networking (SDN) and Network Function Virtualization (NFV). To this end, this thesis addresses the challenges faced on the road of transforming the legacy networking infrastructure to a more dynamic and agile virtualized environment to meet the rapidly increasing demand for network services and serve as an enabler for key emerging technologies such as the Internet of Things (IoT) and 5G networking. The thesis considers different approaches and platforms to serve as an NFV/SDN based cloud applications while closely considering how such an environment deploys its virtualized services to optimize the network and reducing their costs. The thesis starts first by defining the standards of adopting microservices as architecture for NFV. Then, it focuses on the latency-aware deployment approach of virtual network functions (VNFs) forming service function chains (SFC) in a cloud environment. This approach ensures that NSPs still meet their strict quality of service and service level agreements while considering both functional and non-functional constraints of the NFV-based applications such as, delay, resource allocation, and intercorrelation between VNF instances. In addition, the thesis proposes a detailed approach on recovering and handling of those instances by optimizing the decision of migrating or re-instantiating the virtualized services upon a sudden event (failure/overload…). All the proposed approaches contribute to the orchestration of NFV applications to meet the requirements of the IoT and NGNs era

    Resource Management In Cloud And Big Data Systems

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    Cloud computing is a paradigm shift in computing, where services are offered and acquired on demand in a cost-effective way. These services are often virtualized, and they can handle the computing needs of big data analytics. The ever-growing demand for cloud services arises in many areas including healthcare, transportation, energy systems, and manufacturing. However, cloud resources such as computing power, storage, energy, dollars for infrastructure, and dollars for operations, are limited. Effective use of the existing resources raises several fundamental challenges that place the cloud resource management at the heart of the cloud providers\u27 decision-making process. One of these challenges faced by the cloud providers is to provision, allocate, and price the resources such that their profit is maximized and the resources are utilized efficiently. In addition, executing large-scale applications in clouds may require resources from several cloud providers. Another challenge when processing data intensive applications is minimizing their energy costs. Electricity used in US data centers in 2010 accounted for about 2% of total electricity used nationwide. In addition, the energy consumed by the data centers is growing at over 15% annually, and the energy costs make up about 42% of the data centers\u27 operating costs. Therefore, it is critical for the data centers to minimize their energy consumption when offering services to customers. In this Ph.D. dissertation, we address these challenges by designing, developing, and analyzing mechanisms for resource management in cloud computing systems and data centers. The goal is to allocate resources efficiently while optimizing a global performance objective of the system (e.g., maximizing revenue, maximizing social welfare, or minimizing energy). We improve the state-of-the-art in both methodologies and applications. As for methodologies, we introduce novel resource management mechanisms based on mechanism design, approximation algorithms, cooperative game theory, and hedonic games. These mechanisms can be applied in cloud virtual machine (VM) allocation and pricing, cloud federation formation, and energy-efficient computing. In this dissertation, we outline our contributions and possible directions for future research in this field

    Resource Management In Cloud And Big Data Systems

    Get PDF
    Cloud computing is a paradigm shift in computing, where services are offered and acquired on demand in a cost-effective way. These services are often virtualized, and they can handle the computing needs of big data analytics. The ever-growing demand for cloud services arises in many areas including healthcare, transportation, energy systems, and manufacturing. However, cloud resources such as computing power, storage, energy, dollars for infrastructure, and dollars for operations, are limited. Effective use of the existing resources raises several fundamental challenges that place the cloud resource management at the heart of the cloud providers\u27 decision-making process. One of these challenges faced by the cloud providers is to provision, allocate, and price the resources such that their profit is maximized and the resources are utilized efficiently. In addition, executing large-scale applications in clouds may require resources from several cloud providers. Another challenge when processing data intensive applications is minimizing their energy costs. Electricity used in US data centers in 2010 accounted for about 2% of total electricity used nationwide. In addition, the energy consumed by the data centers is growing at over 15% annually, and the energy costs make up about 42% of the data centers\u27 operating costs. Therefore, it is critical for the data centers to minimize their energy consumption when offering services to customers. In this Ph.D. dissertation, we address these challenges by designing, developing, and analyzing mechanisms for resource management in cloud computing systems and data centers. The goal is to allocate resources efficiently while optimizing a global performance objective of the system (e.g., maximizing revenue, maximizing social welfare, or minimizing energy). We improve the state-of-the-art in both methodologies and applications. As for methodologies, we introduce novel resource management mechanisms based on mechanism design, approximation algorithms, cooperative game theory, and hedonic games. These mechanisms can be applied in cloud virtual machine (VM) allocation and pricing, cloud federation formation, and energy-efficient computing. In this dissertation, we outline our contributions and possible directions for future research in this field
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