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ردیف | عنوان | نوع |
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1 |
Mining and Utilization of Special Information for Archives Management Based on 5G Network and Internet of Things
استخراج و استفاده از اطلاعات ویژه برای مدیریت بایگانی بر اساس شبکه 5G و اینترنت اشیا-2020 5G technology is currently in the process of demographic data, data mining, the next-generation mobile networks are considered to be one of the main factors. Through research and data analysis, are expected to overcome the complexity of these networks, and it will be possible to carry out dynamic management and business operations. It is a trade item in that category, which is a particular file. Data collection chosen field of study is the core part. These files are considered to know how it organize their files and save them for future posterity. Finally, deal with digitized archive material; these traditional archives sought to highlight the problems faced by the digital age. Issues related to critical skills of a digitized archive of documents as extended support for mobile telephone networks, and can be considered the next generation of ultra-fast 5G network technology. 5G network includes all kinds of advanced technology, to provide excellent service. Therefore, new architecture and applications of new technology service management solutions should be advised to resolve reliability issues and ensure data transmission capacity, high data rates, and Quality of services (QoS). Cloud computing, networking, as well as software-defined network technology are some of the core networks 5G. Cloud-based service, providing flexible and efficient solutions for information and communication technologies by reducing the cost of the investment and management of information technology infrastructure. In terms of functionality are decoupled control and data planes to support programmability, flexibility and adaptability in a changing network architecture promising architecture. Keywords: Quality of services (QoS) | Internet of things (IoT) | Programmability | Flexibility | 5G network |
مقاله انگلیسی |
2 |
MARVEL: Enabling controller load balancing in software-defined networks with multi-agent reinforcement learning
MARVEL: امکان ایجاد توازن بار کنترل کننده در شبکه های تعریف شده توسط نرم افزار با یادگیری تقویتی چند عامل-2020 The control plane plays a significant role in Software-Defined Networking (SDN). A large SDN usually implements its control plane with several distributed controllers, each controlling a subset of switches and synchronizing with other controllers to maintain a consistent network view. Under the fluctuating network traffic, a static controller- switch mapping relationship could lead to imbalanced workload allocation. Controllers may getoverloaded and reject new requests, eventually reducing the control plane’s request processing ability. Most existing schemes have relied heavily on iterative optimization algorithms to manipulate the mapping relationship between con- trollers and switches, which are either time-consuming or less satisfactory in terms of performance. In this paper, we propose a dynamic controller workload balancing scheme, that is termed MARVEL, based on multi-agent re- inforcement learning for generation of switch migration actions. MARVEL works in two phases: offline training and online decision making. In the training phase, each agent learns how to migrate switches through interacting with the network. In the online phase, MARVEL is deployed to make decisions on migrating switches. Experimen- tal results show that MARVEL outperforms competing existing schemes by improving the control plane’s request processing ability at least 27.3% while using 25% less processing time. Index Terms: Multi-agent reinforcement learning | Neural networks | Software-defined networking | Switch migration |
مقاله انگلیسی |
3 |
SmartFCT: Improving power-efficiency for data center networks with deep reinforcement learning
SmartFCT: بهبود بهره وری انرژی برای شبکه های مرکز داده با یادگیری تقویتی عمیق-2020 Reducing the power consumption of Data Center Networks (DCNs) and guaranteeing the Flow Completion Time (FCT) of applications in DCNs are two major concerns for data center operators. However, existing works cannot realize the two goals together because of two issues: (1) dynamic traffic pattern in DCNs is hard to accurately model; (2) an optimal flow scheduling scheme is computationally expensive. In this paper, we propose SmartFCT, which employs the Deep Reinforcement Learning (DRL) coupled with Software-Defined Networking (SDN) to improve the power efficiency of DCNs and guarantee FCT. SmartFCT dynamically collects traffic distribution from switches to train its DRL model. The well-trained DRL agent of SmartFCT can quickly analyze the complicated traffic characteristics using neural networks and adaptively gen- erate a action for scheduling flows and deliberately configuring margins for different links. Following the gen- erated action, flows are consolidated into a few of active links and switches for saving power, and fine-grained margin configuration for active links avoids FCT violation of unexpected flow bursts. Simulation results show that SmartFCT can guarantee FCT and save up to 12.2% power consumption, compared with the state-of-the-art solutions. Keywords: Data center networks | Software-Defined networking | Power efficiency | Flow completion time | Deep reinforcement learning |
مقاله انگلیسی |
4 |
QoS provisioning for various types of deadline-constrained bulk data transfers between data centers
تامین کیفیت سرویس برای انواع مختلف انتقال داده های فشرده محدود بین مراکز داده-2020 An increasing number of applications in scientific and other domains have moved or are in active
transition to clouds, and the demand for big data transfers between geographically distributed cloudbased
data centers is rapidly growing. Many modern backbone networks leverage logically centralized
controllers based on software-defined networking (SDN) to provide advance bandwidth reservation
for data transfer requests. How to fully utilize the bandwidth resources of the links connecting data
centers with guaranteed quality of service for each user request is an important problem for cloud
service providers. Most existing work focuses on bandwidth scheduling for a single request for data
transfer or multiple requests using the same service model. In this work, we construct rigorous cost
models to quantify user satisfaction degree, and formulate a generic problem of bandwidth scheduling
for multiple deadline-constrained data transfer requests of different types to maximize the request
scheduling success ratio while minimizing the data transfer completion time of each request. We prove
this problem to be not only NP-complete but also non-approximable, and hence design a heuristic
algorithm. For performance evaluation, we establish a proof-of-concept emulated SDN testbed and
also generate large-scale simulation networks. Both experimental and simulation results show that the
proposed scheduling scheme significantly outperforms existing methods in terms of user satisfaction
degree and scheduling success ratio. Keywords: Big data | Data center | High-performance networks | Software-defined networking | Bandwidth scheduling |
مقاله انگلیسی |
5 |
تقویت میان افزار بر مبنی کاربردهای اینترنت اشیا از طریق مکانیسم مدیریت زمان اجرای قابل جابجایی کیفیت سرویس کاربرد برای یک M2M سازگار با میان افزار IOT
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 9 - تعداد صفحات فایل doc فارسی: 22 در سال های اخیر؛ در مخابرات و شبکه های کامپیوتری از طریق مجازی سازی عملکرد شبکه (NFV) و شبکه های تعریف شده نرم افزار (SDN)، مفاهیم و تکنولوژی های جدیدی را شاهد بوده اید. SDN، به برنامه های کاربردی برای کنترل شبکه اجازه می دهد، و NFV، اجازه می دهد تا توزیع توابع شبکه در محیط های مجازی، امکان پذیر شوند، اینها دو نمونه ای هستند که به طور فزاینده ای برای اینترنت اشیا (IoT) استفاده می شود. این اینترنت (IoT) وعده را به ارمغان می آورد که در چند سال آینده میلیاردها دستگاه را به هم متصل کند، و چالش های علمی متعددی را به ویژه در مورد رضایت از کیفیت خدمات (QoS) مورد نیاز برنامه های کاربردی IOT افزایش دهد. به منظور حل این مشکل، ما دو چالش را با توجه به QoS شناسایی کرده ایم: شبکه های متقاطع و نهادهای میانجی که اجازه می دهد تا برنامه با دستگاه های IoT ارتباط برقرار کند. در این مقاله؛ در ابتدا یک چشم انداز نواورانه از یک "عملکرد شبکه" با توجه به محیط توسعه و استقرار آن ارائه می کنیم. سپس، رویکرد کلی از یک راه حل که شامل گسترش پویا، مستقل و یکپارچه از مکانیزم های مدیریت QoS است، را توصیف می کنیم. همچنین مقررات اجرای چنین رویکردی را توصیف می کنیم. در نهایت؛ یک مکانیزم هدایتگر ارائه می کنیم، که به عنوان یک تابع شبکه اجرا می شود، و اجازه کنترل یکپارچه مسیر داده ها از یک ترافیک میان افزار مشخص را می دهد. این مکانیسم از طریق استفاده مربوط به حمل و نقل خودرو ارزیابی می شود.
کلمات کلیدی: اینترنت اشیا | کیفیت سرویس | میان افزار | چارچوب نمونه | گسترش پویا | عملکرد شبکه | محاسبات خودکار. |
مقاله ترجمه شده |
6 |
Optimal Decision Making for Big Data Processing at Edge-Cloud Environment: An SDN Perspective
تصمیم گیری بهینه برای پردازش داده های بزرگ در محیط لبه-ابر: چشم انداز SDN-2018 With the evolution of Internet and extensive usage of smart devices for computing and storage, cloud computing has become popular. It provides seamless services
such as e-commerce, e-health, e-banking, etc., to the end
users. These services are hosted on massive geodistributed
data centers (DCs), which may be managed by different service providers. For faster response time, such a data explosion creates the need to expand DCs. So, to ease the load on DCs, some of the applications may be executed on the edge
devices near to the proximity of the end users. However,
such a multiedge-cloud environment involves huge data
migrations across the underlying network infrastructure,
which may generate long migration delay and cost. Hence,
in this paper, an efficient workload slicing scheme is proposed for handling data-intensive applications in multiedgecloud environment using software-defined networks (SDN).
To handle the inter-DC migrations efficiently, an SDN-based
control scheme is presented, which provides energy-aware
network traffic flow scheduling. Finally, a multileader multifollower Stackelberg game is proposed to provide costeffective inter-DC migrations. The efficacy of the proposed
scheme is evaluated on Google workload traces using various parameters. The results obtained show the effectiveness of the proposed scheme.
Index Terms: Cloud data centers, edge computing, energy efficiency, software-defined networks (SDNs), Stackel berg game |
مقاله انگلیسی |
7 |
Big Data Analysis-Based Secure Cluster Management for Optimized Control Plane in Software-Defined Networks
مدیریت خوشه امن مبتنی بر تحلیل داده های بزرگ برای کنترل بهینه هواپیما در شبکه های تعریف شده توسط نرم افزار-2018 In software-defined networks (SDNs), the abstracted
control plane is its symbolic characteristic, whose core component is the software-based controller. The control plane is logically
centralized, but the controllers can be physically distributed and
composed of multiple nodes. To meet the service management
requirements of large-scale network scenarios, the control plane
is usually implemented in the form of distributed controller clusters. Cluster management technology monitors all types of events
and must maintain a consistent global network status, which usually leads to big data in SDNs. Simultaneously, the cluster security
is an open issue because of the programmable and dynamic
features of SDNs. To address the above challenges, this paper
proposes a big data analysis-based secure cluster management
architecture for the optimized control plane. A security authentication scheme is proposed for cluster management. Moreover,
we propose an ant colony optimization approach that enables big
data analysis scheme and the implementation system that optimizes the control plane. Simulations and comparisons show the
feasibility and efficiency of the proposed scheme. The proposed
scheme is significant in improving the security and efficiency SDN
control plane.
Index Terms: Software-defined networks, big data, swarm computing, security, cluster management |
مقاله انگلیسی |
8 |
Optimized Big Data Management across Multi-Cloud Data Centers: Software-Defined Network-Based Analysis
مدیریت داده های بزرگ بهینه شده در سراسر مراکز داده چند ابری: تحلیل مبتنی بر شبکه نرم افزار تعریف شده-2018 With an exponential increase in smart device users, there is an increase in the bulk amount of data generation from various smart devices, which varies with respect to all the essential Vs used to categorize it as big data. Generally, most service providers, including Google, Amazon, Microsoft and so on, have deployed a large number of geographically distributed data centers to process this huge amount of data generated from various smart devices so that users can get quick response time. For this purpose, Hadoop, and SPARK are widely used by these service providers for processing large datasets. However, less emphasis has been given on the underlying infrastructure (the network through which data flows), which is one of the most important components for successful implementation of any designed solution in this environment. In the worst case, due to heavy network traffic with respect to data migrations across different data centers, the underlying network infrastructure may not be able to transfer data packets from source to destination, resulting in performance degradation. Focusing on all these issues, in this article, we propose a novel SDN-based big data management approach with respect to the optimized network resource consumption such as network bandwidth and data storage units. We analyze various components at both the data and control planes that can enhance the optimized big data analytics across multiple cloud data centers. For example, we analyze the performance of the proposed solution using Bloom-filter-based insertion and deletion of an element in the flow table maintained at the OpenFlow controller, which makes most of the decisions for network traffic classification using the rule-and-action-based mechanism. Using the proposed solution, developers can deploy and analyze real-time traffic behavior for the future big data applications in MCE.
Keywords: Big Data,cloud computing, computer centres, software defined networking, telecommunication traffic |
مقاله انگلیسی |
9 |
Leveraging the Big Data Produced by the Network to Take Intelligent Decisions on Flow Management
بهره برداری از داده های بزرگ تولید شده توسط شبکه تصمیم گیری هوشمندانه بر روی مدیریت جریان-2018 Software-defined network (SDN) offers a very advantageous feature of programming the
network at run time by decoupling the control plane from data plane to have centralized and better forwarding
decisions. To attain the maximum throughput and relatively less latency within a large network, we have
captured the real-time big data produced by the network. Big data is revolutionizing the modern computer
science world to analyze the extremely large datasets to predict the future requirements. Therefore, it would
be useful to embed intelligence in the systems. With the power of SDN, we programmed our network at
run time using Ryu controller to dump the network traffic and engineer it accordingly. We proposed a
methodology for taking intelligent decisions with traffic engineering in SDN. Results have shown that SDN
leverages the big data to decrease the latency time by adding programs in the controller of SDN. An algorithm
is presented to maximize the bandwidth utilization to possess higher throughput after shaping the traffic. It is
found through the results that our proposed methodology of applying intelligence on traffic management in
SDN outperforms those without intelligence decision making.
INDEX TERMS : SDN, big data, Mininet, intelligent decisions, flow management |
مقاله انگلیسی |
10 |
Proactive defense mechanisms for the software-defined Internet of Things with non-patchable vulnerabilities
مکانیسم های دفاعی بیش فعال برای اینترنت اشیا نرم افزار-تعریف شده با آسیب پذیری های غیر قابل آسیب پذیری-2017 The Internet of Things (IoT) contains a large number of heterogeneous devices with a variety of vul
nerabilities. As the vulnerabilities can be exploited by the attackers to break into the system, it is of
vital importance to patch all vulnerabilities. However, some vulnerabilities are impossible to patch (e.g.,
forever-day vulnerabilities). In order to deal with non-patchable vulnerabilities, we propose to change
the attack surface of the IoT network to increase the attack effort. With the support of software-defined
networking (SDN), we develop two proactive defense mechanisms that reconfigure the IoT network
topology. We analyze how the security and performance change when the proposed solutions are
deployed by using a graphical security model and various metrics in simulations. The results show our
proactive defense mechanisms in the SD-IoT effectively increase the attack effort, while maintaining the
average shortest path length.
Keywords: Internet of Things | Software-defined networking | Attack graphs | Security modeling |
مقاله انگلیسی |