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ردیف | عنوان | نوع |
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1 |
Multi-objective scheduling of extreme data scientific workflows in Fog
زمانبندی چند هدفه گردش کار علمی علمی شدید در مه-2020 The concept of ‘‘extreme data’’ is a recent re-incarnation of the ‘‘big data’’ problem, which is distinguished
by the massive amounts of information that must be analyzed with strict time requirements. In
the past decade, the Cloud data centers have been envisioned as the essential computing architectures
for enabling extreme data workflows. However, the Cloud data centers are often geographically
distributed. Such geographical distribution increases offloading latency, making it unsuitable for
processing of workflows with strict latency requirements, as the data transfer times could be very
high. Fog computing emerged as a promising solution to this issue, as it allows partial workflow
processing in lower-network layers. Performing data processing on the Fog significantly reduces data
transfer latency, allowing to meet the workflows’ strict latency requirements. However, the Fog layer
is highly heterogeneous and loosely connected, which affects reliability and response time of task
offloading. In this work, we investigate the potential of Fog for scheduling of extreme data workflows
with strict response time requirements. Moreover, we propose a novel Pareto-based approach for
task offloading in Fog, called Multi-objective Workflow Offloading (MOWO). MOWO considers three
optimization objectives, namely response time, reliability, and financial cost. We evaluate MOWO
workflow scheduler on a set of real-world biomedical, meteorological and astronomy workflows
representing examples of extreme data application with strict latency requirements. Keywords: Scheduling | Scientific workflows | Fog computing | Task offloading | Monte-Carlo simulation | Multi-objective optimization |
مقاله انگلیسی |
2 |
HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments
HealthFog: یک سیستم هوشمند درمانی هوشمند مبتنی بر یادگیری عمیق برای تشخیص خودکار بیماری های قلبی در محیط های IoT و محاسبات مه-2020 Cloud computing provides resources over the Internet and allows a plethora of applications to be
deployed to provide services for different industries. The major bottleneck being faced currently in
these cloud frameworks is their limited scalability and hence inability to cater to the requirements
of centralized Internet of Things (IoT) based compute environments. The main reason for this is
that latency-sensitive applications like health monitoring and surveillance systems now require
computation over large amounts of data (Big Data) transferred to centralized database and from
database to cloud data centers which leads to drop in performance of such systems. The new paradigms
of fog and edge computing provide innovative solutions by bringing resources closer to the user and
provide low latency and energy efficient solutions for data processing compared to cloud domains. Still,
the current fog models have many limitations and focus from a limited perspective on either accuracy
of results or reduced response time but not both. We proposed a novel framework called HealthFog
for integrating ensemble deep learning in Edge computing devices and deployed it for a real-life
application of automatic Heart Disease analysis. HealthFog delivers healthcare as a fog service using IoT
devices and efficiently manages the data of heart patients, which comes as user requests. Fog-enabled
cloud framework, FogBus is used to deploy and test the performance of the proposed model in terms
of power consumption, network bandwidth, latency, jitter, accuracy and execution time. HealthFog
is configurable to various operation modes which provide the best Quality of Service or prediction
accuracy, as required, in diverse fog computation scenarios and for different user requirements. Keywords: Fog computing | Internet of things | Healthcare | Deep learning | Ensemble learning | Heart patient analysis |
مقاله انگلیسی |
3 |
Potentials, trends, and prospects in edge technologies: Fog, cloudlet, mobile edge, and micro data centers
پتانسیل ها، گرایشات و چشم اندازها در روشهای لبه ای: مراکز داده ای مات، تکه ابر، لبه ای سیار و میکرو-2018 Advancements in smart devices, wearable gadgets, sensors, and communication paradigm have enabled the vision of smart cities, pervasive healthcare, augmented reality and interactive multimedia, Internet of Every Thing (IoE), and cognitive assistance, to name a few. All of these visions have one thing in common, i.e., delay sensitivity and instant response. Various new technologies designed to work at the edge of the network, such as fog computing, cloudlets, mobile edge computing, and micro data centers have emerged in the near past. We use the name ``edge computing for this set of emerging technologies. Edge computing is a promising paradigm to offer the required computation and storage resources with minimal delays because of ``being near to the users or terminal devices. Edge computing aims to bring cloud resources and services at the edge of the network, as a middle layer between end user and cloud data centers, to offer prompt service response with minimal delay. Two major aims of edge computing can be denoted as: (a) minimize response delay by servicing the users’ request at the network edge instead of servicing it at far located cloud data centers, and (b) minimize downward and upward traffic volumes in the network core. Minimization of network core traffic inherently brings energy efficiency and data cost reductions. Downward network traffic can be minimized by servicing set of users at network edge instead of service providers data centers (e.g., multimedia and shared data) Content Delivery Networks (CDNs), and upward traffic can be minimized by processing and filtering raw data (e.g., sensors monitored data) and uploading the processed information to cloud. This survey presents a detailed overview of potentials, trends, and challenges of edge computing. The survey illustrates a list of most significant applications and potentials in the area of edge computing. State of the art literature on edge computing domain is included in the survey to guide readers towards the current trends and future opportunities in the area of edge computing.
keywords: Edge computing| Fog computing| Internet of Things |
مقاله انگلیسی |
4 |
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 |
5 |
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 |
6 |
Fast and Parallel Trust Computing Scheme Based on Big Data Analysis For Collaboration Cloud Service
طرح محاسبات سریع و موازنه اعتماد بر اساس تجزیه و تحلیل داده های بزرگ برای همکاری خدمات ابر-2018 Providing high trustworthy service is the most
fundamental task for any cloud computing platform. Users are
willing to deliver their computing tasks and the most sensitive
data to cloud data centers, which is based on the trust relationship
established between users and cloud service providers. However,
with the development of collaboration cloud computing, how
to provider fast response for a large number of users’ service
requests becomes a challenging problem. In order to quickly
provide highly trustworthy services, the service platform must
efficiently and quickly reply tens of millions of service requests,
and automatically match-make tens of thousands of service
resources. In this context, lightweight and fast (high-speed, lowoverhead) trust computing schemes become the fundamental
demand for implementing a trustworthy and collaborative cloud
service. In this paper, we propose an innovative and parallel trust
computing scheme based on big data analysis for the trustworthy
cloud service environment. First, a distributed and modular
perceiving architecture for large-scale virtual machines’ service
behavior is proposed relying on distributed monitoring agents.
Then, an adaptive, lightweight, and parallel trust computing
scheme is proposed for big monitored data. To the best of
our knowledge, this paper is the first to use a blocked and
parallel computing mechanism, the speed of trust calculation is
greatly accelerated, which makes this trust computing scheme
very suitable for a large-scale cloud computing environment.
Performance analysis and experimental results verify feasibility
and effectiveness of the proposed scheme
Index Terms: Cloud computing, service behavior monitoring,trust computing, big data analysis |
مقاله انگلیسی |
7 |
تجدید نظر در شبکه سازی مراکز داده: معماری، پروتکل های شبکه و به اشتراک گذاری منابع
سال انتشار: 2014 - تعداد صفحات فایل pdf انگلیسی: 16 - تعداد صفحات فایل doc فارسی: 53 مراکز داده ی بزرگ مقیاس، دوره ی جدیدی از محاسبات ابر را ممکن ساخته و زیرساختی اصلی جهت برآورده ساختن مقتضیات محاسبه و ذخیره سازی هم برای نیازهای فناوری اطلاعات شرکت ها و هم برای سرویس های مبتنی بر ابر فراهم نموده است. به منظور پشتیبانی از نیازهای روبه رشد محاسبات ابر، شمار از سرویس ها در مراکز داد ه ی کنونی به شیوه ای نمایی در حال افزایش است، که این به نوبه ی خود منجر به چالش هایی عظیم در طراحی یک شبکه ی مرکز داده ی کارآمد و مقرون به صرفه می شود. با دسترس پذیری داده ها و مخاطرات امنیتی، مسائل مربوط به شبکه های مراکز داده از هر زمان دیگری اهمیت بیشتری یافته اند. با انگیزه ی پرداختن به این چالش ها و مسائل، کارهای پژوهشی خلاقانه و بدیع بسیاری در سالیان اخیر پیشنهاد شده-است. در این مقاله، یک بررسی پیرامون شبکه های مراکز داده انجام می دهیم سپس تحلیل و مروری کلی بر متون و مقالات حوزه های پژوهشی مختلفِ تحت پوشش این موضوع از جمله معماری های پیوند متقابل شبکه ی مرکز داده، پروتکل های شبکه برای شبکه های مرکز داده، و به اشتراک گذاری منابع شبکه در مراکز داده ی ابر چند مستأجری ارائه خواهیم نمود. کار خود را با مروری بر شبکه های مراکز داده شروع کرده و با نیازمندی های آن در جهت طرح های شبکه ی مرکز داده پیش می رویم. سپس سه مقاله ی پژوهشی مرتبط با موضوع پژوهشی مذکور را در بخش های بعدی ارائه می دهیم. در پایان به نتیجه گیری نهایی می رسیم.
واژه های شاخص: شبکه های مراکز داده | معماری | پروتکل های شبکه | به اشتراک گذاری منابع
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مقاله ترجمه شده |
8 |
Hybrid Shuffled Frog Leaping Algorithm for Energy-Efficient Dynamic Consolidation of Virtual Machines in Cloud Data Centers
الگوریتم ترکیبی بر زده شده جهش قورباغه برای تحکیم پویای مصرف انرژی-کارآمد ماشین های مجازی در مراکز داده ابر-2014 Cloud computing aims to provide dynamic leasing of server capabilities as scalable
virtualized services to end users. However, datacenters hosting cloud applications consume vast
amounts of electrical energy, thereby contributing to high operational costs and carbon footprints.
Green cloud computing solutions that can not onlyminimize the operational costs but also reduce
the environmental impact are necessary. This study focuses on the Infrastructure as a Service
model, where custom virtual machines (VMs) are launched in appropriate servers available in a
data center. A complete data center resource management scheme is presented in this paper. The
scheme can not only ensure user quality of service (through service level agreements) but can also
achieve maximum energy saving and green computing goals. Considering that the data center host
is usually tens of thousands in size and that using an exact algorithm to solve the resource
allocation problem is difficult, the modified shuffled frog leaping algorithm and improved
extremal optimization are employed in this study tosolve the dynamic allocation problem of VMs.
Experimental results demonstrate that the proposed resource management scheme exhibits
excellent performance in green cloud computing.
Keywords: Cloud Computing; Data Center; Resource Management; Intelligent Algorithm |
مقاله انگلیسی |