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ردیف | عنوان | نوع |
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1 |
Missing the Forest for the Trees: End-to-End AI Application Performance in Edge Data Centers
از دست دادن جنگل برای درختان: عملکرد کاربردی هوش مصنوعی تا پایان در مراکز داده لبه ای -2020 Artificial intelligence and machine learning are experiencing
widespread adoption in the industry, academia, and even
public consciousness. This has been driven by the rapid advances
in the applications and accuracy of AI through increasingly
complex algorithms and models; this, in turn, has
spurred research into developing specialized hardware AI
accelerators. The rapid pace of the advances makes it easy
to miss the forest for the trees: they are often developed and
evaluated in a vacuum without considering the full application
environment in which they must eventually operate.
In this paper, we deploy and characterize Face Recognition,
an AI-centric edge video analytics application built using
open source and widely adopted infrastructure and ML
tools. We evaluate its holistic, end-to-end behavior in a production-
size edge data center and reveal the “AI tax” for
all the processing that is involved. Even though the application
is built around state-of-the-art AI and ML algorithms, it
relies heavily on pre- and post-processing code which must
be executed on a general-purpose CPU. As AI-centric applications
start to reap the acceleration promised by so many
accelerators, we find they impose stresses on the underlying
software infrastructure and the data center’s capabilities:
storage and network bandwidth become major bottlenecks
with increasing AI acceleration. By not having to
serve a wide variety of applications, we show that a purposebuilt
edge data center can be designed to accommodate the
stresses of accelerated AI at 15% lower TCO than one derived
from homogeneous servers and infrastructure. We also
discuss how our conclusions generalize beyond Face Recognition
as many AI-centric applications at the edge rely upon
the same underlying software and hardware infrastructure. |
مقاله انگلیسی |
2 |
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 |
3 |
A Joint Power Efficient Server and Network Consolidation approach for virtualized data centers
یک سرور توانی کارآمد مشترک و دیدگاه یکپارچه سازی شبکه برای مراکز داده ای مجازی-2018 Cloud computing and virtualization are enabling technologies for designing energy-aware resource management mechanisms in virtualized data centers. Indeed, one of the main challenges of big data centers is to decrease the power consumption, both to cut costs and to reduce the environmental impact. To this extent, Virtual Machine (VM) consolidation is often used to smartly reallocate the VMs with the objective of reducing the power consumption, by exploiting the VM live migration. The consolidation problem consists in finding the set of migrations that allow to keep turned on the minimum number of servers needed to host all the VMs. However, most of the proposed consolidation approaches do not consider the network related consumption, which represents about 10–20% of the total energy consumed by IT equipment in real data centers. This paper proposes a novel joint server and network consolidation model that takes into account the power efficiency of both the switches forwarding the traffic and the servers hosting the VMs. It powers down switch ports and routes traffic along the most energy efficient path towards the least energy consuming server under QoS constraints. Since the model is complex, a fast Simulated Annealing based Resource Consolidation algorithm (SARC) is proposed. Our numerical results demonstrate that our approach is able to save on average 50% of the network related power consumption compared to a network unaware consolidation.
keywords: Cloud| Virtualization| Power| Green computing| Simulated annealing |
مقاله انگلیسی |
4 |
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 |
5 |
فشرده سازی هوشمند برای داده های بزرگ: مرور
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 11 - تعداد صفحات فایل doc فارسی: 40 در سال های اخیر، شبکه هوشمند توجه گسترده ای از سراسر جهان را به خود جلب کرده است. داده های مقیاس بزرگ توسط سنسور ها و دستگاه های اندازه گیری در یک شبکه هوشمند جمع آوری می شوند. مقیاس هوشمند می تواند اطلاعات دقیق در مورد مصرف الکتریسیته را در زمان واقعی به ثبت برساند، بنابراین داده های بزرگ در مقیاس هوشمند اندازه گیری می شود. داده های بزرگ مقیاس هوشمند فرصت های جدیدی برای پیش بینی بار الکتریکی، کشف عادت ها و مدیریت تقاضا ارائه داده است. با این حال، ابعاد بزرگ و داده های بزرگ در مقیاس هوشمند عظیم نه تنها فشار زیادی را بر خطوط انتقال داده ایجاد می کند، بلکه هزینه های ذخیره سازی زیادی را در مراکز داده نیز به همراه می آورد. بنابراین، برای کاهش فشار انتقال و ارتفاع محل ذخیره سازی، برای بهبود راندمان استخراج داده ها، و به اين ترتيب ظرفیت های تحقق هوشمند داده های بزرگ 130 سانتی متری است. مقاله پیش رو یک مطالعه جامع در مورد تکنیک های فشرده سازی داده های بزرگ هوشمند را ارائه می دهد. توسعه شبکه های هوشمند و خصوصیات و چالش های کاربرد داده های بزرگ الکتریکی ابتدا معرفی شده و سپس تجزیه و تحلیل ویژگی ها و مزایای داده های بزرگ مقیاس بزرگ انجام می پذیرد. در نهایت، این مطالعه بر روی روش های فشرده سازی اطلاعات بالقوه برای داده های بزرگ هوشمند تمرکز می کند و روش های ارزیابی فشرده سازی داده های مقیاس هوشمند را مورد بحث قرار می دهد.
کلمات کلیدی: شبکه هوشمند | مقیاس هوشمند | داده های بزرگ انرژی | فشرده سازی داده ها. |
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 |
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 |
8 |
Migration-Based Online CPSCN Big Data Analysis in Data Centers
تحلیل داده های بزرگ CPSCN آنلاین مبتنی بر مهاجرت در مراکز داده-2018 It is critical to schedule online data-intensive jobs effectively for various applications, including
cyber-physical-system and social network system. It is also useful to support timely decision making and
better prediction. In this paper, we investigate the online job scheduling problem with data migration for
global job execution time reduction. We first establish a time model based on the real experimental results,
and propose an online job placement algorithm by taking into account the benefit of both instantaneity and
locality for the jobs. We then introduce data migration to the job placement algorithm. The core idea is to
make a tradeoff between the migration cost and remote access cost. The simulation results demonstrate that
our algorithm has a significant improvement than FIFO, and data migration shows effectiveness on global
job execution time reduction. Our algorithms also provide an acceptable fairness for jobs.
INDEX TERMS : Big data analysis, CPSCN, data center, data placement, online job scheduling |
مقاله انگلیسی |
9 |
EDOM: Improving energy efficiency of database operations on multicore servers
EDOM: بهبود بهره وری انرژی عملیات پایگاه داده در سرورهای چند هسته ای-2017 In this paper, we propose a toolkit called EDOM facilitating the evaluation and optimization of energy
efficient multicore-based database systems. The two core components in EDOM are a benchmarking
toolkit and a multicore manager to improve energy efficiency of database systems running on multicore
servers. We start this study by analyzing the energy efficiency of two popular database operations (i.e.,
cross join and outer join) processed on multicore processors. We describe the criteria and challenges of
building an energy efficiency benchmark for databases on multicore servers. We build a benchmarking
toolkit, which is comprised a configuration module, a test driver, and a power monitor. We develop a
multicore manager to optimize the number of cores, thereby making good tradeoff between performance
and energy efficiency in multicore database servers. At the heart of the multicore manager is a memory
usage model that estimates memory utilization from queries and database characteristics. An appropriate
number of cores is determined using the estimated memory usage to avert unnecessary memory
swapping. We make use of the proposed benchmark toolkit to quantitatively evaluate the performance
of our novel multicore manager. Our benchmarking tool of EDOM shows that the multicore and CPU
utilizations have significant impacts on energy efficiency. More importantly, extensive experimental
results show that our multicore manager in EDOM provides a simple yet powerful solution for improving
energy efficiency of database applications running on multicore servers.
Keywords: Energy efficiency | Database operations | Multicore processors | Benchmarks | Data centers | Database applications |
مقاله انگلیسی |
10 |
IAU Meteor Data Center—the shower database: A status report
مراکز داده بارش شهابی IAU - پایگاه داده رگباری: گزارش وضعیت-2017 Currently, the meteor shower part of Meteor Data Center database includes: 112 established showers, 563 in the
working list, among them 36 have the pro tempore status. The list of shower complexes contains 25 groups, 3
have established status and 1 has the pro tempore status.
In the past three years, new meteor showers submitted to the MDC database were detected amongst the
meteors observed by CAMS stations (Cameras for Allsky Meteor Surveillance), those included in the EDMOND
(European viDeo MeteOr Network Database), those collected by the Japanese SonotaCo Network, recorded in
the IMO (International Meteor Organization) database, observed by the Croatian Meteor Network and on the
Southern Hemisphere by the SAAMER radar.
At the XXIX General Assembly of the IAU in Honolulu, Hawaii in 2015, the names of 18 showers were
officially accepted and moved to the list of established ones. Also, one shower already officially named (3/SIA
the Southern iota Aquariids) was moved back to the working list of meteor showers. At the XXIX GA IAU the
basic shower nomenclature rule was modified, the new formulation predicates “The general rule is that a meteor
shower (and a meteoroid stream) should be named after the constellation that contains the nearest star to the
radiant point, using the possessive Latin form”.
Over the last three years the MDC database was supplemented with the earlier published original data on
meteor showers, which permitted verification of the correctness of the MDC data and extension of bibliographic
information. Slowly but surely new database software options are implemented, and software bugs are corrected.
Keywords: Meteoroid streams | Meteor showers | Established meteor showers | IAU MDC | Meteor database | Meteor showers nomenclature rules |
مقاله انگلیسی |