با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
ردیف | عنوان | نوع |
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1 |
Retargetable Optimizing Compilers for Quantum Accelerators via a Multilevel Intermediate Representation
کامپایلرهای بهینه سازی مجدد قابل هدف گیری برای شتاب دهنده های کوانتومی از طریق یک نمایش میانی چند سطحی-2022 We present a multilevel quantum–classical intermediate representation (IR) that
enables an optimizing, retargetable compiler for available quantum languages.
Our work builds upon the multilevel intermediate representation (MLIR)
framework and leverages its unique progressive lowering capabilities to map
quantum languages to the low-level virtual machine (LLVM) machine-level IR.
We provide both quantum and classical optimizations via the MLIR pattern
rewriting subsystem and standard LLVM optimization passes, and demonstrate
the programmability, compilation, and execution of our approach via standard
benchmarks and test cases. In comparison to other standalone language and
compiler efforts available today, our work results in compile times that are
1,000 faster than standard Pythonic approaches, and 5–10 faster than
comparative standalone quantum language compilers. Our compiler provides
quantum resource optimizations via standard programming patterns that result
in a 10 reduction in entangling operations, a common source of program
noise. We see this work as a vehicle for rapid quantum compiler prototyping.
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مقاله انگلیسی |
2 |
A survey on blockchain, SDN and NFV for the smart-home security
مروری بر بلاک چین، SDN و NFV برای امنیت خانه های هوشمند-2022 Due to millions of loosely coupled devices, the smart-home security is gaining the attention
of industry professionals, attackers, and academic researchers. The smart home is a typical
home where many sensors, actuators, and IoT devices are used to automate home users’ daily
activities. Although a smart home provides comfort, safety, and satisfaction to users, it opens up
multiple challenging security issues when automating and offering intelligent services. Recent
studies have investigated not only blockchain but SDN and NFV to address these challenges.
We present a comprehensive survey on blockchain, SDN, and NFV for smart-home security.
The paper also proposes a new architecture of the smart-home security. First, we describe the
features of the smart home and its current security issues. Next, we outline the characteristics of
blockchain, SDN, and NFV, including their contribution to improving the smart-home security.
While SDN enhances the management and access control of the home network by providing a
programmable controller to home nodes, NFV implements the functions of network appliances
(e.g., network monitoring, firewall) as virtual machines and ensures the high availability of the
network. Blockchain reinforces IoT data’s privacy, integrity, and security and improves the trust
in transactions among untrusted IoT devices. Finally, we discuss open issues and challenges in
the field and propose recommendations towards high-level security for the smart home.
Keywords: Smart homes | IoT | Privacy | Security | Trust | Blockchain | SDN | NFV |
مقاله انگلیسی |
3 |
Cost-efficient dynamic scheduling of big data applications in apache spark on cloud
برنامه ریزی پویا مقرون به صرفه برنامه های داده های بزرگ در آپاچی اسپارک روی ابر-2020 Job scheduling is one of the most crucial components in managing resources, and efficient execution of big data applications. Specifically, scheduling jobs in a cloud-deployed cluster are challenging as the cloud offers different types of Virtual Machines (VMs) and jobs can be heterogeneous. The default big data processing framework schedulers fail to reduce the cost of VM usages in the cloud environment while satisfying the performance constraints of each job. The existing works in cluster scheduling mainly focus on improving job performance and do not leverage from VM types on the cloud to reduce cost. In this paper, we propose efficient scheduling algorithms that reduce the cost of resource usage in a cloud-deployed Apache Spark cluster. In addition, the proposed algorithms can also prioritise jobs based on their given deadlines. Besides, the proposed scheduling algorithms are online and adaptive to clus- ter changes. We have also implemented the proposed algorithms on top of Apache Mesos. Furthermore, we have performed extensive experiments on real datasets and compared to the existing schedulers to showcase the superiority of our proposed algorithms. The results indicate that our algorithms can reduce resource usage cost up to 34% under different workloads and improve job performance. Keywords: Cloud | Apache spark | Scheduling | Cost-efficiency |
مقاله انگلیسی |
4 |
Static malware detection and attribution in android byte-code through an end-to-end deep system
شناسایی بدافزارهای استاتیکی و انتساب در بایت کد اندرویدی از طریق یک سیستم عمیق انتها به انتها-2020 Android reflects a revolution in handhelds and mobile devices. It is a virtual machine based, an
open source mobile platform that powers millions of smartphone and devices and even a larger no.
of applications in its ecosystem. Surprisingly in a short lifespan, Android has also seen a colossal
expansion in application malware with 99% of the total malware for smartphones being found in
the Android ecosystem. Subsequently, quite a few techniques have been proposed in the literature
for the analysis and detection of these malicious applications for the Android platform. The increasing
and diversified nature of Android malware has immensely attenuated the usefulness of prevailing
malware detectors, which leaves Android users susceptible to novel malware. Here in this paper,
as a remedy to this problem, we propose an anti-malware system that uses customized learning
models, which are sufficiently deep, and are ’End to End deep learning architectures which detect
and attribute the Android malware via opcodes extracted from application bytecode’. Our results
show that Bidirectional long short-term memory (BiLSTMs) neural networks can be used to detect
static behavior of Android malware beating the state-of-the-art models without using handcrafted
features. For our experiments in our system, we also choose to work with distinct and independent
deep learning models leveraging sequence specialists like recurrent neural networks, Long Short Term
Memory networks and its Bidirectional variation as well as those are more usual neural architectures
like a network of all connected layers(fully connected), deep convnets, Diabolo network (autoencoders)
and generative graphical models like deep belief networks for static malware analysis on Android. To
test our system, we have also augmented a bytecode dataset from three open and independently
maintained state-of-the-art datasets. Our bytecode dataset, which is on an order of magnitude large,
essentially suffice for our experiments. Our results suggests that our proposed system can lead to
better design of malware detectors as we report an accuracy of 0.999 and an F1-score of 0.996 on a
large dataset of more than 1.8 million Android applications. Keywords: End-to-end architecture | Malware analysis | Deep neural networks | Android and big data |
مقاله انگلیسی |
5 |
Ignis: An efficient and scalable multi-language Big Data framework
Ignis: یک چارچوب داده های بزرگ چند زبانه کارآمد و مقیاس پذیر-2020 Most of the relevant Big Data processing frameworks (e.g., Apache Hadoop, Apache Spark) only support
JVM (Java Virtual Machine) languages by default. In order to support non-JVM languages, subprocesses
are created and connected to the framework using system pipes. With this technique, the impossibility
of managing the data at thread level arises together with an important loss in the performance. To
address this problem we introduce Ignis, a new Big Data framework that benefits from an elegant way
to create multi-language executors managed through an RPC system. As a consequence, the new system
is able to execute natively applications implemented using non-JVM languages. In addition, Ignis allows
users to combine in the same application the benefits of implementing each computational task in the
best suited programming language without additional overhead. The system runs completely inside
Docker containers, isolating the execution environment from the physical machine. A comparison with
Apache Spark shows the advantages of our proposal in terms of performance and scalability. Keywords: Big data | Multi-language | Performance | Scalability | Container |
مقاله انگلیسی |
6 |
تعادل بار در محاسبات ابری: یک تصویر بزرگ
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 10 - تعداد صفحات فایل doc فارسی: 27 زمان بندی یا تخصیص درخواست کاربر (وظایف) در محیط ابری یک مساله بهینه سازی NP-hard است. مطابق با زیرساخت ابری و درخواست های کاربران، سیستم ابری همراه با برخی بارها (که ممکن است کم باری یا اضافه بار یا بار متعادل باشد) اختصاص داده می شود. شرایطی همانند کم باری یا اضافه بار سبب خرابی سیستم مرتبط با مصرف توان، زمان اجرا، خرابی ماشین و غیره شود. بنابراین، توازن بار برای غلبه بر تمامی مشکلات اشاره شده فوق مورد نیاز است. این توازن بار کارها (آن ها ممکن است وابسته یا مستقل باشند) بر ماشین های مجازی (VM) جنبه مهمی از زمان بندی کارها در ابرها است. انواع مختلف بارها در شبکه ابری همانند بار حافظه، بار محاسباتی (CPU)، بار شبکه و غیره وجود دارد. توازن بار مکانیزم شناسایی نودهای اضافه بار و کم بار و سپس توزان بار در بین آن ها است. محققان روش های مختلف توازن بار را در محاسبات ابری برای بهینه سازی پارمترهای مختلف عملکرد پیشنهاد داده اند. ما یک طبقه بندی را برای الگوریتم های توزان بار در ابر ارائه کرده ایم. توضیح کوتاهی از پارامترهای عملکرد در ادبیات و اثرات آن ها در این مقاله ارائه شده است. به منظور تحلیل عملکرد الگوریتم های مبتنی بر اکتشاف ، شبیه سازی ها در شبیه ساز CloudSim انجام شده است و نتایج به طور کامل ارائه شده است.
کلید واژه ها: محاسبات ابری | مصرف انرژی | تعادل بار | مجازی سازی | ماشین مجازی | تخصیص وظیفه |
مقاله ترجمه شده |
7 |
Deriving ChaCha20 key streams from targeted memory analysis
استخراج جریان کلیدی ChaCha20 از تجزیه و تحلیل حافظه هدف-2019 There can be performance and vulnerability concerns with block ciphers, thus stream ciphers can used as an alternative. Although many symmetric key stream ciphers are fairly resistant to side-channel at- tacks, cryptographic artefacts may exist in memory. This paper identifies a significant vulnerability within OpenSSH and OpenSSL and which involves the discovery of cryptographic artefacts used within the ChaCha20 cipher. This can allow for the cracking of tunneled data using a single targeted memory ex- traction. With this, law enforcement agencies and/or malicious agents could use the vulnerability to take copies of the encryption keys used for each tunnelled connection. The user of a virtual machine would not be alerted to the capturing of the encryption key, as the method runs from an extraction of the running memory. Methods of mitigation include making cryptographic artefacts difficult to discover and limiting memory access. Keywords: Network traffic | Decryption | Memory analysis | Virtual machine introspection | Secure shell | Transport layer security | Stream ciphers | Chacha20 |
مقاله انگلیسی |
8 |
Blockchain data-based cloud data integrity protection mechanism
مکانیسم حفاظت از داده های ابر مبتنی بر داده بلاکچین -2019 Despite the rapid development of cloud computing for many years, data security and trusted computing
are still the main challenges in current cloud computing applications. In order to solve this problem,
many scholars have carried out a lot of research on this, and proposed many models including data
integrity test and secure multi-party calculation. However, most of these solutions face problems
such as excessive computational complexity or lack of scalability. This paper studies the use of
blockchain techniques to improve this situation. Blockchain is a decentralized new distributed computing
paradigm. Applying blockchain technology to cloud computing, using the security mechanism
of the former to improve the performance of the latter’s secure storage and secure computing is a
promising research topic. In this paper, the distributed virtual machine agent model is deployed in the
cloud by using mobile agent technology. The virtual machine agent enables multi-tenants to cooperate
with each other to ensure data trust verification. The tasks of reliable data storage, monitoring and
verification are completed by virtual machine agent mechanism. This is also a necessary condition
for building a blockchain integrity protection mechanism. The blockchain-based integrity protection
framework is built by the virtual machine proxy model, and the unique hash value corresponding to
the file generated by the Merkel hash tree is used to monitor the data change by means of the smart
contract on the blockchain, and the data is owned in time. The user issues a warning message for data
tampering; in addition, a ‘‘block-and-response’’ mode is used to construct a blockchain-based cloud
data integrity verification scheme. Keywords: Blockchain | Cloud data | Integrity verification | Merkel hash tree |
مقاله انگلیسی |
9 |
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 |
10 |
Cost optimization for deadline-aware scheduling of big-data processing jobs on clouds
بهینه سازی هزینه برای زمانبندی دقیق پردازش داده های بزرگ کارها در ابرها-2018 Cloud computing has been widely regarded as a capable solution for big data processing. Nowadays cloud
service providers usually offer users virtual machines with various combinations of configurations and
prices. As this new service scheme emerges, the problem of choosing the cost-minimized combination
under a deadline constraint is becoming more complex for users. The complexity of determining the cost
minimized combination may be resulted from different causes: the characteristics of user applications,
and providers’ setting on the configurations and pricing of virtual machine. In this paper, we proposed
a variety of algorithms to help the users to schedule their big data processing workflow applications on
clouds so that the cost can be minimized and the deadline constraints can be satisfied. The proposed
algorithms were evaluated by extensive simulation experiments with diverse experimental settings.
Keywords: Big-data ، Scheduling ، Cost-efficient ، Cloud computing |
مقاله انگلیسی |