scholarly journals Modeling Strategies to Improve the Dependability of Cloud Infrastructures

2018 ◽  
Author(s):  
Erica Teixeira Gomes de Sousa ◽  
Fernando Antonio Aires Lins
Keyword(s):  
Cloud Infrastructures

Cloud-Based Multimedia Content Protection System

2020 ◽  
pp. 164-169
Author(s):  
B. Aparna ◽  
S. Madhavi ◽  
G. Mounika ◽  
P. Avinash ◽  
S. Chakravarthi
Keyword(s):  
Large Scale ◽  
Protection System ◽  
Multimedia Content ◽  
Computationally Efficient ◽  
Content Protection ◽  
Protection Systems ◽  
Multimedia Content Protection ◽  
High Scalability ◽  
Cloud Infrastructures ◽  
Rapid Deployment

We propose a new design for large-scale multimedia content protection systems. Our design leverages cloud infrastructures to provide cost efficiency, rapid deployment, scalability, and elasticity to accommodate varying workloads. The proposed system can be used to protect different multimedia content types, including videos, images, audio clips, songs, and music clips. The system can be deployed on private and/or public clouds. Our system has two novel components: (i) method to create signatures of videos, and (ii) distributed matching engine for multimedia objects. The signature method creates robust and representative signatures of videos that capture the depth signals in these videos and it is computationally efficient to compute and compare as well as it requires small storage. The distributed matching engine achieves high scalability and it is designed to support different multimedia objects. We implemented the proposed system and deployed it on two clouds: Amazon cloud and our private cloud. Our experiments with more than 11,000 videos and 1 million images show the high accuracy and scalability of the proposed system. In addition, we compared our system to the protection system used by YouTube and our results show that the YouTube protection system fails to detect most copies of videos, while our system detects more than 98% of them.

scholarly journals GPU-Enabled Serverless Workflows for Efficient Multimedia Processing

2021 ◽  
Vol 11 (4) ◽  
pp. 1438
Author(s):  
Sebastián Risco ◽  
Germán Moltó
Keyword(s):  
Video Processing ◽  
Cost Effective ◽  
Multimedia Processing ◽  
Computing Services ◽  
Video Frames ◽  
Open Source Framework ◽  
Cloud Infrastructures ◽  
Aws Lambda ◽  
Runtime Environments

Serverless computing has introduced scalable event-driven processing in Cloud infrastructures. However, it is not trivial for multimedia processing to benefit from the elastic capabilities featured by serverless applications. To this aim, this paper introduces the evolution of a framework to support the execution of customized runtime environments in AWS Lambda in order to accommodate workloads that do not satisfy its strict computational requirements: increased execution times and the ability to use GPU-based resources. This has been achieved through the integration of AWS Batch, a managed service to deploy virtual elastic clusters for the execution of containerized jobs. In addition, a Functions Definition Language (FDL) is introduced for the description of data-driven workflows of functions. These workflows can simultaneously leverage both AWS Lambda for the highly-scalable execution of short jobs and AWS Batch, for the execution of compute-intensive jobs that can profit from GPU-based computing. To assess the developed open-source framework, we executed a case study for efficient serverless video processing. The workflow automatically generates subtitles based on the audio and applies GPU-based object recognition to the video frames, thus simultaneously harnessing different computing services. This allows for the creation of cost-effective highly-parallel scale-to-zero serverless workflows in AWS.

scholarly journals Cost-Performance Evaluation of a Recognition Service of Livestock Activity Using Aerial Images

2021 ◽  
Vol 13 (12) ◽  
pp. 2318
Author(s):  
Darío G. Lema ◽  
Oscar D. Pedrayes ◽  
Rubén Usamentiaga ◽  
Daniel F. García ◽  
Ángela Alonso
Keyword(s):  
Performance Evaluation ◽  
Local Solution ◽  
Aerial Images ◽  
Cost Ratio ◽  
Activity Detection ◽  
Average Precision ◽  
Cost Performance ◽  
Detection Algorithms ◽  
Cloud Infrastructures ◽  
Animal Detection

The recognition of livestock activity is essential to be eligible for subsides, to automatically supervise critical activities and to locate stray animals. In recent decades, research has been carried out into animal detection, but this paper also analyzes the detection of other key elements that can be used to verify the presence of livestock activity in a given terrain: manure piles, feeders, silage balls, silage storage areas, and slurry pits. In recent years, the trend is to apply Convolutional Neuronal Networks (CNN) as they offer significantly better results than those obtained by traditional techniques. To implement a livestock activity detection service, the following object detection algorithms have been evaluated: YOLOv2, YOLOv4, YOLOv5, SSD, and Azure Custom Vision. Since YOLOv5 offers the best results, producing a mean average precision (mAP) of 0.94, this detector is selected for the creation of a livestock activity recognition service. In order to deploy the service in the best infrastructure, the performance/cost ratio of various Azure cloud infrastructures are analyzed and compared with a local solution. The result is an efficient and accurate service that can help to identify the presence of livestock activity in a specified terrain.

BlastFunction: A Full-stack Framework Bringing FPGA Hardware Acceleration to Cloud-native Applications

2022 ◽  
Vol 15 (2) ◽  
pp. 1-27
Author(s):  
Andrea Damiani ◽  
Giorgia Fiscaletti ◽  
Marco Bacis ◽  
Rolando Brondolin ◽  
Marco D. Santambrogio
Keyword(s):  
Heterogeneous Computing ◽  
Performance Metrics ◽  
State Of The Art ◽  
Hardware Acceleration ◽  
Standard Approach ◽  
Cloud Models ◽  
Experimental Campaign ◽  
Computing Platforms ◽  
Cloud Infrastructures

“Cloud-native” is the umbrella adjective describing the standard approach for developing applications that exploit cloud infrastructures’ scalability and elasticity at their best. As the application complexity and user-bases grow, designing for performance becomes a first-class engineering concern. As an answer to these needs, heterogeneous computing platforms gained widespread attention as powerful tools to continue meeting SLAs for compute-intensive cloud-native workloads. We propose BlastFunction, an FPGA-as-a-Service full-stack framework to ease FPGAs’ adoption for cloud-native workloads, integrating with the vast spectrum of fundamental cloud models. At the IaaS level, BlastFunction time-shares FPGA-based accelerators to provide multi-tenant access to accelerated resources without any code rewriting. At the PaaS level, BlastFunction accelerates functionalities leveraging the serverless model and scales functions proactively, depending on the workload’s performance. Further lowering the FPGAs’ adoption barrier, an accelerators’ registry hosts accelerated functions ready to be used within cloud-native applications, bringing the simplicity of a SaaS-like approach to the developers. After an extensive experimental campaign against state-of-the-art cloud scenarios, we show how BlastFunction leads to higher performance metrics (utilization and throughput) against native execution, with minimal latency and overhead differences. Moreover, the scaling scheme we propose outperforms the main serverless autoscaling algorithms in workload performance and scaling operation amount.

scholarly journals Denial-of-Service on FPGA-based Cloud Infrastructures — Attack and Defense

2021 ◽  
pp. 441-464
Author(s):  
Tuan La ◽  
Khoa Pham ◽  
Joseph Powell ◽  
Dirk Koch
Keyword(s):  
Denial Of Service ◽  
Design Rule ◽  
Side Channel ◽  
Fpga Design ◽  
Side Channel Attacks ◽  
Dynamic Power ◽  
Denial Of Service Attack ◽  
Service Attack ◽  
Attack And Defense ◽  
Cloud Infrastructures

This paper presents attacks targeting the FPGAs of AWS F1 instances at the electrical level through power-hammering, where excessive dynamic power is used to crash FPGA instances. We demonstrate different power-hammering attacks that pass all AWS security fences implemented on F1 instances, including the FPGA vendor design rule checks. In addition, we fingerprint the FPGA instances to observe the responsiveness of the instances, which indicates a successful denial-of-service attack. Most importantly, we provide an FPGA virus scanner framework, which was improved to support large datacenter FPGAs for preventing such attacks, including virtually all currently demonstrated side-channel attacks. Our experiments showed that an AWS F1 instance crashes immediately by starting an FPGA design demanding 369W. By using FPGA-fingerprinting, we found that crashed instances are unavailable for about one to over 200 hours.

scholarly journals Resource-Aware Network Topology Management Framework

2020 ◽  
Author(s):  
Aaqif Afzaal Abbasi ◽  
Shahab Shamshirband ◽  
Mohammed A. A. Al-qaness ◽  
Almas Abbasi ◽  
Nashat T. AL-Jallad ◽  
...  
Keyword(s):  
Network Reliability ◽  
Service Level ◽  
Cloud Infrastructure ◽  
Topology Management ◽  
Management Framework ◽  
Provider Network ◽  
Computing Services ◽  
Cloud Infrastructures ◽  
Resource Aware ◽  
Path Computation Element

Cloud infrastructure provides computing services where computing resources can be adjusted on-demand. However, the adoption of cloud infrastructures brings concerns like reliance on the service provider network, reliability, compliance for service level agreements (SLAs), etc. Software-defined networking (SDN) is a networking concept that suggests the segregation of a network’s data plane from the control plane. This concept improves networking behavior. In this paper, we present an SDN-enabled resource-aware topology framework. The proposed framework employs SLA compliance, Path Computation Element (PCE) and shares fair loading to achieve better topology features. We also present an evaluation, showcasing the potential of our framework.

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