Cost-Efficient and Robust On-Demand Video Transcoding Using Heterogeneous Cloud Services

AbstractTrans-cloud applications consist of multiple interacting components deployed across different cloud providers and at different service layers (IaaS and PaaS). In such complex deployment scenarios, fault handling and recovery need to deal with heterogeneous cloud offerings and to take into account inter-component dependencies. We propose a methodology for self-healing trans-cloud applications from failures occurring in application components or in the cloud services hosting them, both during deployment and while they are being operated. The proposed methodology enables reducing the time application components rely on faulted services, hence residing in “unstable” states where they can suddenly fail in cascade or exhibit erroneous behaviour. We also present an open-source prototype illustrating the feasibility of our proposal, which we have exploited to carry out an extensive evaluation based on controlled experiments and monkey testing.

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An Open-Source Cloud-FPGA Gene Regulatory Accelerator

10.5753/wscad.2021.18527 ◽

2021 ◽

Author(s):

Lucas Bragança ◽

Jeronimo Penha ◽

Michael Canesche ◽

Dener Ribeiro ◽

José Augusto M. Nacif ◽

...

Keyword(s):

Open Source ◽

Gene Regulatory Networks ◽

High Performance ◽

Regulatory Networks ◽

Cloud Services ◽

On Demand ◽

Speed Up ◽

Gene Regulatory ◽

High Level ◽

Better Than

FPGAs are suitable to speed up gene regulatory network (GRN) algorithms with high throughput and energy efficiency. In addition, virtualizing FPGA using hardware generators and cloud resources increases the computing ability to achieve on-demand accelerations across multiple users. Recently, Amazon AWS provides high-performance Cloud's FPGAs. This work proposes an open source accelerator generator for Boolean gene regulatory networks. The generator automatically creates all hardware and software pieces from a high-level GRN description. We evaluate the accelerator performance and cost for CPU, GPU, and Cloud FPGA implementations by considering six GRN models proposed in the literature. As a result, the FPGA accelerator is at least 12x faster than the best GPU accelerator. Furthermore, the FPGA reaches the best performance per dollar in cloud services, at least 5x better than the best GPU accelerator.

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Integration Model between Heterogeneous Data Services in a Cloud

JUCS - Journal of Universal Computer Science ◽

10.3897/jucs.67046 ◽

2021 ◽

Vol 27 (4) ◽

pp. 387-412

Author(s):

Marcelo Aires Vieira ◽

Elivaldo Lozer Fracalossi Ribeiro ◽

Daniela Barreiro Claro ◽

Babacar Mane

Keyword(s):

Heterogeneous Data ◽

Cloud Services ◽

Test Case ◽

Integration Model ◽

Data Services ◽

On Demand ◽

Database As A Service ◽

And Performance ◽

Data As A Service ◽

Integrate Data

With the growth of cloud services, many companies have begun to persist and make their data available through services such as Data as a Service (DaaS) and Database as a Service (DBaaS). The DaaS model provides on-demand data through an Application Programming Inter- face (API), while DBaaS model provides on-demand database management systems. Different data sources require efforts to integrate data from different models. These model types include unstructured, semi-structured, and structured data. Heterogeneity from DaaS and DBaaS makes it challenging to integrate data from different services. In response to this problem, we developed the Data Join (DJ) method to integrate heterogeneous DaaS and DBaaS sources. DJ was described through canonical models and incorporated into a middleware as a proof-of-concept. A test case and three experiments were performed to validate our DJ method: the first experiment tackles data from DaaS and DBaaS in isolation; the second experiment associates data from different DaaS and DBaaS through one join clause; and the third experiment integrates data from three sources (one DaaS and two DBaaS) based on different data type (relational, NoSQL, and NewSQL) through two join clauses. Our experiments evaluated the viability, functionality, integration, and performance of the DJ method. Results demonstrate that DJ method outperforms most of the related work on selecting and integrating data in a cloud environment.

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Cloud solutions for GxP laboratories: considerations for data storage

Bioanalysis ◽

10.4155/bio-2021-0137 ◽

2021 ◽

Author(s):

Scott Davis ◽

Joel Usansky ◽

Shibani Mitra-Kaushik ◽

John Kellie ◽

Kimberly Honrine ◽

...

Keyword(s):

Pharmaceutical Industry ◽

Best Practices ◽

Drug Development ◽

Data Storage ◽

Real World ◽

Cloud Storage ◽

Low Cost ◽

Cloud Services ◽

Significant Shift ◽

On Demand

Challenges for data storage during drug development have become increasingly complex as the pharmaceutical industry expands in an environment that requires on-demand availability of data and resources for users across the globe. While the efficiency and relative low cost of cloud services have become increasingly attractive, hesitancy toward the use of cloud services has decreased and there has been a significant shift toward real-world implementation. Within GxP laboratories, the considerations for cloud storage of data include data integrity and security, as well as access control and usage for users around the globe. In this review, challenges and considerations when using cloud storage options for the storage of laboratory-based GxP data are discussed and best practices are defined.

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Examining IoT's Applications Using Cloud Services

Advances in Wireless Technologies and Telecommunication - Examining Cloud Computing Technologies Through the Internet of Things ◽

10.4018/978-1-5225-3445-7.ch008 ◽

2018 ◽

pp. 147-163 ◽

Cited By ~ 9

Author(s):

Saravanan K ◽

P. Srinivasan

Keyword(s):

Cloud Computing ◽

Internet Of Things ◽

Distributed Computing ◽

Knowledge Sharing ◽

Effective Communication ◽

Cloud Services ◽

On Demand ◽

Computing Paradigm ◽

Iot Devices ◽

And Storage

Cloud IoT has evolved from the convergence of Cloud computing with Internet of Things (IoT). The networked devices in the IoT world grow exponentially in the distributed computing paradigm and thus require the power of the Cloud to access and share computing and storage for these devices. Cloud offers scalable on-demand services to the IoT devices for effective communication and knowledge sharing. It alleviates the computational load of IoT, which makes the devices smarter. This chapter explores the different IoT services offered by the Cloud as well as application domains that are benefited by the Cloud IoT. The challenges on offloading the IoT computation into the Cloud are also discussed.

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Cloud Computing for On-Demand Virtual Desktops and Labs

Cloud Computing for Teaching and Learning ◽

10.4018/978-1-4666-0957-0.ch008 ◽

2012 ◽

pp. 111-125

Author(s):

Christoph Reich ◽

Sandra Hübner ◽

Hendrik Kuijs

Keyword(s):

Cloud Computing ◽

Learning Experience ◽

Cloud Services ◽

The Internet ◽

It Infrastructure ◽

On Demand ◽

E Learning ◽

Collaboration Platforms ◽

Computer Resources ◽

Support Research

Cloud computing is used to provide users with computer resources on-demand any time over the Internet. At the Hochschule Furtwangen University (HFU) students, lecturers, and researchers can leverage cloud computing to enhance their e-learning experience. This chapter presents how cloud computing provides on-demand virtual desktops for problem solving, on-demand virtual labs for special courses, and on-demand collaboration platforms to support research groups. The focus is how cloud services can be used, how they can be integrated into the existing HFU-IT infrastructure, and how new didactic models could look.

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Towards AiP as a Service

International Journal of Information Systems in the Service Sector ◽

10.4018/ijisss.2015040101 ◽

2015 ◽

Vol 7 (2) ◽

pp. 1-17 ◽

Cited By ~ 5

Author(s):

Djamel Benmerzoug

Keyword(s):

Cloud Computing ◽

Business Processes ◽

Rapid Development ◽

Cloud Services ◽

Agent Based ◽

Software Artifacts ◽

Cloud Application ◽

Business Application ◽

Agent Interaction ◽

Heterogeneous Cloud

The challenges that Cloud computing poses to business processes integration, emphasize the need for addressing two major issues: (i) which integration approach should be used allowing an adequate description of interaction aspects of the composed software components ? (ii) how are these interaction descriptions stored and shared to allow other software artifacts to (re)use them ? To address these issues, in this paper the authors propose an Agent Interaction Protocols (AiP)-based approach for reusing and aggregating existing Cloud services to create a new desired business application. The proposed approach facilitates rapid development and provisioning of composite Cloud services by specifying what to compose as an AiP. Furthermore, the authors develop an agent-based architecture that supports flexible scaling of business processes in a virtualized Cloud computing environment. The main goal of the proposed architecture is to address and tackle interoperability challenges at the Cloud application level. It solves the interoperability issues between heterogeneous Cloud services environments by offering a harmonized API. Also, it enables the deployment of applications at public, private or hybrid multi-Cloud environments.

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A Novel Cost-Efficient Framework for Critical Heartbeat Task Scheduling Using the Internet of Medical Things in a Fog Cloud System

Sensors ◽

10.3390/s20020441 ◽

2020 ◽

Vol 20 (2) ◽

pp. 441 ◽

Cited By ~ 4

Author(s):

Qurat-ul-ain Mastoi ◽

Teh Ying Wah ◽

Ram Gopal Raj ◽

Abdullah Lakhan

Keyword(s):

Health Care ◽

Task Scheduling ◽

Minimum Cost ◽

Cloud Services ◽

Cloud System ◽

Scheduling Problems ◽

Healthcare Applications ◽

Algorithm Framework ◽

Internet Of Medical Things ◽

Cost Efficient

Recently, there has been a cloud-based Internet of Medical Things (IoMT) solution offering different healthcare services to wearable sensor devices for patients. These services are global, and can be invoked anywhere at any place. Especially, electrocardiogram (ECG) sensors, such as Lead I and Lead II, demands continuous cloud services for real-time execution. However, these services are paid and need a lower cost-efficient process for the users. In this paper, this study considered critical heartbeat cost-efficient task scheduling problems for healthcare applications in the fog cloud system. The objective was to offer omnipresent cloud services to the generated data with minimum cost. This study proposed a novel health care based fog cloud system (HCBFS) to collect, analyze, and determine the process of critical tasks of the heartbeat medical application for the purpose of minimizing the total cost. This study devised a health care awareness cost-efficient task scheduling (HCCETS) algorithm framework, which not only schedule all tasks with minimum cost, but also executes them on their deadlines. Performance evaluation shows that the proposed task scheduling algorithm framework outperformed the existing algorithm methods in terms of cost.

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