scholarly journals Intercloud Resource Discovery: A Future Perspective using Blockchain Technology

2019 ◽  
Vol 10 (2) ◽  
pp. 89-96
Author(s):  
Mekhla Sharma ◽  
Ankur Gupta
Keyword(s):  
Conceptual Model ◽  
Resource Discovery ◽  
Resource Provisioning ◽  
Current Work ◽  
Future Perspective ◽  
On Demand ◽  
Blockchain Technology ◽  
Efficient Resource ◽  
Work Done

Intercloud is a single logical entity orchestrating resources from different individual clouds providing on-demand resource provisioning in a seamless manner. However, achieving efficient resource discovery in the intercloud environment remains a challenging task owing to the heterogeneity of resources and diversity of cloud platforms. The paper briefs about intercloud resource discovery, outlines the current work done using existing approaches and examines the challenges involved. Finally, the paper explains the concept of blockchain and presents an innovative conceptual model for efficient resource discovery in intercloud.

Optimizing the Performance of Containerized Cloud Software Systems Using Adaptive PID Controllers

2021 ◽  
Vol 15 (3) ◽  
pp. 1-27
Author(s):  
Mikael Sabuhi ◽  
Nima Mahmoudi ◽  
Hamzeh Khazaei
Keyword(s):  
Adaptive Controller ◽  
Service Level ◽  
Resource Provisioning ◽  
Performance Model ◽  
Software Systems ◽  
Adaptive Controllers ◽  
Efficient Resource ◽  
Reliable Performance ◽  
Auto Scaling ◽  
Compare And Contrast

Control theory has proven to be a practical approach for the design and implementation of controllers, which does not inherit the problems of non-control theoretic controllers due to its strong mathematical background. State-of-the-art auto-scaling controllers suffer from one or more of the following limitations: (1) lack of a reliable performance model, (2) using a performance model with low scalability, tractability, or fidelity, (3) being application- or architecture-specific leading to low extendability, and (4) no guarantee on their efficiency. Consequently, in this article, we strive to mitigate these problems by leveraging an adaptive controller, which is composed of a neural network as the performance model and a Proportional-Integral-Derivative (PID) controller as the scaling engine. More specifically, we design, implement, and analyze different flavours of these adaptive and non-adaptive controllers, and we compare and contrast them against each other to find the most suitable one for managing containerized cloud software systems at runtime. The controller’s objective is to maintain the response time of the controlled software system in a pre-defined range, and meeting the Service-level Agreements, while leading to efficient resource provisioning.

scholarly journals Best Trade-Off Point Method for Efficient Resource Provisioning in Spark

Algorithms ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 190
Author(s):  
Peter Nghiem
Keyword(s):  
Resource Allocation ◽  
Cluster Computing ◽  
High Energy ◽  
Optimal Number ◽  
Resource Provisioning ◽  
Trade Off ◽  
Energy Efficient Computing ◽  
Hadoop Mapreduce ◽  
Efficient Resource ◽  
Mathematical Formulas

Considering the recent exponential growth in the amount of information processed in Big Data, the high energy consumed by data processing engines in datacenters has become a major issue, underlining the need for efficient resource allocation for more energy-efficient computing. We previously proposed the Best Trade-off Point (BToP) method, which provides a general approach and techniques based on an algorithm with mathematical formulas to find the best trade-off point on an elbow curve of performance vs. resources for efficient resource provisioning in Hadoop MapReduce. The BToP method is expected to work for any application or system which relies on a trade-off elbow curve, non-inverted or inverted, for making good decisions. In this paper, we apply the BToP method to the emerging cluster computing framework, Apache Spark, and show that its performance and energy consumption are better than Spark with its built-in dynamic resource allocation enabled. Our Spark-Bench tests confirm the effectiveness of using the BToP method with Spark to determine the optimal number of executors for any workload in production environments where job profiling for behavioral replication will lead to the most efficient resource provisioning.

scholarly journals A Conceptual Model for Cloud Computing Adoption by SMEs in Australia

2016 ◽  
pp. 307-334 ◽  
Author(s):  
Ishan Senarathna ◽  
Matthew Warren ◽  
William Yeoh ◽  
Scott Salzman
Keyword(s):  
Cloud Computing ◽  
Conceptual Model ◽  
Business Service ◽  
Perceived Benefits ◽  
Public Cloud ◽  
Total Cost ◽  
The Public ◽  
Software Applications ◽  
On Demand ◽  
Cloud Computing Adoption

Cloud Computing is an increasingly important worldwide development in business service provision. The business benefits of Cloud Computing usage include reduced IT overhead costs, greater flexibility of services, reduced TCO (Total Cost of Ownership), on-demand services, and improved productivity. As a result, Small and Medium-Sized Enterprises (SMEs) are increasingly adopting Cloud Computing technology because of these perceived benefits. The most economical deployment model in Cloud Computing is called the Public Cloud, which is especially suitable for SMEs because it provides almost immediate access to hardware resources and reduces their need to purchase an array of advanced hardware and software applications. The changes experienced in Cloud Computing adoption over the past decade are unprecedented and have raised important issues with regard to privacy, security, trust, and reliability. This chapter presents a conceptual model for Cloud Computing adoption by SMEs in Australia.

scholarly journals Water Management for Rural Environments and IoT

2019 ◽  
pp. 83-99 ◽  
Author(s):  
José Jasnau Caeiro ◽  
João Carlos Martins
Keyword(s):  
Water Management ◽  
Water Quality ◽  
Computer Security ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Monitoring Systems ◽  
Blockchain Technology ◽  
Rural Environments ◽  
General Architecture ◽  
Work Done

Internet of Things (IoT) systems are starting to be developed for applications in the management of water quality monitoring systems. The chapter presents some of the work done in this area and also shows some systems being developed by the authors for the Alentejo region. A general architecture for water quality monitoring systems is discussed. The important issue of computer security is mentioned and connected to recent publications related to the blockchain technology. Web services, data transmission technology, micro web frameworks, and cloud IoT services are also discussed.

scholarly journals Scalability and Performance Management of Internet Applications in the Cloud

2013 ◽  
pp. 434-464
Author(s):  
Wesam Dawoud ◽  
Ibrahim Takouna ◽  
Christoph Meinel
Keyword(s):  
Performance Management ◽  
Resource Provisioning ◽  
Infrastructure As A Service ◽  
Internet Applications ◽  
Application Performance ◽  
Total Cost ◽  
On Demand ◽  
And Performance ◽  
The Impact

Elasticity and on-demand are significant characteristics that attract many customers to host their Internet applications in the cloud. They allow quick reacting to changing application needs by adding or releasing resources responding to the actual rather than to the projected demand. Nevertheless, neglecting the overhead of acquiring resources, which mainly is attributed to networking overhead, can result in periods of under-provisioning, leading to degrading the application performance. In this chapter, the authors study the possibility of mitigating the impact of resource provisioning overhead. They direct the study to an Infrastructure as a Service (IaaS) provisioning model where application scalability is the customer’s responsibility. The research shows that understanding the application utilization models and a proper tuning of the scalability parameters can optimize the total cost and mitigate the impact of the overhead of acquiring resources on-demand.

Human-Computer Cloud and Its Applications in E-Tourism

2021 ◽  
pp. 1143-1176
Author(s):  
Andrew Ponomarev ◽  
Nikolay Shilov
Keyword(s):  
Information Processing ◽  
Decision Support ◽  
Resource Management ◽  
Ad Hoc ◽  
Dynamic Environments ◽  
Resource Provisioning ◽  
Cloud Environment ◽  
On Demand ◽  
Building Information ◽  
Developing Area

The chapter addresses two problems that typically arise during the creation of decision support systems that include humans in the information processing workflow, namely, resource management and complexity of decision support in dynamic environments, where it is impossible (or impractical) to implement all possible information processing workflows that can be useful for a decision-maker. The chapter proposes the concept of human-computer cloud, providing typical cloud features (elasticity, on demand resource provisioning) to the applications that require human input (so-called human-based applications) and, on top of resource management functionality, a facility for building information processing workflows for ad hoc tasks in an automated way. The chapter discusses main concepts lying behind the proposed cloud environment, as well as its architecture and some implementation details. It is also shown how the proposed human-computer cloud environment solves information and decision support demands in the dynamic and actively developing area of e-tourism.

QoS Based Efficient Resource Allocation and Scheduling in Cloud Computing

2019 ◽  
Vol 15 (4) ◽  
pp. 13-29
Author(s):  
Harvinder Chahal ◽  
Anshu Bhasin ◽  
Parag Ravikant Kaveri
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Resource Provisioning ◽  
Cloud Environment ◽  
Allocation Decision ◽  
Research Article ◽  
Large Pool ◽  
Efficient Resource ◽  
Algorithm Implementation

The Cloud environment is a large pool of virtually available resources that perform thousands of computational operations in real time for resource provisioning. Allocation and scheduling are two major pillars of said provisioning with quality of service (QoS). This involves complex modules such as: identification of task requirement, availability of resource, allocation decision, and scheduling operation. In the present scenario, it is intricate to manage cloud resources, as Service provider aims to provide resources to users on productive cost and time. In proposed research article, an optimized technique for efficient resource allocation and scheduling is presented. The proposed policy used heuristic based, ant colony optimization (ACO) for well-ordered allocation. The suggested algorithm implementation done using simulation, shows better results in terms of cost, time and utilization as compared to other algorithms.

Sign in / Sign up

Export Citation Format

Share Document