scholarly journals Efficient Resource Provisioning by Means of Sub-domain Based Ontology and Dynamic Pricing in Grid Computing

2013 ◽  
pp. 1-15 ◽  
Author(s):  
Abdul Khalique Shaikh ◽  
Aminul Haque ◽  
Saadat Alhashmi ◽  
R. Parthiban
Keyword(s):  
Grid Computing ◽  
Dynamic Pricing ◽  
Resource Provisioning ◽  
Efficient Resource

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.

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.

A Distributed Blockchain-based Broker for Efficient Resource Provisioning in 5G Networks

2020 ◽  
Author(s):  
Mohammed Amine Togou ◽  
Ting Bi ◽  
Kapal Dev ◽  
Kevin McDonnell ◽  
Aleksandar Milenovic ◽  
...  
Keyword(s):  
Resource Provisioning ◽  
5G Networks ◽  
Efficient Resource
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