scholarly journals Special Issue on Algorithms for the Resource Management of Large Scale Infrastructures

Algorithms ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 200 ◽  
Author(s):  
Danilo Ardagna ◽  
Claudia Canali ◽  
Riccardo Lancellotti
Keyword(s):  
Energy Consumption ◽  
Distributed Systems ◽  
Resource Management ◽  
Large Scale ◽  
Efficient Solutions ◽  
Special Issue ◽  
Emerging Trends ◽  
Innovative Solutions ◽  
Application Deployment ◽  
Global Energy Consumption

Modern distributed systems are becoming increasingly complex as virtualization is being applied at both the levels of computing and networking. Consequently, the resource management of this infrastructure requires innovative and efficient solutions. This issue is further exacerbated by the unpredictable workload of modern applications and the need to limit the global energy consumption. The purpose of this special issue is to present recent advances and emerging solutions to address the challenge of resource management in the context of modern large-scale infrastructures. We believe that the four papers that we selected present an up-to-date view of the emerging trends, and the papers propose innovative solutions to support efficient and self-managing systems that are able to adapt, manage, and cope with changes derived from continually changing workload and application deployment settings, without the need for human supervision.

A Survey and Taxonomy of Energy Efficient Resource Management Techniques in Platform as a Service Cloud

2017 ◽  
pp. 410-454 ◽  
Author(s):  
Sareh Fotuhi Piraghaj ◽  
Amir Vahid Dastjerdi ◽  
Rodrigo N. Calheiros ◽  
Rajkumar Buyya
Keyword(s):  
Energy Consumption ◽  
Resource Management ◽  
Energy Efficient ◽  
Power Efficiency ◽  
Large Scale ◽  
Data Centers ◽  
Cloud Service ◽  
Efficient Resource ◽  
Management Techniques ◽  
Increasing Demand

The numerous advantages of cloud computing environments, including scalability, high availability, and cost effectiveness have encouraged service providers to adopt the available cloud models to offer solutions. This rise in cloud adoption, in return encourages platform providers to increase the underlying capacity of their data centers so that they can accommodate the increasing demand of new customers. Increasing the capacity and building large-scale data centers has caused a drastic growth in energy consumption of cloud environments. The energy consumption not only affects the Total Cost of Ownership but also increases the environmental footprint of data centers as CO2 emissions increases. Hence, energy and power efficiency of the data centers has become an important research area in distributed systems. In order to identify the challenges in this domain, this chapter surveys and classifies the energy efficient resource management techniques specifically focused on the PaaS cloud service models.

A dynamic rescheduling algorithm for resource management in large scale dependable distributed systems

2012 ◽  
Vol 63 (9) ◽  
pp. 1409-1423 ◽  
Author(s):  
Alexandra Olteanu ◽  
Florin Pop ◽  
Ciprian Dobre ◽  
Valentin Cristea
Keyword(s):  
Distributed Systems ◽  
Resource Management ◽  
Large Scale ◽  
Dynamic Rescheduling

Interference mitigation in point to point wireless sensor networks using LSP protocol and time division multiplexing approach

2020 ◽  
Vol 39 (4) ◽  
pp. 5449-5458
Author(s):  
A. Arokiaraj Jovith ◽  
S.V. Kasmir Raja ◽  
A. Razia Sulthana
Keyword(s):  
Energy Consumption ◽  
Large Scale ◽  
Control Parameter ◽  
Interference Mitigation ◽  
Wireless Sensor ◽  
Point To Point ◽  
A Minor ◽  
Two Stages ◽  
Time Division ◽  
And Control

Interference in Wireless Sensor Network (WSN) predominantly affects the performance of the WSN. Energy consumption in WSN is one of the greatest concerns in the current generation. This work presents an approach for interference measurement and interference mitigation in point to point network. The nodes are distributed in the network and interference is measured by grouping the nodes in the region of a specific diameter. Hence this approach is scalable and isextended to large scale WSN. Interference is measured in two stages. In the first stage, interference is overcome by allocating time slots to the node stations in Time Division Multiple Access (TDMA) fashion. The node area is split into larger regions and smaller regions. The time slots are allocated to smaller regions in TDMA fashion. A TDMA based time slot allocation algorithm is proposed in this paper to enable reuse of timeslots with minimal interference between smaller regions. In the second stage, the network density and control parameter is introduced to reduce interference in a minor level within smaller node regions. The algorithm issimulated and the system is tested with varying control parameter. The node-level interference and the energy dissipation at nodes are captured by varying the node density of the network. The results indicate that the proposed approach measures the interference and mitigates with minimal energy consumption at nodes and with less overhead transmission.

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