On the classification and quantification of server consolidation overheads

2020 ◽  
Vol 77 (1) ◽  
pp. 23-43 ◽  
Author(s):  
Belen Bermejo ◽  
Carlos Juiz
Keyword(s):  
Server Consolidation

Towards Autonomic Workload Management in DBMSs

2011 ◽  
pp. 154-173 ◽  
Author(s):  
Baoning Niu ◽  
Patrick Martin ◽  
Wendy Powley
Keyword(s):  
Database Management ◽  
Work Flow ◽  
Management Systems ◽  
Differentiated Service ◽  
Computing Systems ◽  
Workload Management ◽  
Server Consolidation ◽  
Business Objective ◽  
High Utilization ◽  
Future Work

Workload management is the discipline of effectively managing, controlling and monitoring work flow across computing systems. It is an increasingly important requirement of database management systems (DBMSs) in view of the trends towards server consolidation and more diverse workloads. Workload management is necessary so the DBMS can be business-objective oriented, can provide efficient differentiated service at fine granularity and can maintain high utilization of resources with low management costs. We see that workload management is shifting from offline planning to online adaptation. In this paper we discuss the objectives of workload management in autonomic DBMSs and provide a framework for examining how current workload management mechanisms match up with these objectives. We then use the framework to study several mechanisms from both DBMS products and research efforts. We also propose directions for future work in the area of workload management for autonomic DBMSs.

Server Consolidation

2005 ◽  
pp. 161-196
Author(s):  
Al Muller ◽  
Seburn Wilson ◽  
Don Happe ◽  
Gary J. Humphrey ◽  
Ralph Troupe
Keyword(s):  
Server Consolidation

A server consolidation method with integrated deep learning predictor in local storage based clouds

2018 ◽  
Vol 30 (23) ◽  
pp. e4503 ◽  
Author(s):  
Guoliang Zhang ◽  
Weidong Bao ◽  
Xiaomin Zhu ◽  
Weiwei Zhao ◽  
Huining Yan
Keyword(s):  
Deep Learning ◽  
Server Consolidation ◽  
Local Storage

Energy-efficient 5G cloud RAN with virtual BBU server consolidation and base station sleeping

2020 ◽  
Vol 177 ◽  
pp. 107302
Author(s):  
Tshiamo Sigwele ◽  
Yim Fun Hu ◽  
Misfa Susanto
Keyword(s):  
Energy Efficient ◽  
Base Station ◽  
Server Consolidation ◽  
Cloud Ran

scholarly journals Server Consolidation Based on Culture Multiple-Ant-Colony Algorithm in Cloud Computing

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2724 ◽  
Author(s):  
Yuan ◽  
Sun
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Ant Colony Algorithm ◽  
Data Centers ◽  
Virtual Machines ◽  
Ant Colony ◽  
Virtual Machine Migration ◽  
Server Consolidation ◽  
Cloud Data ◽  
Cloud Data Centers

High-energy consumption in data centers has become a critical issue. The dynamic server consolidation has significant effects on saving energy of a data center. An effective way to consolidate virtual machines is to migrate virtual machines in real time so that some light load physical machines can be turned off or switched to low-power mode. The present challenge is to reduce the energy consumption of cloud data centers. In this paper, for the first time, a server consolidation algorithm based on the culture multiple-ant-colony algorithm was proposed for dynamic execution of virtual machine migration, thus reducing the energy consumption of cloud data centers. The server consolidation algorithm based on the culture multiple-ant-colony algorithm (CMACA) finds an approximate optimal solution through a specific target function. The simulation results show that the proposed algorithm not only reduces the energy consumption but also reduces the number of virtual machine migration.

Sign in / Sign up

Export Citation Format

Share Document