Advances, Systems and Applications
From: Integrated resource management pipeline for dynamic resource-effective cloud data center
Authors | Architecture | Target | When to migrate | Performance Metric | Conclusion |
---|---|---|---|---|---|
Nathuji and | Centralized | minimize power | - Periodic reallocation | - Power (W) | The overall energy |
Schwan [12] | Â | consumption without | Â | Â | consumption could be reduced significantly, up |
 |  | performance penalties |  |  | to 34%, without appreciable losses in performance. |
Verma et al. [13] | Centralized | minimize power | - Periodic reallocation | - Power (watts) | pMapper was considered |
 |  | consumption, |  | - Energy (kilojoules) | an efficient solution to |
 |  | considering the |  | - Migration cost | minimize power |
 |  | VM migration |  | - Overall cost | consumption (less than |
 |  | cost |  | - Power savings (%) | 0.2% penalty). |
Zhu et al. [15] | De-centralized | improve | - Periodic reallocation | - Response time | The integration of node |
 | - Pod controllers | workload | - Threshold-based heuristics | (seconds) | and pod controllers |
 | - Node controllers | management to ensure efficient use of data center resources |  | - Number of migrations | improved performance by 32% and 23% over fixed allocation and over non-integrated controllers, and reduced migrations for high priority workloads. |
Gmach et al. [16] | De-centralized | minimize power | - Periodic reallocation | - Migration overhead | The integration between |
 | - workload placement | consumption, | - Threshold-based heuristics | - CPU quality | reactive migration |
 | controller | taking into |  | violations | controller and periodic |
 | - migration controller | account the Qos and the number of VM migrations |  | - Power consumption | workload placement controller presented the best approach for power and SLA, but needs more migrations. |
VMware Distributed | Centralized | minimize power | - Fixed threshold heuristics | Â | Fixed threshold heuristics |
Power Management | Â | consumption | Â | Â | are unsuitable for real |
(DPM) [17] | Â | Â | Â | Â | systems with dynamic and unknown workloads. |
Li et al. [18] | Centralized | minimize power | - Dynamic threshold heuristics | - Energy | The double threshold with |
 |  | consumption and QoS |  | - Number of migrations | multi-resource utilization with the MPSO algorithm |
 |  | guarantee |  | - Number of active physical servers | reduces energy consumption and |
 |  |  |  | - load balance degree | improves the QoS. |
Beloglazov et | Centralized | minimize power | - Fixed threshold heuristics | - Energy (kWh) | It is not a suitable decision |
al. [24] | Â | consumption, | Â | - SLAVs (%) | for keeping the utilization |
 |  | taking into account QoS |  | - Number of migrations | threshold constant as the workload is in continuous change. |
Beloglazov and | De-centralized | minimize power | - Decision based on statistical | - Energy (kWh) | The proposed LR |
Buyya [23] | Â | consumption, | analysis of historical data | - ESV | algorithm remarkably |
 |  | taking into |  | - SLAVs | outperformed other |
 |  | account QoS |  | - PDM (%) | dynamic VM consolidation |
 |  |  |  | - Number of migrations | algorithms. |
Guenter et al. [21] | De-centralized | minimize power | - Decision based on statistical | - Power saving | Predicting demand was |
 |  | consumption, | analysis of historical data | - normalized daily | used to switch on servers |
 |  | considering the |  | energy savings | before require and avoid |
 |  | trade-off between cost, performance, |  | (MWh) | switching on unnecessary servers. |
 |  | and reliability |  |  |  |
Bobroff et al. [22] | Centralized | minimize power | - Decision based on statistical | - Time-averaged | The proposed algorithm |
 |  | consumption | analysis of historical data | number of servers | used decreased the |
 |  |  |  | used | number of servers needed |
 |  |  |  | - Capacity of overflow | to support a certain SLA by 50% compared to static consolidation. |
This work | De-centralized | minimize power | - Fixed threshold heuristics | - AITF (%) | Our combination of DES, |
 |  | consumption, respect | - Decision based on statistical | - AOTF (%) | MMTMC 2, and MF |
 |  | overall and end-user’s | analysis of historical data | - Number of migrations | algorithms improved |
 |  | SLA, and eliminate |  | - Energy saving (%) | performance in power |
 |  | unnecessary migrations |  |  | saving, QoS, and network traffic. The number of migrations reduced by 49.44% compared to default algorithms. |