Thermal-Aware Virtual Machine Allocation for Heterogeneous Cloud Data Centers

Abstract

In recent years, a large and growing body of literature has addressed the energy-efficient resource management problem in data centers. Due to the fact that cooling costs still remain the major portion of the total data center energy cost, thermal-aware resource management techniques have been employed to make additional energy savings. In this paper, we formulate the problem of minimizing the total energy consumption of a heterogeneous data center (MITEC) as a non-linear integer optimization problem. We consider both computing and cooling energy consumption and provide a thermal-aware Virtual Machine (VM) allocation heuristic based on the genetic algorithm. Experimental results show that, using the proposed formulation, up to 30 % energy saving is achieved compared to thermal-aware greedy algorithms and power-aware VM allocation heuristics.

Heating Homes with Servers: Workload Scheduling for Heat Reuse in Distributed Data Centers

“…They rely on an optimization problem, defined either reactively or proactively, whose complexity is highly dependent on the representation of the thermodynamics processes and the correlations considered among workload and power and heat demand [ 50 , 51 ]. Workload placement strategies considered are based on zones discretization, minimize the heat recirculation, and prioritize the servers for task allocation by observing hot airflow within the DC [ 47 , 48 , 51 ]. Scheduling methodologies common in DCs such as first-come-first-serve or backfilling do not usually consider the thermal perspective [ 52 , 53 ].…”

“…Thermal aware workload scheduling algorithms for heat reuse are derived from scheduling algorithms developed to minimize the cooling system energy consumption [12,47]. The main goal of these scheduling algorithms is to distribute the workload in a data center to maintain a low ambient temperature and avoid hotspot formation [47,48]. In the case of heat reuse, the workload scheduling aims to increase the efficiency of heat pump operation and to meet the heat demand of the district heating network [49].…”

HUNTER: AI based Holistic Resource Management for Sustainable Cloud Computing

“…The 'holistic' column represents whether the approach provides an end-to-end solution for scheduling, considering all parameters for sustainable cloud computing [6]. TOPSIS [20] Threshold Based MALE [21] Memory Mapping CRUZE [6] Cuckoo Optimization MITEC [8] Genetic Algorithm PADQN [12] Deep Q Learning ANN [10] Neural Network SDAE-MMQ [13] Autoencoders HDIC [9] NARX Network HUNTER Surrogate Modelling…”

“…Most prior work presents meta-heuristic algorithms [6] and deep learning techniques [7]. Most state-ofthe-art models use meta-heuristic approaches like genetic algorithms or integer linear programming [6,8,9,10,11]. Other recent methods use reinforcement learning (RL); specifically, the traditional tabular models like Q-Learning [12,13].…”

HUNTER: AI based holistic resource management for sustainable cloud computing

“…Most prior work presents meta-heuristic algorithms [6] and deep learning techniques [7]. Most state-ofthe-art models use meta-heuristic approaches like genetic algorithms or integer linear programming [6,8,9,10,11]. Other recent methods use reinforcement learning (RL); specifically, the traditional tabular models like Q-Learning [12,13].…”