## 2 Background and Related Work
### Power Consumption Adjustment of Datacenters  
Recently, the participation of datacenters towards demand response requirements has become increasingly important given their high and increasing energy consumption and their flexibility in demand management compared to conventional industrial facilities. The huge yet flexible power adjustments of datacenters make them promising resources for demand response, which requiresa certain amount of power adjustment at a certain time. Datacenters could dynamically adjust the power consumption themselves by leveraging the IT computing knobs such as geographical load balancing [18; 19], dynamic capacity provisioning [20], and workload shifting [21], as well as non-IT knobs including batteries and cooling systems [22; 23]. One of the most comprehensive studies describing the potential of different hardware components in the datacenters and strategies providing a demand response was released by Lawrence Berkeley National Laboratories [24]. Some of the initial work in the area comes from [PERSON] et al. [25], which proposed an approach for adjusting the power consumption of datacenters by using energy storage to shift peak demand away from high peak periods. While the design of workload planning algorithms for datacenters has received considerable attention over the recent years [26; 27; 28; 29; 30; 31; 32; 33; 34; 35], all of the proposed flexible datacenter workload load planning methods could change the overall power consumption as needed. A more complex approach was presented in Reference [36]. It took advantage of two datacenter flexibility mechanisms--workload shifting and local generation (local diesel generators and local renewable energy). Using these mechanisms, algorithms were developed in order to avoid the coincidental peak and reduce the energy costs. They relied on the prediction of a coincident peak occurrence based on historical data to optimize the workload allocation and local generation and to minimize the expected cost. In Reference [37] a technique was proposed for balancing and keeping the peak power consumption of the datacenter under a given threshold according to the electricity pricing but at the same time allowing the datacenter to respond to the regulation control signals that may request an increase in power consumption. [PERSON] et al in Reference [38], on the basis of real-time electricity price consideration, proposed to reduce the energy consumption cost of datacenters by dynamically adjusting the server capacity and performing workload transfers in each time slot. Besides, some researches have focused on dynamically controlling the number of active servers based on the load and regard it as an effective means of power control [39; 40; 41]. In References [20; 42; 43], dynamic speed/voltage scaling (DVS) could change processor power consumption, which could also adjust the datacenter power consumption on demand by adjusting the frequency based on the instantaneous power demand. Furthermore, our research team also conducted relevant studies to dynamically adjust the power consumption amount according to the variation of the power supply. [PERSON] et al. [44] designed adaptive scheduling algorithms and deployed renewable energy in the datacenters, aiming at scheduling approximate applications, in order to meet the user demands as well as maximizing the utilization of renewable energy. The main purpose was to appropriately utilize the renewable energy and at the same time aimed at typical approximate applications based on the trade-off of performance and accuracy to schedule and manage these tasks. By running such applications, the power consumption of the computing nodes could follow a trend of changing energy input as much as possible, which showed the effect of adjusting the datacenter power consumption. [PERSON] et al.