Simulation Platform for Energy Optimization of Server SystemHongjie Ma, Edward Smart, David Brown

Institute of Industrial Research

University of Portsmouth

AbstractThe electricity cost is even higher than theinfrastructure cost for a data center especiallywhen they are deployed in large clusterconfigurations[1]. However, due to the lack offlexible controllable hardware researchplatforms, commercial server system-levelenergy optimization algorithm are not veryeffective[2]. To address this issue, we build aflexible business server simulation platformbased on these principles and the energy -efficiency (EE) measurement data for themain components, such as Central ProcessorUnit (CPU), Hard Disk Drive (HDD), Memoryand Power Supply Unit (PSU).

Introduction:The demand for data centers of IT industry isgradually increasing year by year. The surveyin 2013 shows that a full 90% of all the data inthe world has been generated over the lasttwo years[3]. The explosive growth of the datacenter is a major challenge to the demand forelectricity. Data center electricity consumptionis projected to increase to roughly 140 billionkWh annually by 2020, equivalent to theannual output of 50 power plants, costingAmerican businesses 13 billion US dollarsannually in electricity bills, and emitting nearly100 million metric tons of carbon pollution peryear[4].

The main power of the data center isconsumed by the server (26%), networkhardware (11%), power conversion systemand cooling system composed (61%). Thiscascade effect makes it very important toreduce server power consumption to improvedata center efficiency. Research has shownthat one Watt reduction at the servercomponent level results in an additional1.84Watts saving in the power supply, powerdistribution system, UPS system, coolingsystem, and building entrance switchgear andmedium voltage transformer.

At present, the server vendor is subject to thelimitations of cost, development cycle or otherfactors, so there are rarely able to provide afully flexible hardware platform to providedecoupling energy efficiency data for allcomponents and fully flexible hardwareoptimization interface. To address thischallenge, we build a server simulationplatform in Simulink® environment.

.

System configuration

AcknowledgmentsThis project was funded by Innovate UK (102258).

References: [1]. Economou D, Rivoire S, Kozyrakis C,

Ranganathan P. Full-system power analysis and modeling for server environments.; 2006: International Symposium on Computer Architecture-IEEE; 2006.[2]. Lin W, Wu W, Wang H, Wang JZ, Hsu C.

Experimental and quantitative analysis of server power model for cloud data centers. Future Generation Computer Systems 2016.[3]. SINTEF. Big Data, for better or worse: 90%

of world's data generated over last two years., 2013.[4]. America's Data Centers Consuming and

Wasting Growing Amounts of Energy., 2015.

Simulator platform:The block diagram of the simulation platformis shown in the Fig. 2. It consists of virtualsoftware, optimization algorithm interface andEE models of main components. Thesimulation platform can be used forcomponent analysis and system optimization,such as the effect of the number of activecores, frequency, and multi-corecommunication overhead on the EE of CPUor the different hardware configurations onthe EE of system. This means that thesimulation platform can be used for onlineoptimization algorithm development as well asa server hardware selection guidance.

Fig. 1. Server(Overdrive 3000) from SoftIron.

Fig. 2. Block diagram of the simulation platform.

Application cases of the platform:As shown in Fig. 3, this study simulates theserver response latency and powerconsumption of Overdrive 3000, based on theactual service requests to the U.S. securitiesand exchange commission web server.

The simulation results are shown in Fig. 4 andFig. 5. In addition, as shown in Fig. 6 basedon the simulation platform and the real worldservice request data, we used an energyoptimization algorithm to achieve theimproving both in request response latency(10%) and power consumption (38.11%).

Fig. 3. Service requests from a web server (U.S. Securities and Exchange Commission 2004/02/07).

Fig. 4. Response delay of different hardware configurations (2 cores and 8 cores with 1700MHz).

Fig. 5. Percent of timeout services and power consumption of different configurations (2 cores and 8 cores with 1700MHz).

0 10800 21600 32400 43200 54000 64800 75600 864000

8

16

24

32

40

48

56

Tim

e ou

t (%

)

Time (s)

5%

The total time for which the timeout ratio (threshold = 5s) is greater than 5%:[1]. 50 minutes for 8 cores;[2]. 140 minutes for 2 cores;

20

24

28

32

36

40

44

48

52

56

60

Tim

e ou

t (W

)

Power is reduced 32.26%

Fig. 6. Quality of service and power consumption before and after optimization.

0 7200 14400 21600 28800 36000 43200 50400 57600 64800 72000 79200 864000

1

2

3

4

5

6

7

8 cores 2 cores

Res

pons

del

ay (

s)

Sevice request timing (s)

Long delay: Partial service request timeout(threshold = 5s).

0 10800 21600 32400 43200 54000 64800 75600 864000

10M

20M

30M

40M

50M

0 10 20 30 40 50 600.0

300.0k

600.0k

900.0k

1.2M

1.5M

Siz

e (B

ytes

)

Sevice request timing (s)

Siz

e (B

ytes

)

Sevice request timing (s)

NumberAvgSize

NumberAvgSize

NumberAvgSize

0 10800 21600 32400 43200 54000 64800 75600 864000

8

16

24

32

40

48

56

Timeout percent of 8 cores Timeout percent after optimizaiton

Tim

e ou

t (%

)

Time (s)

5%

20

24

28

32

36

40

44

48

52

56

60

1. The time, which the timout percent is bigger than 5% is redueced 10%.

2. Power is reduced 38.11%

Power of 8 cores Power after optimizaiton

Tim

e ou

t (W

)