Challenges towards Elastic Power Management in Internet Data Center

Introduction  Fast growing of IT power consumption.  Cloud Computing -> Internet Data Center(IDCs).  The electronic and mechanical systems for power distribution & cooling is the biggest portion of IDC  Services and independent. Workload is diverse.The resource demands in IDCs change dramatically.  Thus, the resource mechanism have to be elastic.  Cannot be solved by computer systems alone or physical systems alone. (CS : such as application organization, load distribution, machine virtualization PS : such as power distribution, cooling control It needs coordinations.

The physical infrastructure in IDCs  Power Distribution and Cost  Cooling  Slow dynamics/excessive cooling

Elastic of Data Center Computing  Example: Windows Live messenger.  Total user connected: 1 million.  Login rate : 1400/sec.  Flash crowd effect – a large number of users login in a short period of time.

What was showed  The demand for data center software service experience natural fluctuation and spikes.  This drastic demand variations require software applications in data centers to be elastic. They can take advantage of server-level parallelism to scale out in addition to scale up. They must be easily replicated and migrated at anytime and anywhere. They can leverage data center level software infrastructure such as MapReduce, Dryad,EC2 to perform data intensive parallel operations. Their performances can degrade gracefully when reaching resource limitations.  The slow cooling v.s fast change computing activity. (Load balancing, VM migration server repurpose.)

Methods of elastic  Oversubscription of resources If one app’s requirement is low, fulfill it by re-purposing. Use it to improve the utilization.  Macro-Resource Management

Method of elastic  Macro-Resource Management It takes information such as service-level agreement, application structures, and environmental conditions, and physical facility constraints from facility and applications designs ; monitors the operation status from application, system, and physical data collected over and across data centers; and makes decisions that affect power provisioning, cooling control, server allocation, service placement, load balancing,and job priorities. An important role: to build and refine models to predict performance impacts and risks on resource allocation decisions and to diagnose possible failures. Such models may in turn become abstractions that designer can use to refine their design so resource utilization can be further optimized. It may consist multiple sub-layers that are distributed. Challenge : HOW to organize this layer to perform desired coordination with efficient communication among sub-modules

Micro-foundations for Macro-Resource Management  Device Architecture CPU energy-efficient techniques. Chip Multi-Processing technology.  Dynamic Voltage and Frequency Scaling(DVFS)  Sleep(On/Off) Scheduling  Virtual Machine Management  Cooling Management

Virtual Machine Management  VMs could share the conventional hardware is a secure and resource-managed fashion while each VM is hosting its own operating system and applications.  VMM would provide the support for the source management in such a shared hosting platform, which enables applications such as server-consolidation, co- located hosting facilities and even the distributed web services.  Utilize the “Virtual Power ” to represent the soft versions of the hardware power state, to facilitate the deployment of the power management policies.  Dynamically migrate VMs to improve resource utilizations on active servers, shutting down inactive servers. Challenge : HOW to group VMs together since hardware resource utilization across VMs are not additive. Example: two disk IO intensive applications on the same host machine may cause significant throughput degradation due to disk contention.

Future Research  Modeling and control Challenge: How to integrate techniques at different layers. (DVFS v.s. On/Off, sensitivity of CRAC)  Cyber-Physical Co-Design Challenge: lack of consistent abstraction and modularity in computation and physical dynamics.  Data Management deal with amount of data, such as VM migration how to structure the systems,what data to sense…

Conclusion  A coordination layer must take into account information from both cyber-activity and physical-dynamics to make resource utilization follow the elasticity of software services.