Power Management: Research Review

Power Management:Research Review Bithika Khargharia Aug 5th, 2005

Single data-center rack: Somefigures • Cost of power and cooling equipment ~ $52,800 over 10 yr lifespan • Electricity costs for a typical 300W server Energy consumption/year = 2,628 kWh Cooling/year = 748 kWh Electricity/kWh = $ 0.10 • Excludes energy costs due to air circulation and power delivery sub-systems • Electricity cost/10 years for typical data center rack = $22,800 Total = $ 338/year

Motivation: Reduce TCO Power Equipment = 36% Cooling Equipment = 8% Electricity = 19% ----------------------------- Total =63% of the TCO of data-center’s physical infrastructure

Some Objectives • Explore possible power savings areas • Reduce TCO by operating within a reduced power budget. • Develop QoS aware power management techniques. • Develop power aware resource scheduling, resource partitioning techniques.

Power management : Problem Domains • Battery-operated devices • Server systems – App Servers, Storage Servers, Front-end Servers - Local schemes per server - Partition-wide schemes - Component-wide schemes • Whole data centers – Server systems, Interconnect switches, power supplies, disk-arrays - Heterogeneous cluster-wide schemes - Homogeneous cluster-wide schemes

Battery-operated devices: Power management • Transition hardware components between high and low power states (Hsu & Kremer, ’03, Rutgers, Weiser, ’94, Xerox PARC) • Deactivation decisions involve Power Usage Prediction - Periods of inactivity e.g. time between disk accesses (Douglis, Krishnan, Marsh, ’94, Li, ’94, UCB) - Other high-level information (Health, ’02, Rutgers, Weissel et al, ’02, University of Erlangen) • Mechanism supported by ACPI technology • Usually incurs both energy and performance penalties

Power management : Problem Domains • Battery-operated devices • Server systems – App Servers, Storage Servers, Front-end Servers - Local schemes per server - Partition-wide schemes - Component-wide schemes • Whole data centers – Server systems, Interconnect switches, power supplies, disk-arrays - Heterogeneous cluster-wide schemes - Homogeneous cluster-wide schemes

Power management Schemes: Server Systems • Battery-operated devices • Server systems – App Servers, Storage Servers, Front-end Servers - Local schemes per server - Partition-wide schemes - Component-wide schemes • Whole data centers – Server systems, Interconnect switches, power supplies, disk-arrays - Heterogeneous cluster-wide schemes - Homogeneous cluster-wide schemes

Server Power management: Local Schemes Attacks processor power usage (Elnozahy, Kistler, Rajamony, ’03, IBM, Austin) • DVS - extends DVS to server environments with concurrent tasks (Flautner, Reinhardt, Mudge, ’01, UMich) - conserves the most energy for intermediate load intensities • Request Batching - processor awakens when accumulated requests pending time > batch time-out - conserves the most energy for low load intensities • Combination of both - conserves energy for wide range of load intensities

Server Power management: QoS driven Local Schemes Specified QoS Apply Management Strategies QoS aware management strategies Compute QoS Actual QoS Fig: Feed-back driven control framework

Server Power management: QoS driven Local Schemes Some results (Elnozahy, Kistler, Rajamony, ’03, IBM, Austin) • Measured QoS is 90th percentile response time of 50ms • Validated Web-server simulator • Web workload from real Web server systems - Nagano Olympics 98 server - Financial Services company site - Disk-intensive workload.

Server Power management: QoS driven Local Schemes Savings increase with workload, stabilize and then reduce Some results Finance Workload Disk-intensive Workload

Server Power management: QoS driven Local Schemes Results Summary • DVS saves 8.7 to 38 % of the CPU energy • Request Batching saves 3.1 to 27 % of CPU energy • Combined technique saves 17 to 42% for all the three workload types for different load intensities.

Server Power management: Local Schemes Storage servers: Attacks disk power usage • Multi-speed disks for servers (Carrera, Pinheiro, Bianchini, ’02 ,Rutgers, Gurumurthi, PennState, IBM T.J Watson, ’03,) - dynamically adjust speed according to load imposed on the disk - performance and power models exist for multi-speed disks - based on disk response time, transition speeds dynamically - results with simulation and synthetic workload: energy savings up to 60%

Server Power management: Local Schemes Storage servers: Attacks disk power usage (Carrera, Pinheiro, Bianchini, ’02 ,Rutgers) • Four disk energy management techniques - combines laptop and SCSI disks - results with kernel level implementation and real workloads; Up to 41% energy savings for over-provisioned servers - two-speed disks (15,000 rpm and 10,000 rpm) - results with emulation and same real workload: energy savings up to 20% for properly provisioned servers.

Server Power management: Local Schemes Alternation of server load peaks and valleys Lighter weekend loads 22% energy savings Switch to 15,000 rpm only 3 times

Server Power management: Local Schemes Storage servers: Attacks database servers power usage • Effect of RAID parameters for disk-array based servers (Gurumurthi, ’03, PennState) - RAID level, stripe size, number of disks parameters - effect of varying these parameters on performance and energy consumption for database servers running transaction workloads

Server Power management: Local Schemes Storage servers: Attacks disks power usage • Storage cache replacement techniques (Zhu ’04, UIUC) - Increase disk idle time by selectively keeping certain disk blocks in main memory cache • Dynamically adjusted memory partitions for caching disk data (Zhu, Shankar, Zhou ’04, UIUC)

Server Power management: Local Schemes Storage servers: Attacks disks power usage, involves data Movement • Using MAID (massive array of idle disks) (Colarelli, GrunWald, ’02, U of Colorado, Boulder) - replace old tape back-up archives - copy accessed data to cache-disks, spin down all disks - LRU to implement cache disk replacement - write back when dirty - sacrifice access time in favor of energy conservation

Server Power management: Local Schemes Storage servers: Attacks disks power usage, involves data movement • Popular data concentration (PDC) technique (Pinheiro, Bianchini, ’04, Rutgers) - heavily skewed file access frequencies for server workloads - concentrate most popular disk data on a sub-set of disks - other disks are idle longer - sacrifice access time in favor of energy conservation

Server Power management: Local Schemes Some results: Comparing MAID and PDC(Pinheiro, Bianchini, ’04, Rutgers) • MAID and PDC can only conserve energy when server is very low • Using 2-speed disks MAID and PDC can conserve 30-40% of disk energy with small fraction of delayed requests • Overall PDC is more consistent and robust than MAID

Power management Schemes: Server Systems • Battery-operated devices • Server systems – App Servers,Storage Servers,Front-end Servers - Local schemes per server - Partition-wide schemes - Component-wide schemes • Whole data centers – Server systems, Interconnect switches, power supplies, disk-arrays - Heterogeneous cluster-wide schemes - Homogeneous cluster-wide schemes

Server Power management: Local Schemes • Power management schemes for application servers has not • been much explored.

Power management Schemes: Server Systems • Battery-operated devices • Server systems – App Servers,Storage Servers,Front-end Servers - Local schemes per server - Partition-wide schemes - Component-wide schemes • Whole data centers – Server systems, Interconnect switches, power supplies, disk-arrays - Heterogeneous cluster-wide schemes - Homogeneous cluster-wide schemes

Server Power management: Partition-wide Schemes • No known work done so far

Power management Schemes: Server Systems • Battery-operated devices • Server systems – App Servers,Storage Servers, Front-end Servers - Local schemes per server - Partition-wide schemes - Component-wide schemes • Whole data centers – Server systems, Interconnect switches, power supplies, disk-arrays - Heterogeneous cluster-wide schemes - Homogeneous cluster-wide schemes

Server Power management: Component-wide Schemes • The power management schemes in this space are mostly the ones used by battery-operated devices • Scheme applies to transitioning single device (CPU, memory, NIC etc) into different power modes • These schemes normally work independently of each other, even when applied to server power management techniques at the local level

Server Power management: HeterogeneousCluster-wide Schemes • Not much work done in this space

Server Power management: HomogeneousCluster-wide Schemes Front-end Web servers: (Pinheiro, ’03, Rutgers, Chase, ’01, Duke) • Load Concentration (LC) technique - dynamically distributes load offered to a server cluster under light load - idles some hardware and puts them in low power mode - under heavy load the system brings back resources to high power mode

Server Power management: Cluster-wide Schemes As load increases, # of nodes increases Some results 38% energy savings

Server Power management: HomogeneousCluster-wide Schemes Front-end Web server clusters: Attacks CPU power usage (Elnozahy, Kistler, Rajamony, ’03, IBM, Austin) • Independent voltage scaling (IVS) - server independently decides CPU operating points (voltage , frequency) at runtime • Co-coordinated voltage scaling (CVS) - servers co-ordinate to determine CPU operating points (voltage , frequency) for overall energy conservation.

Server Power management: HomogeneousCluster-wide Schemes Hot server clusters: Thermal Management (Weissel, Bellosa,Virginia) • Throttling processes to keep CPU temperatures in server clusters - CPU performance counters to infer the energy that each process consumes - CPU halt cycles introduced if energy consumption is more than permitted • Results - Implementation in Linux Kernel for a server cluster with one Web, one factorization and one database server - Can schedule client requests according to pre-established energy allotments when throttling CPU

Server Power management: HomogeneousCluster-wide Schemes Hot server clusters: Thermal Management for Data centers (Moore et al, HP Labs) • Hot spots can develop at certain parts irrespective of cooling - temperature modeling work by HP Labs • Temperature aware load-distribution policies - adjusts load distribution to racks according to temperature distribution between racks on the same row. - moved load away from regions directly affected by failed air-conditioners

Challenges • No existing tool to model power and energy consumption. • Develop schemes that intelligently exploit SLA’s such as ‘request priorities to increase savings’. • Develop accurate workload based power usage prediction. • Partition-wide power management schemes are not yet explored. • Power management schemes for application servers has not been much explored. - use CPU and memory intensively - store state typically not replicated - challenge is to correctly trade-off energy savings and performance overheads

Challenges • No previous work for energy conservation in memory servers - Challenge is to properly lay-out data across main memory banks and chips to exploit low power states more extensively. • Power management for Interconnects and interfaces - 32 port gigabit ethernet switch consumes 700W when idle. • Thermal Management - very good understanding of components and system lay-outs, air-flow in server enclosures and data centers required. - accurate temperature monitoring mechanisms

Challenges • Peak power management - dynamic power management can limit over provisioning of cooling - challenge is to provide the best performance under fixed smaller power budget - IBM Austin is doing some work related to memory - power shifting project dynamically redistributes budget between active and inactive components - lightweight mechanisms to control power and performance of different system components. - automatic work-load characterization techniques. - algorithms for allocating power among components

Discussion

Power related decision making QoS aware adaptive Power-management Schemes • Translate certain power envelope to compute & IO power. • 2. Add a new parameter to workload requirements characterization – Power • 3. Power usage prediction for different devices (CPU, ,memory, disks etc) and server systems under • different kinds of workloads – like compute-intensive, IO intensive etc • 4. Global power states for servers and data-center systems like ACPI –(ACPI has rudimentary global states right now) 4. Exploit SLA’s such as ‘request priorities to increase savings’. 5. Exploit Server characteristics to increase power savings – workloads, replication, frequency of access for disk array servers • For devices that exists in the ‘battery-operated world’ – • - CPU NIC, memory etc • (additional power savings ?) e.g. • 2. For new devices introduced • by data-centers – disk-arrays, interconnect switches etc. • 3. Relate power consumption with the • ability to self-optimize a platform to • achieve promised QoS • - Power & QoS aware scheduling • - Power & QoS aware resource • aggregation to provision • platforms on demand. • - Power & QoS aware Resource • partitioning.

Power related decision making • Translate certain power envelope to compute & IO power. • 2.Power usage prediction for different devices (CPU, ,memory, disks etc) and server systems under • kinds of workloads – like compute-intensive, IO intensive etc • 3. Add a new parameter to workload requirements characterization – Power • 4. Global power states for servers and data-center systems like ACPI –(ACPI has rudimentary global states right now)

Power related decision making • Translate certain power envelope to compute & IO power. • 2. Power usage prediction for different devices (CPU, ,memory, disks etc) and server systems under • kinds of workloads – like compute-intensive, IO intensive etc • 3. Add a new parameter to workload requirements characterization – Power • 4. Global power states for servers and data-center systems like ACPI –(ACPI has rudimentary global states right now)

Power related decision making QoS aware adaptive Power-management Schemes • Translate certain power envelope to compute & IO power. • 2. Add a new parameter to workload requirements characterization – Power • 3. Power usage prediction for different devices (CPU, ,memory, disks etc) and server systems under • kinds of workloads – like compute-intensive, IO intensive etc • 4. Global power states for servers and data-center systems like ACPI –(ACPI has rudimentary global states right now) • For devices that exists in the ‘battery-operated world’ – • - CPU NIC, memory etc • (additional power savings ?) e.g. • 2. For new devices introduced • by data-centers – disk-arrays, interconnect switches etc.

Power related decision making QoS aware adaptive Power-management Schemes • Translate certain power envelope to compute & IO power. • 2. Add a new parameter to workload requirements characterization – Power • 3. Power usage prediction for different devices (CPU, ,memory, disks etc) and server systems under • kinds of workloads – like compute-intensive, IO intensive etc • 4. Global power states for servers and data-center systems like ACPI –(ACPI has rudimentary global states right now) • For devices that exists in the ‘battery-operated world’ – • - CPU NIC, memory etc • (additional power savings • 2. For new devices introduced • by data-centers – disk-arrays, interconnect switches etc. • 3. Relate power consumption with the • ability to self-optimize a platform to • achieve promised QoS • - Power & QoS aware scheduling • - Power & QoS aware resource • aggregation to provision • platforms on demand. • - Power & QoS aware Resource • partitioning.

Power related decision making QoS aware adaptive Power-management Schemes • Translate certain power envelope to compute & IO power. • 2. Add a new parameter to workload requirements characterization – Power • 3. Power usage prediction for different devices (CPU, ,memory, disks etc) and server systems under • kinds of workloads – like compute-intensive, IO intensive etc • 4. Global power states for servers and data-center systems like ACPI –(ACPI has rudimentary global states right now) • Exploit SLA’s such as ‘request priorities to increase savings’. • 5. Exploit Server characteristics • to increase power savings • workloads, replication, • frequency of access for disk • array servers • For devices that exists in the ‘battery-operated world’ – • - CPU NIC, memory etc • (additional power savings • 2. For new devices introduced • by data-centers – disk-arrays, interconnect switches etc.

Power related decision making QoS aware adaptive Power-management Schemes • Translate certain power envelope to compute & IO power. • 2. Add a new parameter to workload requirements characterization – Power • 3. Power usage prediction for different devices (CPU, ,memory, disks etc) and server systems under • kinds of workloads – like compute-intensive, IO intensive etc • 4. Global power states for servers and data-center systems like ACPI –(ACPI has rudimentary global states right now) • Exploit SLA’s such as ‘request priorities to increase savings’. • 5. Exploit Server characteristics • to increase power savings – • workloads, replication, • frequency of access for disk- • array servers • For devices that exists in the ‘battery-operated world’ – • - CPU NIC, memory etc • (additional power savings ?) e.g. • 2. For new devices introduced • by data-centers – disk-arrays, interconnect switches etc. • 3. Relate power consumption with the • ability to self-optimize a platform to • achieve promised QoS • - Power & QoS aware scheduling • - Power & QoS aware resource • aggregation to provision • platforms on demand. • - Power & QoS aware Resource • partitioning.

Power Management: Research Review