# Enhancing Data Center Sustainability Through Energy Adaptive Computing

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9 Enhancing Data Center Sustainability Through Energy Adaptive Computing A:9 PMU is at level 2 and server/switch level PMUs are at level 1. With such a multilevel Fig. 2. A simple example of multi-level power control in a datacenter power management architecture our control scheme attempts to provide the scalability required for handling energy and thermal adaptation in large data centers with minimum impact on the underlying networks. In the hierarchical power control model that we have assumed, the power budget in every level gets distributed to its children nodes in proportion to their demands. All the leaf nodes are in level 0. The component in each level l+1 has configuration information about the children nodes in level l. For example the rack level power manager has to have knowledge of the power and thermal characteristics of the individual components in the rack. The components at level l continuously monitor the demands and utilization levels and report them to level l + 1. This helps level l + 1 to continuously adjust the power budgets. Level l+1 then directs the components in level l as to what control action needs to be taken. The granularities at which the monitoring of power usage and the allocation adjustments are done are different and are discussed later in Section Energy-Temperature relationship In the design of our control scheme we limit the power consumption of a device based on its thermal limits as follows. Let t denote time, T(t) the temperature of the component as a function of time, P(t) power consumption as a function of time, and c 1, c 2 be the appropriate thermal constants. Also, let T a denote the ambient temperature, i.e., temperature of the medium right outside the component. The component will eventually achieve this temperature if no power is supplied to it. Then the rate of change of temperature is given by dt(t) = [c 1 P(t)+c 2 (T(t) T a )]dt (1) Being a first-order linear differential equation, this equation has an explicit solution. Let T(0) denote the temperature at time t = 0. Then, T(t) = [T a +[T(0) T a ]e c2t ]+c 1 e c2t t 0 P(τ)e c2τ dτ (2) where the first term relates to cooling and tends to the ambient temperature T a and the second term relates to heating. Let T limit denote the limit on the temperature and P limit is the limit on power consumption so that the temperature does not exceed T limit during the next adjustment window of s seconds. It is easy to see that, T(τ) = T a +P limit c 1 /c 2 [1 e c2 s ]+[T(0) T a ]e c2 s (3)

12 A:12 K.Kant, M.Murugan et al. Power Margin (P min ): The minimum amount of surplus that has to be present after a migration in both the source and target nodes of the migration. This helps in mitigating the effects of fluctuations in the demands. Migration Cost: The migration cost is a measure of the amount of work done in the source and target nodes of the migrations as well as in the switches involved in the migrations. This cost is added as a temporary power demand to the nodes involved. A migration is done if and only if the source and target nodes can have a surplus of at least P min. Also migrations are done at the application level and hence the demand is not split between multiple nodes. Finally Willow also does resource consolidation to save power whenever possible. When the utilization in a node is really small the demand from that node is migrated away from it and the node is deactivated. The matching of power deficits to surpluses is done by a variable sized bin packing algorithm called FFDLR [Friesen and Langston 1986] solves a bin packing problem of sizenin timeo(nlogn). The optimality bound guaranteed for the solution is (3/2) OPT +1 where OPT is the solution given by an optimal bin packing strategy QoS Aware Scheduler Traditional scheduling algorithms like round robin and priority based scheduling are not QoS aware and do not have any feedback mechanism to guarantee the QoS requirements of jobs. A few algorithms that attempt to guarantee some level of QoS do so by mechanisms like priority treatment and admission control. Our objective in this work is to allow for energy adaptation while respecting QoS needs of various applications to the maximum extent possible. In this regard we implemented a QoS aware scheduling algorithm in the nodes as shown in Figure 3. The scheduler uses an integral controller to adjust the weights of the applications at regular intervals based on the delay violations of the applications. The applications are allocated CPU shares proportional to their weights. Applications with higher delay violations get more CPU share. It is well known from classic queuing theory [Kant 1992] that in an asymptotic sense, as U 1 the wait time of jobs is directly proportional to 1/(1 U) where U is the overall queue utilization. Hence the integral gain for the controller is calculated as (1 U). Using the overall utilization as the integral gain also avoids oscillations and keeps the system stable. Fig. 3. QoS Aware Scheduler

13 Enhancing Data Center Sustainability Through Energy Adaptive Computing A:13 Fig. 4. Various adaptations done in Willow and the different time granularities The error in delay E(t) for each application is the difference between the delay bound and the measured delay. At the end of each sample interval t (of size t), the new weights are calculated as follows. weight(t) = weight(t 1)+K i E(t) (7) Then the CPU shares are allocated to the applications proportional to their weights. Figure 4 shows an overall picture of the different adaptation mechanisms that are done in Willow at different time granularities. The scheduler works in the individual nodes at the smallest time granularity. At the next higher time granularity the demand side adaptations are done that include migration of deficits to surplus nodes. At the next higher granularity the supply side adaptations such as allocating power budgets at different levels takes place. At the largest time granularity consolidation related decisions are made that include shutting down of nodes with very low utilization. 7. EXPERIMENTAL EVALUATION 7.1. Assumptions and QoS model Fig. 5. Configuration used in simulation We built a simulator in Java for evaluating the ability of our control scheme to cater to the QoS requirements of tasks when there are energy variations. The Java simulator can be configured to simulate any number of nodes and levels in the hierarchy. For our evaluations we used the configuration shown in Figure 5. There are 18 nodes and 3 types of applications. The application types and their SLA requirements are shown

14 A:14 K.Kant, M.Murugan et al. Table I. Utilization vs power consumption Application Type SLA Requirement Mean Runtime Type I Average Delay 120ms, cannot be migrated 10ms Type II Average Delay 180ms, can be migrated 15ms Type III Average Delay 200ms, can be migrated 20ms in Table I. For simplicity we assume that each application is hosted in a VM and can be run on any of the 18 nodes. To begin with, each node is assigned a random mix of applications. The static power consumption of nodes is assumed to be 20% of the maximum power limit (450W ). There is a fixed power cost associated with migrations. In the configuration that we use for our experiments, we assume a single SAN storage that can be accessed by all nodes in the data center. Storage migration is done when the VM disk files have to be migrated across shared storage arrays due to shortage of storage capacity and is usually dealt with separately (eg. Storage VMotion [VMWare vsphere ]) so as to reduce the delays involved. Since we deal with compute intensive applications in our experiments, we assume a single shared storage domain is large enough to support the applications and we do not account for delays involving data migrations. Since our experimental platform consists of multiple VMs with independent traffic patterns, initially we ran our experiments for the case where the traffic to each individual VM was a Poisson process. In order to test our proposed scheme with real world traces, we used the Soccer World Cup 98 [M. Arlitt and T. Jin ] traces for our evaluation. In this paper, we present only the results with the World Cup 98 trace. The trace dataset consists of all the requests made to the 1998 Soccer World Cup Web site during April to July, Since the trace data was collected from multiple physical servers, we had to process the traces before we could use them in our virtualized environment. We used traces collected on different days for different VMs. We scaled the arrival rates of queries to achieve the target utilization levels. We assume that the queries in the traces belonged to the three classes of applications as shown in Table I. The service times for queries for each application class is different and the energy consumed by a query is assumed to be proportional to its runtime. The time constant multipliers for discrete time control η 1 and η 2 in Section are assumed to be 4 and 7 respectively. Unless specified otherwise the ambient temperature of the nodes was assumed to be 25 C. Also the thermal limit of the servers and switches is assumed to be 70 C. The thermal constants in Equation 1 were determined to be c 1 =0.2 c 2 = from the experiments as described in [Kant et al. 2011]. We use these values for our simulations as well. We measure the utilization of a VM based on CPU time spent by the VM in servicing the requests. However the actual utilization may be higher. For instance the actual utilization of a task may be increased due to CPU stalls that are caused by memory contention from other tasks or context switches between multiple VMs. In our simulations we include a factor called the interference penalty for the utilization and power/energy calculations. Basically the idea is that when n tasks are running on a server, the utilization of each task is increased due to the interference from the other (n 1) tasks. Hence the actual utilization of an application is calculated as follows. U i = U i + α j U j, j {1,2,...n} {i} U node = min[1.0, n i=1 U i] where n is the total number of applications in the node U i is the utilization of i th application,i {1,2,...n}. U node is the actual utilization of the node.

15 Enhancing Data Center Sustainability Through Energy Adaptive Computing A:15 We then calculate the average power consumed in the node based on the actual average utilization of the node. We conducted a few experiments on a Dell machine running VMWare ESX server to determine the value of α. We varied the number of VMs and their utilization levels and compared the sum of their utilizations with the actual utilization reported by the ESX server. We then calculated the the value of α using simple first order linear regression. The value of α was found to be 0.01 Note that the workload that we used in our experiments was totally CPU bound. For other workloads that involve memory or network contention, the interference penalty might be higher Simulation Results In order to evaluate the performance of the integral controller we placed all 3 types of applications on a single node and the incoming traffic to each of the applications was assumed to be a Poisson process. Figure 6 shows the delays of queries normalized to their delay bounds at a utilization level of 60%. Any value of delay greater than 1 implies an SLA violation. A QoS ignorant scheduler simply serves queries as they arrive. This leads to increased delay violations for queries. For instance, when there are too many Type I queries that have a smaller delay bound waiting in the queue, a QoS aware scheduler will make Type III queries to wait a little longer in the queue since they can tolerate much longer delays. On the other hand a QoS ignorant scheduler continues to favor both Type I and Type III queries equally and hence leading to delay violations for Type I queries. It can be seen from Figure 6 that almost during the entire time, the feedback based integral controller successfully keeps the delays of all 3 types of queries below the bounds specified by their SLA requirements. Fig. 6. Time series of average delays of queries normalized to delay bounds with simple Round Robin and QoS Aware Scheduling We use the response time as a metric to quantify the impact of EAC in the presence of variations in energy. The response time includes the time that the queries wait in

16 A:16 K.Kant, M.Murugan et al. Fig. 7. Power Supply Profiles Used - Histogram of power supply values and the utilization levels supported the queue to be scheduled and the run time of the queries. We show that the response times are improved when adaptations to the energy variations are done in Willow. The significance of Willow is realized the most when the devices are operating at the edge - that is when the power budgets are just enough to meet the aggregate demand in the data center and there is no over provisioning. To demonstrate this, we tested the performance of Willow with two different cases of power budgets. Let P U be the power required to support the data center operations when the average utilization of the servers is U%. Figure 7 shows the histogram of the total available power budget values during the simulation of 350 minutes and the number of minutes for which the particular power budget value was available. The first case (Case 1) is when the total available power budget varies between P 100 and P 60. The second case (Case 2) is when the total power budget varies between t P 80 and P 50. (a) (b) Fig. 8. % of queries with delay violations when total power budget varies around the power supply value required to support (a) 80% utilization (b) 100% utilization Figure 8 (a) compares the percentage of queries with delay violations in Case1when the QoS aware scheduler alone is used and when the scheduler is used in combination with Willow. We see that at low utilizations the performance of Willow is not significantly better than when the QoS aware scheduler alone is used. However at high

17 Enhancing Data Center Sustainability Through Energy Adaptive Computing A:17 Fig. 9. Average delay values normalized to delay bounds utilizations Willow performs better than the case when the QoS aware scheduler alone is used since there are no adaptation related migrations. Figure 8 (b) shows the percentage of queries with delay violations in Case 2. It can be seen that the benefits of Willow are very significant in Case 2 as compared to Case 1, especially at moderate to high utilization levels. Figure 8 shows that an efficient QoS aware scheduler alone cannot do any good in the presence of energy variations. Willow improves the possibility of meeting the QoS requirements significantly with the help of systematic migrations. Figure 9 shows the average delay of different application types normalized to their delay bounds at different utilization levels for power profiles as in Case 2. As expected the average delays increase with increase in utilization due to increased wait times. It can be seen that at higher utilizations, the average normalized delay is higher for Type I queries. The reason is two fold. Firstly, according to the SLA requirements of Type I queries that we assume in Table I, they cannot be migrated. This reduces the flexibility for these applications when the available power budget in the server becomes very low. Secondly, the absolute values of the delay bound is much smaller than the other two types and hence the average delay when normalized to the delay bound is naturally higher. Since the delay requirements of Type II and Type III are almost the same, they are equally impacted. It can be seen that even at low utilizations there are queries that have delay violations. This is because when the available energy drops, the demand side adaptations are done only after Dl in Willow. During that time if the estimated demand is less than the actual demand some queries have to wait longer to be scheduled. Moreover the utilization levels are just an average (calculated at a much higher granularity) and there can be periods of congestion even at those low utilization levels. This is especially handled very poorly when there are no migrations. Figures 10 and 11 demonstrate the thermal adaptability of Willow. We set the ambient temperatures of servers 1 4 at 45 C and the other servers at 25 C. In order to enforce power budgets we reduce the available CPU shares proportional to their allocated power budgets. Figure 10 shows the average delays in the servers when the first four servers are at a higher temperature than others. To avoid clutter we show the average response times only for the first 9 servers. As expected in all servers the response time increases with increase in utilization. However at low to moderately high utilization levels ( 60%) it can be seen that the average response time is lower in high temperature severs. This is because Willow is able to migrate most of the load

18 A:18 K.Kant, M.Murugan et al. Fig. 10. Avg. delay values for Type I queries when servers 1 4 are at a higher ambient temperature away from high temperature servers. Hence the waiting time for queries is reduced which in turn improves the response times in the high temperature servers. (a) (b) Fig. 11. % of queries with delay violations when servers 1 4 are at high temperature at (a) 60% utilization (b) 80% utilization Figure 11 (a) shows the percentage of queries with delay violations when the ambient temperature of servers 1 4 is 45 C at 60% utilization. As explained before, at a moderately high utilization level(60%), Willow migrates applications away from high temperature servers and hence they run at lower utilizations. This in turn reduces the number of queries with delay violations. However at high utilization (80%) the high temperature servers have no choice but to increase the delay of queries because no migrations can be done. This is shown in Figure 11 (b). 8. CONCLUSION AND FUTURE WORK In this paper we have discussed the challenges involved in adapting to the energy and thermal profiles in a data center. EAC puts power/thermal controls at the heart of distributed computing. We discussed how EAC can make IT more sustainable and elaborated on three different types of EAC scenarios. We have presented the design of Willow, a simple control scheme for energy and thermal adaptive computing. A major goal of this work is to inspire right-sizing the otherwise over designed infrastructure in terms of power and ensure the possibility of addressing the ensuing challenges via smarter control. In this work, we have tested the performance of Willow in the case of transactional workloads in simulations for specificity. In our experiments we used applications that

20 A:20 K.Kant, M.Murugan et al. KANT, K., MURUGAN, M., AND H.C.DU, D Willow: A Control System for Energy and Thermal Adaptive Computing. In Proceedings of 25th IEEE International Parallel & Distributed Processing Symposium, IPDPS 11. KELENYI, I. AND NURMINEN, J Energy Aspects of Peer Cooperation Measurements with a Mobile DHT System. In Communications Workshops, ICC Workshops 08. IEEE International Conference on KELÉNYI, I. AND NURMINEN, J. K Bursty content sharing mechanism for energy-limited mobile devices. In Proceedings of the 4th ACM workshop on Performance monitoring and measurement of heterogeneous wireless and wired networks. PM2HW2N 09. K.KANT Challenges in Distributed Energy Adaptive Computing. In Proceedings of ACM HotMetrics. KRISHNAN, B., AMUR, H., GAVRILOVSKA, A., AND SCHWAN, K VM power metering: feasibility and challenges. SIGMETRICS Perform. Eval. Rev. 38, M. ARLITT AND T. JIN World Cup Web Site Access Logs. MOORE, J., CHASE, J., RANGANATHAN, P., AND SHARMA, R Making scheduling cool : temperatureaware workload placement in data centers. In ATEC 05: Proceedings of the annual conference on USENIX Annual Technical Conference NAGARAJAN, A. B., MUELLER, F., ENGELMANN, C., AND SCOTT, S. L Proactive fault tolerance for hpc with xen virtualization. In Proceedings of the 21st annual international conference on Supercomputing. ICS NATHUJI, R. AND SCHWAN, K VirtualPower: Coordinated power management in virtualized enterprise systems. In SOSP 07: Proceedings of twenty-first ACM SIGOPS symposium on Operating systems principles NEDEVSCHI, S., POPA, L., IANNACCONE, G., RATNASAMY, S., AND WETHERALL, D Reducing network energy consumption via sleeping and rate-adaptation. In NSDI 08: Proceedings of the 5th USENIX Symposium on Networked Systems Design and Implementation. PARIKH, D., SKADRON, K., ZHANG, Y., AND STAN, M Power-Aware Branch Prediction: Characterization and Design. IEEE Trans. Comput. 53, 2, PETRUCCI, V., LOQUES, O., AND MOSSÉ, D A framework for dynamic adaptation of power-aware server clusters. In Proceedings of the 2009 ACM symposium on Applied Computing. SAC 09. RAGHAVENDRA, R., RANGANATHAN, P., TALWAR, V., WANG, Z., AND ZHU, X No power struggles: coordinated multi-level power management for the data center. SIGARCH Comput. Archit. News 36. SHARMA, N., BARKER, S., IRWIN, D., AND SHENOY, P Blink: Managing server clusters on intermittent power. In Proceedings of the sixteenth international conference on Architectural support for programming languages and operating systems (ASPLOS). VENKATACHALAM, V. AND FRANZ, M Power reduction techniques for microprocessor systems. ACM Comput. Surv. 37, 3, VERMA, A., AHUJA, P., AND NEOGI, A Power-aware dynamic placement of HPC applications. In Proceedings of the 22nd annual international conference on Supercomputing. ICS 08. VMWARE VSPHERE. WANG, X. AND WANG, Y Coordinating Power Control and Performance Management for Virtualized Server Clusters. IEEE Transactions on Parallel and Distributed Systems 99. WANG, Y., MA, K., AND WANG, X Temperature-constrained power control for chip multiprocessors with online model estimation. SIGARCH Comput. Archit. News 37, 3, YANG, C. AND ORAILOGLU, A Power efficient branch prediction through early identification of branch addresses. In CASES 06: Proceedings of the 2006 international conference on Compilers, architecture and synthesis for embedded systems.

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