Energy Efficient in Cloud Computing
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1 Energy Efficient in Cloud Computing Christoph Aschberger Franziska Halbrainer May 24, of 25
2 Introduction Energy consumption by Google 2011: 2,675,898 MWh. We found that we use roughly as much electricity globally as 220,000 people, based on electricity use per capita in the US. An aisle in a Google serverfarm 2 of 25
3 Modelling of Energy Consumption Identify the components that consume energy How much energy they consume on different workloads The energy consumption of a cloud over a time span can be calculated as shown in [8]: E Cloud = t2 t1 E Nodes + E Switches + E Storages + E Others dt (1) E Node = E CPU + E Memory + E Disk + E Mainboard + E NIC (2) E Switch = E Chassis + E Linecards + E Ports (3) E Storage = E NASServer + E StorageController + E DiskArray (4) 3 of 25
4 How to save energy Use energy efficient hardware No energy needed if a machine is not running Fewer machines running less energy usage Always run only as many machines as currently needed? Algorithms needed to decide which machines should run 4 of 25
5 Cloud Model Task vs VM Task: Many requirements are known in advance (CPU, duration) Will not suddenly stop Will not suddenly need more VM: Only upper bounds are known Can need less resources than agreed on Resource usage might change suddenly Lifetime is not known Can stop suddenly or run indefinitely Can migrate from one machine to another 5 of 25
6 Dynamic Voltage Frequency Scaling [6] Using a near-optimal list-scheduling heuristic for mapping interdependent tasks to resources The energy usage E of a task E = k v 2 f t Where k is a device dependant constant, v is the voltage, f is the frequency and t is the execution time. Lower voltage means lower max frequency Lower voltage means less energy consumption Earliest start time: time when all predecessors are finished. Latest finish time: minimum of latest start time of all successors. Slack time: difference between this timespan and the execution time at highest frequency. Slack time can be used to slow the computation down and save energy. 6 of 25
7 Dynamic Voltage Frequency Scaling [6] In a loop start with the task with the latest latest finish time Find set S: tasks on the same processor with overlapping execution times T exec(s): sum of all execution times of tasks in this set T total (S): earliest earliest start time - latest latest finish time f global (task) = f max max( T exec(task) Texec (S), ) T exec(task) +T slack(task) T total (S) scheduling task t 3 on r 0 [7] 7 of 25
8 Task consolidation [7] Not running one task alone, but more tasks at once on the same machine Two different heuristics are suggested for consolidation Algorithm 1 Find machine to run task t j on Input: A task t j and a set R of r cloud resources Output: A task resource match Let r = 2: for r i ɛr do Compute the cost function value f i,j of t j on r i 4: if f i,j > f,j then Let r = r i 6: Let f,j = f i,j end if 8: end for Assign t j on r 8 of 25
9 Task consolidation [7] ECTC (Energy-conscious task consolidation) f i,j = p min (τ 0 τ 1 ) τ 0 :total running time of task τ 1 : time at which task runs alone on machine p min : energy consumption of idle machine Worst case: τ 0 = τ 1 than f i,j = 0 Best case: τ 1 = 0 scheduling task t 3 on r 1 [7] 9 of 25
10 Task consolidation [7] MaxUtil f i,j = τ0 τ=1 U i τ 0 U i is Utilisation of a resource r i at a given time Best case: when utilisation of period is max Worst case: when there are no other task running on this machine scheduling task t 3 on r 0 [7] 10 of 25
11 VM migration Possibility: a VM does not need the agreed performance Chance: allocate VMs on PMs according to used performance Migrate: VM needs more than available on its PM move to other PM (Live) migration however comes with costs: Performance of a VM during migration may be degraded Additional energy is needed as the VM is allocated on two PMs The network load is increased Trade off: Using as few PMs as possible and risking migrating often Minimizing the number of migrations at the cost of powering more PMs. 11 of 25
12 VM migration [3] VM selection for migration With single fixed Threshold Define upper utilization threshold for hosts Place VMs on host as long as the threshold is not violated Reserve resources to avoid immediate migration if demand increases With upper and lower Threshold Keep the utilization between thresholds by migrating Three different VM selection policies Minimization of Migrations - migrate least number of VMs Highest Potential Growth - migrate VMs with lowest usage of CPU relatively to requested Random Choice - choose necessary number of VMs randomly 12 of 25
13 VM migration [3] Dynamic Utilization Thresholds Assume utilization created by VM is a random variable u j CPU utilization of a host represented as U i as sum of utilizations Assume distributions of VMs are different Distribution of the host s utilization is normal and can be modeled by the t-distribution With the inverse cumulative probability function an interval of utilization which will be reached with low probability can be determined With this information the upper threshold can be set The lower threshold is a global value across all hosts It aims to identify the hosts with lower utilization than the average 13 of 25
14 VM migration [3] VM Placement Modified Best Fit Decreasing to consider power usage Sort VMs by decreasing order of CPU utilization Allocate a VM to the host with the least increase of power consumption This maximizes the utilization of power efficient hosts 14 of 25
15 VM migration [4] Knowledgebase: Knows all about VMs, PMs, Current memory/cpu utilization, agreed SLAs Makes decision upon rules Every resource has 2 Thresholds Span between Threshold must be big enough to prevent oscilating behavior 15 of 25
16 VM migration [4] Alloc / Realloc Algorithm First Fit: Realloc half of most loaded and all of least loaded Monte Carlo: Alloc in Round Robin Realloc calculates cost for current alloc and for random sets Vector Packing: Sorts VMs based on resource utilization, highest first Takes a VM from the list and tries to put it on the most energy efficient machine Realloc: load balances some VMs on most loaded PMs that will not be empty in future iterations PM Power Management Power off: Number of PMs to switch off = NumberofemptyPMs a Turns off PMs in an exponential manner Power on: if average utilization of a resource exceeds its threshold power on as many PMs as necessary 16 of 25
17 Decentralized VM migration [9] A homogeneous hardware basis is assumed Each host is capable of holding the same amount of same-sized virtual machines Protocol can easily be extended to handle heterogeneous hardware and VMs Gossip protocol Two threads running on each physical machine The active thread The passive thread 17 of 25
18 Decentralized VM migration [9] Algorithm 2 Procedure ACTIVE THREAD loop 2: Wait for all j ɛ GetNeighbors(i) do 4: Send (H i ) to j Receive (H i ) from j 6: H i H i end for 8: end loop 18 of 25
19 Decentralized VM migration [9] Algorithm 3 Procedure PASSIVE THREAD loop 2: Wait for message H j from j if H i H j then 4: D min(h j, C H i ) Send (H j D) to j 6: H i H i + D else 8: D min(h i, C H j ) Send (H j + D) to j 10: H i H i D end if 12: end loop 19 of 25
20 Artificial Intelligence Approach [5] MultiDimensional Bin Packing Problem Goal: to use as few machines as possible Ant colony as model to optimize workload placement Ants communicate through pheromones Pheromone is emitted by an ant Pheromone evaporates after some time Shortest path to food source has highest concentration of pheromones Ants tend to prefer paths with higher concentration Technical implementation: Artificial ants act as multi-agent system Construct a complex solution based on indirect low-level communication 20 of 25
21 Artificial Intelligence Approach [5] Ant knows about all running VMs and all PMs. Ant allocates a VM on a PM based on a probabilistic decision rule Based on current pheromone level Heuristic information Pheromone level is calculated Decreasing the old pheromone level by a factor Adding pheromones from the best ant in this round Pheromone concentration must lay between thresholds to prevent early stagnation Termination: restricting the algorithm to a fixed amount of iterations Output is chosen to be the global best solution so far 21 of 25
22 Conclusion There is no standard test set all authors used Some are more detailed than others In the presented papers only node power usage is considered 22 of 25
23 References (1) [1] [2] [3] Anton Beloglazov and Rajkumar Buyya. Adaptive threshold-based approach for energy-efficient consolidation of virtual machines in cloud data centers. In Proceedings of the 8th International Workshop on Middleware for Grids, Clouds and e-science, MGC 10, pages 4:1 4:6, New York, NY, USA, ACM. [4] Damien Borgetto, Michael Maurer, Georges Da-Costa, Jean-Marc Pierson, and Ivona Brandic. Energy-efficient and SLA-aware management of IaaS clouds. In Proceedings of the 3rd International Conference on Future Energy Systems: Where Energy, Computing and Communication Meet, e-energy 12, pages 25:1 25:10, New York, NY, USA, ACM. 23 of 25
24 References (2) [5] Eugen Feller, Louis Rilling, and Christine Morin. Energy-Aware Ant Colony Based Workload Placement in Clouds. In Proceedings of the 2011 IEEE/ACM 12th International Conference on Grid Computing, GRID 11, pages 26 33, Washington, DC, USA, IEEE Computer Society. [6] Qingjia Huang, Sen Su, Jian Li, Peng Xu, Kai Shuang, and Xiao Huang. Enhanced Energy-Efficient Scheduling for Parallel Applications in Cloud. In Proceedings of the th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012), CCGRID 12, pages , Washington, DC, USA, IEEE Computer Society. [7] YoungChoon Lee and AlbertY. Zomaya. Energy efficient utilization of resources in cloud computing systems. The Journal of Supercomputing, 60(2): of 25
25 References (3) [8] Liang Luo, Wenjun Wu, Dichen Di, Fei Zhang, Yizhou Yan, and Yaokuan Mao. A resource scheduling algorithm of cloud computing based on energy efficient optimization methods. In Green Computing Conference (IGCC), 2012 International, pages 1 6, [9] M. Marzolla, O. Babaoglu, and F. Panzieri. Server consolidation in Clouds through gossiping. In World of Wireless, Mobile and Multimedia Networks (WoWMoM), 2011 IEEE International Symposium on a, pages 1 6, of 25
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