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A Study on Task Scheduling in Could Data Centers for Energy Efficacy
This document discusses energy efficiency in task scheduling for cloud data centers, highlighting the increasing electricity consumption and CO2 emissions attributed to their operations. It outlines various challenges and solutions, including dynamic voltage and frequency scaling (DVFS), task optimization strategies, and the need for new energy-aware algorithms amidst diverse system architectures. The study emphasizes the necessity for innovative energy management techniques, particularly in real-time and many-core systems, to achieve significant energy savings.
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A Study on Task Scheduling in Could Data Centers for Energy Efficacy
- 1. A Study onTask Scheduling in Could Data Centers for Energy Efficacy By: Ehsan Sharifi Esfahani Awday Korde Viktor Bakayov
- 2. Outline Motivations Possibleapproaches Different taxonomies Challenges Main reasons of high energy consumptions in this data centers Energy proportionally Server utilizations Solutions for the problem Aggregation DVFS Trend in Task Scheduling System Modeling to Design a New Task Scheduler Energy Modeling ECS Algorithm Future Possible Works Conclusions
- 3. Motivations Cost In2013, data centers in the US consumed electricity for about 91 billion kilowatt-hours, which can accommodate all the households in New York City for two years. Moreover, this figure will reach 140 billion kilowatt hours by 2020, or about $11 billion in electricity costs. Environmental Impact CO2 emission form Data center worldwide is estimated to increase from 80 Megatons in 2007 to 340 MT in 2020, more than double of CO2 emission in the Netherland (145 MT) Improve system performance and uses Negative impacts for density, reliability, and scalability of datacenter hardware.
- 4. Global Cloud DataCenter IP Traffic Trend • As the Cloud traffic is increasing, the concern for energy consumption is also rising
- 5. Which actions canbe taken to reduce energy consumption? Simply turning off unused devices. Reducing unnecessary traffic. Using energy-aware algorithms. Adjusting fan speed to server load and temperature. Using Nano Data centers !!! It is still under study. Using symbols and elements with more energy efficient (We named it modification element density). Our suggestion! Etc …
- 6. Different taxonomies forpossible approaches Power-aware Power-aware technologies either use low power energy-efficient hardware equipment, or reduce energy usage based on the knowledge of current resource utilization and application workloads. Thermal-aware (Explicitly have some other parameter inside) The thermal-aware approaches take the temperature in their energy model. The temperature depends on the power consumption of each processing element, dimension, and relative location on the embedded system platform. The goal of thermal-aware approaches is to minimize peak air inlet temperature resulting in minimization of the cost of cooling.
- 7. Single (Singleoptimization problem) The aims is only reduce energy consumption Composite (Multi optimization problem) Energy function deals with minimizing energy consumption and improving another parameter such as QoS or execution time of tasks. Our Suggestion Software-base approaches Dynamic power management (DPM) Energy efficient task scheduling Hardware-base approaches Energy efficient hardware Energy efficient topologies
- 8. Challenges Heterogeneous networkarchitectures. Trade-off between performance, scalability, availability, reliability and energy consumption. Designing energy-aware algorithms means moving to multi- optimization problems and thereby more complex algorithms and more energy consumption. Need significant changes in most applications and equipment and how to incorporate these new energy-aware protocols into real systems is still an open problem.
- 9. Approximate Distribution ofenergy Usage in Data Centers
- 10. Main reasons ofhigh energy consumption in servers Energy Proportionality (Inefficient trend in server energy usage) Utilization
- 11. Server utilization The figure is shown that the average CPU utilization of 5,000 Google servers during a 6-month period in 2007. A common trend is that, on average, servers spend relatively little aggregate time at high load levels. Instead, most of the time is spent within the 10–50% CPU utilization range.
- 12. Aggregation (Consolidation) 25% 29% 18% 35% 48%10% 78% 87% Aggregation
- 13. Dynamic Voltage andFrequency Scaling (DVFS) 𝑃𝐶𝑃𝑈 = 𝑃𝑠𝑡𝑎𝑡𝑖𝑐 + 𝑃 𝐷𝑦𝑛𝑎𝑚𝑖𝑐 𝑃 𝐷𝑦𝑛𝑎𝑚𝑖𝑐 = ACV2 f = αV2 f 𝐸 = 𝑃 𝑓 𝑓 = 𝑘. (𝑉 − 𝑉𝑡ℎ)2 𝑉𝑑𝑑 𝑃 ≈ 𝑉3 , 𝐸 ≈ 𝑉2 , 𝑇 ≈ 1 𝑓 ≈ 1 𝑉 C Capacity Load A A=Average number of switches and the circuit in time unit V Voltage f Working frequency
- 14. Dynamic Voltage andFrequency Scaling For example, If a program can run with frequency f in 10 seconds, then the energy consumption and power consumption during the period are P and E, respectively. Then if voltage get half then frequency get half too, and execution time become 20 second. However, energy consumption and power consumption would be as follows: 𝑃′ = α 𝑉 2 2 𝑓 2 = 1 8 α V2f = 1 8 𝑃 𝐸′ = 𝑃′ 𝑡′ = 1 8 . 𝑃. 20 = 5 2 . 𝐸 10 = 1 4 𝐸
- 15. Trend in TaskScheduling on Physical Machines (PMs) (Our suggestion) Single Machine, Single Core per processors Which tasks should be select? Single Machine, Multiple processors or core. Which task, on which processor or core? (Specially on heterogeneous environment) Single Machine, Multiple core and Multiple processors Which task, on which core and processor? Multiple machine, multiple processors, multiple cores Which task, Which machine, on which processors, on which cores. Multiple machine, multiple processors, multiple cores with DVFS-enables capability Which task, Which machine, on which processor and core, with which frequency. (Or which VM with which frequency, in many-core systems dark silicon property also should be taken into account).
- 16. Task Scheduling Static(Offline) Which is usually done before compile time, the characteristics of the program are known before execution. This method does not cause any overhead on the system during runtime. Dynamic (Online) Which characteristics ought to be determined before execution, then scheduling process have to be done during the course of scheduling according to the state of the system. This strategy is good for independent tasks.
- 17. System Modeling toDesign a New Task Scheduler Machines Model: Homogeneous, Heterogeneous Fully connected or not If processors are Multi-cores or many-cores If they are per core DVFS-enabled or per processors And etc. Application Model: If each service should run in serial or they are multi-task or multi-thread Arrival rate Duration is unknown or not And etc.
- 18. Virtual machinemodel How many VM is needed for each arrival service request? If each VM can run in its own specific range of frequency (𝑓 𝑚𝑖𝑛 𝑠 , 𝑓𝑚𝑎𝑥 𝑠 ) or all use the same protocol. How many cores will be occupied by each VM However, if 𝑉𝑀 𝑛,𝑠 𝑡 is the number of VM from type of s at t time is n then: ∀ 𝑥∈ 𝑉𝑀 𝑛,𝑠 𝑡 𝑥 ≤ 𝑇𝑜𝑡𝑎𝑙 𝑟𝑒𝑠𝑜𝑢𝑟𝑐𝑒𝑠 Working load model Non-periodic Independent Have Poisson process property Server selection model Goal (Performance, Time complexity, improving efficacy like energy consumption, or communication cost)
- 19. Energy Model (Firstmodel and most common) 𝑃𝑎𝑐𝑡𝑖𝑣𝑒 =∝ 𝑣𝑓2 𝑃𝑂𝑡ℎ𝑒ℎ𝑟 = 𝑃𝑚𝑎𝑥 − 𝑃 𝑚𝑖𝑛 ∗ 𝑢 + 𝑃 𝑚𝑖𝑛 𝑃𝑖 = 𝑃𝑂𝑡ℎ𝑒𝑟 + 𝑃𝑎𝑐𝑡𝑖𝑣𝑒 𝑇𝐸𝐶𝑡 = 𝑖=1 𝑘 𝑗=1 𝑛 (𝑃𝑗𝑖. 𝑥𝑗𝑖 + 𝑃𝑖𝑑𝑙𝑒,𝑗𝑖. (1 − 𝑥𝑗𝑖)). 𝑦𝑖 𝑃𝑖𝑑𝑙𝑒,𝑖𝑗 is the energy consumption of each processor when it is in idle or sleep mode.
- 20. Energy Model (Secondand Third Models) 𝑝 = 𝑛𝑒1 + 𝑚 (𝑒2 − 𝑒1) n=number of idle servers and 𝑒1 is the amount of energy consumed by them 𝑒2=amount of energy consumption by active servers 𝑚=average number of active servers Disadvantage= Not so accurate! Generic but accurate 0 𝑚𝑎𝑘𝑒𝑠𝑝𝑎𝑛 𝑝 𝑢 𝑡 𝑑𝑡 Where u(t) is the immediate PM utilization at the given time t and p(u(t)) is the power consumption associated with u(t)
- 21. Energy Conscious SchedulingAlgorithm (ECS) Firstly, all of tasks will be ordered according to 𝑏 − 𝑙𝑒𝑏𝑒𝑙𝑖 parameter 𝑏 − 𝑙𝑒𝑏𝑒𝑙𝑖 = 𝑚𝑎𝑥 𝑝𝑎𝑡ℎ 𝑚 𝜖𝑆𝑃𝑎𝑡ℎ𝑖 ( 𝑛 𝑗∈𝑝𝑎𝑡ℎ 𝑚 𝑢𝑗 𝑤𝑗 + 𝑒 𝑘∈𝑝𝑎𝑡ℎ 𝑚 𝑒 𝑘) Then, schedule the tasks one by one from the beginning of the queue and assign them to processors that have more RS(Relative Superiority). It shows which processor with which frequency is consumed less energy.
- 22. A Comparison BetweenSeveral Suggestions
- 23. Conclusion and Futurepossible works Task scheduling is a NP-complete problem. Most of proposed algorithms are dedicated for offline environment which DAG graph and the other parameters should already determined. In this environment, heuristic method is a common way to solve this problem. So, How about unknown systems with unknown DAG or unknown parameters? Our solution can be using MDP and Machine Learning techniques. Many standard energy efficiency techniques do not work for cloud computing environments. This is due to the stratification of the cloud computing infrastructure into a widespread groups.
- 24. Conclusion and Futurepossible works None of the previous works have clearly addressed the energy efficient resource management problem from application engineering perspective. Rarely research have been done with more accurate energy model. There is a big opportunity to research in real-time systems. Most of algorithms can not be applied to many-core systems, so we need to design new ones for this environments. Only about one streaking research as a Ph.D thesis in 2015 has been done to improve energy consumption in whole cloud environments so far!
- 25. Thank you foryour attention Any Questions?