2/8/2018 Day 9 Optimization of Cloud Data Centre Energy Consumption https://www.ncbi.nlm.nih.gov/pmc/articles/P MC4446568/ Agenda for Today • Why energy is an issue in Cloud data center? • How is energy consumed in Cloud data center ? • What are the various methods used for optimizing energy consumption? • How do we formulate energy optimization problem statement? • Modeling the Cloud data center energy consumption • What are some open research problems with respect to energy optimization? • Practical work in energy optimization with CloudSim. Introduction • Traditionally performance have been the main interest in system design and development • With energy price souring and environmental concerns, energy consumption management has become an important issue in various domains. – Cloud data center – IoT devices (e.g., portable medical devices) – Embedded systems - Mobile and portable devices (e.g., digital camcorders, mobile phones), laptops – Sensor network applications • This session discusses some of the energy consumption management techniques 3 1
2/8/2018 Motivation • Why energy efficiency become such a significant problem? • Economic issues – Energy consumption, as illustrated by the estimated average power use across three classes of servers, is continually increasing year after year – Today, 50 cents are spent on energy for every dollar of hardware This is expected to increase by 54% over the next four years – The ever increasing energy consumption of computing systems has started to limit further performance growth due to overwhelming electricity bills Motivation • Impact to end user – Energy impacts end users in terms of resource usage costs. – Higher power consumption results in • Increased electricity bills, which cuts the revenue of the service providers • additional requirements to a cooling system and power delivery infrastructure (i.e. Uninterruptible Power Supplies (UPS), Power Distribution Units (PDU), etc. ) • Environmental impact – The rising concern of the environmental impact in terms of carbon dioxide (CO2) emissions caused by high energy consumption. • Therefore, the reduction of power and energy consumption has become a first-order objective in the design of modern computing systems. Energy Usage in Data center • How is energy typically used in the data center? IT Load 55% 45% Power and Cooling 2
2/8/2018 Energy consumption at different levels in computing systems. • What is the difference between power and energy? – Power is the rate at which the system performs the work, – Energy is the total amount of work performed over a period of time. 𝑄 = 𝑋 𝑈 , 𝐹 = 𝑄𝑈 – P is power, T is a period of time, W is the total work performed in that period of time. • Note that reduction of the power consumption does not always reduce the consumed energy. Implications of Lowering Power • Running a task at a slower speed saves power Energy Energy ¼ energy savings with the Task running double execution time frequency ‘ f ’ Task running ‘½ f ’ frequency Execution time Execution time • The problem is that it will lake longer to finish the task thus affecting the performance • What if we could reduce the energy used with minimal performance impact? 3
2/8/2018 Dynamic Power Management • Dynamic Power Management (DPM) is a design methodology for energy and power management of dynamically reconfiguring systems. • The goal for a DPM system is to provide the requested services and performance with a minimum power consumption. • An example of DPM is the ‘Dynamic Voltage and Frequency Scaling (DVFS).’ Dynamic voltage and frequency scaling • The power consumption is mainly governed by the following equation: 𝑄 = 𝐷𝑊 � 𝐺 – P is the power, – C is the switching capacitance, – V is the supplied voltage and – F is the working frequency. • From the equation, it is clearly evident that by simply adjusting voltage–frequency pairs, it is possible to control the amount of consumed power Dynamic voltage and frequency scaling • The main idea of this technique is to – down-scale the voltage and frequency of CPU when it is not fully utilized – In ideal case, this is expected to result in cubic reduction of the dynamic power consumption. • Question – How can we determine the suitable voltage- frequency setting? 4
2/8/2018 Dynamic voltage and frequency scaling • Although DVFS can provide substantial energy savings, real-world systems raise many complexities that have to be considered. – The complex architectures of modern CPUs (i.e. pipelining, multi-level cache, etc.) make it difficult the prediction of the required CPU clock frequency that will meet application’s performance requirements. – Power consumption by a CPU may not be quadratic to its supply voltage. For example, if the program is memory or I/O bounded, CPU speed will not have a dramatic effect on the execution time. – Furthermore, slowing down the CPU may lead to changes in the order in which tasks are scheduled. Virtualization • Another technology that can improve the utilization of resources, and thus reduce the power consumption is virtualization of computer resources. • Virtualization technology allows one to create several Virtual Machines (VMs) on a physical server and, therefore, reduce the amount of hardware in use and improve the utilization of resources. Energy-Aware Consolidation for Cloud Computing • The power management problem becomes more complicated when considered from the data center level. In this case the system is represented by a set of interconnected computing nodes that need to be managed as a single resource in order to minimize the energy consumption. • Common limitations of the most of the works are that no other system resource except for CPU are considered in the optimization 5
2/8/2018 Cloud Datacenter Energy Consumption Modeling • Energy consumption model plays an important role in Cloud datacenter energy management and control – It is essential for guiding energy-aware algorithms such as resource provisioning policies – mechanisms such as virtual machine migration policies. – Moreover, it affects the pricing mechanism which cloud service providers charge their customers. Energy Consumption Modeling • The most common approach used is the one built on the assumption that the power consumption by a server grows linearly with the growth of CPU utilization 𝑄 𝑣 = 𝑄 ���� + (𝑄 ���� − 𝑄 ���� ) ∙ 𝜈 – 𝑄 is the estimated power consumption, – 𝑄 ���� is the power consumption by an idle server, – 𝑄 ���� is the power consumed by the server when it is fully utilized, and – 𝜈 is current CPU utilization. • Issue with this mode – Zhang et al. [19] argue that the relationship between the energy consumption and the CPU utilization is not linear and instead it is a cubic. Modeling the Cloud Datacenter Energy Consumption • Modeling the Cloud data center energy consumption has received little attention – Existing approaches primarily focus on CPU and memory subsystems energy consumption, – Tend to be complicated as it collects too many events leading to high overheads – The disk and network subsystems have become major contributors of data center energjy consumption – Existing approaches do not consider application characteristics when modeling Cloud datacenters energy consumption. – As application imposes different resource requirements, considering application characteristics in the development of the model also becomes a primary concern. 6
2/8/2018 Cloud Datacenter Energy Consumption Modeling • We proposed an approach for Cloud datacenter energy consumption model – the proposed approach takes into account the energy consumption of the • processing unit, • memory, • disk • NIC (Network Interface Card) • the application characteristics. Zhou Zhou; Jemal H. Abawajy; Fangmin Li; Zhigang Hu; Morshed Chowdhury; Abdulhameed Alelaiwi; Keqin Li, Fine-grained Energy Consumption Model of Servers Based on Task Characteristics in Cloud Data Center, IEEE Access , Year: 2018, Volume: PP, Issue: 99 Cloud Datacenter Energy Consumption Modeling • Cloud datacenter energy consumption model framework Zhou Zhou; Jemal H. Abawajy; Fangmin Li; Zhigang Hu; Morshed Chowdhury; Abdulhameed Alelaiwi; Keqin Li, Fine-grained Energy Consumption Model of Servers Based on Task Characteristics in Cloud Data Center, IEEE Access , Year: 2018, Volume: PP, Issue: 99 Energy Consumption Modeling • The feature extraction step is responsible for collecting features of the resources and applications relevant to the energy consumption modeling. • This step can be performed by using either resource utilization based method or performance-monitor-counter (PMC) based models. We used the latter with some modification Zhou Zhou; Jemal H. Abawajy; Fangmin Li; Zhigang Hu; Morshed Chowdhury; Abdulhameed Alelaiwi; Keqin Li, Fine-grained Energy Consumption Model of Servers Based on Task Characteristics in Cloud Data Center, IEEE Access , Year: 2018, Volume: PP, Issue: 99 7
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