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CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms
, 2010
"... Cloud computing is a recent advancement wherein IT infrastructure and applications are provided as “services ” to endusers under a usage-based payment model. They can leverage virtualized services even on the fly based on requirements (workload patterns and QoS) varying with time. The application se ..."
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Cited by 199 (23 self)
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Cloud computing is a recent advancement wherein IT infrastructure and applications are provided as “services ” to endusers under a usage-based payment model. They can leverage virtualized services even on the fly based on requirements (workload patterns and QoS) varying with time. The application services hosted under Cloud computing model have complex provisioning, composition, configuration, and deployment requirements. Evaluating the performance of Cloud provisioning policies, application workload models, and resources performance models in a repeatable manner under varying system and user configurations and requirements is difficult to achieve. To overcome this challenge, we propose CloudSim: an extensible simulation toolkit that enables modeling and simulation of Cloud computing systems and application provisioning environments. The CloudSim toolkit supports both system and behaviour modeling of Cloud system components such as data centers, virtual machines (VMs) and resource provisioning policies. It implements generic application provisioning techniques that can be extended with ease and limited efforts. Currently, it supports modeling and simulation of Cloud computing environments consisting of both single and inter-networked clouds (federation of clouds). Moreover, it exposes custom interfaces for implementing policies and provisioning techniques for allocation of VMs under inter-networked Cloud computing scenarios. Several researchers from organisations such as HP Labs in USA are using CloudSim in their investigation on Cloud resource provisioning and energy-efficient management of data center resources.
Optimal Power Allocation in Server Farms
, 2009
"... Server farms today consume more than 1.5 % of the total electricity in the U.S. at a cost of nearly $4.5 billion. Given the rising cost of energy, many industries are now seeking solutions for how to best make use of their available power. An important question which arises in this context is how to ..."
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Cited by 102 (3 self)
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Server farms today consume more than 1.5 % of the total electricity in the U.S. at a cost of nearly $4.5 billion. Given the rising cost of energy, many industries are now seeking solutions for how to best make use of their available power. An important question which arises in this context is how to distribute available power among servers in a server farm so as to get maximum performance. By giving more power to a server, one can get higher server frequency (speed). Hence it is commonly believed that, for a given power budget, performance can be maximized by operating servers at their highest power levels. However, it is also conceivable that one might prefer to run servers at their lowest power levels, which allows more servers to be turned on for a given power budget. To fully understand the effect of power allocation on performance in a server farm with a fixed power budget, we introduce a queueing theoretic model, which allows us to predict the optimal power allocation in a variety of scenarios. Results are verified via extensive experiments on an IBM BladeCenter. We find that the optimal power allocation varies for different scenarios. In particular, it is not always optimal to run servers at their maximum power levels. There are scenarios where it might be optimal to run servers at their lowest power levels or at some intermediate power levels. Our analysis shows that the optimal power allocation is non-obvious and depends on many factors such as the power-to-frequency relationship in the processors, the arrival rate of jobs, the maximum server frequency, the lowest attainable server frequency and the server farm configuration. Furthermore, our theoretical model allows us to explore more general settings than we can implement, including arbitrarily large server farms and different power-to-frequency curves. Importantly, we show that the optimal power allocation can significantly
Minimizing electricity cost: Optimization of distributed internet data centers in a multi-electricity-market environment
- In Proc. of INFOCOM
, 2010
"... Abstract—The study of Cyber-Physical System (CPS) has been an active area of research. Internet Data Center (IDC) is an important emerging Cyber-Physical System. As the demand on Internet services drastically increases in recent years, the power used by IDCs has been skyrocketing. While most existin ..."
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Cited by 98 (9 self)
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Abstract—The study of Cyber-Physical System (CPS) has been an active area of research. Internet Data Center (IDC) is an important emerging Cyber-Physical System. As the demand on Internet services drastically increases in recent years, the power used by IDCs has been skyrocketing. While most existing research focuses on reducing power consumptions of IDCs, the power management problem for minimizing the total electricity cost has been overlooked. This is an important problem faced by service providers, especially in the current multi-electricity market, where the price of electricity may exhibit time and location diversities. Further, for these service providers, guaranteeing quality of service (i.e. service level objectives-SLO) such as service delay guarantees to the end users is of paramount importance. This paper studies the problem of minimizing the total electricity cost under multiple electricity markets environment while guaranteeing quality of service geared to the location diversity and time diversity of electricity price. We model the problem as a constrained mixed-integer programming and propose an efficient solution method. Extensive evaluations based on reallife electricity price data for multiple IDC locations illustrate the efficiency and efficacy of our approach. I.
GreenCloud: a new architecture for green data center.
- In Proceedings of the 6th International Conference on Autonomic Computing,
, 2009
"... ABSTRACT Nowadays, power consumption of data centers has huge impacts on environments. Researchers are seeking to find effective solutions to make data centers reduce power consumption while keep the desired quality of service or service level objectives. Virtual Machine (VM) technology has been wi ..."
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Cited by 71 (2 self)
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ABSTRACT Nowadays, power consumption of data centers has huge impacts on environments. Researchers are seeking to find effective solutions to make data centers reduce power consumption while keep the desired quality of service or service level objectives. Virtual Machine (VM) technology has been widely applied in data center environments due to its seminal features, including reliability, flexibility, and the ease of management. We present the GreenCloud architecture, which aims to reduce data center power consumption, while guarantee the performance from users' perspective. GreenCloud architecture enables comprehensive online-monitoring, live virtual machine migration, and VM placement optimization. To verify the efficiency and effectiveness of the proposed architecture, we take an online real-time game, Tremulous, as a VM application. Evaluation results show that we can save up to 27% of the energy when applying GreenCloud architecture.
Delivering Energy Proportionality with Non Energy-Proportional Systems – Optimizing the Ensemble
"... With power having become a critical issue in the operation of data centers today, there has been an increased push towards the vision of “energy-proportional computing”, in which no power is used by idle systems, very low power is used by lightly loaded systems, and proportionately higher power at h ..."
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Cited by 61 (0 self)
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With power having become a critical issue in the operation of data centers today, there has been an increased push towards the vision of “energy-proportional computing”, in which no power is used by idle systems, very low power is used by lightly loaded systems, and proportionately higher power at higher loads. Unfortunately, given the state of the art of today’s hardware, designing individual servers that exhibit this property remains an open challenge. However, even in the absence of redesigned hardware, we demonstrate how optimization-based techniques can be used to build systems with off-the-shelf hardware that, when viewed at the aggregate level, approximate the behavior of energy-proportional systems. This paper explores the viability and tradeoffs of optimization-based approaches using two different case studies. First, we show how different power-saving mechanisms can be combined to deliver an aggregate system that is proportional in its use of server power. Second, we show early results on delivering a proportional cooling system for these servers. When compared to the power consumed at 100 % utilization, results from our testbed show that optimization-based systems can reduce the power consumed at 0 % utilization to 15 % for server power and 32 % for cooling power. 1
A Taxonomy and Survey of Energy-Efficient Data Centers and Cloud Computing Systems
"... Traditionally, the development of computing systems has been focused on performance improvements driven by the demand of applications from consumer, scientific and business domains. However, the ever increasing energy consumption of computing systems has started to limit further performance growth d ..."
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Cited by 58 (4 self)
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Traditionally, the development of computing systems has been focused on performance improvements driven by the demand of applications from consumer, scientific and business domains. However, the ever increasing energy consumption of computing systems has started to limit further performance growth due to overwhelming electricity bills and carbon dioxide footprints. Therefore, the goal of the computer system design has been shifted to power and energy efficiency. To identify open challenges in the area and facilitate future advancements it is essential to synthesize and classify the research on power and energy-efficient design conducted to date. In this work we discuss causes and problems of high power / energy consumption, and present a taxonomy of energy-efficient design of computing systems covering the hardware, operating system, virtualization and data center levels. We survey various key works in the area and map them to our taxonomy to guide future design and development efforts. This chapter is concluded with a discussion of advancements identified in energy-efficient computing and our vision on future
Renewable and Cooling Aware Workload Management for Sustainable Data Centers ∗
"... The demand for data center computing increased significantly in recent years resulting in huge energy consumption. Data centers typically comprise three main subsystems: IT equipment provides services to customers; power infrastructure supports the IT and cooling equipment; and the cooling infrastru ..."
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Cited by 54 (2 self)
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The demand for data center computing increased significantly in recent years resulting in huge energy consumption. Data centers typically comprise three main subsystems: IT equipment provides services to customers; power infrastructure supports the IT and cooling equipment; and the cooling infrastructure removes the generated heat. This work presents a novel approach to model the energy flows in a data center and optimize its holistic operation. Traditionally, supply-side constraints such as energy or cooling availability were largely treated independently from IT workload management. This work reduces cost and environmental impact using a holistic approach that integrates energy supply, e.g., renewable supply and dynamic pricing, and cooling supply, e.g., chiller and outside air cooling, with IT workload planning to improve the overall attainability of data center operations. Specifically, we predict renewable energy as well as IT demand and design an IT workload management plan that schedules IT workload and allocates IT resources within a data center according to time varying power supply and cooling efficiency. We have implemented and evaluated our approach using traces from real data centers and production systems. The results demonstrate that our approach can reduce the recurring power costs and the use of non-renewable energy by as much as 60 % compared to existing, non-integrated techniques, while still meeting operational goals and Service Level Agreements. 1.
Benefits and Limitations of Tapping into Stored Energy For Datacenters
, 2011
"... Datacenter power consumption has a significant impact on both its recurring electricity bill (Op-ex) and one-time construction costs (Cap-ex). Existing work optimizing these costs has relied primarily on throttling devices or workload shaping, both with performance degrading implications. In this p ..."
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Cited by 53 (7 self)
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Datacenter power consumption has a significant impact on both its recurring electricity bill (Op-ex) and one-time construction costs (Cap-ex). Existing work optimizing these costs has relied primarily on throttling devices or workload shaping, both with performance degrading implications. In this paper, we present a novel knob of energy buffer (eBuff) available in the form of UPS batteries in datacenters for this cost optimization. Intuitively, eBuff stores energy in UPS batteries during “valleys”- periods of lower demand, which can be drained during “peaks ”- periods of higher demand. UPS batteries are normally used as a fail-over mechanism to transition to captive power sources upon utility failure. Furthermore, frequent discharges can cause UPS batteries to fail prematurely. We conduct detailed analysis of battery operation to figure out feasible operating regions given such battery lifetime and datacenter availability concerns. Using insights learned from this analysis, we develop peak reduction algorithms that combine the UPS battery knob with existing throttling based techniques for minimizing datacenter power costs. Using an experimental platform, we offer insights about Op-ex savings offered by eBuff for a wide range of workload peaks/valleys, UPS provisioning, and application SLA constraints. We find that eBuff can be used to realize 15-45 % peak power reduction, corresponding to 6-18 % savings in Op-ex across this spectrum. eBuff can also play a role in reducing Cap-ex costs by allowing tighter overbooking of power infrastructure components and we quantify the extent of such Cap-ex savings. To our knowledge, this is the first paper to exploit stored energy- typically lying untapped in the datacenter- to address the peak power draw problem.
Statistical Profiling-based Techniques for Effective Power Provisioning in Data Centers
"... Abstract: Current capacity planning practices based on heavy over-provisioning of power infrastructure hurt (i) the operational costs of data centers as well as (ii) the computational work they can support. We explore a combination of statistical multiplexing techniques to improve the utilization of ..."
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Cited by 50 (6 self)
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Abstract: Current capacity planning practices based on heavy over-provisioning of power infrastructure hurt (i) the operational costs of data centers as well as (ii) the computational work they can support. We explore a combination of statistical multiplexing techniques to improve the utilization of the power hierarchy within a data center. At the highest level of the power hierarchy, we employ controlled underprovisioning and over-booking of power needs of hosted workloads. At the lower levels, we introduce the novel notion of soft fuses to flexibly distribute provisioned power among hosted workloads based on their needs. Our techniques are built upon a measurement-driven profiling and prediction framework to characterize key statistical properties of the power needs of hosted workloads and their aggregates. We characterize the gains in terms of the amount of computational work (CPU cycles) per provisioned unit of power – Computation per Provisioned Watt (CPW). Our technique is able to double the CPW offered by a Power Distribution Unit (PDU) running the e-commerce benchmark TPC-W compared to conventional provisioning practices. Over-booking the PDU by 10 % based on tails of power profiles yields a further improvement of 20%. Reactive techniques implemented on our Xen VMM-based servers dynamically modulate CPU DVFS states to ensure power draw below the limits imposed by soft fuses. Finally, information captured in our profiles also provide ways of controlling application performance degradation despite overbooking. The 95 th percentile of TPC-W session response time only grew from 1.59 sec to 1.78 sec—a degradation of 12%.
Joint Optimization of Idle and Cooling Power in Data Centers While Maintaining Response Time
"... Server power and cooling power amount to a significant fraction of modern data centers ’ recurring costs. While data centers provision enough servers to guarantee response times under the maximum loading, data centers operate under much less loading most of the times (e.g., 30-70 % of the maximum lo ..."
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Cited by 48 (0 self)
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Server power and cooling power amount to a significant fraction of modern data centers ’ recurring costs. While data centers provision enough servers to guarantee response times under the maximum loading, data centers operate under much less loading most of the times (e.g., 30-70 % of the maximum loading). Previous serverpower proposals exploit this under-utilization to reduce the server idle power by keeping active only as many servers as necessary and putting the rest into low-power standby modes. However, these proposals incur higher cooling power due to hot spots created by concentrating the data center loading on fewer active servers, or degrade response times due to standby-to-active transition delays, or both. Other proposals optimize the cooling power but incur considerable idle power. To address the first issue of power, we propose PowerTrade, which trades-off idle power and cooling power for each other, thereby reducing the total power. To address the second issue of response time, we propose SurgeGuard to overprovision the number of active servers beyond that needed by the current loading so as to absorb future increases in the loading. SurgeGuard is a two-tier scheme which uses well-known over-provisioning at coarse time granularities (e.g., one hour) to absorb the common, smooth increases in the loading, and a novel fine-grain replenishment of the over-provisioned reserves at fine time granularities (e.g., five minutes) to handle the uncommon, abrupt loading surges. Using real-world traces, we show that combining Power-Trade and SurgeGuard reduces total power by 30 % compared to previous low-power schemes while maintaining response times within 1.7%.
