Dynamic Deferral of Workload for Capacity Provisioning in Data Centers

Recent increase in energy prices has led researchers to find better ways for capacity provisioning in data centers to reduce energy wastage due to the variation in workload. This paper explores the opportunity for cost saving utilizing the flexibility from the Service Level Agreements (SLAs) and proposes a novel approach for capacity provisioning under bounded latency requirements of the workload. We investigate how many servers to be kept active and how much workload to be delayed for energy saving while meeting every deadline. We present an offline LP formulation for capacity provisioning by dynamic deferral and give two online algorithms to determine the capacity of the data center and the assignment of workload to servers dynamically. We prove the feasibility of the online algorithms and show that their worst case performance are bounded by a constant factor with respect to the offline formulation. We validate our algorithms on a MapReduce workload by provisioning capacity on a Hadoop cluster and show that the algorithms actually perform much better in practice compared to the naive `follow the workload' provisioning, resulting in 20-40% cost-savings.Comment: 12 pages, 13 figures, 4 table

Planificación de transferencias masivas en entornos multi-PoP

Desde los comienzos de la computación, períodos de baja carga han sido aprovechados para programar tareas no interactivas. Hoy en día, entre estas tareas destaca la planificación de transferencias masivas —aquellas transferencias de gran volumen sin exigencias precisas en cuanto a su momento de realización, como pueden ser la distribución de bases de datos o la replicación de recursos o copias de seguridad—, debido a su efecto directo tanto en el rendimiento como en el coste de las redes. A través de una revisión e inspección visual de las curvas de demandas de tráfico de diversos puntos de presencia (PoP), ya sean una red, enlace, ISP o IXP, se hace evidente que los períodos de bajo uso de ancho de banda se producen a primera hora de la mañana, mostrando una apreciable forma convexa en ese momento. Tal observación nos ha llevado a estudiar y modelar el instante cuando tales demandas alcanzan su mínimo, en lo que hemos denominado momento valle como una aproximación al lapso ideal para realizar transferencias masivas. Después de estudiar y modelar escenarios de PoPs individuales buscando homogeneidad temporal y espacial en el fenómeno, así como su extensión a escenarios multi-PoP —metanodos construidos a partir de la agregación de varios PoPs—, se propone un sistema predictor para el momento valle. Esta herramienta funciona como un oráculo para la planificación de transferencias masivas, con diferentes versiones según las escalas de tiempo y el equilibrio deseado entre precisión y complejidad, y tiene en cuenta las diferentes zonas horarias de cada uno de los nodos; por lo tanto, está pensada para redes geodistribuidas. La evaluación del sistema, denominado VTO, ha demostrado su utilidad, con errores inferiores a una hora en la estimación de momentos valle, así como errores en torno al 10% en términos de ancho de banda entre la predicción y el tráfico del valle real. Asimismo, se ha calculado el impacto en el percentil 95 de uso de una red real de efectuar transferencias masivas con VTO, mostrando su mejora frente a un sistema de hora fija.Periods of light load have been employed for the scheduling of non-interactive tasks since the early stages of computing. Nowadays, among such tasks it stands out the scheduling of bulk transfers—i.e., large-volume transfers without a precise timing—such as database distribution, resources replication or backups, given its direct effect on both the performance and billing of networks. Through a review and visual inspection of traffic-demand curves of diverse points of presence (PoP), either a network, link, ISP or IXP, it becomes apparent that low-use periods of bandwidth demands occur at early morning, showing a noticeable convex shape. Such observation conducted us to study and model the time when such demands come to their minimum, on what we have named valley time of a PoP as an approximation to the ideal moment to carry out bulk transfers. After studying and modeling single-PoP scenarios both temporally and spatially seeking homogeneity in the phenomenon, as well as its extension to multi-PoP scenarios or paths—a meta-PoP constructed as the aggregation of several PoPs—, a final predictor system is proposed for the valley time. This tool works as an oracle for scheduling bulk transfers, with different versions according to time scales and the desired trade-off between precision and complexity, and takes into account different time zones; hence, it is intended to serve geodistributed networks. The evaluation of the system, named VTO, has proven its usefulness with errors below an hour on estimating the occurrence of valley times, as well as errors about 10% in terms of bandwidth between the prediction and the actual valley traffic. Likewise, the impact of effecting bulk transfers with VTO on the 95th percentile usage of a real network has been calculated, showing an improvement over a fixed-time system

Demand-Side Management for Energy-efficient Data Center Operations with Renewable Energy and Demand Response

In recent years, we have noticed tremendous increase of energy consumption and carbon pollution in the industrial sector, and many energy-intensive industries are striving to reduce energy cost and to have a positive impact on the environment. In this context, this dissertation is motivated by opportunities to reduce energy cost from demand-side perspective. Specifically, industries have an opportunity to reduce energy consumption by improving energy-efficiency in their system operations. By improving utilization of their resources, they can reduce waste of energy, and thus, they are able to prevent paying unnecessary energy cost. In addition, because of today‘s high penetration of renewable generation (e.g. wind or solar), many industries consider renewable energy as a promising solution to reduce energy cost and carbon pollution, and they have tried to utilize renewable energy to meet their power demand by installing on-site generation facilities (e.g. PV panels on roof top) or making a contract with renewable generation farms. Moreover, it is becoming common for energy markets to allow industries to directly purchase electricity from them while providing the industries with day-ahead and real-time electricity price information. In this situation, industries have an opportunity to adjust purchase and consumption of energy in response to time-varying electricity price and intermittent renewable generation to reduce their energy procurement cost, which are called demand response. Considering these opportunities, it is anticipated that the industrial sector can save a significant amount of energy cost, however, time-varying behavior, uncertainty and stochasticity in system operations, power demand, renewable energy, and electricity price make it challenging to determine optimal operational decision. Motivated by the aforementioned opportunities as well as challenges, this dissertation focuses on developing decision-making methodologies tailored for demand-side energy system operations to improve energy-efficiency based on energy-aware system operations and reduce energy procurement cost by utilizing renewable energy and demand response in an integrated fashion to optimally reduce energy cost. For practical application, this dissertation considers real-world practices in data centers including their operations management and power procurement for the following research tasks: (i) develop a server provisioning algorithm that dynamically adapts server operations in response to heterogeneous and time-varying workloads to reduce energy consumption while providing performance guarantees based on time-stability; (ii) propose stochastic optimization models for optimal energy procurement to determine purchase and consumption of energy based on day-ahead and real-time energy market operations considering utilization of renewable energy based on demand response; (iii) suggest a decision-making model that integrate the proposed server provisioning algorithm with energy procurement to achieve energy-efficiency in data center operations to reduce both energy consumption and energy cost against variability and uncertainty. In terms of methodologies, this study uses operations research techniques including deterministic and stochastic models, such as, queueing analysis, mixed-integer program, Markov decision process, two-stage stochastic program, and probabilistic constrained program. In conclusion, this dissertation claims that renewable energy, demand response, and energy storage are worth to be considered for data center operations to reduce energy consumption and procurement cost. Although variability and uncertainty in system operations, renewable generation, and electricity price make it challenging to determine optimal operational decisions, numerical results show that the proposed optimization problems can be efficiently solved by the developed algorithm. The proposed decision-making methodologies can also be extended to other industries, and thus, this dissertation study would be a good starting point to study demand-side management in energy system operations