DReAM: An approach to estimate per-Task DRAM energy in multicore systems

Accurate per-task energy estimation in multicore systems would allow performing per-task energy-aware task scheduling and energy-aware billing in data centers, among other applications. Per-task energy estimation is challenged by the interaction between tasks in shared resources, which impacts tasks’ energy consumption in uncontrolled ways. Some accurate mechanisms have been devised recently to estimate per-task energy consumed on-chip in multicores, but there is a lack of such mechanisms for DRAM memories. This article makes the case for accurate per-task DRAM energy metering in multicores, which opens new paths to energy/performance optimizations. In particular, the contributions of this article are (i) an ideal per-task energy metering model for DRAM memories; (ii) DReAM, an accurate yet low cost implementation of the ideal model (less than 5% accuracy error when 16 tasks share memory); and (iii) a comparison with standard methods (even distribution and access-count based) proving that DReAM is much more accurate than these other methods.Peer ReviewedPostprint (author's final draft

Per-task energy accounting in computing systems

We present for the first time the concept of per-task energy accounting (PTEA) and relate it to per-task energy metering (PTEM). We show the benefits of supporting both in future computing systems. Using the shared last-level cache (LLC) as an example: (1) We illustrate the complexities in providing PTEM and PTEA; (2) we present an idealized PTEM model and an accurate and low-cost implementation of it; and (3) we introduce a hardware mechanism to provide accurate PTEA in the cache.Postprint (published version

Review of the environmental and organisational implications of cloud computing: final report.

Cloud computing – where elastic computing resources are delivered over the Internet by external service providers – is generating significant interest within HE and FE. In the cloud computing business model, organisations or individuals contract with a cloud computing service provider on a pay-per-use basis to access data centres, application software or web services from any location. This provides an elasticity of provision which the customer can scale up or down to meet demand. This form of utility computing potentially opens up a new paradigm in the provision of IT to support administrative and educational functions within HE and FE. Further, the economies of scale and increasingly energy efficient data centre technologies which underpin cloud services means that cloud solutions may also have a positive impact on carbon footprints. In response to the growing interest in cloud computing within UK HE and FE, JISC commissioned the University of Strathclyde to undertake a Review of the Environmental and Organisational Implications of Cloud Computing in Higher and Further Education [19]

On the feasibility of collaborative green data center ecosystems

The increasing awareness of the impact of the IT sector on the environment, together with economic factors, have fueled many research efforts to reduce the energy expenditure of data centers. Recent work proposes to achieve additional energy savings by exploiting, in concert with customers, service workloads and to reduce data centers’ carbon footprints by adopting demand-response mechanisms between data centers and their energy providers. In this paper, we debate about the incentives that customers and data centers can have to adopt such measures and propose a new service type and pricing scheme that is economically attractive and technically realizable. Simulation results based on real measurements confirm that our scheme can achieve additional energy savings while preserving service performance and the interests of data centers and customers.Peer ReviewedPostprint (author's final draft

Per-task energy metering and accounting in the multicore era

Energy has become arguably the most expensive resource in a computing system. As multi-core processors are the preferred processing platform across different computing domains, measuring the energy usage draws vast attention. In this thesis, for the first time, we formalize the need for per-task energy measurement in multicore by establishing a two-fold concept: per-task energy metering and sensible energy accounting. The former, for a task running in a multi-core system, provides estimates on the actual energy consumption corresponding to its resource usage. The latter provides estimates on the energy the task would have consumed running in isolation with a given fraction of the shared resources. We have shown how these two concepts can be applied to the main components of a computing system: the processor and the memory system

Планирование загрузки ресурсов центра обработки данных на основе статистических данных

Якість обслуговування клієнтів залежить від процедури підтримки прикладних програм в центрі обробки даних постачальника зв'язку. У статті розглядається підхід контролю динамічного використання ресурсів для забезпечення обслуговування вхідного потоку, який враховує випадковий характер надходження заявок і використовує короткострокові і довгострокові статистичні навантаження. Запропонований підхід складається з двох методів, які керують кількістю обслуговуючих вузлів. Результати моделювання управління технічними ресурсами були представлені для інфраструктури ЦОД провайдера зв'язку, що доводить ефективність запропонованих методів.The customer service quality depends on the procedure of the application maintenance in data center of the communication provider. In the article the control approach of dynamic resource involvement has been suggested in order to ensure the input flow maintenance that takes into account the random nature of applications’ inflow and utilizes both short-term and long-term load statistics. The proposed approach consists of two methods that manage the number of the implicated serving nodes. The first one verifies the resource amount adequacy, provides the evaluation of input load’s dynamics based on the short-term statistics as well as the current state of the technical facilities. The second one accounts for the long-term statistics according to which the implication of additional resources can be scheduled during the load peaks. The simulation results of technical resources management have been presented for the data center infrastructure of the communication provider, that prove the effectiveness of the proposed methods.Качество обслуживания клиентов зависит от процедуры поддержки приложений в центре обработки данных поставщика связи. В статье рассматривается подход контроля динамического использования ресурсов для обеспечения обслуживания входящего потока, который принимает во внимание случайный характер поступления заявок и использует краткосрочные и долгосрочные статистические нагрузки. Предложенный подход состоит из двух методов, которые управляют количеством обслуживающих узлов. Результаты моделирования управления техническими ресурсами были представлены для инфраструктуры ЦОД провайдера связи, что доказывает эффективность предложенных методов