TOWARDS AN INTEGRATED APPROACH FOR RESOURCE-EFFICIENCY IN SERVER ROOMS AND DATA CENTERS

This paper conceptualizes a hybrid package of products and services which ensures the energy efficient planning, realization and operation of IT infrastructure. It follows the well-established procedure model of product-service systems engineering and pursues a qualitative research approach that is based on two industry workshops with more than 60 participants from science and practice and on semi-structured interviews with 8 experts. Based on the findings from the interviews and the workshops, customer requirements to the hybrid package are described, a product model is developed and evaluated. Finally, an application scenario is derived

Architecting Efficient Data Centers.

Data center power consumption has become a key constraint in continuing to scale Internet services. As our society’s reliance on “the Cloud” continues to grow, companies require an ever-increasing amount of computational capacity to support their customers. Massive warehouse-scale data centers have emerged, requiring 30MW or more of total power capacity. Over the lifetime of a typical high-scale data center, power-related costs make up 50% of the total cost of ownership (TCO). Furthermore, the aggregate effect of data center power consumption across the country cannot be ignored. In total, data center energy usage has reached approximately 2% of aggregate consumption in the United States and continues to grow. This thesis addresses the need to increase computational efficiency to address this grow- ing problem. It proposes a new classes of power management techniques: coordinated full-system idle low-power modes to increase the energy proportionality of modern servers. First, we introduce the PowerNap server architecture, a coordinated full-system idle low- power mode which transitions in and out of an ultra-low power nap state to save power during brief idle periods. While effective for uniprocessor systems, PowerNap relies on full-system idleness and we show that such idleness disappears as the number of cores per processor continues to increase. We expose this problem in a case study of Google Web search in which we demonstrate that coordinated full-system active power modes are necessary to reach energy proportionality and that PowerNap is ineffective because of a lack of idleness. To recover full-system idleness, we introduce DreamWeaver, architectural support for deep sleep. DreamWeaver allows a server to exchange latency for full-system idleness, allowing PowerNap-enabled servers to be effective and provides a better latency- power savings tradeoff than existing approaches. Finally, this thesis investigates workloads which achieve efficiency through methodical cluster provisioning techniques. Using the popular memcached workload, this thesis provides examples of provisioning clusters for cost-efficiency given latency, throughput, and data set size targets.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91499/1/meisner_1.pd

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

Rubik: fast analytical power management for latency-critical systems

Latency-critical workloads (e.g., web search), common in datacenters, require stable tail (e.g., 95th percentile) latencies of a few milliseconds. Servers running these workloads are kept lightly loaded to meet these stringent latency targets. This low utilization wastes billions of dollars in energy and equipment annually. Applying dynamic power management to latency-critical workloads is challenging. The fundamental issue is coping with their inherent short-term variability: requests arrive at unpredictable times and have variable lengths. Without knowledge of the future, prior techniques either adapt slowly and conservatively or rely on application-specific heuristics to maintain tail latency. We propose Rubik, a fine-grain DVFS scheme for latency-critical workloads. Rubik copes with variability through a novel, general, and efficient statistical performance model. This model allows Rubik to adjust frequencies at sub-millisecond granularity to save power while meeting the target tail latency. Rubik saves up to 66% of core power, widely outperforms prior techniques, and requires no application-specific tuning. Beyond saving core power, Rubik robustly adapts to sudden changes in load and system performance. We use this capability to design RubikColoc, a colocation scheme that uses Rubik to allow batch and latency-critical work to share hardware resources more aggressively than prior techniques. RubikColoc reduces datacenter power by up to 31% while using 41% fewer servers than a datacenter that segregates latency-critical and batch work, and achieves 100% core utilization.National Science Foundation (U.S.) (Grant CCF-1318384

Energy Proportionality and Workload Consolidation for Latency-Critical Applications

Energy proportionality and workload consolidation are important objectives towards increasing efficiency in large-scale datacenters. Our work focuses on achieving these goals in the presence of applications with microsecond-scale tail latency requirements. Such applications represent a growing subset of datacenter workloads and are typically deployed on dedicated servers, which is the simplest way to ensure low tail latency across all loads. Unfortunately, it also leads to low energy efficiency and low resource utilization during the frequent periods of medium or low load. We present the OS mechanisms and dynamic control needed to adjust core allocation and voltage/frequency settings based on the measured delays for latency-critical workloads. This allows for energy proportionality and frees the maximum amount of resources per server for other background applications, while respecting service-level objectives. The two key mechanism allow us to detect increases in queuing latencies and to re-assign flow groups between the threads of a latency-critical application in milliseconds without dropping or reordering packets. We compare the efficiency of our solution to the Pareto-optimal frontier of 224 distinct static configurations. Dynamic resource control saves 44%–54% of processor energy, which corresponds to 85%–93% of the Pareto-optimal upper bound. Dynamic resource control also allows background jobs to run at 32%–46% of their standalone throughput, which corresponds to 82%–92% of the Pareto bound

Bridging the gap between dataplanes and commodity operating systems

The conventional wisdom is that aggressive networking requirements, such as high packet rates for small messages and microsecond-scale tail latency, are best addressed outside the kernel, in a user-level networking stack. In particular, dataplanes borrow design elements from network middleboxes to run tasks to completion in tight loops. In its basic form, the dataplane design leverages sweeping simplifications such as the elimination of any resource management and any task scheduling to improve throughput and lower latency. As a result, dataplanes perform best when the request rate is predictable (since there is no resource management) and the service time of each task has a low execution time and a low dispersion. On the other hand, they exhibit poor energy proportionality and workload consolidation, and suffer from head-of-line blocking. This thesis proposes the introduction of resource management to dataplanes. Current dataplanes decrease latency by constantly polling for incoming network packets. This approach trades energy usage for latency. We argue that it is possible to introduce a control plane, which manages the resources in the most optimal way in terms of power usage without affecting the performance of the dataplane. Additionally, this thesis proposes the introduction of scheduling to dataplanes. Current designs operate in a strict FIFO and run-to-completion manner. This method is effective only when the incoming request requires a minimal amount of processing in the order of a few microseconds. When the processing time of requests is (a) longer or (b) follows a distribution with higher dispersion, the transient load imbalances and head-of-line blocking deteriorate the performance of the dataplane. We claim that it is possible to introduce a scheduler to dataplanes, which routes requests to the appropriate core and effectively reduce the tail latency of the system while at the same time support a wider range of workloads