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219
Dremel: Interactive Analysis of Web-Scale Datasets
"... Dremel is a scalable, interactive ad-hoc query system for analysis of read-only nested data. By combining multi-level execution trees and columnar data layout, it is capable of running aggregation queries over trillion-row tables in seconds. The system scales to thousands of CPUs and petabytes of da ..."
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Cited by 140 (1 self)
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Dremel is a scalable, interactive ad-hoc query system for analysis of read-only nested data. By combining multi-level execution trees and columnar data layout, it is capable of running aggregation queries over trillion-row tables in seconds. The system scales to thousands of CPUs and petabytes of data, and has thousands of users at Google. In this paper, we describe the architecture and implementation of Dremel, and explain how it complements MapReduce-based computing. We present a novel columnar storage representation for nested records and discuss experiments on few-thousand node instances of the system. 1.
Megastore: Providing Scalable, Highly Available Storage for Interactive Services
- CONFERENCE ON INNOVATIVE DATABASE RESEARCH (CIDR) 2011
, 2011
"... Megastore is a storage system developed to meet the requirements of today’s interactive online services. Megastore blends the scalability of a NoSQL datastore with the convenience of a traditional RDBMS in a novel way, and provides both strong consistency guarantees and high availability. We provide ..."
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Cited by 121 (0 self)
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Megastore is a storage system developed to meet the requirements of today’s interactive online services. Megastore blends the scalability of a NoSQL datastore with the convenience of a traditional RDBMS in a novel way, and provides both strong consistency guarantees and high availability. We provide fully serializable ACID semantics within fine-grained partitions of data. This partitioning allows us to synchronously replicate each write across a wide area network with reasonable latency and support seamless failover between datacenters. This paper describes Megastore’s semantics and replication algorithm. It also describes our experience supporting a wide range of Google production services built with Megastore.
Optimal power cost management using stored energy in data centers
, 2011
"... Since the electricity bill of a data center constitutes a significant portion of its overall operational costs, reducing this has become important. We investigate cost reduction opportunities that arise by the use of uninterrupted power supply (UPS) units as energy storage devices. This represents a ..."
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Cited by 79 (10 self)
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Since the electricity bill of a data center constitutes a significant portion of its overall operational costs, reducing this has become important. We investigate cost reduction opportunities that arise by the use of uninterrupted power supply (UPS) units as energy storage devices. This represents a deviation from the usual use of these devices as mere transitional fail-over mechanisms between utility and captive sources such as diesel generators. We consider the problem of opportunistically using these devices to reduce the time average electric utility bill in a data center. Using the technique of Lyapunov optimization, we develop an online control algorithm that can optimally exploit these devices to minimize the time average cost. This algorithm operates without any knowledge of the statistics of the workload or electricity cost processes, making it attractive in the presence of workload and pricing uncertainties. An interesting feature of our algorithm is that its deviation from optimality reduces as the storage capacity is increased. Our work opens up a new area in data center power management.
Characterizing Cloud Computing Hardware Reliability
"... Modern day datacenters host hundreds of thousands of servers that coordinate tasks in order to deliver highly available cloud computing services. These servers consist of multiple hard disks, memory modules, network cards, processors etc., each of which while carefully engineered are capable of fail ..."
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Cited by 57 (0 self)
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Modern day datacenters host hundreds of thousands of servers that coordinate tasks in order to deliver highly available cloud computing services. These servers consist of multiple hard disks, memory modules, network cards, processors etc., each of which while carefully engineered are capable of failing. While the probability of seeing any such failure in the lifetime (typically 3-5 years in industry) of a server can be somewhat small, these numbers get magnified across all devices hosted in a datacenter. At such a large scale, hardware component failure is the norm rather than an exception. Hardware failure can lead to a degradation in performance to end-users and can result in losses to the business. A sound understanding of the numbers as well as the causes behind these failures helps improve operational experience by not only allowing us to be better equipped to tolerate failures but also to bring down the hardware cost through engineering, directly leading to a saving for the company. To the best of our knowledge, this paper is the first attempt to study server failures and hardware repairs for large datacenters. We present a detailed analysis of failure characteristics as well as a preliminary analysis on failure predictors. We hope that the results presented in this paper will serve as motivation to foster further research in this area.
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.
SRCMap: Energy Proportional Storage using Dynamic Consolidation
"... We investigate the problem of creating an energy proportional storage system through power-aware dynamic storage consolidation. Our proposal, Sample-Replicate-Consolidate Mapping (SRCMap), is a storage virtualization layer optimization that enables energy proportionality for dynamic I/O workloads by ..."
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Cited by 41 (9 self)
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We investigate the problem of creating an energy proportional storage system through power-aware dynamic storage consolidation. Our proposal, Sample-Replicate-Consolidate Mapping (SRCMap), is a storage virtualization layer optimization that enables energy proportionality for dynamic I/O workloads by consolidating the cumulative workload on a subset of physical volumes proportional to the I/O workload intensity. Instead of migrating data across physical volumes dynamically or replicating entire volumes, both of which are prohibitively expensive, SRCMap samples a subset of blocks from each data volume that constitutes its working set and replicates these on other physical volumes. During a given consolidation interval, SRCMap activates a minimal set of physical volumes to serve the workload and spins down the remaining volumes, redirecting their workload to replicas on active volumes. We present both theoretical and experimental evidence to establish the effectiveness of SRCMap in minimizing the power consumption of enterprise storage systems. 1
Paragon: Qos-aware scheduling for heterogeneous datacenters
- In Proceedings of the eighteenth international
, 2013
"... Large-scale datacenters (DCs) host tens of thousands of diverse applications each day. However, interference between colocated workloads and the difficulty to match applications to one of the many hardware platforms available can degrade performance, violating the quality of service (QoS) guarantees ..."
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Cited by 37 (7 self)
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Large-scale datacenters (DCs) host tens of thousands of diverse applications each day. However, interference between colocated workloads and the difficulty to match applications to one of the many hardware platforms available can degrade performance, violating the quality of service (QoS) guarantees that many cloud workloads require. While previous work has identified the impact of heterogeneity and interference, existing solutions are computationally intensive, cannot be applied online and do not scale beyond few applications. We present Paragon, an online and scalable DC scheduler that is heterogeneity and interference-aware. Paragon is derived from robust analytical methods and instead of profiling each application in detail, it leverages information the system already has about applications it has previously seen. It uses collaborative filtering techniques to quickly and accurately classify an unknown, incoming workload with respect to heterogeneity and interference in multiple shared resources, by identifying similarities to previously scheduled applications. The classification allows Paragon to greedily schedule applications in a manner that minimizes interference and maximizes server utilization. Paragon scales to tens of thousands of servers with marginal scheduling overheads in terms of time or state. We evaluate Paragon with a wide range of workload scenarios, on both small and large-scale systems, including 1,000 servers on EC2. For a 2,500-workload scenario, Paragon enforces performance guarantees for 91 % of applications, while significantly improving utilization. In comparison, heterogeneity-oblivious, interference-oblivious and least-loaded schedulers only provide similar guarantees for 14%, 11 % and 3 % of workloads. The differences are more striking in oversubscribed scenarios where resource efficiency is more critical.
Rethinking DRAM Design and Organization for Energy-Constrained Multi-Cores
"... DRAM vendors have traditionally optimized the cost-perbit metric, often making design decisions that incur energy penalties. A prime example is the overfetch feature in DRAM, where a single request activates thousands of bitlines in many DRAM chips, only to return a single cache line to the CPU. The ..."
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Cited by 35 (5 self)
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DRAM vendors have traditionally optimized the cost-perbit metric, often making design decisions that incur energy penalties. A prime example is the overfetch feature in DRAM, where a single request activates thousands of bitlines in many DRAM chips, only to return a single cache line to the CPU. The focus on cost-per-bit is questionable in modern-day servers where operating costs can easily exceed the purchase cost. Modern technology trends are also placing very different demands on the memory system: (i) queuing delays are a significant component of memory access time, (ii) there is a high energy premium for the level of reliability expected for business-critical computing, and (iii) the memory access stream emerging from multi-core systems exhibits limited locality. All of these trends necessitate an
Micro-Pages: Increasing DRAM Efficiency with Locality-Aware Data Placement
"... Power consumption and DRAM latencies are serious concerns in modern chip-multiprocessor (CMP or multi-core) based compute systems. The management of the DRAM row buffer can significantly impact both power consumption and latency. Modern DRAM systems read data from cell arrays and populate a row buff ..."
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Cited by 34 (6 self)
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Power consumption and DRAM latencies are serious concerns in modern chip-multiprocessor (CMP or multi-core) based compute systems. The management of the DRAM row buffer can significantly impact both power consumption and latency. Modern DRAM systems read data from cell arrays and populate a row buffer as large as 8 KB on a memory request. But only a small fraction of these bits are ever returned back to the CPU. This ends up wasting energy and time to read (and subsequently write back) bits which are used rarely. Traditionally, an open-page policy has been used for uni-processor systems and it has worked well because of spatial and temporal locality in the access stream. In future multi-core processors, the possibly independent access streams of each core are interleaved, thus destroying the available locality and significantly under-utilizing the contents of the row buffer. In this work, we attempt to improve row-buffer utilization for future multi-core systems. The schemes presented here are motivated by our observations that a large number of accesses within heavily accessed OS pages are to small, contiguous “chunks ” of cache blocks. Thus, the colocation of chunks (from different OS pages) in a row-buffer will improve the overall utilization of the row buffer contents, and consequently reduce memory energy consumption and access time. Such co-location can be achieved in many ways, notably involving a reduction in OS page size and software or hardware assisted migration of data within DRAM. We explore these mechanisms and discuss the trade-offs involved along with energy and performance improvements from each scheme. On average, for applications with room for improvement, our best performing scheme increases performance by 9 % (max. 18%) and reduces memory energy consumption by 15 % (max. 70%).
Data center demand response: Avoiding the coincident peak via workload shifting and local generation
- In ACM SIGMETRICS
, 2013
"... Demand response is a crucial aspect of the future smart grid. It has the potential to provide significant peak demand reduction and to ease the incorporation of renewable energy into the grid. Data centers ’ participation in demand response is becoming increasingly important given their high and inc ..."
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Cited by 32 (3 self)
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Demand response is a crucial aspect of the future smart grid. It has the potential to provide significant peak demand reduction and to ease the incorporation of renewable energy into the grid. Data centers ’ participation in demand response is becoming increasingly important given their high and increasing energy consumption and their flexibility in demand management compared to conventional industrial facilities. In this paper, we study two demand response schemes to reduce a data center’s peak loads and energy expenditure: workload shifting and the use of local power generations. We conduct a detailed characterization study of coincident peak data over two decades from Fort Collins Utilities, Colorado and then develop two optimization based algorithms by combining workload scheduling and local power generation to avoid the coincident peak and reduce the energy expenditure. The first algorithm optimizes the expected cost and the second one provides the optimal worst-case guarantee. We evaluate these algorithms via trace-based simulations. The results show that using workload shifting in combination with local generation can provide significant cost savings compared to either alone. 1.
