Wikipedia workload analysis for decentralized hosting

Urdaneta, Guido; Pierre, Guillaume; Steen, Maarten van

doi:10.1016/j.comnet.2009.02.019

Cited by 287 publications

(135 citation statements)

References 23 publications

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“…All three vary between nearly idle and maximum throughput within a four minute period: the slope pattern gradually raises the target load from 0 and 6.2M RPS and then reduces its load; the step pattern increases load by 500 KRPS every 10 seconds; and finally the sine+noise pattern is a basic sinusoidal pattern modified by randomly adding sharp noise that is uniformly distributed over [-250,+250] KRPS and re-computed every 5 seconds. The slope pattern provides a baseline to study smooth changes, the step pattern models abrupt and massive changes, while the sine+noise pattern is representative of daily web patterns [48]. Fig.…”

Section: Discussionmentioning

confidence: 99%

Energy proportionality and workload consolidation for latency-critical applications

Prekas

Primorac

Belay

et al. 2015

Proceedings of the Sixth ACM Symposium on Cloud Computing

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Energy proportionality and workload consolidation are important objectives towards increasing efficiency in largescale datacenters. Our work focuses on achieving these goals in the presence of applications with µs-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. Monitoring hardware queue depths allows us to detect increases in queuing latencies. Carefully coordinated adjustments to the NIC's packet redirection table enable us to reassign 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.

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Section: Discussionmentioning

confidence: 99%

Energy proportionality and workload consolidation for latency-critical applications

Prekas

Primorac

Belay

et al. 2015

Proceedings of the Sixth ACM Symposium on Cloud Computing

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show abstract

“…To build our workloads in the simulator, we use a trace of Internet traffic from Wikipedia.org [45]. In particular, we use this tracefile with 2-month long data, which contains 10% of user requests that arrived at Wikipedia between October 1st, 2007 and November 30th, 2007.…”

Section: Real-world Workload Tracesmentioning

confidence: 99%

GreenWare: Greening Cloud-Scale Data Centers to Maximize the Use of Renewable Energy

Zhang

Wang

2011

Lecture Notes in Computer Science

137

111

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Abstract. To reduce the negative environmental implications (e.g., CO2 emission and global warming) caused by the rapidly increasing energy consumption, many Internet service operators have started taking various initiatives to operate their cloud-scale data centers with renewable energy. Unfortunately, due to the intermittent nature of renewable energy sources such as wind turbines and solar panels, currently renewable energy is often more expensive than brown energy that is produced with conventional fossil-based fuel. As a result, utilizing renewable energy may impose a considerable pressure on the sometimes stringent operation budgets of Internet service operators. Therefore, two key questions faced by many cloud-service operators are 1) how to dynamically distribute service requests among data centers in different geographical locations, based on the local weather conditions, to maximize the use of renewable energy, and 2) how to do that within their allowed operation budgets.In this paper, we propose GreenWare, a novel middleware system that conducts dynamic request dispatching to maximize the percentage of renewable energy used to power a network of distributed data centers, subject to the desired cost budget of the Internet service operator. Our solution first explicitly models the intermittent generation of renewable energy, e.g., wind power and solar power, with respect to varying weather conditions in the geographical location of each data center. We then formulate the core objective of GreenWare as a constrained optimization problem and propose an efficient request dispatching algorithm based on linear-fractional programming (LFP). We evaluate GreenWare with realworld weather, electricity price, and workload traces. Our experimental results show that GreenWare can significantly increase the use of renewable energy in cloud-scale data centers without violating the desired cost budget, despite the intermittent supplies of renewable energy in different locations and time-varying electricity prices and workloads.

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“…As we do not have data on availability requirements of website owners, we generate this dataset based on the frequency of end user HTTP requests directed at different websites and counting missed requests, similarly to how reliability is determined from the mean time between failures [17]. A public dataset of HTTP requests made to Wikipedia [19] is used. To obtain data for different websites, we consider Wikipedia in each language as an individual website, because of its unique group of end users (different in number and usage pattern).…”

Section: User Requirements Modelmentioning

confidence: 99%

Economics of Grids, Clouds, Systems, and Services

Lučanin

Jrad

Brandić

et al. 2014

Lecture Notes in Computer Science

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Abstract. Novel energy-aware cloud management methods dynamically reallocate computation across geographically distributed data centers to leverage regional electricity price and temperature differences. As a result, a managed virtual machine (VM) may suffer occasional downtimes. Current cloud providers only offer high availability VMs, without enough flexibility to apply such energyaware management. In this paper we show how to analyse past traces of dynamic cloud management actions based on electricity prices and temperatures to estimate VM availability and price values. We propose a novel service level agreement (SLA) specification approach for offering VMs with different availability and price values guaranteed over multiple SLAs to enable flexible energy-aware cloud management. We determine the optimal number of such SLAs as well as their availability and price guaranteed values. We evaluate our approach in a user SLA selection simulation using Wikipedia and Grid'5000 workloads. The results show higher customer conversion and 39% average energy savings per VM.

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Wikipedia workload analysis for decentralized hosting

Cited by 287 publications

References 23 publications

Energy proportionality and workload consolidation for latency-critical applications

Energy proportionality and workload consolidation for latency-critical applications

GreenWare: Greening Cloud-Scale Data Centers to Maximize the Use of Renewable Energy

Economics of Grids, Clouds, Systems, and Services

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