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IEEE COMPUTER ARCHITECTURE LETTERS 2018年第2期17卷 155-158页

作者： Gan, Yu Delimitrou, Christina Cornell Univ Ithaca NY 14850 USA

Cloud services have recently undergone a shift from monolithic applications to microservices, with hundreds or thousands of loosely-coupled microservices comprising the end-to-end application. Microservices present both opportunities and challenges when optimizing for quality of service (QoS) and cloud utilization. In this paper we explore the implications cloud microservices have on system bottlenecks, and datacenter server design. We first present and characterize an end-to-end application built using tens of popular open-source microservices that implements a movie renting and streaming service, and is modular and extensible. We then use the end-to-end service to study the scalability and performance bottlenecks of microservices, and highlight implications they have on the design of datacenter hardware. Specifically, we revisit the long-standing debate of brawny versus wimpy cores in the context of microservices, we quantify the I-cache pressure they introduce, and measure the time spent in computation versus communication between microservices over RPCs. As more cloud applications switch to this new programming model, it is increasingly important to revisit the assumptions we have previously used to build and manage cloud systems.

关键词： Super (very large) computers distributed applications application studies resulting in better multiple-processor systems

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Security Implications of Data Mining in Cloud Scheduling

IEEE COMPUTER ARCHITECTURE LETTERS

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IEEE COMPUTER ARCHITECTURE LETTERS 2016年第2期15卷 109-112页

作者： Delimitrou, Christina Kozyrakis, Christos Stanford Univ Dept Elect Engn Stanford CA 94305 USA

Cloud providers host an increasing number of popular applications, on the premise of resource flexibility and cost efficiency. Most of these systems expose virtualized resources of different types and sizes. As instances share the same physical host to increase utilization, they contend on hardware resources, e.g., last-level cache, making them vulnerable to side-channel attacks from co-scheduled applications. In this work we show that using data mining techniques can help an adversarial user of the cloud determine the nature and characteristics of co-scheduled applications and negatively impact their performance through targeted contention injections. We design Bolt, a simple runtime that extracts the sensitivity of co-scheduled applications to various types of interference and uses this signal to determine the type of these applications by applying a set of data mining techniques. We validate the accuracy of Bolt on a 39-server cluster. Bolt correctly identifies the type and characteristics of 81 percent out of 108 victim applications, and constructs specialized contention signals that degrade their performance. We also use Bolt to find the most commonly-run applications on EC2. We hope that underlining such security vulnerabilities in modern cloud facilities will encourage cloud providers to introduce stronger resource isolation primitives in their systems.

关键词： Super (very large) computers security and privacy protection scheduling and task partitioning application studies resulting in better multiple-processor systems

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The Netflix Challenge: Datacenter Edition

IEEE COMPUTER ARCHITECTURE LETTERS

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IEEE COMPUTER ARCHITECTURE LETTERS 2013年第1期12卷 29-32页

作者： Delimitrou, Christina Kozyrakis, Christos Stanford Univ Stanford CA 94305 USA

The hundreds of thousands of servers in modern warehouse scale systems make performance and efficiency optimizations pressing design challenges. These systems are traditionally considered homogeneous. However, that is not typically the case. multiple server generations compose a heterogeneous environment, whose performance opportunities have not been fully explored since techniques that account for platform heterogeneity typically do not scale to the tens of thousands of applications hosted in large-scale cloud providers. We present ADSM, a scalable and efficient recommendation system for application-to-server mapping in large-scale datacenters (DCs) that is QoS-aware. ADSM overcomes the drawbacks of previous techniques, by leveraging robust and computationally efficient analytical methods to scale to tens of thousands of applications with minimal overheads. It is also OoS-aware, mapping applications to platforms while enforcing strict QoS guarantees. ADSM is derived from validated analytical models, has low and bounded prediction errors, is simple to implement and scales to thousands of applications without significant changes to the system. Over 390 real DC workloads, ADSM improves performance by 16% on average and up to 2.5x and efficiency by 22% in a DC with 10 different server configurations.

关键词： Super (very large) computers Heterogeneous (hybrid) systems Scheduling and task partitioning application studies resulting in better multiple-processor systems

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A case for on-machine load balancing

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2011年第4期71卷 556-564页

作者： Ali, Shoukat Eslamnour, Behdis Shah, Zehra Intel Corp Platform Validat Enabling Folsom CA 95630 USA Missouri Univ Sci & Technol Dept Elect & Comp Engn Rolla MO 65409 USA Lahore Univ Management Sci Dept Comp Sci Comp Vis & Pattern Classificat lab Machine Learning & Data Min Grp Lahore Pakistan

This paper diverges from the traditional load balancing, and introduces a new principle called the on-machine load balance rule. The on-machine load balance rule leads to resource allocations that are better in tolerating uncertainties in the processing times of the tasks allocated to the resources when compared to other resource allocations that are derived using the conventional "across-the-machines" load balancing rule. The on-machine load balance rule calls for the resource allocation algorithms to allocate similarly sized tasks on a machine (in addition to optimizing some primary performance measures such as estimated makespan and average response time). The on-machine load balance rule is very different from the usual across-the-machines load balance rule that strives to balance load across resources so that all resources have similar finishing times. We give a mathematical justification for the on-machine load balance rule requiring only liberal assumptions about task processing times. Then we validate with extensive simulations that the resource allocations derived using on-machine load balance rule are indeed more tolerant of uncertain task processing times. (C) 2010 Elsevier Inc. All rights reserved.

关键词： Load balancing and task assignment application studies resulting in better multiple-processor systems Measurement, evaluation, modeling, simulation of multiple-processor systems Resource allocation

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Implementing an OFDM receiver on the RaPiD reconfigurable architecture

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IEEE TRANSACTIONS ON COMPUTERS 2004年第11期53卷 1436-1448页

作者： Ebeling, C Fisher, C Xing, GB Shen, MY Liu, H Univ Washington Dept Comp Sci & Engn Seattle WA 98195 USA Kaon Syst Inc Bothell WA 98201 USA Univ Washington Dept Elect Engn Seattle WA 98195 USA

Field-programmable gate arrays (FPGAs) have become an extremely popular implementation technology for custom hardware because they offer a combination of low cost and very fast turnaround. Because of their in-system reconfigurability, FPGAs have also been suggested as an efficient replacement for application-specific integrated circuits (ASICs) and digital signal processors (DSPs) for applications that require a combination of high performance, low cost, and flexibility. Unfortunately, the use of FPGAs in mobile embedded systems platforms is hampered by the very large overhead of FPGA-based architectures. Coarse-grained configurable architectures can reduce this overhead substantially by taking advantage of the application domain to specialize the reconfigurable architecture via coarse-grained components and interconnects. This paper presents the design and implementation of an OFDM receiver in the RaPiD reconfigurable architecture as a case study for comparing the relative cost and performance of ASIC, DSP, FPGA, and coarse-grained reconfigurable architectures. RaPiD is a coarse-grained reconfigurable architecture specialized to the domain of signal and image processing. The RaPiD architecture provides a reconfigurable pipelined datapath controlled by efficient reconfigurable control logic. We have implemented the computationally intensive parts of an OFDM receiver on the RaPiD architecture and have developed careful estimates of corresponding implementations in representative ASIC, DSP and FPGA technology. Our results show that, for this application, RaPiD fills the cost/performance gap between programmable DSP and ASIC architectures, achieving a factor of 6 better than a DSP implementation but a factor of 6 less than an ASIC implementation.

关键词： data communications devices wireless systems adaptable architectures heterogeneous (hybrid) systems special-purpose and application-based systems application studies resulting in better multiple-processor systems reconfigurable hardware signal processing systems design studies

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Implementing an OFDM receiver on the RaPiD reconfigurable architecture

Implementing an OFDM receiver on the RaPiD reconfigurable ar...

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13th International Conference on Field-Programmable Logic and applications (FPL 2003)

ISBN: (纸本)3540408223

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