Managing contamination delay to improve Timing Speculation architectures

Timing Speculation (TS) is a widely known method for realizing better-than-worst-case systems. Aggressive clocking, realizable by TS, enable systems to operate beyond specified safe frequency limits to effectively exploit the data dependent circuit delay. However, the range of aggressive clocking for performance enhancement under TS is restricted by short paths. In this paper, we show that increasing the lengths of short paths of the circuit increases the effectiveness of TS, leading to performance improvement. Also, we propose an algorithm to efficiently add delay buffers to selected short paths while keeping down the area penalty. We present our algorithm results for ISCAS-85 suite and show that it is possible to increase the circuit contamination delay by up to 30% without affecting the propagation delay. We also explore the possibility of increasing short path delays further by relaxing the constraint on propagation delay and analyze the performance impact

Improving Job Processing Speed through Shuffle Phase Optimization for SSD-based Hadoop MapReduce System

학위논문 (석사)-- 서울대학교 융합과학기술대학원 : 융합과학기술대학원 융합과학부(지능형융합시스템전공), 2015. 8. 홍성수.맵리듀스는 클라우드 데이터센터에서 대용량 데이터 처리를 위해 널리 사용되는 분산 처리 프로그래밍 모델이다. 맵리듀스는 맵, 셔플, 리듀스의 3단계로 구성된다. 하둡 맵리듀스는 맵리듀스 프로그래밍 모델을 구현한 프레임워크 중 가장 많이 쓰이는 것 중 하나이다. 현재 하둡 맵리듀스의 셔플 단계는 동일 데이터의 중복된 읽기/쓰기로 대량의 I/O를 발생시키며, 네트워크 전송에 의한 긴 지연을 발생시킨다. 이 문제를 해결하기 위하여 본 논문에서는 SSD 기반 하둡 맵리듀스 시스템에서 데이터 주소 기반의 셔플 메커니즘을 제안한다. 데이터 주소 기반의 셔플 메커니즘은 (1) 데이터 주소 기반 정렬 방법, (2) 데이터 주소 기반 병합 방법과 (3) 맵 출력 데이터 선 전송 방법으로 구성된다. 이는 임의 읽기/쓰기 속도가 빠른 SSD의 특징을 활용하여 대량의 중간 데이터 전체를 정렬하는 대신 작은 크기의 데이터 주소정보만을 정렬하고, 맵 태스크에서 리듀스 태스크로의 데이터 전송을 맵 출력 파일이 아닌 스필 파일과 주소정보 파일로 함으로써 네트워크 전송 시작을 앞당길 수 있는 메커니즘이다. 이를 활용하여 (1) 로컬 저장장치에 대한 읽기/쓰기 횟수와 데이터 양을 줄이고, (2) 네트워크 전송을 위한 지연 시간을 줄여 하둡 맵리듀스 셔플 단계의 수행시간을 단축하였다. 데이터 주소 기반의 셔플 메커니즘을 하둡 1.2.1에 구현하고 실험하였다. 실험결과 데이터 주소 기반의 셔플 메커니즘은 Terasort 벤치마크와 Wordcount 벤치마크의 평균 실행시간이 각각 8%와 1% 감소시킴을 보였다.초 록 i 목 차 iii 표 목차 iv 그림 목차 v 제 1 장 서 론 1 제 2 장 관련 연구 5 2.1 하둡 맵리듀스 성능 개선 연구 5 2.2 SSD 기반 하둡 시스템 연구 6 제 3 장 배 경 9 3.1 맵리듀스 프로그래밍 모델 9 3.2 하둡 맵리듀스 11 3.3 SSD (Solid State Drive) 특성 13 제 4 장 시스템 모델 15 4.1 SSD 기반의 하둡 시스템 15 4.2 하둡 맵리듀스의 셔플 단계 16 제 5 장 문제 정의 19 5.1 동일 데이터의 중복 읽기/쓰기 문제 19 5.2 네트워크 전송의 지연 문제 20 제 6 장 데이터 주소 기반 셔플 메커니즘 22 6.1 데이터 주소 기반 정렬 22 6.2 데이터 주소 기반 병합 23 6.3 맵 출력 데이터 선 전송 26 제 7 장 실험 및 평가 28 7.1 실험 환경 28 7.2 실험 결과 및 평가 30 제 8 장 결 론 35 참고 문헌 37 Abstract 40Maste

A storage architecture for data-intensive computing

The assimilation of computing into our daily lives is enabling the generation of data at unprecedented rates. In 2008, IDC estimated that the "digital universe" contained 486 exabytes of data [9]. The computing industry is being challenged to develop methods for the cost-effective processing of data at these large scales. The MapReduce programming model has emerged as a scalable way to perform data-intensive computations on commodity cluster computers. Hadoop is a popular open-source implementation of MapReduce. To manage storage resources across the cluster, Hadoop uses a distributed user-level filesystem. This filesystem --- HDFS --- is written in Java and designed for portability across heterogeneous hardware and software platforms. The efficiency of a Hadoop cluster depends heavily on the performance of this underlying storage system. This thesis is the first to analyze the interactions between Hadoop and storage. It describes how the user-level Hadoop filesystem, instead of efficiently capturing the full performance potential of the underlying cluster hardware, actually degrades application performance significantly. Architectural bottlenecks in the Hadoop implementation result in inefficient HDFS usage due to delays in scheduling new MapReduce tasks. Further, HDFS implicitly makes assumptions about how the underlying native platform manages storage resources, even though native filesystems and I/O schedulers vary widely in design and behavior. Methods to eliminate these bottlenecks in HDFS are proposed and evaluated both in terms of their application performance improvement and impact on the portability of the Hadoop framework. In addition to improving the performance and efficiency of the Hadoop storage system, this thesis also focuses on improving its flexibility. The goal is to allow Hadoop to coexist in cluster computers shared with a variety of other applications through the use of virtualization technology. The introduction of virtualization breaks the traditional Hadoop storage architecture, where persistent HDFS data is stored on local disks installed directly in the computation nodes. To overcome this challenge, a new flexible network-based storage architecture is proposed, along with changes to the HDFS framework. Network-based storage enables Hadoop to operate efficiently in a dynamic virtualized environment and furthers the spread of the MapReduce parallel programming model to new applications

Cooperative caching for object storage

Data is increasingly stored in data lakes, vast immutable object stores that can be accessed from anywhere in the data center. By providing low cost and scalable storage, today immutable object-storage based data lakes are used by a wide range of applications with diverse access patterns. Unfortunately, performance can suffer for applications that do not match the access patterns for which the data lake was designed. Moreover, in many of today's (non-hyperscale) data centers, limited bisectional bandwidth will limit data lake performance. Today many computer clusters integrate caches both to address the mismatch between application performance requirements and the capabilities of the shared data lake, and to reduce the demand on the data center network. However, per-cluster caching; i) means the expensive cache resources cannot be shifted between clusters based on demand, ii) makes sharing expensive because data accessed by multiple clusters is independently cached by each of them, and iii) makes it difficult for clusters to grow and shrink if their servers are being used to cache storage. In this dissertation, we present two novel data-center wide cooperative cache architectures, Datacenter-Data-Delivery Network (D3N) and Directory-Based Datacenter-Data-Delivery Network (D4N) that are designed to be part of the data lake itself rather than part of the computer clusters that use it. D3N and D4N distribute caches across the data center to enable data sharing and elasticity of cache resources where requests are transparently directed to nearby cache nodes. They dynamically adapt to changes in access patterns and accelerate workloads while providing the same consistency, trust, availability, and resilience guarantees as the underlying data lake. We nd that exploiting the immutability of object stores significantly reduces the complexity and provides opportunities for cache management strategies that were not feasible for previous cooperative cache systems for le or block-based storage. D3N is a multi-layer cooperative cache that targets workloads with large read-only datasets like big data analytics. It is designed to be easily integrated into existing data lakes with only limited support for write caching of intermediate data, and avoiding any global state by, for example, using consistent hashing for locating blocks and making all caching decisions based purely on local information. Our prototype is performant enough to fully exploit the (5 GB/s read) SSDs and (40, Gbit/s) NICs in our system and improve the runtime of realistic workloads by up to 3x. The simplicity of D3N has enabled us, in collaboration with industry partners, to upstream the two-layer version of D3N into the existing code base of the Ceph object store as a new experimental feature, making it available to the many data lakes around the world based on Ceph. D4N is a directory-based cooperative cache that provides a reliable write tier and a distributed directory that maintains a global state. It explores the use of global state to implement more sophisticated cache management policies and enables application-specific tuning of caching policies to support a wider range of applications than D3N. In contrast to previous cache systems that implement their own mechanism for maintaining dirty data redundantly, D4N re-uses the existing data lake (Ceph) software for implementing a write tier and exploits the semantics of immutable objects to move aged objects to the shared data lake. This design greatly reduces the barrier to adoption and enables D4N to take advantage of sophisticated data lake features such as erasure coding. We demonstrate that D4N is performant enough to saturate the bandwidth of the SSDs, and it automatically adapts replication to the working set of the demands and outperforms the state of art cluster cache Alluxio. While it will be substantially more complicated to integrate the D4N prototype into production quality code that can be adopted by the community, these results are compelling enough that our partners are starting that effort. D3N and D4N demonstrate that cooperative caching techniques, originally designed for file systems, can be employed to integrate caching into today’s immutable object-based data lakes. We find that the properties of immutable object storage greatly simplify the adoption of these techniques, and enable integration of caching in a fashion that enables re-use of existing battle tested software; greatly reducing the barrier of adoption. In integrating the caching in the data lake, and not the compute cluster, this research opens the door to efficient data center wide sharing of data and resources

A Survey on Automatic Parameter Tuning for Big Data Processing Systems

Big data processing systems (e.g., Hadoop, Spark, Storm) contain a vast number of configuration parameters controlling parallelism, I/O behavior, memory settings, and compression. Improper parameter settings can cause significant performance degradation and stability issues. However, regular users and even expert administrators grapple with understanding and tuning them to achieve good performance. We investigate existing approaches on parameter tuning for both batch and stream data processing systems and classify them into six categories: rule-based, cost modeling, simulation-based, experiment-driven, machine learning, and adaptive tuning. We summarize the pros and cons of each approach and raise some open research problems for automatic parameter tuning.Peer reviewe

Commodity single board computer clusters and their applications

© 2018 Current commodity Single Board Computers (SBCs) are sufficiently powerful to run mainstream operating systems and workloads. Many of these boards may be linked together, to create small, low-cost clusters that replicate some features of large data center clusters. The Raspberry Pi Foundation produces a series of SBCs with a price/performance ratio that makes SBC clusters viable, perhaps even expendable. These clusters are an enabler for Edge/Fog Compute, where processing is pushed out towards data sources, reducing bandwidth requirements and decentralizing the architecture. In this paper we investigate use cases driving the growth of SBC clusters, we examine the trends in future hardware developments, and discuss the potential of SBC clusters as a disruptive technology. Compared to traditional clusters, SBC clusters have a reduced footprint, are low-cost, and have low power requirements. This enables different models of deployment—particularly outside traditional data center environments. We discuss the applicability of existing software and management infrastructure to support exotic deployment scenarios and anticipate the next generation of SBC. We conclude that the SBC cluster is a new and distinct computational deployment paradigm, which is applicable to a wider range of scenarios than current clusters. It facilitates Internet of Things and Smart City systems and is potentially a game changer in pushing application logic out towards the network edge

Novel information and data exchange within power systems using enhanced blockchain technologies

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonCurrent energy systems are primarily designed for centralized power generation and supplying bulk electricity to users with stable and predictable usage patterns. However, with the increasing penetration of renewable energy sources (RES), future energy systems will require greater flexibility and wider distribution of both demand and supply. Integrating RES on a large scale poses challenges to the hosting capacity of distribution systems. To address these challenges, the digitalization of energy systems through novel Information and Communication Technologies (ICT) infrastructure is essential. The shift from centralized to highly distributed systems necessitates increased coordination and communication efforts. This is because a distributed system is composed of multiple independent entities that need to communicate and collaborate effectively to accomplish a shared objective. Coordination and communication are necessary to ensure that the system is operating efficiently and effectively. Traditional centralized cloud-based data exchange schemes depend on a single trusted third party, this may lead to single-point failure and lack of data privacy and access control. To overcome these issues, a novel approach is proposed for exchanging data within power systems using blockchain technology. This approach enables users to securely exchange data while maintaining ownership. The experiments conducted demonstrate that the proposed approach can handle more users and enables information and data exchange within power systems. Secondly, this thesis proposes an Artificial Neural Network (ANN) based prediction model to optimize the performance of the blockchain-enabled data exchange approach. A use case for exchanging data within the power system is implemented on the proposed platform using various performance metrics. The results of the proposed approach are compared to two other schemes: the baseline scheme and an optimized scheme. The evaluation results indicate that the proposed approach can enhance network performance when compared to the baseline and optimized schemes. In summary, the proposed novel approach to ICT infrastructure for successfully exchanging information and data within power systems entities. The performance of the novel approach is evaluated based on the ability to handle multiple users, scalability, reliability, and security

Composable architecture for rack scale big data computing

The rapid growth of cloud computing, both in terms of the spectrum and volume of cloud workloads, necessitate re-visiting the traditional rack-mountable servers based datacenter design. Next generation datacenters need to offer enhanced support for: (i) fast changing system configuration requirements due to workload constraints, (ii) timely adoption of emerging hardware technologies, and (iii) maximal sharing of systems and subsystems in order to lower costs. Disaggregated datacenters, constructed as a collection of individual resources such as CPU, memory, disks etc., and composed into workload execution units on demand, are an interesting new trend that can address the above challenges. In this paper, we demonstrated the feasibility of composable systems through building a rack scale composable system prototype using PCIe switch. Through empirical approaches, we develop assessment of the opportunities and challenges for leveraging the composable architecture for rack scale cloud datacenters with a focus on big data and NoSQL workloads. In particular, we compare and contrast the programming models that can be used to access the composable resources, and developed the implications for the network and resource provisioning and management for rack scale architecture