SoK: Diving into DAG-based Blockchain Systems

Blockchain plays an important role in cryptocurrency markets and technology services. However, limitations on high latency and low scalability retard their adoptions and applications in classic designs. Reconstructed blockchain systems have been proposed to avoid the consumption of competitive transactions caused by linear sequenced blocks. These systems, instead, structure transactions/blocks in the form of Directed Acyclic Graph (DAG) and consequently re-build upper layer components including consensus, incentives, \textit{etc.} The promise of DAG-based blockchain systems is to enable fast confirmation (complete transactions within million seconds) and high scalability (attach transactions in parallel) without significantly compromising security. However, this field still lacks systematic work that summarises the DAG technique. To bridge the gap, this Systematization of Knowledge (SoK) provides a comprehensive analysis of DAG-based blockchain systems. Through deconstructing open-sourced systems and reviewing academic researches, we conclude the main components and featured properties of systems, and provide the approach to establish a DAG. With this in hand, we analyze the security and performance of several leading systems, followed by discussions and comparisons with concurrent (scaling blockchain) techniques. We further identify open challenges to highlight the potentiality of DAG-based solutions and indicate their promising directions for future research.Comment: Full versio

Architecting Data Centers for High Efficiency and Low Latency

Modern data centers, housing remarkably powerful computational capacity, are built in massive scales and consume a huge amount of energy. The energy consumption of data centers has mushroomed from virtually nothing to about three percent of the global electricity supply in the last decade, and will continuously grow. Unfortunately, a significant fraction of this energy consumption is wasted due to the inefficiency of current data center architectures, and one of the key reasons behind this inefficiency is the stringent response latency requirements of the user-facing services hosted in these data centers such as web search and social networks. To deliver such low response latency, data center operators often have to overprovision resources to handle high peaks in user load and unexpected load spikes, resulting in low efficiency. This dissertation investigates data center architecture designs that reconcile high system efficiency and low response latency. To increase the efficiency, we propose techniques that understand both microarchitectural-level resource sharing and system-level resource usage dynamics to enable highly efficient co-locations of latency-critical services and low-priority batch workloads. We investigate the resource sharing on real-system simultaneous multithreading (SMT) processors to enable SMT co-locations by precisely predicting the performance interference. We then leverage historical resource usage patterns to further optimize the task scheduling algorithm and data placement policy to improve the efficiency of workload co-locations. Moreover, we introduce methodologies to better manage the response latency by automatically attributing the source of tail latency to low-level architectural and system configurations in both offline load testing environment and online production environment. We design and develop a response latency evaluation framework at microsecond-level precision for data center applications, with which we construct statistical inference procedures to attribute the source of tail latency. Finally, we present an approach that proactively enacts carefully designed causal inference micro-experiments to diagnose the root causes of response latency anomalies, and automatically correct them to reduce the response latency.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144144/1/yunqi_1.pd

Flexible and efficient computation in large data centres

Increasingly, online computer applications rely on large-scale data analyses to offer personalised and improved products. These large-scale analyses are performed on distributed data processing execution engines that run on thousands of networked machines housed within an individual data centre. These execution engines provide, to the programmer, the illusion of running data analysis workflows on a single machine, and offer programming interfaces that shield developers from the intricacies of implementing parallel, fault-tolerant computations. Many such execution engines exist, but they embed assumptions about the computations they execute, or only target certain types of computations. Understanding these assumptions involves substantial study and experimentation. Thus, developers find it difficult to determine which execution engine is best, and even if they did, they become “locked in” because engineering effort is required to port workflows. In this dissertation, I first argue that in order to execute data analysis computations efficiently, and to flexibly choose the best engines, the way we specify data analysis computations should be decoupled from the execution engines that run the computations. I propose an architecture for decoupling data processing, together with Musketeer, my proof-of-concept implementation of this architecture. In Musketeer, developers express data analysis computations using their preferred programming interface. These are translated into a common intermediate representation from which code is generated and executed on the most appropriate execution engine. I show that Musketeer can be used to write data analysis computations directly, and these can execute on many execution engines because Musketeer automatically generates code that is competitive with optimised hand-written implementations. The diverse execution engines cause different workflow types to coexist within a data centre, opening up both opportunities for sharing and potential pitfalls for co-location interference. However, in practice, workflows are either placed by high-quality schedulers that avoid co-location interference, but choose placements slowly, or schedulers that choose placements quickly, but with unpredictable workflow run time due to co-location interference. In this dissertation, I show that schedulers can choose high-quality placements with low latency. I develop several techniques to improve Firmament, a high-quality min-cost flow-based scheduler, to choose placements quickly in large data centres. Finally, I demonstrate that Firmament chooses placements at least as good as other sophisticated schedulers, but at the speeds associated with simple schedulers. These contributions enable more efficient and effective use of data centres for large-scale computation than current solutions.Google fellowship in distributed system

Efficient algorithms for new computational models

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.Includes bibliographical references (p. 155-163).This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Advances in hardware design and manufacturing often lead to new ways in which problems can be solved computationally. In this thesis we explore fundamental problems in three computational models that are based on such recent advances. The first model is based on new chip architectures, where multiple independent processing units are placed on one chip, allowing for an unprecedented parallelism in hardware. We provide new scheduling algorithms for this computational model. The second model is motivated by peer-to-peer networks, where countless (often inexpensive) computing devices cooperate in distributed applications without any central control. We state and analyze new algorithms for load balancing and for locality-aware distributed data storage in peer-to-peer networks. The last model is based on extensions of the streaming model. It is an attempt to capture the class of problems that can be efficiently solved on massive data sets. We give a number of algorithms for this model, and compare it to other models that have been proposed for massive data set computations. Our algorithms and complexity results for these computational models follow the central thesis that it is an important part of theoretical computer science to model real-world computational structures, and that such effort is richly rewarded by a plethora of interesting and challenging problems.by Jan Matthias Ruhl.Ph.D

GRID superscalar: a programming model for the Grid

Durant els darrers anys el Grid ha sorgit com una nova plataforma per la computació distribuïda. La tecnologia Gris permet unir diferents recursos de diferents dominis administratius i formar un superordinador virtual amb tots ells. Molts grups de recerca han dedicat els seus esforços a desenvolupar un conjunt de serveis bàsics per oferir un middleware de Grid: una capa que permet l'ús del Grid. De tota manera, utilitzar aquests serveis no és una tasca fácil per molts usuaris finals, cosa que empitjora si l'expertesa d'aquests usuaris no està relacionada amb la informàtica.Això té una influència negativa a l'hora de que la comunitat científica adopti la tecnologia Grid. Es veu com una tecnologia potent però molt difícil de fer servir. Per facilitar l'ús del Grid és necessària una capa extra que amagui la complexitat d'aquest i permeti als usuaris programar o portar les seves aplicacions de manera senzilla.Existeixen moltes propostes d'eines de programació pel Grid. En aquesta tesi fem un resum d'algunes d'elles, i podem veure que existeixen eines conscients i no-conscients del Grid (es programen especificant o no els detalls del Grid, respectivament). A més, molt poques d'aquestes eines poden explotar el paral·lelisme implícit de l'aplicació, i en la majoria d'elles, l'usuari ha de definir aquest paral·lelisme de manera explícita. Una altra característica que considerem important és si es basen en llenguatges de programació molt populars (com C++ o Java), cosa que facilita l'adopció per part dels usuaris finals.En aquesta tesi, el nostre objectiu principal ha estat crear un model de programació pel Grid basat en la programació seqüencial i els llenguatges més coneguts de la programació imperativa, capaç d'explotar el paral·lelisme implícit de les aplicacions i d'accelerar-les fent servir els recursos del Grid de manera concurrent. A més, com el Grid és de naturalesa distribuïda, heterogènia i dinàmica i degut també a que el nombre de recursos que pot formar un Grid pot ser molt gran, la probabilitat de que es produeixi una errada durant l'execució d'una aplicació és elevada. Per tant, un altre dels nostres objectius ha estat tractar qualsevol tipus d'error que pugui sorgir durant l'execució d'una aplicació de manera automàtica (ja siguin errors relacionats amb l'aplicació o amb el Grid). GRID superscalar (GRIDSs), la principal contribució d'aquesta tesi, és un model de programació que assoleix elsobjectius mencionats proporcionant una interfície molt petita i simple i un entorn d'execució que és capaç d'executar en paral·lel el codi proporcionat fent servir el Grid. La nostra interfície de programació permet a un usuari programar una aplicació no-conscient del Grid, amb llenguatges imperatius coneguts i populars (com C/C++, Java, Perl o Shell script) i de manera seqüencial, per tant dóna un pas important per ajudar als usuaris a adoptar la tecnologia Grid.Hem aplicat el nostre coneixement de l'arquitectura de computadors i el disseny de microprocessadors a l'entorn d'execució de GRIDSs. Tal com es fa a un processador superescalar, l'entorn d'execució de GRIDSs és capaç de realitzar un anàlisi de dependències entre les tasques que formen l'aplicació, i d'aplicar tècniques de renombrament per incrementar el seu paral·lelisme. GRIDSs genera automàticament a partir del codi principal de l'usuari un graf que descriu les dependències de dades en l'aplicació. També presentem casos d'ús reals del model de programació en els camps de la química computacional i la bioinformàtica, que demostren que els nostres objectius han estat assolits.Finalment, hem estudiat l'aplicació de diferents tècniques per detectar i tractar fallades: checkpoint, reintent i replicació de tasques. La nostra proposta és proporcionar un entorn capaç de tractar qualsevol tipus d'errors, de manera transparent a l'usuari sempre que sigui possible. El principal avantatge d'implementar aquests mecanismos al nivell del model de programació és que el coneixement a nivell de l'aplicació pot ser explotat per crear dinàmicament una estratègia de tolerància a fallades per cada aplicació, i evitar introduir sobrecàrrega en entorns lliures d'errors.During last years, the Grid has emerged as a new platform for distributed computing. The Grid technology allows joining different resources from different administrative domains and forming a virtual supercomputer with all of them.Many research groups have dedicated their efforts to develop a set of basic services to offer a Grid middleware: a layer that enables the use of the Grid. Anyway, using these services is not an easy task for many end users, even more if their expertise is not related to computer science. This has a negative influence in the adoption of the Grid technology by the scientific community. They see it as a powerful technology but very difficult to exploit. In order to ease the way the Grid must be used, there is a need for an extra layer which hides all the complexity of the Grid, and allows users to program or port their applications in an easy way.There has been many proposals of programming tools for the Grid. In this thesis we give an overview on some of them, and we can see that there exist both Grid-aware and Grid-unaware environments (programmed with or without specifying details of the Grid respectively). Besides, very few existing tools can exploit the implicit parallelism of the application and in the majority of them, the user must define the parallelism explicitly. Another important feature we consider is if they are based in widely used programming languages (as C++ or Java), so the adoption is easier for end users.In this thesis, our main objective has been to create a programming model for the Grid based on sequential programming and well-known imperative programming languages, able to exploit the implicit parallelism of applications and to speed them up by using the Grid resources concurrently. Moreover, because the Grid has a distributed, heterogeneous and dynamic nature and also because the number of resources that form a Grid can be very big, the probability that an error arises during an application's execution is big. Thus, another of our objectives has been to automatically deal with any type of errors which may arise during the execution of the application (application related or Grid related).GRID superscalar (GRIDSs), the main contribution of this thesis, is a programming model that achieves these mentioned objectives by providing a very small and simple interface and a runtime that is able to execute in parallel the code provided using the Grid. Our programming interface allows a user to program a Grid-unaware application with already known and popular imperative languages (such as C/C++, Java, Perl or Shell script) and in a sequential fashion, therefore giving an important step to assist end users in the adoption of the Grid technology.We have applied our knowledge from computer architecture and microprocessor design to the GRIDSs runtime. As it is done in a superscalar processor, the GRIDSs runtime system is able to perform a data dependence analysis between the tasks that form an application, and to apply renaming techniques in order to increase its parallelism. GRIDSs generates automatically from user's main code a graph describing the data dependencies in the application.We present real use cases of the programming model in the fields of computational chemistry and bioinformatics, which demonstrate that our objectives have been achieved.Finally, we have studied the application of several fault detection and treatment techniques: checkpointing, task retry and task replication. Our proposal is to provide an environment able to deal with all types of failures, transparently for the user whenever possible. The main advantage in implementing these mechanisms at the programming model level is that application-level knowledge can be exploited in order to dynamically create a fault tolerance strategy for each application, and avoiding to introduce overhead in error-free environments

Efficient resource sharing for big data applications in shared clusters

Modern data centers are shifting to shared clusters where the resources are shared among multiple users and frameworks. A key enabler for such shared clusters is a cluster resource management system which allocates resources among different frameworks. One key problem in these shared clusters is how to efficiently share cluster resources between multiple applications and users in an elastic and non-disruptive manner. Current cluster schedulers typically utilize kill-based preemption to coordinate resource sharing, achieve fairness and satisfy SLOs during resource contention by simply killing low priority jobs and restarting them later when resources are available. This simple preemption policy ensures fast service times of high priority jobs and prevents a single user/application from occupying too many resources and starving others; however, without saving the progress of preempted jobs, this policy causes significant resource waste and delays the response time of long running or low priority jobs. The issue of dynamic resource sharing becomes even more problematic when there are different types of applications running on the same cluster (e.g., batch processing systems running alongside real-time streaming systems). Different application types will often have varying quality of service metrics (e.g., higher throughput versus lower latency) which can make resource sharing among these applications contentious. In this dissertation, we show the impact of kill-based preemption in modern shared clusters and propose two solutions to more efficiently share resources in shared cluster environments by utilizing checkpoint-based preemption and supporting elasticity in distributed data stream processing systems.Ph.D

Big data analytics for large-scale wireless networks: Challenges and opportunities

© 2019 Association for Computing Machinery. The wide proliferation of various wireless communication systems and wireless devices has led to the arrival of big data era in large-scale wireless networks. Big data of large-scale wireless networks has the key features of wide variety, high volume, real-time velocity, and huge value leading to the unique research challenges that are different from existing computing systems. In this article, we present a survey of the state-of-art big data analytics (BDA) approaches for large-scale wireless networks. In particular, we categorize the life cycle of BDA into four consecutive stages: Data Acquisition, Data Preprocessing, Data Storage, and Data Analytics. We then present a detailed survey of the technical solutions to the challenges in BDA for large-scale wireless networks according to each stage in the life cycle of BDA. Moreover, we discuss the open research issues and outline the future directions in this promising area

Sharing GPUs for Real-Time Autonomous-Driving Systems

Autonomous vehicles at mass-market scales are on the horizon. Cameras are the least expensive among common sensor types and can preserve features such as color and texture that other sensors cannot. Therefore, realizing full autonomy in vehicles at a reasonable cost is expected to entail computer-vision techniques. These computer-vision applications require massive parallelism provided by the underlying shared accelerators, such as graphics processing units, or GPUs, to function “in real time.” However, when computer-vision researchers and GPU vendors refer to “real time,” they usually mean “real fast”; in contrast, certifiable automotive systems must be “real time” in the sense of being predictable. This dissertation addresses the challenging problem of how GPUs can be shared predictably and efficiently for real-time autonomous-driving systems. We tackle this challenge in four steps. First, we investigate NVIDIA GPUs with respect to scheduling, synchronization, and execution. We conduct an extensive set of experiments to infer NVIDIA GPU scheduling rules, which are unfortunately undisclosed by NVIDIA and are beyond access owing to their closed-source software stack. We also expose a list of pitfalls pertaining to CPU-GPU synchronization that can result in unbounded response times of GPU-using applications. Lastly, we examine a fundamental trade-off for designing real-time tasks under different execution options. Overall, our investigation provides an essential understanding of NVIDIA GPUs, allowing us to further model and analyze GPU tasks. Second, we develop a new model and conduct schedulability analysis for GPU tasks. We extend the well-studied sporadic task model with additional parameters that characterize the parallel execution of GPU tasks. We show that NVIDIA scheduling rules are subject to fundamental capacity loss, which implies a necessary total utilization bound. We derive response-time bounds for GPU task systems that satisfy our schedulability conditions. Third, we address an industrial challenge of supplying the throughput performance of computer-vision frameworks to support adequate coverage and redundancy offered by an array of cameras. We re-think the design of convolution neural network (CNN) software to better utilize hardware resources and achieve increased throughput (number of simultaneous camera streams) without any appreciable increase in per-frame latency (camera to CNN output) or reduction of per-stream accuracy. Fourth, we apply our analysis to a finer-grained graph scheduling of a computer-vision standard, OpenVX, which explicitly targets embedded and real-time systems. We evaluate both the analytical and empirical real-time performance of our approach.Doctor of Philosoph

Optimizing Resource Management in Cloud Analytics Services

The fundamental challenge in the cloud today is how to build and optimize machine learning and data analytical services. Machine learning and data analytical platforms are changing computing infrastructure from expensive private data centers to easily accessible online services. These services pack user requests as jobs and run them on thousands of machines in parallel in geo-distributed clusters. The scale and the complexity of emerging jobs lead to increasing challenges for the clusters at all levels, from power infrastructure to system architecture and corresponding software framework design. These challenges come in many forms. Today's clusters are built on commodity hardware and hardware failures are unavoidable. Resource competition, network congestion, and mixed generations of hardware make the hardware environment complex and hard to model and predict. Such heterogeneity becomes a crucial roadblock for efficient parallelization on both the task level and job level. Another challenge comes from the increasing complexity of the applications. For example, machine learning services run jobs made up of multiple tasks with complex dependency structures. This complexity leads to difficulties in framework designs. The scale, especially when services span geo-distributed clusters, leads to another important hurdle for cluster design. Challenges also come from the power infrastructure. Power infrastructure is very expensive and accounts for more than 20% of the total costs to build a cluster. Power sharing optimization to maximize the facility utilization and smooth peak hour usages is another roadblock for cluster design. In this thesis, we focus on solutions for these challenges at the task level, on the job level, with respect to the geo-distributed data cloud design and for power management in colocation data centers. At the task level, a crucial hurdle to achieving predictable performance is stragglers, i.e., tasks that take significantly longer than expected to run. At this point, speculative execution has been widely adopted to mitigate the impact of stragglers in simple workloads. We apply straggler mitigation for approximation jobs for the first time. We present GRASS, which carefully uses speculation to mitigate the impact of stragglers in approximation jobs. GRASS's design is based on the analysis of a model we develop to capture the optimal speculation levels for approximation jobs. Evaluations with production workloads from Facebook and Microsoft Bing in an EC2 cluster of 200 nodes show that GRASS increases accuracy of deadline-bound jobs by 47% and speeds up error-bound jobs by 38%. Moving from task level to job level, task level speculation mechanisms are designed and operated independently of job scheduling when, in fact, scheduling a speculative copy of a task has a direct impact on the resources available for other jobs. Thus, we present Hopper, a job-level speculation-aware scheduler that integrates the tradeoffs associated with speculation into job scheduling decisions based on a model generalized from the task-level speculation model. We implement both centralized and decentralized prototypes of the Hopper scheduler and show that 50% (66%) improvements over state-of-the-art centralized (decentralized) schedulers and speculation strategies can be achieved through the coordination of scheduling and speculation. As computing resources move from local clusters to geo-distributed cloud services, we are expecting the same transformation for data storage. We study two crucial pieces of a geo-distributed data cloud system: data acquisition and data placement. Starting from developing the optimal algorithm for the case of a data cloud made up of a single data center, we propose a near-optimal, polynomial-time algorithm for a geo-distributed data cloud in general. We show, via a case study, that the resulting design, Datum, is near-optimal (within 1.6%) in practical settings. Efficient power management is a fundamental challenge for data centers when providing reliable services. Power oversubscription in data centers is very common and may occasionally trigger an emergency when the aggregate power demand exceeds the capacity. We study power capping solutions for handling such emergencies in a colocation data center, where the operator supplies power to multiple tenants. We propose a novel market mechanism based on supply function bidding, called COOP, to financially incentivize and coordinate tenants' power reduction for minimizing total performance loss while satisfying multiple power capping constraints. We demonstrate that COOP is "win-win", increasing the operator's profit (through oversubscription) and reducing tenants' costs (through financial compensation for their power reduction during emergencies).</p