Control-based Scheduling in a Distributed Stream Processing System

Stream processing systems receive continuous streams of messages with raw information and produce streams of messages with processed information. The utility of a stream-processing system depends, in part, on the accuracy and timeliness of the output. Streams in complex event processing systems are processed on distributed systems; several steps are taken on different processors to process each incoming message, and messages may be enqueued between steps. This paper deals with the problems of distributed dynamic control of streams to optimize the total utility provided by the system. A challenge of distributed control is that timeliness of output depends only on the total end-toend time and is otherwise independent of the delays at each separate processor whereas the controller for each processor takes action to control only the steps on that processor and cannot directly control the entire network. This paper identifies key problems in distributed control and analyzes two scheduling algorithms that help in an initial analysis of a difficult problem

Efficient Online Scheduling in Distributed Stream Data Processing Systems

General-purpose Distributed Stream Data Processing Systems (DSDPSs) have attracted extensive attention from industry and academia in recent years. They are capable of processing unbounded big streams of continuous data in a distributed and real (or near-real) time manner. A fundamental problem in a DSDPS is the scheduling problem, i.e., assigning threads (carrying workload) to workers/machines with the objective of minimizing average end-to-end tuple processing time (or simply tuple processing time). A widely-used solution is to distribute workload over machines in the cluster in a round-robin manner, which is obviously not efficient due to the lack of consideration for communication delay among processes/machines. A scheduling solution makes a significant impact on the average tuple processing time. However, their relationship is very subtle and complicated. It does not even seem possible to have a mathematical programming formulation for the scheduling problem if its objective is to directly minimize the average tuple processing time. In this dissertation, we first propose a model-based approach that accurately models the correlation between a scheduling solution and its objective value (i.e. average tuple processing time) for a given scheduling solution according to the topology of the application graph and runtime statistics. A predictive scheduling algorithm is then presented, which as- signs tasks (threads) to machines under the guidance of the proposed model. This approach achieves an average of 24.9% improvement over Storm’s default scheduler. However, the model-based approach still has its limitations: the model may not be able to fully capture the features of a DSDPS; prediction may not be accurate enough; and a large amount of high-dimensional data may lead to high overhead. To address the limitations, we develop a model-free approach that can learn to control a DSDPS from its experience rather than adopting accurate and mathematically solvable system models, just as a human learns a skill (such as cooking, driving, swimming, etc.). Recent breakthrough of Deep Reinforcement Learning (DRL) provides a promising approach for enabling effective model-free control. The proposed DRL-based model-free approach minimizes the average end-to-end tuple processing time by jointly learning the system environment via collecting very limited runtime statistics and making decisions under the guidance of powerful Deep Neural Networks (DNNs). This approach achieves great performance improvement over the current practice and the state-of-the-art model-based approach. Moreover, there is still room for improvement for the above model-free approach: For the above model-free approach and most existing methods, a user specifies the number of threads for an application in advance without knowing much about runtime needs, which, however, remains unchanged during runtime. This could severely affect the performance of a DSDPS. Therefore, we further develop another model-free approach using DRL, EXTRA, which enables the dynamic use of a variable number of threads at runtime. It has been shown by extensive experimental results, by adding this new feature, EXTRA can achieve further performance improvement and greater flexibility on scheduling

Using Dedicated and Opportunistic Networks in Synergy for a Cost-effective Distributed Stream Processing Platform

This paper presents a case for exploiting the synergy of dedicated and opportunistic network resources in a distributed hosting platform for data stream processing applications. Our previous studies have demonstrated the benefits of combining dedicated reliable resources with opportunistic resources in case of high-throughput computing applications, where timely allocation of the processing units is the primary concern. Since distributed stream processing applications demand large volume of data transmission between the processing sites at a consistent rate, adequate control over the network resources is important here to assure a steady flow of processing. In this paper, we propose a system model for the hybrid hosting platform where stream processing servers installed at distributed sites are interconnected with a combination of dedicated links and public Internet. Decentralized algorithms have been developed for allocation of the two classes of network resources among the competing tasks with an objective towards higher task throughput and better utilization of expensive dedicated resources. Results from extensive simulation study show that with proper management, systems exploiting the synergy of dedicated and opportunistic resources yield considerably higher task throughput and thus, higher return on investment over the systems solely using expensive dedicated resources.Comment: 9 page

DRS: Dynamic Resource Scheduling for Real-Time Analytics over Fast Streams

In a data stream management system (DSMS), users register continuous queries, and receive result updates as data arrive and expire. We focus on applications with real-time constraints, in which the user must receive each result update within a given period after the update occurs. To handle fast data, the DSMS is commonly placed on top of a cloud infrastructure. Because stream properties such as arrival rates can fluctuate unpredictably, cloud resources must be dynamically provisioned and scheduled accordingly to ensure real-time response. It is quite essential, for the existing systems or future developments, to possess the ability of scheduling resources dynamically according to the current workload, in order to avoid wasting resources, or failing in delivering correct results on time. Motivated by this, we propose DRS, a novel dynamic resource scheduler for cloud-based DSMSs. DRS overcomes three fundamental challenges: (a) how to model the relationship between the provisioned resources and query response time (b) where to best place resources; and (c) how to measure system load with minimal overhead. In particular, DRS includes an accurate performance model based on the theory of \emph{Jackson open queueing networks} and is capable of handling \emph{arbitrary} operator topologies, possibly with loops, splits and joins. Extensive experiments with real data confirm that DRS achieves real-time response with close to optimal resource consumption.Comment: This is the our latest version with certain modificatio

Computing infrastructure issues in distributed communications systems : a survey of operating system transport system architectures

The performance of distributed applications (such as file transfer, remote login, tele-conferencing, full-motion video, and scientific visualization) is influenced by several factors that interact in complex ways. In particular, application performance is significantly affected both by communication infrastructure factors and computing infrastructure factors. Several communication infrastructure factors include channel speed, bit-error rate, and congestion at intermediate switching nodes. Computing infrastructure factors include (among other things) both protocol processing activities (such as connection management, flow control, error detection, and retransmission) and general operating system factors (such as memory latency, CPU speed, interrupt and context switching overhead, process architecture, and message buffering). Due to a several orders of magnitude increase in network channel speed and an increase in application diversity, performance bottlenecks are shifting from the network factors to the transport system factors.This paper defines an abstraction called an "Operating System Transport System Architecture" (OSTSA) that is used to classify the major components and services in the computing infrastructure. End-to-end network protocols such as TCP, TP4, VMTP, XTP, and Delta-t typically run on general-purpose computers, where they utilize various operating system resources such as processors, virtual memory, and network controllers. The OSTSA provides services that integrate these resources to support distributed applications running on local and wide area networks.A taxonomy is presented to evaluate OSTSAs in terms of their support for protocol processing activities. We use this taxonomy to compare and contrast five general-purpose commercial and experimental operating systems including System V UNIX, BSD UNIX, the x-kernel, Choices, and Xinu