3,355 research outputs found
Integrative Dynamic Reconfiguration in a Parallel Stream Processing Engine
Load balancing, operator instance collocations and horizontal scaling are
critical issues in Parallel Stream Processing Engines to achieve low data
processing latency, optimized cluster utilization and minimized communication
cost respectively. In previous work, these issues are typically tackled
separately and independently. We argue that these problems are tightly coupled
in the sense that they all need to determine the allocations of workloads and
migrate computational states at runtime. Optimizing them independently would
result in suboptimal solutions. Therefore, in this paper, we investigate how
these three issues can be modeled as one integrated optimization problem. In
particular, we first consider jobs where workload allocations have little
effect on the communication cost, and model the problem of load balance as a
Mixed-Integer Linear Program. Afterwards, we present an extended solution
called ALBIC, which support general jobs. We implement the proposed techniques
on top of Apache Storm, an open-source Parallel Stream Processing Engine. The
extensive experimental results over both synthetic and real datasets show that
our techniques clearly outperform existing approaches
Model-driven Scheduling for Distributed Stream Processing Systems
Distributed Stream Processing frameworks are being commonly used with the
evolution of Internet of Things(IoT). These frameworks are designed to adapt to
the dynamic input message rate by scaling in/out.Apache Storm, originally
developed by Twitter is a widely used stream processing engine while others
includes Flink, Spark streaming. For running the streaming applications
successfully there is need to know the optimal resource requirement, as
over-estimation of resources adds extra cost.So we need some strategy to come
up with the optimal resource requirement for a given streaming application. In
this article, we propose a model-driven approach for scheduling streaming
applications that effectively utilizes a priori knowledge of the applications
to provide predictable scheduling behavior. Specifically, we use application
performance models to offer reliable estimates of the resource allocation
required. Further, this intuition also drives resource mapping, and helps
narrow the estimated and actual dataflow performance and resource utilization.
Together, this model-driven scheduling approach gives a predictable application
performance and resource utilization behavior for executing a given DSPS
application at a target input stream rate on distributed resources.Comment: 54 page
Integrating Scale Out and Fault Tolerance in Stream Processing using Operator State Management
As users of big data applications expect fresh results, we witness a new breed of stream processing systems (SPS) that are designed to scale to large numbers of cloud-hosted machines. Such systems face new challenges: (i) to benefit from the pay-as-you-go model of cloud computing, they must scale out on demand, acquiring additional virtual machines (VMs) and parallelising operators when the workload increases; (ii) failures are common with deployments on hundreds of VMs - systems must be fault-tolerant with fast recovery times, yet low per-machine overheads. An open question is how to achieve these two goals when stream queries include stateful operators, which must be scaled out and recovered without affecting query results. Our key idea is to expose internal operator state explicitly to the SPS through a set of state management primitives. Based on them, we describe an integrated approach for dynamic scale out and recovery of stateful operators. Externalised operator state is checkpointed periodically by the SPS and backed up to upstream VMs. The SPS identifies individual operator bottlenecks and automatically scales them out by allocating new VMs and partitioning the check-pointed state. At any point, failed operators are recovered by restoring checkpointed state on a new VM and replaying unprocessed tuples. We evaluate this approach with the Linear Road Benchmark on the Amazon EC2 cloud platform and show that it can scale automatically to a load factor of L=350 with 50 VMs, while recovering quickly from failures. Copyright © 2013 ACM
Parallel and Distributed Stream Processing: Systems Classification and Specific Issues
Deploying an infrastructure to execute queries on distributed data streams sources requires to identify a scalable and robust solution able to provide results which can be qualified. Last decade, different Data Stream Management Systems have been designed by exploiting new paradigm and technologies to improve performances of solutions facing specific features of data streams and their growing number. However, some tradeoffs are often achieved between performance of the processing, resources consumption and quality of results. This survey 5 suggests an overview of existing solutions among distributed and parallel systems classified according to criteria able to allow readers to efficiently identify relevant existing Distributed Stream Management Systems according to their needs ans resources
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
Auto-tuning Distributed Stream Processing Systems using Reinforcement Learning
Fine tuning distributed systems is considered to be a craftsmanship, relying
on intuition and experience. This becomes even more challenging when the
systems need to react in near real time, as streaming engines have to do to
maintain pre-agreed service quality metrics. In this article, we present an
automated approach that builds on a combination of supervised and reinforcement
learning methods to recommend the most appropriate lever configurations based
on previous load. With this, streaming engines can be automatically tuned
without requiring a human to determine the right way and proper time to deploy
them. This opens the door to new configurations that are not being applied
today since the complexity of managing these systems has surpassed the
abilities of human experts. We show how reinforcement learning systems can find
substantially better configurations in less time than their human counterparts
and adapt to changing workloads
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