86,957 research outputs found
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
Resource Allocation Optimization through Task Based Scheduling Algorithms in Distributed Real Time Embedded Systems
Distributed embedded system is a type of distributed system, which consists of a large number of nodes, each node having lower computational power when compared
to a node of a regular distributed system (like a cluster). A real time system is the one where every task has an associated dead line and the system works with a continuous stream of data supplied in real time.Such systems find wide applications in various fields such as automobile industry as fly-by-wire,brake-by-wire and steer-by-wire systems. Scheduling and efficient allocation of resources is extremely important in such systems because a distributed embedded real time system must deliver its output within a certain time frame, failing which the output becomes useless.In this paper, we have taken up processing unit number as a resource and have optimized the allocation of it to the various tasks.We use techniques such as model-based redundancy,heartbeat monitoring and check-pointing for fault detection and failure recovery.Our fault tolerance framework uses an existing list-based scheduling algorithm for task scheduling.This helps in diagnosis and shutting down of faulty actuators before the system becomes unsafe. The framework is designed and tested using a new simulation model consisting of virtual nodes working on a message passing system
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
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