5,060 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
A Survey and Comparative Study of Hard and Soft Real-time Dynamic Resource Allocation Strategies for Multi/Many-core Systems
Multi-/many-core systems are envisioned to satisfy the ever-increasing performance requirements of complex applications in various domains such as embedded and high-performance computing. Such systems need to cater to increasingly dynamic workloads, requiring efficient dynamic resource allocation strategies to satisfy hard or soft real-time constraints. This article provides an extensive survey of hard and soft real-time dynamic resource allocation strategies proposed since the mid-1990s and highlights the emerging trends for multi-/many-core systems. The survey covers a taxonomy of the resource allocation strategies and considers their various optimization objectives, which have been used to provide comprehensive comparison. The strategies employ various principles, such as market and biological concepts, to perform the optimizations. The trend followed by the resource allocation strategies, open research challenges, and likely emerging research directions have also been provided
Feedback-control & queueing theory-based resource management for streaming applications
Recent advances in sensor technologies and instrumentation have led to an extraordinary growth of data sources and streaming applications. A wide variety of devices, from smart phones to dedicated sensors, have the capability of collecting and streaming large amounts of data at unprecedented rates. A number of distinct streaming data models have been proposed. Typical applications for this include smart cites & built environments for instance, where sensor-based infrastructures continue to increase in scale and variety. Understanding how such streaming content can be processed within some time threshold remains a non-trivial and important research topic. We investigate how a cloud-based computational infrastructure can autonomically respond to such streaming content, offering Quality of Service guarantees. We propose an autonomic controller (based on feedback control and queueing theory) to elastically provision virtual machines to meet performance targets associated with a particular data stream. Evaluation is carried out using a federated Cloud-based infrastructure (implemented using CometCloud) – where the allocation of new resources can be based on: (i) differences between sites, i.e. types of resources supported (e.g. GPU vs. CPU only), (ii) cost of execution; (iii) failure rate and likely resilience, etc. In particular, we demonstrate how Little’s Law –a widely used result in queuing theory– can be adapted to support dynamic control in the context of such resource provisioning
Design and Experimental Validation of a Software-Defined Radio Access Network Testbed with Slicing Support
Network slicing is a fundamental feature of 5G systems to partition a single
network into a number of segregated logical networks, each optimized for a
particular type of service, or dedicated to a particular customer or
application. The realization of network slicing is particularly challenging in
the Radio Access Network (RAN) part, where multiple slices can be multiplexed
over the same radio channel and Radio Resource Management (RRM) functions shall
be used to split the cell radio resources and achieve the expected behaviour
per slice. In this context, this paper describes the key design and
implementation aspects of a Software-Defined RAN (SD-RAN) experimental testbed
with slicing support. The testbed has been designed consistently with the
slicing capabilities and related management framework established by 3GPP in
Release 15. The testbed is used to demonstrate the provisioning of RAN slices
(e.g. preparation, commissioning and activation phases) and the operation of
the implemented RRM functionality for slice-aware admission control and
scheduling
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