395,077 research outputs found
Distributed Robust Set-Invariance for Interconnected Linear Systems
We introduce a class of distributed control policies for networks of
discrete-time linear systems with polytopic additive disturbances. The
objective is to restrict the network-level state and controls to user-specified
polyhedral sets for all times. This problem arises in many safety-critical
applications. We consider two problems. First, given a communication graph
characterizing the structure of the information flow in the network, we find
the optimal distributed control policy by solving a single linear program.
Second, we find the sparsest communication graph required for the existence of
a distributed invariance-inducing control policy. Illustrative examples,
including one on platooning, are presented.Comment: 8 Pages. Submitted to American Control Conference (ACC), 201
Certain performance aspects of optimal load balancing in distributed computer systems
A distributed computer system is considered to be a collection of autonomous computers (nodes) located at possibly different sites and connected by a communication network. Through the communication network, resources of the system can be shared by users at different locations. Performance enhancement is one of the most important issues in distributed systems. The performance of a distributed computer system can often be improved to an acceptable level by redistributing the workload among nodes. The problem of load redistribution in distributed computer systems is called load balancing. Load balancing policies may be either static or dynamic. Static load balancing policies use only the statistical information on the system (e.g., the average behavior of the system) in making load balancing decisions. On the other hand, dynamic load balancing policies attempt to dynamically balance the workload reflecting the current system state and are therefore thought to be able to further improve the system performance. Generally, the purpose of load balancing policies either static or dynamic is to improve the performance of the system by redistributing the workload among nodes. We can choose between several distinct objectives for performance optimization in many systems including communication networks, distributed computer systems, transportation flow networks, etc. Among them, we have the following three typical objectives or optima: ...Thesis (Ph. D. in Engineering)--University of Tsukuba, (A), no. 3425, 2004.3.25Includes bibliographical reference
On the Convergence of the Holistic Analysis for EDF Distributed Systems
Dynamic scheduling techniques, and EDF (Earliest Deadline First) in particular, have demonstrated their ability to increase the schedulability of real time systems compared to fixed-priority scheduling. In distributed systems, the scheduling policies of the processing nodes tend to be the same as in stand-alone systems and, although few EDF networks exist, it is foreseen that dynamic scheduling will gradually develop into real-time networks. There are some response time analysis techniques for EDF scheduled distributed systems, mostly derived from the holistic analysis developed by Spuri. The convergence of the holistic analysis in context of EDF distributed systems with shared resources had not been studied until now. There is a circular dependency between tasks’ release jitter values, response times and preemption level ceilings of shared resources. In this paper we present an extension of Spuri’s algorithm and we demonstrate that its iterative formulas are non-decreasing, even in the presence of shared resources. This result enables us to assert that the new algorithm converges towards a solution for the response times of the tasks and messages in a distributed system
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Resource Allocation in Distributed Service Networks
The past few years have witnessed significant growth in the use of distributed network analytics involving agile code, data and computational resources. In many such networked systems, for example, Internet of Things (IoT), a large number of smart devices, sensors, processing and storage resources are widely distributed in a geographic region. These devices and resources distributed over a physical space are collectively called a distributed service network. Efficient resource allocation in such high performance service networks remains one of the most critical problems. In this thesis, we model and optimize the allocation of resources in a distributed service network. This thesis contributes to two different types of service networks: caching, and spatial networks; and develops new techniques that optimize the overall performance of these services.
First, we propose a new method to compute an upper bound on hit probability for all non-anticipative caching policies in a distributed caching system. We find our bound to be tighter than state-of-the-art upper bounds for a variety of content request arrival processes. We then develop a utility based framework for content placement in a cache network for efficient and fair allocation of caching resources.
We develop provably optimal distributed algorithms that operate at each network cache to maximize the overall network utility. Next, we develop and evaluate assignment policies that allocate resources to users with a goal to minimize the expected distance traveled by a user request, where both resources and users are located on a line. Lastly, we design and evaluate resource proximity aware user-request allocation policies with a goal to reduce the implementation cost associated with moving a request/job to/from its allocated resource while balancing the number of requests allocated to a resource. Depending on the topology, our proposed policies achieve a 8% - 99% decrease in implementation cost as compared to the state-of-the-art
Unified Approach to Convex Robust Distributed Control given Arbitrary Information Structures
We consider the problem of computing optimal linear control policies for
linear systems in finite-horizon. The states and the inputs are required to
remain inside pre-specified safety sets at all times despite unknown
disturbances. In this technical note, we focus on the requirement that the
control policy is distributed, in the sense that it can only be based on
partial information about the history of the outputs. It is well-known that
when a condition denoted as Quadratic Invariance (QI) holds, the optimal
distributed control policy can be computed in a tractable way. Our goal is to
unify and generalize the class of information structures over which quadratic
invariance is equivalent to a test over finitely many binary matrices. The test
we propose certifies convexity of the output-feedback distributed control
problem in finite-horizon given any arbitrarily defined information structure,
including the case of time varying communication networks and forgetting
mechanisms. Furthermore, the framework we consider allows for including
polytopic constraints on the states and the inputs in a natural way, without
affecting convexity
The Behavior of Epidemics under Bounded Susceptibility
We investigate the sensitivity of epidemic behavior to a bounded
susceptibility constraint -- susceptible nodes are infected by their neighbors
via the regular SI/SIS dynamics, but subject to a cap on the infection rate.
Such a constraint is motivated by modern social networks, wherein messages are
broadcast to all neighbors, but attention spans are limited. Bounded
susceptibility also arises in distributed computing applications with download
bandwidth constraints, and in human epidemics under quarantine policies.
Network epidemics have been extensively studied in literature; prior work
characterizes the graph structures required to ensure fast spreading under the
SI dynamics, and long lifetime under the SIS dynamics. In particular, these
conditions turn out to be meaningful for two classes of networks of practical
relevance -- dense, uniform (i.e., clique-like) graphs, and sparse, structured
(i.e., star-like) graphs. We show that bounded susceptibility has a surprising
impact on epidemic behavior in these graph families. For the SI dynamics,
bounded susceptibility has no effect on star-like networks, but dramatically
alters the spreading time in clique-like networks. In contrast, for the SIS
dynamics, clique-like networks are unaffected, but star-like networks exhibit a
sharp change in extinction times under bounded susceptibility.
Our findings are useful for the design of disease-resistant networks and
infrastructure networks. More generally, they show that results for existing
epidemic models are sensitive to modeling assumptions in non-intuitive ways,
and suggest caution in directly using these as guidelines for real systems
Providing End-to-End Delay Guarantees for Multi-hop Wireless Sensor Networks over Unreliable Channels
Wireless sensor networks have been increasingly used for real-time
surveillance over large areas. In such applications, it is important to support
end-to-end delay constraints for packet deliveries even when the corresponding
flows require multi-hop transmissions. In addition to delay constraints, each
flow of real-time surveillance may require some guarantees on throughput of
packets that meet the delay constraints. Further, as wireless sensor networks
are usually deployed in challenging environments, it is important to
specifically consider the effects of unreliable wireless transmissions.
In this paper, we study the problem of providing end-to-end delay guarantees
for multi-hop wireless networks. We propose a model that jointly considers the
end-to-end delay constraints and throughput requirements of flows, the need for
multi-hop transmissions, and the unreliable nature of wireless transmissions.
We develop a framework for designing feasibility-optimal policies. We then
demonstrate the utility of this framework by considering two types of systems:
one where sensors are equipped with full-duplex radios, and the other where
sensors are equipped with half-duplex radios. When sensors are equipped with
full-duplex radios, we propose an online distributed scheduling policy and
proves the policy is feasibility-optimal. We also provide a heuristic for
systems where sensors are equipped with half-duplex radios. We show that this
heuristic is still feasibility-optimal for some topologies
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