Self-stabilization Research Articles

Self-stabilizing [formula omitted]-alliances with safe convergence

Given two functions f and g mapping nodes to non-negative integers, we give a silent self-stabilizing algorithm that computes a minimal (f,g)-alliance in an asynchronous network with unique node IDs, assuming that every node p has a degree at least g(p) and satisfies f(p)≥g(p). Our algorithm is safely converging in the sense that starting from any configuration, it first converges to a (not necessarily minimal) (f,g)-alliance in at most four rounds, and then continues to converge to a minimal one in at most 5n+4 additional rounds, where n is the size of the network. Our algorithm is written in the shared memory model. It is proven assuming an unfair (distributed) daemon. Its memory requirement is Θ(logn) bits per process, and it takes O(n⋅Δ3) steps to stabilize, where Δ is the degree of the network.

New Self-Stabilizing Algorithms for Minimal Weakly Connected Dominating Sets

In this paper, we propose two new self-stabilizing algorithms, MWCDS-C and MWCDS-D, for minimal weakly connected dominating sets in an arbitrary connected graph. Algorithm MWCDS-C stabilizes in O(n4) steps using an unfair central daemon and space requirement at each node is O(log n) bits at each node for an arbitrary connected graph with n nodes; it uses a designated node while other nodes are identical and anonymous. Algorithm MWCDS-D stabilizes using an unfair distributed daemon with identical time and space complexities, but it assumes unique node IDs. In the literature, the best reported stabilization time for a minimal weakly connected dominating set algorithm is O(nmA) under a distributed daemon [1], where m is the number of edges and A is the number of moves to construct a breadth-first tree.

Model Checking Self-Stabilising in Embedded Systems with Linear Temporal Logic

Over the past two decades, the use of distributed embedded systems is wide in many applications. One way to guarantee that these systems tolerate transient faults is done by making them self-stabilizing systems, which automatically recover from any transient fault. In this paper we present a formalism of self-stabilization concept based on Linear Temporal Logic (LTL), and model checked the self-stabilization in embedded systems. Using a case study inspired by industrial practice, we present in detail a model checking to verify the self stabilization property of our embedded system.

Self-Stabilizing Algorithms for Maximal 2-packing and General k-packing (k ≥ 2) with Safe Convergence in an Arbitrary Graph

In a graph or a network G =( V,E ), a set SâS† V is a 2-packing if ∀ i ∈ V : | N [ i ] ∩S|≤ 1, where N [ i ] denotes the closed neighborhood of node i . A 2-packing is maximal if no proper superset of S is a 2-packing. This paper presents a safely converging self-stabilizing algorithm for maximal 2-packing problem. Under a synchronous daemon, it quickly converges to a 2- packing (a safe state, not necessarily the legitimate state) in three synchronous steps, and then terminates in a maximal one (the legitimate state) in O ( n ) steps without breaking safety during the convergence interval, where n is the number of nodes. Space requirement at each node is O (log n ) bits. This is a significant improvement over the most recent self-stabilizing algorithm for maximal 2-packing that uses O ( n 2 ) synchronous steps with same space complexity and that does not have safe convergence property. We then generalize the technique to design a self- stabilizing algorithm for maximal k -packing, k ≥ 2, with safe convergence that stabilizes in O ( kn 2 ) steps under synchronous daemon; the algorithm has space complexity of O ( kn log n ) bits at each node; existing algorithms for k -packing stabilize in exponential time under a central daemon with O (log n ) space complexity.Â

On the self-stabilization of mobile oblivious robots in uniform rings

We investigate self-stabilizing algorithms for anonymous and oblivious robots in uniform ring networks, that is, we focus on algorithms that can start from any initial configuration (including those with multiplicity points). First, we show that no probabilistic self-stabilizing gathering algorithm exists in the asynchronous (ASYNC) model or if only global-weak and local-strong multiplicity detection is available. This impossibility result implies that a common assumption about initial configurations (no two robots share a node initially) is a very strong one.On the positive side, we give a probabilistic self-stabilizing algorithm for the gathering and orientation problems in the semi-synchronous (SSYNC) model with global-strong multiplicity detection. With respect to impossibility results, those are the weakest system hypotheses. In addition, as an application of the previous algorithm, we provide a self-stabilizing algorithm for the set formation problem. Our results imply that any static set formation can be realized in a self-stabilizing manner in this model.

SKIP +

Peer-to-peer systems rely on a scalable overlay network that enables efficient routing between its members. Hypercubic topologies facilitate such operations while each node only needs to connect to a small number of other nodes. In contrast to static communication networks, peer-to-peer networks allow nodes to adapt their neighbor set over time in order to react to join and leave events and failures. This article shows how to maintain such networks in a robust manner. Concretely, we present a distributed and self-stabilizing algorithm that constructs a (slightly extended) skip graph, SKIP + , in polylogarithmic time from any given initial state in which the overlay network is still weakly connected. This is an exponential improvement compared to previously known self-stabilizing algorithms for overlay networks. In addition, our algorithm handles individual joins and leaves locally and efficiently.

A Self-stabilizing Algorithm for Locating the Center of Maximal Outerplanar Graphs

A Self-stabilizing Algorithm for Locating the Center of Maximal Outerplanar Graphs

A Linear Time Self-stabilizing Algorithm for Minimal Weakly Connected Dominating Sets

In this paper, we propose a new self-stabilizing algorithm for minimal weakly connected dominating sets (called algorithm $$\mathtt{MWCDS}$$MWCDS). For an arbitrary connected graph with $$n$$n nodes, algorithm $$\mathtt{MWCDS}$$MWCDS terminates in $$O(n)$$O(n) steps using a synchronous daemon. The space requirement at each node is $$O(\log n)$$O(logn) bits. In the literature, the best reported stabilization time for a minimal weakly connected dominating set algorithm is $$O(nmA)$$O(nmA) under a distributed daemon, where $$m$$m is the number of edges and $$A$$A is the number of moves to construct a breadth-first tree.

Bounds on Herman's algorithm

Herman's self-stabilization algorithm allows a ring of N processors having any odd number of tokens to reach a stable state where exactly one token remains. McIver and Morgan conjecture that the expected time taken for stabilization is maximized when there are three equally-spaced tokens. We prove exact results on a related cost function, and obtain a bound on expected time which is very close to the conjectured bound.

Synthesis of a simple self-stabilizing system

With the increasing importance of distributed systems as a computing paradigm, a systematic approach to their design is needed. Although the area of formal verification has made enormous advances towards this goal, the resulting functionalities are limited to detecting problems in a particular design. By means of a classical example, we illustrate a simple template-based approach to computer-aided design of distributed systems based on leveraging the well-known technique of bounded model checking to the synthesis setting.

Clustering by Swarm Intelligence in the Ad-Hoc Networks

An Ad-hoc wireless is composed mainly of mobile hosts that communicate with each other without fixed infrastructure and no central administration. The main problems associated with these networks are the unpredictable mobility of hosts and a modest flow of communication. In this context, a major problem is partitioning the network in groups called clusters, giving a hierarchical organization. This work presents a deterministic self-stabilizing clustering algorithm for Ad-Hoc networks based on PSO (Particle Swarm Optimization). The proposed approach creates clusters and controls the nodes mobility to ensure more stability for the system. To increase the network life and reduce the answer time of user queries, it proposed also a replication strategy based on the non-similarity degree. The simulations show that the cooperative approaches (clustering, mobility and replication) minimize the energy consumption and increase the QoS of the system.

Research on the Visual Self-Stabilization System of the Automated Guided Vehicle (AGV)

In view of the unstable phenomenon of the visual system of the AGV in the automatic system, the paper adopts the MEMS gyroscope MPU6050 to collect the status of the visual holder. We can guarantee the stability of the holder, obtain clear pictures and improve the operating validity of the vision-based guided AGV by controlling the brushless motor through the Arduino single chip microcomputer program.

A 4n-move self-stabilizing algorithm for the minimal dominating set problem using an unfair distributed daemon

A distributed system is self-stabilizing if, regardless of its initial state, the system is guaranteed to reach a legitimate (i.e., correct) state in finite time. In 2007, Turau proposed the first linear-time self-stabilizing algorithm for the minimal dominating set (MDS) problem under an unfair distributed daemon [9]; this algorithm stabilizes in at most 9n moves, where n is the number of nodes in the system. In 2008, Goddard et al. [4] proposed a 5n-move algorithm. In this paper, we present a 4n-move algorithm.

Analysis of Distributed Token Circulation Algorithm with Faulty Random Number Generator

Randomization is a technique to improve efficiency and computability of distributed computing. In this paper, we investigate fault tolerance of distributed computing against faults of random number generators. We introduce an RNG (Random Number Generator)-fault as a new class of faults; a random number generator on an RNG-faulty process outputs the same number deterministically. This paper is the first work that considers faults of randomness in distributed computing. We investigate the role of randomization by observing the impact of RNG-faults on performance of a self-stabilizing token circulation algorithm on unidirectional n-node ring networks. In the analysis, we assume there exist nf (0 ≤ nf ≤ n−1) RNG-faulty nodes and each RNG-faulty node always transfers a token to the next node. Our results are threefold: (1) We derive the upper bound on the expected convergence time in the case of nf = n − 1. (2) Our simulation result shows that the expected convergence time is maximum when nf = n − 1. (3) We derive the expected token circulation time for each nf (0 ≤ nf ≤ n − 1).

Enhancing Performance of Partitioned Database in Cloud Computing Environment

Cloud computing is a revolutionary technology which has taken more concern than any other before, especially by large companies, huge online services and every compute-data intensive application; not only because it is new, but cloud computing has moved distributed systems from side to another. This paper describes the design and architecture of our self-stabilization cloud DBMS based on an in-depth benchmark model with focusing on read operations. On the contrast of map-Reduce, which is a powerful traditional distributed system working on a dedicated cluster and uses key-value data store with a low-level query language, our architecture benefits from great cloud characteristics, certainly automatic resource provisioning to scale virtual cluster up or down with an easy and straight way, and from a long-term evolution of RDBMS and their indexes including b-tree, hash and also full text to retrieve data; in addition, the SQL is a standard query language. Our system uses a dedicated benchmark server which we have developed to benchmark virtual nodes used in the system to give the broker, the stabilizer in the system, a benchmark matrix containing number of queries, distributed over data object stairs and time stairs, per time cycle, besides a decision tree, so the broker can build up access pattern matrix such the benchmark matrix, but with using a decision tree to estimate query time stair; depending on the two matrices, The broker stabilizes the system by migrating and replicating data objects, while allocating or releasing nodes

Efficient self-stabilizing algorithms for minimal total k-dominating sets in graphs

We propose the first polynomial self-stabilizing distributed algorithm for the minimal total dominating set problem in an arbitrary graph. Then, we generalize the proposed algorithm for the minimal total k-dominating set problem. Under an unfair distributed scheduler, the proposed algorithms converge in O(mn) moves starting from any arbitrary state, and require O(logn) storage per node.

FLOC-SPANNER: An O(1) Time, Locally Self-Stabilizing Algorithm for Geometric Spanner Construction in a Wireless Sensor Network

We present a distributed algorithm for creation of geometric spanners in a wireless sensor network. Given any connected network, we show that the algorithm terminates in [Formula: see text] time, irrespective of network size. Our algorithm uses an underlying clustering algorithm as a foundation for creating spanners and only relies on the periodic heartbeat messages associated with cluster maintenance for the creation of the spanners. The algorithm is also shown to stabilize locally in the presence of node additions and deletions. The performance of our algorithm is verified using large scale simulations. The average path length ratio for routing along the spanner for large networks is shown to be less than 2.

Dynamic multiagent load balancing using distributed constraint optimization techniques

Resource management is a key challenge in multiagent systems. It is especially important in dynamic environments where decisions need to be made quickly and when decisions can get obsolete quickly. In wireless local area networks WLANs, resource management includes dynamic channel assignment, dynamic transmit power control and load balancing of WLANs traffic. In this work, we present a novel decentralized framework and a distributed optimization algorithm DLB-SDPOP for load balancing in complex WLANs. Our self-stabilizing algorithm focuses on repairing the network structure instead of reconstructing it when network perturbations occur. It controls the complexity of problem solving by utilizing efficient search and leverages uncertainty to reduce the possibility of reaching myopic solutions. The size and number of inter-agent communication messages are significantly reduced using a communication filtering mechanism. We categorize different scenarios based on key characteristics i.e., load, dynamics and uncertainty in WLANs and compare our algorithm with other state of the art distributed constraint optimization algorithms in each scenario. Our empirical results show that our distributed approach improves WLANs load balancing performance significantly.

Efficient distributed lifetime optimization algorithm for sensor networks

One of the main design challenges in Wireless Sensor Networks (WSN) is to prolong the system lifetime, while achieving acceptable quality of service for applications. In WSN, each sensor node is battery powered and it is not convenient to recharge or replace the batteries in many cases, especially in remote and hostile environments. Due to the limited capabilities of sensor nodes, it is usually desirable that a WSN should be deployed with high density and thus redundancy can be exploited to increase the network’s lifetime. In this paper, we introduce an efficient lifetime optimization and self-stabilizing algorithm to enhance the lifetime of wireless sensor networks especially when the reliabilities of sensor nodes are expected to decrease due to use and wear-out effects. Our algorithm seeks to build resiliency by maintaining a necessary set of working nodes and replacing failed ones when needed. We provide some theoretical and simulation results, that fully demonstrate the usefulness of the proposed algorithm.

A Practical Framework for Self-Stabilization in Service-based Mobile Ecosystem *

Mobile devices are widely accepted as a convergence machine, which provides both cell phone capability and a lightweight computing capability. However, mobile devices have a major drawback of limited computing power and resources such as main memory and battery life. Service-based mobile applications are emerged as an efficient solution to overcome this limitation of mobile devices. On the other hand, with the advent of more powerful mobile devices, mobile devices are actively participating as computer nodes and performing enterprise functionality. In this paper, we present a practical framework for dynamically deploying services. With the ever increasing computing power of mobile devices, we project that mobile devices can also be used as computer nodes deploying services. A number of benefits for deploying services on mobile devices existed. In this paper, we define an ecosystem for service-based mobile computing, and present techniques for dynamically deploying services on station nodes and mobile nodes, which are challenging problems. By applying dynamical deployment of services on both station and mobile nodes, the overall quality of the ecosystem can be consistently maintained.