Group-Based Gossip Multicast Protocol for Efficient and Fault Tolerant Message Dissemination in Clouds

Lim, Jong Beom; Lee, Jong Hyuk; Chin, Sung Ho; Yu, Heon Chang

doi:10.1007/978-3-642-20754-9_3

Cited by 10 publications

(6 citation statements)

References 6 publications

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“…In Algorithm 1, initially, WaitFor and ArrayWaitFor are null (lines 1-3). During local execution, which is running independent of gossip execution, a node updates WaitFor and ArrayWaitFor data structures as follows (lines [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. When P i is trying to acquire a resource from P k , it adds <P k , ID resource > to WaitFor i (line 6).…”

Section: Algorithm Descriptionsmentioning

confidence: 99%

“…Gossip-based algorithms guarantee message delivery to all nodes with high probability; their variations can be found in [13][14][15][16][17]. In this paper, we present an unstructured deadlock detection algorithm using a gossip protocol in cloud computing environments, where constituting nodes may join and leave at any time.…”

mentioning

confidence: 99%

“…Gossip-based algorithms guarantee message delivery to all nodes with high probability; their variations can be found in [13][14][15][16][17]. Gossip-based algorithms guarantee message delivery to all nodes with high probability; their variations can be found in [13][14][15][16][17].…”

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Unstructured deadlock detection technique with scalability and complexity‐efficiency in clouds

Lim

Suh

2013

Int J Communication

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To detect deadlock in distributed systems, the initiator should construct an efficient explicit or implicit global wait-for graph. In this paper, we present an unstructured deadlock detection algorithm using a gossip protocol in cloud computing environments, where constituting nodes may join and leave at any time. Because of the inherit properties of a gossip protocol, we argue that our proposed deadlock detection algorithm is scalable, fault-tolerant, and efficient, retaining safety and liveness properties. The correctness proof of the algorithm is also provided. The message complexity of our proposed algorithm is O(n), where n is the number of nodes. Our performance evaluation with scalable settings shows that our approach has a significant advantage over previous deadlock detection algorithms in terms of solving scalability, fault-tolerance, and complexity-efficiency issues.UNSTRUCTURED DEADLOCK DETECTION TECHNIQUE 853 cycle (round), every node in the system selects f (fanout) number of nodes at random and then communicates using either (i) push; (ii) pull; or (iii) push-pull mode. Gossip-based algorithms guarantee message delivery to all nodes with high probability; their variations can be found in [13][14][15][16][17]. Applications of gossip-based algorithms include message dissemination, failure detection services, data aggregation, and clock synchronization [18,19].In this paper, we present an unstructured deadlock detection algorithm based on the gossip-based algorithm. An approach that applies a gossip-based algorithm for resolving the deadlock detection problem is desirable because of the ability of this type of algorithm to deal with scalability and dynamic behavior in cloud computing systems. In the gossip-based algorithm, the essential key to resolving the scalability issue is that each node has only a local view. In other words, each node does not have to maintain all the nodes in the system but only a small number of nodes, and the overlay network is greatly simplified.The remainder of the paper is organized as follows. We present the system model and formally describe the deadlock detection problem in Section 2. In Section 3, we provide the basic idea and descriptions of our unstructured deadlock detection algorithm that uses gossip communication. Evaluation results for the algorithm and their interpretation are given in Section 4; this section also analyzes the message complexity and proves the correctness properties of the algorithm. After reviewing some related studies in Section 5, we present our conclusions in Section 6. SYSTEM MODEL AND PROBLEM STATEMENTS System modelWe assume that the cloud computing infrastructure consists of numerous nodes of resources and individual nodes process arbitrary programs to achieve a common goal. Because of the absence of shared memory, each process or node should communicate with other nodes only by passing messages through a set of channels. In addition, we assume that all the channels are reliable but not restricted to FIFO, meaning messages within a channe...

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Section: Algorithm Descriptionsmentioning

confidence: 99%

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confidence: 99%

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Unstructured deadlock detection technique with scalability and complexity‐efficiency in clouds

Lim

Suh

2013

Int J Communication

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“…In recent years, the epidemic or gossip-based communication model has been employed in many applications in large-scale distributed systems and cloud computing systems. These applications include information dissemination [1], [2], clock synchronization [3], mutual exclusion [4], deadlock detection [5], termination detection [6], video streaming service [7], and BitTorrent (Tribler) [8]. In cloud computing environments, nodes can join or leave the system at will by virtue of virtualization technology [9].…”

Section: Introductionmentioning

confidence: 99%

Gossip Membership Management with Social Graphs for Byzantine Fault Tolerance in Clouds

Lim

Gil

Chung

et al. 2014

Advanced Information Systems Engineering

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As computer systems have become more complex and dynamic, unstructured and decentralized techniques serve as basic building blocks in large-scale systems such as cloud computing systems. In particular, we consider a gossip-based algorithm, one of the unstructured overlay construction techniques. In this paper, we propose a membership management mechanism using the gossip-based algorithm with social graphs for the Byzantine fault tolerance problem. Experimental results show that our membership management mechanism copes with Byzantine nodes effectively in a scalable way without a bottleneck in dynamic computing environments, requiring only n ≥ 2f + 1 nodes.

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“…With this partial view, at each cycle (round), every node in the system selects f (fanout) number of nodes at random and then communicates using one of the following ways: 1) Push, 2) Pull, and 3) Push-pull mode. Gossip-based algorithms guarantee message delivery to all nodes with high probability and their variation can be found in [10,11,12,13,14]. Applications of gossip-based algorithms include message dissemination, failure detection services, data aggregation etc.…”

Section: Introductionmentioning

confidence: 99%

A Termination Detection Technique Using Gossip in Cloud Computing Environments

Lim¹,

Chung

Yu³

2012

Lecture Notes in Computer Science

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Termination detection is a fundamental problem in distributed systems. In previous research, some structures are used (e.g., spanning tree or computational tree) to detect termination. In this work, we present an unstructured termination detection algorithm, which uses a gossip based algorithm to cope with scalability and fault-tolerance issues. This approach allows the algorithm not to maintain structures during runtime due to node joining and leaving. These dynamic behaviors are prevalent in cloud computing environments and little attention has been paid by existing approaches. To measure the complexity of our proposed algorithm, a new metric, self-centered message complexity is used. Our evaluation over scalable settings shows that the unstructured approach can have a significant merit on performance over existing algorithms.

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Group-Based Gossip Multicast Protocol for Efficient and Fault Tolerant Message Dissemination in Clouds

Cited by 10 publications

References 6 publications

Unstructured deadlock detection technique with scalability and complexity‐efficiency in clouds

Unstructured deadlock detection technique with scalability and complexity‐efficiency in clouds

Gossip Membership Management with Social Graphs for Byzantine Fault Tolerance in Clouds

A Termination Detection Technique Using Gossip in Cloud Computing Environments

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