A Local Facility Location Algorithm for Large-scale Distributed Systems

Krivitski, Denis; Schuster, Assaf; Wolff, Ran

doi:10.1007/s10723-007-9069-5

Cited by 20 publications

(24 citation statements)

References 27 publications

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“…These approaches address either general-purpose service placement directly [34,58] or as part of placing the components of a hierarchical service overlay [98].…”

Section: Discussionmentioning

confidence: 99%

“…Krivitski et al propose a local algorithm for the facility location problem for large-scale distributed system in general [58] and for WSNs in particular [57]. Given a set of clients, a set of possible locations for facilities, a cost function, and the desired number of facilities, the proposed algorithm establishes which locations are to be used for the desired number of facilities in order to minimize cost.…”

Section: Local Facility Location In Distributed Systemsmentioning

confidence: 99%

“…This implies that an implementation must either be readily available or it must be feasible to implement the algorithm with reasonable effort. Unfortunately, this criterion rules out some of the more interesting approaches such as [34,58].…”

Section: Candidates For Quantitative Comparisonmentioning

confidence: 99%

See 2 more Smart Citations

Service Placement in Ad Hoc Networks

Wittenburg¹,

Schiller²

2010

PIK - Praxis Der Informationsverarbeitung Und Kommunikation

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Service provisioning in ad hoc networks is challenging given the difficulties of communicating over a wireless channel and the potential heterogeneity and mobility of the devices that form the network. In order to optimize the performance of the network over which a service host provides a service to client nodes, it is necessary to continuously adapt the logical network topology to both external (e.g., wireless connectivity, mobility, churn) and internal (e.g., communication patterns, service demand) factors. Recent proposals advocate that nodes should dynamically choose which nodes in the network are to provide application-level services to other nodes. Services in this context range from infrastructural services such as the Domain Name System (DNS) to user-oriented services such as the World Wide Web (WWW).Service placement is the process of selecting an optimal set of nodes to host the implementation of a service in light of a given service demand and network topology. The main questions addressed by service placement are: How many instances of the same service should be available in the network and cooperate to process clients' service requests; where these service instances should be placed, i.e., which nodes are best suited for hosting them;and when to adapt the current service configuration. The service instances of a distributively operating service are exact copies of the software component that provides the service, including both the executable binary and the application-level data. The set of nodes that host a service instance is referred to as the service configuration. A good service configuration increases the performance of a service according to application-specific quality metrics, while at the same time potentially reducing the overall network load. The key advantage of active service placement in ad hoc networks is that it allows for the service configuration to be adapted continuously at run time.In this work, we propose the SPi service placement framework as a novel approach to service placement in ad hoc networks. The SPi framework takes advantage of the interdependencies between service placement, service discovery and the routing of service requests to minimize signaling overhead. We also propose the Graph Cost / Single Instance (GCSI) and the Graph Cost / Multiple Instances (GCMI) placement algorithms. The SPi framework employs these algorithms to optimize the number and the location of service instances based on usage statistics and a partial network topology derived from routing information. The GCSI and GCMI placement algorithms only require minimal knowledge about the service they are tasked with placing in the network. They are novel in that they take the communication between service instances into account which is required to synchronize the global state of the service. Furthermore, when calculating the optimal timing of their placement decisions, the two algorithms explicitly consider the overhead of the actions required for implementing changes to the current service c...

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“…These approaches address either general-purpose service placement directly [34,58] or as part of placing the components of a hierarchical service overlay [98].…”

Section: Discussionmentioning

confidence: 99%

Section: Local Facility Location In Distributed Systemsmentioning

confidence: 99%

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Service Placement in Ad Hoc Networks

Wittenburg¹,

Schiller²

2010

PIK - Praxis Der Informationsverarbeitung Und Kommunikation

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“…Their work focuses on primal-dual techniques -in order to derive worst-case performance bounds -which are difficult to implement (e.g., impractical communication model) compared to the work presented in this paper. A recent work by Krivitski et al [25] proposes a distributed hill-climbing algorithm based on local majority votes and used by nodes to agree on the next step of the algorithm. The overall overhead is kept low by avoiding unnecessary votes.…”

Section: Related Workmentioning

confidence: 99%

Scalable service migration in autonomic network environments

Οικονόμου

Stavrakakis

2010

IEEE J. Select. Areas Commun.

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Abstract-Service placement is a key problem in communication networks as it determines how efficiently the user service demands are supported. This problem has been traditionally approached through the formulation and resolution of large optimization problems requiring global knowledge and a continuous recalculation of the solution in case of network changes. Such approaches are not suitable for large-scale and dynamic network environments. In this paper, the problem of determining the optimal location of a service facility is revisited and addressed in a way that is both scalable and deals inherently with network dynamicity. In particular, service migration which enables service facilities to move between neighbor nodes towards more communication cost-effective positions, is based on local information. The migration policies proposed in this work are analytically shown to be capable of moving a service facility between neighbor nodes in a way that the cost of service provision is reduced and -under certain conditions -the service facility reaches the optimal (cost minimizing) location, and locks in there as long as the environment does not change; as network conditions change, the migration process is automatically resumed, thus, naturally responding to network dynamicity under certain conditions. The analytical findings of this work are also supported by simulation results that shed some additional light on the behavior and effectiveness of the proposed policies.

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“…Bawa et al [4] developed an approach based on probabilistic counting. In addition, techniques have been developed for addressing more complex data mining/data problems over large-scale dynamic networks: association rule mining [28], facility location [24], outlier detection [9], decision tree induction [7], ensemble classification [25], support vector machine-based classification [1], K-means clustering [11], top-K query processing [3]. A related line of research concerns the monitoring of various kinds of data models over large numbers of data streams.…”

Section: Data Analysis In Large Dynamic Networkmentioning

confidence: 99%

Scalable Distributed Change Detection from Astronomy Data Streams using Local, Asynchronous Eigen Monitoring Algorithms

Das¹,

Bhaduri²,

Arora³

et al. 2009

Proceedings of the 2009 SIAM International Conference on Data Mining

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This paper considers the problem of change detection using local distributed eigen monitoring algorithms for next generation of astronomy petascale data pipelines such as the Large Synoptic Survey Telescopes (LSST). This telescope will take repeat images of the night sky every 20 seconds, thereby generating 30 terabytes of calibrated imagery every night that will need to be coanalyzed with other astronomical data stored at different locations around the world. Change point detection and event classification in such data sets may provide useful insights to unique astronomical phenomenon displaying astrophysically significant variations: quasars, supernovae, variable stars, and potentially hazardous asteroids. However, performing such data mining tasks is a challenging problem for such high-throughput distributed data streams. In this paper we propose a highly scalable and distributed asynchronous algorithm for monitoring the principal components (PC) of such dynamic data streams. We demonstrate the algorithm on a large set of distributed astronomical data to accomplish well-known astronomy tasks such as measuring variations in the fundamental plane of galaxy parameters. The proposed algorithm is provably correct (i.e. converges to the correct PCs without centralizing any data) and can seamlessly handle changes to the data or the network. Real experiments performed on Sloan Digital Sky Survey (SDSS) catalogue data show the effectiveness of the algorithm.

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A Local Facility Location Algorithm for Large-scale Distributed Systems

Cited by 20 publications

References 27 publications

Service Placement in Ad Hoc Networks

Service Placement in Ad Hoc Networks

Scalable service migration in autonomic network environments

Scalable Distributed Change Detection from Astronomy Data Streams using Local, Asynchronous Eigen Monitoring Algorithms

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