Peer-to-Peer Service Discovery for Grid Computing

2010 ◽  
pp. 285-312
Author(s):  
Eddy Caron ◽  
Frédéric Desprez ◽  
Franck Petit ◽  
Cédric Tedeschi
Keyword(s):  
Fault Tolerance ◽  
Load Balancing ◽  
Service Discovery ◽  
Large Scale ◽  
Dynamic Networks ◽  
Peer To Peer ◽  
Indexing System ◽  
Key Points ◽  
Computing Platforms ◽  
Traditional Approaches

Within distributed computing platforms, some computing abilities (or services) are offered to clients. To build dynamic applications using such services as basic blocks, a critical prerequisite is to discover those services. Traditional approaches to the service discovery problem have historically relied upon centralized solutions, unable to scale well in large unreliable platforms. In this chapter, we will first give an overview of the state of the art of service discovery solutions based on peer-to-peer (P2P) technologies that allow such a functionality to remain efficient at large scale. We then focus on one of these approaches: the Distributed Lexicographic Placement Table (DLPT) architecture, that provide particular mechanisms for load balancing and fault-tolerance. This solution centers around three key points. First, it calls upon an indexing system structured as a prefix tree, allowing multi-attribute range queries. Second, it allows the mapping of such structures onto heterogeneous and dynamic networks and proposes some load balancing heuristics for it. Third, as our target platform is dynamic and unreliable, we describe its powerful fault-tolerance mechanisms, based on self-stabilization. Finally, we present the software prototype of this architecture and its early experiments.

Peer-to-Peer Service Discovery for Grid Computing

2012 ◽  
pp. 232-259
Author(s):  
Eddy Caron ◽  
Frédéric Desprez ◽  
Franck Petit ◽  
Cédric Tedeschi
Keyword(s):  
Fault Tolerance ◽  
Load Balancing ◽  
Service Discovery ◽  
Large Scale ◽  
Dynamic Networks ◽  
Peer To Peer ◽  
Indexing System ◽  
Key Points ◽  
Computing Platforms ◽  
Traditional Approaches

Within distributed computing platforms, some computing abilities (or services) are offered to clients. To build dynamic applications using such services as basic blocks, a critical prerequisite is to discover those services. Traditional approaches to the service discovery problem have historically relied upon centralized solutions, unable to scale well in large unreliable platforms. In this chapter, we will first give an overview of the state of the art of service discovery solutions based on peer-to-peer (P2P) technologies that allow such a functionality to remain efficient at large scale. We then focus on one of these approaches: the Distributed Lexicographic Placement Table (DLPT) architecture, that provide particular mechanisms for load balancing and fault-tolerance. This solution centers around three key points. First, it calls upon an indexing system structured as a prefix tree, allowing multi-attribute range queries. Second, it allows the mapping of such structures onto heterogeneous and dynamic networks and proposes some load balancing heuristics for it. Third, as our target platform is dynamic and unreliable, we describe its powerful fault-tolerance mechanisms, based on self-stabilization. Finally, we present the software prototype of this architecture and its early experiments.

Peer-to-Peer Search Techniques for Service-Oriented Computing

2010 ◽  
pp. 44-63
Author(s):  
Lu Liu ◽  
Duncan Russell ◽  
Jie Xu
Keyword(s):  
Service Discovery ◽  
Large Scale ◽  
Resource Discovery ◽  
Peer To Peer ◽  
Great Success ◽  
P2p Networks ◽  
Search Techniques ◽  
P2p Systems ◽  
Service Oriented ◽  
And Performance

Peer-to-peer (P2P) networks attract attentions worldwide with their great success in file sharing networks (e.g., Napster, Gnutella, BitTorrent, and Kazaa). In the last decade, numerous studies have been devoted to the problem of resource discovery in P2P networks. Recent research on structured and unstructured P2P systems provides a series of useful solutions to improve the scalability and performance of service discovery in large-scale service-based systems. In this chapter, the authors systematically review recent research studies on P2P search techniques and explore the potential roles and influence of P2P networking in dependable service-based military systems.

scholarly journals Peer-to-Peer Cloud Provisioning: Service Discovery and Load-Balancing

2010 ◽  
pp. 195-217 ◽  
Author(s):  
Rajiv Ranjan ◽  
Liang Zhao ◽  
Xiaomin Wu ◽  
Anna Liu ◽  
Andres Quiroz ◽  
...  
Keyword(s):  
Load Balancing ◽  
Service Discovery ◽  
Peer To Peer

Load Balancing in Peer-to-Peer Systems

2010 ◽  
pp. 163-190
Author(s):  
Haiying Shen
Keyword(s):  
Load Balancing ◽  
High Performance ◽  
Large Scale ◽  
Overlay Networks ◽  
Rapid Development ◽  
Peer To Peer ◽  
P2p Systems ◽  
Emerging Trends ◽  
Peer To Peer Systems ◽  
P2p Overlay

Structured peer-to-peer (P2P) overlay networks like Distributed Hash Tables (DHTs) map data items to the network based on a consistent hashing function. Such mapping for data distribution has an inherent load balance problem. Thus, a load balancing mechanism is an indispensable part of a structured P2P overlay network for high performance. The rapid development of P2P systems has posed challenges in load balancing due to their features characterized by large scale, heterogeneity, dynamism, and proximity. An efficient load balancing method should flexible and resilient enough to deal with these characteristics. This chapter will first introduce the P2P systems and the load balancing in P2P systems. It then introduces the current technologies for load balancing in P2P systems, and provides a case study of a dynamism-resilient and proximity-aware load balancing mechanism. Finally, it indicates the future and emerging trends of load balancing, and concludes the chapter.

scholarly journals Self-Learning Threshold-Based Load Balancing

2021 ◽  
Author(s):  
Diego Goldsztajn ◽  
Sem C. Borst ◽  
Johan S. H. van Leeuwaarden ◽  
Debankur Mukherjee ◽  
Philip A. Whiting
Keyword(s):  
Load Balancing ◽  
Large Scale ◽  
Data Centers ◽  
Service System ◽  
Computing Platforms ◽  
Load Balancing Algorithm ◽  
And Diffusion ◽  
Theoretical Results ◽  
Allocation Strategies

We consider a large-scale service system where incoming tasks have to be instantaneously dispatched to one out of many parallel server pools. The user-perceived performance degrades with the number of concurrent tasks and the dispatcher aims at maximizing the overall quality of service by balancing the load through a simple threshold policy. We demonstrate that such a policy is optimal on the fluid and diffusion scales, while only involving a small communication overhead, which is crucial for large-scale deployments. In order to set the threshold optimally, it is important, however, to learn the load of the system, which may be unknown. For that purpose, we design a control rule for tuning the threshold in an online manner. We derive conditions that guarantee that this adaptive threshold settles at the optimal value, along with estimates for the time until this happens. In addition, we provide numerical experiments that support the theoretical results and further indicate that our policy copes effectively with time-varying demand patterns. Summary of Contribution: Data centers and cloud computing platforms are the digital factories of the world, and managing resources and workloads in these systems involves operations research challenges of an unprecedented scale. Due to the massive size, complex dynamics, and wide range of time scales, the design and implementation of optimal resource-allocation strategies is prohibitively demanding from a computation and communication perspective. These resource-allocation strategies are essential for certain interactive applications, for which the available computing resources need to be distributed optimally among users in order to provide the best overall experienced performance. This is the subject of the present article, which considers the problem of distributing tasks among the various server pools of a large-scale service system, with the objective of optimizing the overall quality of service provided to users. A solution to this load-balancing problem cannot rely on maintaining complete state information at the gateway of the system, since this is computationally unfeasible, due to the magnitude and complexity of modern data centers and cloud computing platforms. Therefore, we examine a computationally light load-balancing algorithm that is yet asymptotically optimal in a regime where the size of the system approaches infinity. The analysis is based on a Markovian stochastic model, which is studied through fluid and diffusion limits in the aforementioned large-scale regime. The article analyzes the load-balancing algorithm theoretically and provides numerical experiments that support and extend the theoretical results.

Enhancing Computational Grids with Peer-to-Peer Technology for Large Scale Service Discovery

2006 ◽  
Vol 5 (3) ◽  
pp. 337-360 ◽  
Author(s):  
Eddy Caron ◽  
Frédéric Desprez ◽  
Cédric Tedeschi
Keyword(s):  
Service Discovery ◽  
Large Scale ◽  
Peer To Peer ◽  
Computational Grids

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

A Review on Load Balancing Model Using Best Partition Technique

2017 ◽  
Vol 7 (8) ◽  
pp. 284
Author(s):  
M. Chaitanya ◽  
K. Durga Charan
Keyword(s):  
Cloud Computing ◽  
Fault Tolerance ◽  
Load Balancing ◽  
Load Balance ◽  
Large Impact ◽  
Cloud Environment ◽  
Public Cloud ◽  
The Public ◽  
Partition Technique ◽  
Textual Content

Load balancing makes cloud computing greater knowledgeable and could increase client pleasure. At reward cloud computing is among the all most systems which offer garage of expertise in very lowers charge and available all the time over the net. However, it has extra vital hassle like security, load administration and fault tolerance. Load balancing inside the cloud computing surroundings has a large impact at the presentation. The set of regulations relates the sport idea to the load balancing manner to amplify the abilties in the public cloud environment. This textual content pronounces an extended load balance mannequin for the majority cloud concentrated on the cloud segregating proposal with a swap mechanism to select specific strategies for great occasions.

Recent Progress in Machine Learning-based Prediction of Peptide Activity for Drug Discovery

2019 ◽  
Vol 19 (1) ◽  
pp. 4-16 ◽  
Author(s):  
Qihui Wu ◽  
Hanzhong Ke ◽  
Dongli Li ◽  
Qi Wang ◽  
Jiansong Fang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Large Scale ◽  
Recent Progress ◽  
High Specificity ◽  
Learning Approaches ◽  
Anticancer Peptides ◽  
The Past ◽  
Traditional Approaches ◽  
Large Scale Screening

Over the past decades, peptide as a therapeutic candidate has received increasing attention in drug discovery, especially for antimicrobial peptides (AMPs), anticancer peptides (ACPs) and antiinflammatory peptides (AIPs). It is considered that the peptides can regulate various complex diseases which are previously untouchable. In recent years, the critical problem of antimicrobial resistance drives the pharmaceutical industry to look for new therapeutic agents. Compared to organic small drugs, peptide- based therapy exhibits high specificity and minimal toxicity. Thus, peptides are widely recruited in the design and discovery of new potent drugs. Currently, large-scale screening of peptide activity with traditional approaches is costly, time-consuming and labor-intensive. Hence, in silico methods, mainly machine learning approaches, for their accuracy and effectiveness, have been introduced to predict the peptide activity. In this review, we document the recent progress in machine learning-based prediction of peptides which will be of great benefit to the discovery of potential active AMPs, ACPs and AIPs.

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