Heuristic Algorithms for Optimization of Task Allocation and Result Distribution in Peer-to-Peer Computing Systems

Recently, distributed computing system have been gaining much attention due to a growing demand for various kinds of effective computations in both industry and academia. In this paper, we focus on Peer-to-Peer (P2P) computing systems, also called public-resource computing systems or global computing systems. P2P computing systems, contrary to grids, use personal computers and other relatively simple electronic equipment (e.g., the PlayStation console) to process sophisticated computational projects. A significant example of the P2P computing idea is the BOINC (Berkeley Open Infrastructure for Network Computing) project. To improve the performance of the computing system, we propose to use the P2P approach to distribute results of computational projects, i.e., results are transmitted in the system like in P2P file sharing systems (e.g., BitTorrent). In this work, we concentrate on offline optimization of the P2P computing system including two elements: scheduling of computations and data distribution. The objective is to minimize the system OPEX cost related to data processing and data transmission. We formulate an Integer Linear Problem (ILP) to model the system and apply this formulation to obtain optimal results using the CPLEX solver. Next, we propose two heuristic algorithms that provide results very close to an optimum and can be used for larger problem instances than those solvable by CPLEX or other ILP solvers

Performance Analysis of Publish/Subscribe Systems

The Desktop Grid offers solutions to overcome several challenges and to answer increasingly needs of scientific computing. Its technology consists mainly in exploiting resources, geographically dispersed, to treat complex applications needing big power of calculation and/or important storage capacity. However, as resources number increases, the need for scalability, self-organisation, dynamic reconfigurations, decentralisation and performance becomes more and more essential. Since such properties are exhibited by P2P systems, the convergence of grid computing and P2P computing seems natural. In this context, this paper evaluates the scalability and performance of P2P tools for discovering and registering services. Three protocols are used for this purpose: Bonjour, Avahi and Free-Pastry. We have studied the behaviour of theses protocols related to two criteria: the elapsed time for registrations services and the needed time to discover new services. Our aim is to analyse these results in order to choose the best protocol we can use in order to create a decentralised middleware for desktop grid

Enabling JXTA for High Performance Grid Computing

Grid computing has recently emerged as a response to the growing demand for resources (processing power, storage, etc.) exhibited by scientific applications. However, as grid sizes increase, the need for self-organization and dynamic reconfigurations is becoming more and more important. Since such properties are exhibited by P2P systems, the convergence of grid computing and P2P computing seems natural. However, using P2P systems (usually running on the Internet) on a grid infrastructure (generally available as a federation of SAN-based clusters interconnected by high-bandwidth WANs) may raise the issue of the adequacy of the P2P communication mechanisms. This paper evaluates the communication performance of the JXTA P2P library over SANs and WANs, for both J2SE and C bindings. We analyze these results and we evaluate solutions able to improve the performance of JXTA on such grid infrastructures

Coordination and P2P computing

Peer-to-Peer (P2P) refers to a class of systems and/or applications that use distributed resources in a decentralized and autonomous manner to achieve a goal. A number of successful applications, like BitTorrent (for file and content sharing) and SETI@Home (for distributed computing) have demonstrated the feasibility of this approach. As a new form of distributed computing, P2P computing has the same coordination problems as other forms of distributed computing. Coordination has been considered an important issue in distributed computing for a long time and many coordination models and languages have been developed. This research focuses on how to solve coordination problems in P2P computing. In particular, it is to provide a seamless P2P computing environment so that the migration of computation components is transparent. This research extends Manifold, an event-driven coordination model, to meet P2P computing requirements and integrates the P2P-Manifold model into an existing platform. The integration hides the complexity of the coordination model and makes the model easy to use

Towards a Framework for DHT Distributed Computing

Distributed Hash Tables (DHTs) are protocols and frameworks used by peer-to-peer (P2P) systems. They are used as the organizational backbone for many P2P file-sharing systems due to their scalability, fault-tolerance, and load-balancing properties. These same properties are highly desirable in a distributed computing environment, especially one that wants to use heterogeneous components. We show that DHTs can be used not only as the framework to build a P2P file-sharing service, but as a P2P distributed computing platform. We propose creating a P2P distributed computing framework using distributed hash tables, based on our prototype system ChordReduce. This framework would make it simple and efficient for developers to create their own distributed computing applications. Unlike Hadoop and similar MapReduce frameworks, our framework can be used both in both the context of a datacenter or as part of a P2P computing platform. This opens up new possibilities for building platforms to distributed computing problems. One advantage our system will have is an autonomous load-balancing mechanism. Nodes will be able to independently acquire work from other nodes in the network, rather than sitting idle. More powerful nodes in the network will be able use the mechanism to acquire more work, exploiting the heterogeneity of the network. By utilizing the load-balancing algorithm, a datacenter could easily leverage additional P2P resources at runtime on an as needed basis. Our framework will allow MapReduce-like or distributed machine learning platforms to be easily deployed in a greater variety of contexts