Desktop Grids

ABSTRACT: This paper discusses the implementation details of a grid-based rendering framework for POV-Ray on desktop grids. Our goal is to present how enterprises can build desktop grids in Windows environment in order to enable semi-real time rendering for 3D models, both images and animations, defined with POV-Ray. Algorithms, code and technical details are given for easy and efficient implementations. We think this work could be useful for both researchers and developers who are interested in the grid computing technology and its applications.

In this paper, we discuss how Peer-to-Peer data distribution techniques can be adapted to Desktop Grid computing environments, particularly to the BOINC platform. To date, Desktop Grid systems have focused primarily on utilizing spare CPU cycles, yet have neglected to take advantage of client network capabilities. Leveraging client bandwidth will not only benefit current projects by lowering their overheads but will also facilitate Destkop Grid adoption by data-heavy applications. In this paper, weoutline two approaches to Peer-to-Peer data sharing that could be adapted for volunteer computing platforms: the highly successful BitTorrent protocol; and, a more complex, yet secure and customizable, hierarchal Peer-to-Peer data center approach.

Volunteer Computing harnesses computing resources of machines from around the world to perform distributed independent tasks, following a master/worker model. Despite the recent increase in popularity and power in middleware such as BOINC, there are still several limitations in existing systems. Current research is oriented towards optimizing existing applications, while the number of active users and projects has reached a plateau.
A programming paradigm that has been significantly popular and is used by several systems on the cloud is MapReduce. The main advantage of this paradigm is that it can be used to solve a vast amount of different problems, by breaking them into simple steps and taking advantage of distributed resources. Volunteer Computing provides these resources, and although it cannot match the conditions offered by a cluster, it has other advantages that can be leveraged. In this paper, we try to increase the computational power of Volunteer Computing systems by allowing more complex applications and paradigms such as MapReduce to be run, thus opening new avenues and possibilities for the use of computational devices scattered through the Internet.
We created a BOINC prototype that can run MapReduce jobs (BOINC-MR), using a pull-model in which communication is always initiated by the client. By running experiments on a small cluster, with multiple variables, we were able to evaluate a few initial scenarios with this paradigm. We used a simple MapReduce application, word count, as proof of concept, just to demonstrate a typical execution.

In this paper we propose two new data models for BOINC: an approach based on the popular BitTorrent protocol; and a Content Delivery Network based approach. While the latter remains on a theoretical level, we developed a prototype that adds BitTorrent functionality for task distribution and conducted medium-scale tests of the environment. Our preliminary results indicate that the BitTorrent client had a negligible influence on the BOINC client’s computation time, even in the case where it was seeding extensively.
The BOINC server, on the contrary, showed an unexpectedly low bandwidth output when seeding the file, as well as spikes on CPU usage. Current results show the BitTorrent scenario allows clients to share the burden of data distribution on BOINC with almost no negative influence on compute time. This paper will discuss the tests that were performed, how they were evaluated, as well as some improvements that could be made in future research on both approaches.

Peer-to-Peer (P2P) Desktop Grids are computing infrastructures that aggregate a set of desktop-class machines in which all the participating entities have the same roles, responsibilities, and rights. In this paper, we present ShareGrid, a P2P Desktop Grid infrastructure based on the OurGrid middleware, that federates the resources provided by a set of small research laboratories to easily share and use their computing resources. We discuss the techniques and tools we employed to ensure scalability, efficiency, and usability, and describe the various applications used on it. We also demonstrate the ability of ShareGrid of providing good performance and scalability by reporting the results of experimental evaluations carried out by running various applications with different resource requirements. Our experience with ShareGrid indicates that P2P Desktop Grids can represent an effective answer to the computing needs of small research laboratories, as long as they provide both ease of management and use, and good scalability and performance.

As Map-Reduce emerges as a leading programming paradigm for data-intensive computing, today’s frameworks which support it still have substantial shortcomings that limit its potential scalability. In this paper we discuss several directions where there is room for such progress: they concern storage efficiency under massive data access concurrency, scheduling, volatility and fault tolerance. We place our discussion in the perspective of the current evolution towards an increasing integration of large-scale distributed platforms (clouds, cloud federations, enterprise desktop grids, etc.). We propose an approach which aims to overcome the current limitations of existing Map Reduce frameworks, in order to achieve scalable, concurrency optimized, fault-tolerant Map-Reduce data processing on hybrid infrastructures. This approach will be evaluated with real-life bio-informatics applications on existing Nimbus-powered cloud testbeds interconnected with desktop grids.

In this paper we show how we applied BitTorrent data distribution techniques to the BOINC middleware. Our goal was to decentralize BOINC’s data model to take advantage of client network capabilities. To achieve this, we developed a prototype that adds BitTorrent functionality for task distribution and conducted small-scale tests of the environment. Additionally, we measured the impact of the BitTorrent components in both the BOINC client and server, and compared it with the original implementation. Our preliminary results indicate that the BitTorrent client had a negligible influence on the BOINC client’s computation time, even in the case where it was seeding extensively. The BOINC server, on the contrary, showed an unexpectedly low bandwidth output when seeding the file, as well as spikes on CPU usage. Current results show the BitTorrent scenario allows clients to share the burden of data distribution on BOINC with almost no negative influence on compute time. This paper will discuss the tests that were performed, how they were evaluated, as well as some improvements that could be made to future tests to enhance server-side efficiency.

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In this paper we present a CPU scavenging architecture suitable for desktop resources, and we study its appropriateness in exploiting the PC Laboratory resources of the Greek School Network and their integration to the existing HellasGrid national infrastructure. School laboratories form an extensive network equipped with computational systems and fast Internet connections. As this infrastructure is utilized at most 8 h per day and 5 days per week, it could be made available during its remaining idle time for computational purposes through the use of Grid technology. The structure and organization of the school laboratories and backbone network enables the CPU scavenging service, as an independent and additional service, which will not violate the operational rules and policies of the school network, while it will add additional resources to the current HellasGrid infrastructure with low adaptation cost.