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

Reconsidering big data security and privacy in cloud and mobile cloud systems

Large scale distributed systems in particular cloud and mobile cloud deployments provide great services improving people\u27s quality of life and organizational efficiency. In order to match the performance needs, cloud computing engages with the perils of peer-to-peer (P2P) computing and brings up the P2P cloud systems as an extension for federated cloud. Having a decentralized architecture built on independent nodes and resources without any specific central control and monitoring, these cloud deployments are able to handle resource provisioning at a very low cost. Hence, we see a vast amount of mobile applications and services that are ready to scale to billions of mobile devices painlessly. Among these, data driven applications are the most successful ones in terms of popularity or monetization. However, data rich applications expose other problems to consider including storage, big data processing and also the crucial task of protecting private or sensitive information. In this work, first, we go through the existing layered cloud architectures and present a solution addressing the big data storage. Secondly, we explore the use of P2P Cloud System (P2PCS) for big data processing and analytics. Thirdly, we propose an efficient hybrid mobile cloud computing model based on cloudlets concept and we apply this model to health care systems as a case study. Then, the model is simulated using Mobile Cloud Computing Simulator (MCCSIM). According to the experimental power and delay results, the hybrid cloud model performs up to 75% better when compared to the traditional cloud models. Lastly, we enhance our proposals by presenting and analyzing security and privacy countermeasures against possible attacks

A Case for P2P Delivery of Paid Content

P2P file sharing provides a powerful content distribution model by leveraging users' computing and bandwidth resources. However, companies have been reluctant to rely on P2P systems for paid content distribution due to their inability to limit the exploitation of these systems for free file sharing. We present TP2, a system that combines the more cost-effective and scalable distribution capabilities of P2P systems with a level of trust and control over content distribution similar to direct download content delivery networks. TP2 uses two key mechanisms that can be layered on top of existing P2P systems. First, it provides strong authentication to prevent free file sharing in the system. Second, it introduces a new notion of trusted auditors to detect and limit malicious attempts to gain information about participants in the system to facilitate additional out-of-band free file sharing. We analyze TP2 by modeling it as a novel game between malicious users who try to form free file sharing clusters and trusted auditors who curb the growth of such clusters. Our analysis shows that a small fraction of trusted auditors is sufficient to protect the P2P system against unauthorized file sharing. Using a simple economic model, we further show that TP2 provides a more cost-effective content distribution solution, resulting in higher profits for a content provider even in the presence of a large percentage of malicious users. Finally, we implemented TP2 on top of BitTorrent and use PlanetLab to show that our system can provide trusted P2P file sharing with negligible performance overhead

The state of peer-to-peer network simulators

Networking research often relies on simulation in order to test and evaluate new ideas. An important requirement of this process is that results must be reproducible so that other researchers can replicate, validate and extend existing work. We look at the landscape of simulators for research in peer-to-peer (P2P) networks by conducting a survey of a combined total of over 280 papers from before and after 2007 (the year of the last survey in this area), and comment on the large quantity of research using bespoke, closed-source simulators. We propose a set of criteria that P2P simulators should meet, and poll the P2P research community for their agreement. We aim to drive the community towards performing their experiments on simulators that allow for others to validate their results