Peer-to-Peer (P2P) computing offers new research challenges in the field of
distributed computing. This paradigm can take advantage of a huge number of
idle CPU cycles through Internet in order to solve very complex computational
problems. All these resources are provided voluntarily by millions of users
spread over the world. This means the cost of allocating and maintaining
the resources is split and assumed by each owner/peer. For this reason, P2P
computing can be seen as a low-cost alternative to expensive super-computers.
Obviously, not every kind of parallel application is suitable for a P2P computing
environment. Those with high communication requirements between
tasks or with high QoS needs should still be performed in a Local Area Networking
(LAN) environment. Otherwise, problems with huge computational
requirements that can be easily split into millions of independent tasks are
suitable for P2P computing, especially as solving these problems with a supercomputer
would be extremely expensive.
One of the most critical aspects in the design of P2P systems is the development
of incentive techniques to enforce cooperation and resource sharing
among participants. Incentive policies in P2P distributed computing systems
is a new research field that requires specific policies to fight against malicious
and selfish behavior by peers. Encouraging peers to collaborate in file-sharing
has been widely investigated but, in the P2P computing field, this issue is still
at a very early stage of research. Furthermore, the dynamics of peer participation
are an inherent property of P2P systems and critical for design and
evaluation. This further increases the difficulty of P2P computing.
Another critical aspect of P2P computing systems is the development of
scheduling techniques to achieve an efficient and scalable management of the
computational resources. Unlike file-sharing, based on such immutable resources
as files, the mutable ones, such as CPU and Memory are the principal
resources involved in P2P computing. Inside the scheduling field, P2P computing
can be seen as a particular variant of Grid computing. In a similar way
as with the incentive polices, an extensive list of publications can be found that
study the scheduling problems for distributed computing, such as Clusters or
Grid computing, but few of these focus on P2P computing. For this reason,
the scheduling problem in this kind of network is a field that still requires
research in depth.
In this thesis we propose a Distributed Incentive and Scheduling Integrated
Mechanism (DISIM) with a two-level topology and designed to work on largescale
distributed computing P2P systems. The low level is formed by associations
of peers controlled by super-peers with major responsibilities in managing
and gathering information about the state of these groups. Scalability limitations
on the first level are avoided by providing the mechanism with an upper
level, made up of super-peers interconnected through a logical overlay.
Regarding incentives, we propose a mechanism based on credits with a twolevel
topology designed to operate on different platforms of shared computing
networks. One of the main contributions is a new policy for managing the
credits, called Weighted, that increases peer participation significantly. This
mechanism reflects P2P user dynamics, penalizes free-riders efficiently and
encourages peer participation. Moreover, the use of a popular pricing strategy,
called reverse Vickrey Auction, protects the system against malicious peer
behavior. Simulation results show that our policy outperforms alternative
approaches, maximizing system throughput and limiting free-riding behavior
by peers.
From the scheduling point of view, the low-level scheduler takes user dynamism
into account and is almost optimal since it holds all the status information
about the workload and computational power of its constituent peers.
Our main contribution at the upper level is to propose three criteria that only
use local information for scheduling tasks, providing the overall system with
scalability. By setting these criteria, the system can easily, dynamically and
rapidly adapt its behavior to very different kinds of parallel jobs in order toachieve an efficient performance. The results obtained proved the efficiency
of the overall model and the convergence with the best assignment, achieved
with an ideal centralized policy with global information.
| Identifer | oai:union.ndltd.org:TDX_UDL/oai:www.tdx.cat:10803/63288 |
| Date | 25 January 2012 |
| Creators | Rius Torrentó, Josep Maria |
| Contributors | Solsona Tehàs, Francesc, Cores Prado, Fernando, Universitat de Lleida. Departament d'Informàtica i Enginyeria Industrial |
| Publisher | Universitat de Lleida |
| Source Sets | Universitat de Lleida |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/doctoralThesis, info:eu-repo/semantics/publishedVersion |
| Format | 124 p., application/pdf |
| Source | TDX (Tesis Doctorals en Xarxa) |
| Rights | info:eu-repo/semantics/openAccess, ADVERTIMENT. L'accés als continguts d'aquesta tesi doctoral i la seva utilització ha de respectar els drets de la persona autora. Pot ser utilitzada per a consulta o estudi personal, així com en activitats o materials d'investigació i docència en els termes establerts a l'art. 32 del Text Refós de la Llei de Propietat Intel·lectual (RDL 1/1996). Per altres utilitzacions es requereix l'autorització prèvia i expressa de la persona autora. En qualsevol cas, en la utilització dels seus continguts caldrà indicar de forma clara el nom i cognoms de la persona autora i el títol de la tesi doctoral. No s'autoritza la seva reproducció o altres formes d'explotació efectuades amb finalitats de lucre ni la seva comunicació pública des d'un lloc aliè al servei TDX. Tampoc s'autoritza la presentació del seu contingut en una finestra o marc aliè a TDX (framing). Aquesta reserva de drets afecta tant als continguts de la tesi com als seus resums i índexs. |
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