The presented work provided a design basis for the implementation of a fully fledged P2P backup application prototype. The elementary backup and retrieve operations are carried out as follows. A given user, interested in the backup service, bootstraps in the system. Using a control interface to the tracker, the user indicates the amount of data it wants to backup, and sets a bootstrap value for its online availability and dedicated bandwidth. Provided these character- istics, the tracker offers potential remote peers to make storage exchange links with, based on the findings that selfish users eventually self-organize into cliques holding peers with nearly the same uptime and bandwidth values. Once a stable neighborhood is found, the user faces two
CHAPTER 8. CONCLUSIONS 81
options: it keeps its initial contribution level, with the local storage required to dedicate to the system, or lowers the quantity of shared local storage at the cost of an increased quality. When the decision is made the backup data with a well-suited redundancy ratio that corresponds to the characteristics of the partners, is swapped between the parties.
Every time a user wants to insert new data to the system, the backup operation is carried out. Retrieve works similarly: the user looks up its account at the tracker to find the coordinates of remote peers in order to download the backed up data. Then it contacts online peers to gather the necessary data fragments from them.
A P2P backup system operated by the Internet Service Provider (ISP) offers benefits for both users and ISPs, compared to existing central storage services. Users do not pay for the backup, but exploit unused end-user resources instead. ISPs spare costly inter-ISP traffic due to large data shipments from subscribers to data centers, and/or avoid the burden of maintaining own data centers in order to offer storage service themselves.
Once deployed, the system can be exploited to collect measurements about the natural het- erogeneity of user demand in terms of storage requirements, and of realistic bootstrap resource contributions that users would set. This could give an insight about the validity of our assump- tion about uncorrelated user availability, dedicated bandwidth, shared storage space amount. Furthermore, these conditions would determine the theoretic equilibrium state of the system, the number of cliques, their sizes and the grades within, and also could give answers on the realistic user responses, given to the incentives that the system employs.
Another a measurement research direction is to find the optimal size of backup objects, which in turn requires knowledge about patterns of data production, not only the initial amount of data that a user wants to back after its registration to the system.
Measuring the amount of resources a peer dedicates to the system represents an important issue. The observation of the availability and uplink capacity of remote peers is necessary for our approximation techniques; we assume that monitoring is performed by the tracker and any peer in the system can query it to obtain the qualities of other peers in the system. Indeed, it is common practice (e.g., in Wuala) to rely on a centralized infrastructure to monitor peer resources. However, a decentralized approach to resource monitoring is an appealing research subject, e.g., if the connectivity or availability of a peer is measured by other peers in the system, they may estimate it differently. A nearby peer may experience faster communication, a peer that has positively correlated uptimes with the observed peer will measure higher availability.
Part II
Distributed Dynamic Spectrum
Allocation
Chapter 9
Introduction
We propose a distributed spectrum management framework to allocate radio frequency bands for wireless service providers dynamically. Our goal is to reach high efficiency in frequency utilization, i.e., to allocate spectrum to those licensees that value it the most. We build a self-organizing scheme in which the participants, i.e., the wireless service providers, manage the allocation of their frequency bands at arbitrary points in time. We give the possibility of choosing the adequate band and the activation time of the license in the hands of the participants.
We model interference effects among the participants by reflecting realistic constraints of their co-existence despite abstract simplification. In case a participant cannot fit on a given frequency band due to excessive interference caused by others, we allow for exclusions: a newly allocating participant (as any other participant) may eliminate other, actively operating participant(s) if this action potentially improves the efficiency of spectrum utilization. The exclusions are based on the pricing of spectrum which is managed in a distributed way.
The central authority plays regulative role and controls only the interference and payments of active frequency-leasers. By design, our framework favors the application of modern radio technologies which are interference-tolerant, furthermore, it takes into account the selfishness of participants (in the game theoretic sense) and supports dynamics in allocation demands. The goal is to maximize frequency utilization, which is the most important objective of introducing DSA.
9.1
Static versus dynamic spectrum allocation
Radio spectrum exploitation is historically regulated by governmental authorities, e.g., the Fed- eral Communications Commission in USA. The regulation, lead by these national bodies, results in the static allocation of frequency bands with rigid specification on the geographic operation and on the usage purpose (e.g., broadcast radio/TV, cellular services, wireless LAN) of the li- cense. The growing need for spectrum, generated by many novel applications, claims the revision
CHAPTER 9. INTRODUCTION 86
of this management scheme, since the current static frequency allocation results in suboptimal spectrum utilization due to well-known reasons.
The capital intensive governmental licenses make the frequency bands, auctioned for long- term, access-limited (“big player syndrome”); moreover, the peak traffic planning causes temporal underutilization in less busy periods. Furthermore, the spatial and spectral restrictions on fre- quency re-usage due to rigid interference handling policies exclude many potential frequency exploitation opportunities.
The emergence of novel radio technologies enables the application of advantageous spectrum policies wherein allocating spectrum bands for licensees is performed with various spectral, spatial and temporal parameters, thus possibly improving spectrum utilization. While actual spectrum allocation is not efficient because of the aforementioned constraints and fixed frequency ranges for existing services, new generation radio interfaces support flexible transmission frequencies (e.g., dynamic frequency bands in the Long Term Evolution (LTE) project), furthermore the convergence of services makes actual restrictions seem out of date.
A well-suited dynamic spectrum allocation (DSA) framework must offer solution for every key issue. Interference relations among frequency leasers (e.g., caused interference when operat- ing on the same frequency band) must be taken into account without simplifying assumptions, thus reflecting appropriate spectral and spatial constraints when allocating spectrum bands to the licensees. Furthermore, it must fulfill the basic requirements of general resource distribution, when limited resource is to be divided among selfish participants. An ideal framework should not impose temporal constraints on licenses in terms of duration and renewal periods of alloca- tion. Albeit the existing literature that tackles possible spectrum allocation models is vast, our approach provides novelty in many aspects.
9.2
Focus of the work
If licensee candidates are interested in allocating frequency bands for time periods that are not synchronized and do not have the same lengths, auction-based management schemes are not particularly well-suited. We consider the issue generated by delayed activation times of selfish frequency leasers. Furthermore, distributed schemes often fail to mitigate the effects of selfishness among the participants. Our distributed allocation framework provides solution for both: possible exclusion of active licensees allows the candidates to enter the spectrum at arbitrary points in time, and our pricing scheme guarantees successful allocation in exchange for adequate payments.
Our contribution also involves excessive discussion on the complexity of decision problems related to exclusions and frequency band selection, moreover well-motivated heuristics are pro- posed for these latter. We compare the performance of the heuristic algorithms, that are built
CHAPTER 9. INTRODUCTION 87
on the observations of analytically tractable scenarios, in numerical evaluations. We prove that the proposed strategies are light-weighted and perform well in realistic situations.
We overview the related work in Chapter 10.
In Chapter 11 we present our framework through the introduction of the node and the interference models, moreover we introduce the allocation and pricing policies and define the notion of node arrival sequence. We discuss the complexity of allocation decisions, generated by our framework, and propose reasonable policy choices for node exclusion strategies built on the insights to the algorithmic characteristics of the frequency band selection.
In Chapter 12 we evaluate the proposed heuristics numerically. Chapter 13 concludes our work.
Chapter 10
Related work
Since the appearance of enabling technologies, the management of systems implementing DSA has received much attention. Therefore many related works have appeared so far. We keep our focus on papers that present allocation and pricing solutions for DSA and we do not consider the large field of research on underlying technological issues.
10.1
Central allocation
The management of DSA was first discussed in [86]. The authors presented the concepts of DSA as an alternative to fixed allocation schemes. They showed the potential for gains in spectrum efficiency and several issues related to improvements to the fairness and effectiveness of the allocation scheme.
The seminal work of Buddhikot and Ryan [22] initiated the sequence of papers from Bud- dhikot et al. focusing on spectrum allocation and pricing. All of them present models in which a central spectrum broker allocates governmental licenses of spectrum for short leasing times. They define the concept of coordinated DSA and the spectrum broker (i.e., who owns the spectrum and leases it), along with different allocation algorithm types (online vs. batched), the authors of [22] also introduce the important notion of interference conflict graph, and the cascading ef- fects among frequency leasers on blocked list. The authors also provide some linear programming formulation of the spectrum allocation with feasibility constraints: maximal service vs. minimal interference, maximal broker revenue vs. max-min fairness.
In [115] the authors go further and analyze pricing issues: they propose models on auction- based and peak-load pricing depending on demand and supply. Furthermore, Buddhikot et
al. [123] propose fast heuristic algorithms to perform the central allocation by optimizing the
same metrics as before: obtaining maximal satisfied demand or minimal interference. Based on the conflict graph, the authors arrive to well-known NP-hard graph theory problems (coloring and cutting problems respectively), and provide nice theorems and proofs on the efficiency of
CHAPTER 10. RELATED WORK 90
their heuristic algorithms under some interference graph assumptions.
The work in [137] highlights the weaknesses of the widely-employed interference modeling tool, i.e., the pairwise conflict graph, and they show how to derive this latter from physical interference models so that it produces near-optimal allocation.