Papers by Thanasis Papaioannou
AnIncentives' MechanismPromoting Truthful Feedback inPeer-to-Peer Systems*
Wepropose amechanism forproviding theincentives for reporting truthful feedback inapeer-to-peer s... more Wepropose amechanism forproviding theincentives for reporting truthful feedback inapeer-to-peer system for exchanging services. Thismechanism istocomplement reputation mechanisms that employ ratings'feedback on thevarious transactions inorder toprovide incentives to peers foroffering better services toothers. Underour approach, bothtransacting peers(rather thanjustthe client) submit ratings onperformance oftheir mutual transaction. Ifthese areindisagreement, thenboth transacting peers arepunished, since suchanoccasion is asignthat oneofthemislying. Theseverity ofeach peer's punishment isdetermined byhiscorresponding non-credibility metric; thisismaintained by the mechanism andevolves according tothepeer's record. Whenunder punishment, apeerisnotallowed totransact withothers. Wepresent theresults ofa multitude of experiments of dynamically evolving peer-to-peer systems. Theresults showclearly thatourmechanism detects andisolates effectively liar peers, while rendering lying costly. Also,ourmechanism diminishes the efficiency losses induced tosincere peers bythepresence oflarge subsets ofthepopulation ofpeers that provide their ratings either falsely oraccording tovarious unfair strategies. Finally, weexplain howourapproach canbe implemented inpractical cases ofpeer-to-peer systems.
The participatory sensing paradigm, through the growing availability of cheap sensors in mobile d... more The participatory sensing paradigm, through the growing availability of cheap sensors in mobile devices, enables applications of great social and business interest, e.g. electrosmog exposure measurement, early earthquake detection, etc. However, users' privacy concerns regarding their activity traces need to be adequately addressed first. Existing static privacy-enabling approaches, which hide or obfuscate data, offer some protection at the expense of data value, but no privacy guarantees, while heterogeneous user privacy requirements cannot be met. In this report, we propose a user-side privacy protection scheme that adaptively adjusts its parameters, in order to meet personalized location-privacy protection requirements against adversaries in a measurable manner. As proved by simulation experiments with artificial and real data traces of electrosmog participatory sensing, our approach not only always satisfies personal location-privacy concerns when feasible, but also maximizes data utility (in terms of error, data availability, area coverage), as compared to static privacy-protection schemes.
Zenodo (CERN European Organization for Nuclear Research), Dec 20, 2022
The Internet is becoming more centralized, more asymmetric in terms of information and power dist... more The Internet is becoming more centralized, more asymmetric in terms of information and power distribution, more biased, less secure and less trustworthy. Blockchain technologies already allow the decentralized exchange of digital assets in a secure and fair manner, but its application to information transmission is mostly unexplored. This article outlines our vision for ONTOCHAIN, a semantically enhanced blockchain software ecosystem that enables the creation of secure distributed applications that empower users, ensure their privacy, high quality of service, and ultimately encourage pluralism and democracy. The primary goal of ONTOCHAIN is to achieve trustworthy service exchange and content handling for a variety of disciplines, including health, economy, public services, energy and sustainability, news, media, entertainment, industry 4.0, and tourism, employing advanced knowledge management mechanisms.
The proliferation of mobile devices with ubiquitous Internet access has made content access patte... more The proliferation of mobile devices with ubiquitous Internet access has made content access patterns highly volatile and spatio-temporally varying. At the same time, virtualization techniques have enabled the emergence of virtual Content Delivery Network (vCDN) providers, that bundle together a virtual infrastructure by utilizing storage resources anywhere along the path between the access network and the corresponding data center, where the requested content actually resides. In this context, efficient content placement in the various storage layers depends on accurately estimating content access patterns from the different access networks at any given time. At the same time, different vCDN providers compete against each other to provide low content retrieval latency to their users. However, there are some interesting synergies that might emerge among otherwise competing vCDN providers: (i) host content of another vCDN provider and, (ii) provide own content to the users/customers of other vCDN providers. In this paper, we formulate the overall problem of content placement for multiple vCDN providers that employ collaboration as an overall social-welfare maximization problem. The solution to this problem gives the optimal placement, achievable only in the case of full information. Alleviating the need for full information and considering vCDN providers separately as profit seekers, we also devise a distributed algorithm for content placement by exchanging limited information among them. Extensive simulation experiments show that business collaboration among competing vCDN providers is beneficial, as compared to isolated offerings, and allows them to adapt faster to content pattern changes.
Automated Demand Response (ADR) programs play key role in alleviating the energy supply and deman... more Automated Demand Response (ADR) programs play key role in alleviating the energy supply and demand imbalances by (i) controlling user loads either directly or indirectly, and (ii) economically mitigating the uncertainties that impact power system operations in an automated (precontracted) way. In general, users are assumed to act rationally, i.e., optimize their decision-making process so as to maximize their financial net benefit. However, an extensive literature on behavioural economics (BE) contends that the decision-making process of users is far more complex, not always self-interested and depends on a number of individual factors, such as altruism. Our work aims to advance our knowledge on how to engage in ADR contracts users that may exhibit different degrees of altruism and motivate them effectively to ultimately optimize the overall use of energy. We investigate the impact of altruism on the total financial incentives to be offered by the provider and on the social welfare, and identify the optimal demand reduction and user targeting strategies for performing ADR in such populations. Based on experiments with real and synthetic data, we find that appropriate targeting policies and demand reduction strategies that take advantage of altruism can be beneficial for the social welfare of the users and the incentive costs of the provider. However, leveraging altruists should be performed carefully, since saddling them with high power reductions, although yielding lower total incentives, can prove inefficient for the social welfare of the system.
Web intelligence, Nov 23, 2015
Today's complex online applications often require the interaction of multiple (web) services that... more Today's complex online applications often require the interaction of multiple (web) services that belong to potentially different business entities. Interoperability is a core element of such an environment, yet not a straightforward one due to the lack of common data semantics. The problem is often approached by means of standardization procedures in a top-down manner with limited adoption in practice. (De facto) standards for semantic interoperability most commonly emerge in a bottom-up approach, i.e., involving the interaction and information exchange among self-interested industrial agents. In this paper, we argue that the emergence of semantic interoperability can be seen as an economic process among rational agents and, although interoperability can be mutually beneficial for the involved parties, it may also be costly and might fail to emerge. As a sample scenario, we consider the emergence of semantic interoperability among rational web service agents in service-oriented architectures (SOAs), and we analyze their individual economic incentives with respect to utility, risk and cost. We model this process as a positive-sum game and study its equilibrium and evolutionary dynamics. According to our analysis, which is also experimentally verified, certain conditions on the communication cost, the cost of technological adaptation, the expected mutual benefit from interoperability, as well as the expected loss from isolation, drive the process.
Springer eBooks, 2010
Ranking systems such as those in product review sites and recommender systems usually use ratings... more Ranking systems such as those in product review sites and recommender systems usually use ratings to rank favorite items based on both their quality and popularity. Since higher ranked items are more likely selected and yield more revenues for their owners, providers of unpopular and low quality items have strong incentives to strategically manipulate their ranking. This paper analyzes the adversary cost for manipulating these rankings in a variety of scenarios. Particularly, we analyze and compare the adversarial cost to attack ranking systems that use various trust measures to detect and eliminate malicious ratings to systems that use no such a trust management mechanism. We provide theoretical results showing the relation between the capability of the trust mechanism in detecting malicious ratings and the minimal adversarial cost for successfully changing the ranking. Furthermore, we study the impact of sharing trust information between ranking systems to the adversarial cost. It is proved that sharing information between two ranking systems on common user identities and malicious behaviors detected can increase considerably the minimal adversarial cost to successfully attack the two systems under certain assumptions. The numerical evaluation of our results shows that the estimated adversary cost for manipulating the item ranking can be made significant when proper trust mechanisms are employed or combined.
Failures of any type are common in current datacenters, partly due to the higher scales of the da... more Failures of any type are common in current datacenters, partly due to the higher scales of the data stored. As data scales up, its availability becomes more complex, while different availability levels per application or per data item may be required. In this paper, we propose a self-managed key-value store that dynamically allocates the resources of a data cloud to several applications in a costefficient and fair way. Our approach offers and dynamically maintains multiple differentiated availability guarantees to each different application despite failures. We employ a virtual economy, where each data partition (i.e. a key range in a consistent-hashing space) acts as an individual optimizer and chooses whether to migrate, replicate or remove itself based on net benefit maximization regarding the utility offered by the partition and its storage and maintenance cost. As proved by a game-theoretical model, no migrations or replications occur in the system at equilibrium, which is soon reached when the query load and the used storage are stable. Moreover, by means of extensive simulation experiments, we have proved that our approach dynamically finds the optimal resource allocation that balances the query processing overhead and satisfies the availability objectives in a cost-efficient way for different query rates and storage requirements. Finally, we have implemented a fully working prototype of our approach that clearly demonstrates its applicability in real settings.
Designing an Iot-Enabled Gamified App for Employee Energy Conservation at the Workplace
Proceedings of SPIE, Feb 2, 2001
Most often, incentives involved in Automated Demand Response (ADR) contracts are statically defin... more Most often, incentives involved in Automated Demand Response (ADR) contracts are statically defined and assume full customer rationality, thus hindering sustained customer enrollment to them of customers with other characteristics (e.g. altruism). In this paper, we derive appropriate incentives for ADR contracts, so that non-fully rational customers are compensated even when information for consumer utilities is not available. In case such information is hidden, we assume that customers provide feedback on their satisfaction from direct endowments, albeit sustaining energyconsumption reduction. We mathematically model the customer and the utility company's problems for the aforementioned cases of full and hidden information and solve them algebraically or in a distributed manner. Based on numerical evaluation and simulation experiments, we showcase the validity of our analytical framework in realistic scenarios and that, for the case of hidden information, customer feedback is adequate for calculating incentives that lead to successful DR campaigns.
2012 International Conference for High Performance Computing, Networking, Storage and Analysis, Nov 1, 2012
A growing amount of data is produced daily resulting in a growing demand for storage solutions. W... more A growing amount of data is produced daily resulting in a growing demand for storage solutions. While cloud storage providers offer a virtually infinite storage capacity, data owners seek geographical and provider diversity in data placement, in order to avoid vendor lock-in and to increase availability and durability. Moreover, depending on the customer data access pattern, a certain cloud provider may be cheaper than another. In this paper 1 , we introduce Scalia, a cloud storage brokerage solution that continuously adapts the placement of data based on its access pattern and subject to optimization objectives, such as storage costs. Scalia efficiently considers repositioning of only selected objects that may significantly lower the storage cost. By extensive simulation experiments, we prove the cost-effectiveness of Scalia against static placements and its proximity to the ideal data placement in various scenarios of data access patterns, of available cloud storage solutions and of failures.
How to split teams and what rewards to give? Select rewards and number of teams K, so as to maxim... more How to split teams and what rewards to give? Select rewards and number of teams K, so as to maximize total net savings of the building: 1) Change "bad" energy-consumption behaviors at work context We employ a serious-game approach to maximize user engagement
The continuous growth of energy needs and the fact that unpredictable energy demand is mostly ser... more The continuous growth of energy needs and the fact that unpredictable energy demand is mostly served by unsustainable (i.e. fossil-fuel) power generators have given rise to the development of Demand Response (DR) mechanisms for flattening energy demand. Building effective DR mechanisms and user awareness on power consumption can significantly benefit from fine-grained monitoring of user consumption at the appliance level. However, installing and maintaining such a monitoring infrastructure in residential settings can be quite expensive. In this paper, we study the problem of fine-grained appliance power-consumption monitoring based on one houselevel meter and few plug-level meters. We explore the trade-off between monitoring accuracy and cost, and exhaustively find the minimum subset of plug-level meters that maximize accuracy. As exhaustive search is time-and resource-consuming, we define a heuristic approach that finds the optimal set of plug-level meters without utilizing any other sets of plug-level meters. Based on experiments with real data, we found that few plug-level meterswhen appropriately placed-can very accurately disaggregate the total real power consumption of a residential setting and verified the effectiveness of our heuristic approach.
Foundations and Trends in Networking, 2016
Peer-to-peer environments have become popular as a framework for exchange of services. In these e... more Peer-to-peer environments have become popular as a framework for exchange of services. In these environments, certain peers may fail to provide their services. Reputation can be a proper means of discovering low-performing peers, without affecting significantly inherent characteristics of Peer-to-Peer environments, such as anonymity and privacy. However, the accurate calculation of the reputation metrics may not be sufficient to provide the right incentives to peers. In this paper, we show that the straightforward approach for peers to exploit the reputation metrics (i.e. by just selecting as a providing peer the one with the highest reputation) may lead to unexpectedly low efficiency for high-performing peers. We argue and justify experimentally that the calculation of the reputation values has to be complemented by reputation-based policies that define the pairs of peers eligible to interact. We introduce two orthogonal dimensions constituting the reputationbased policies: "provider selection" and "contention resolution". We argue and show by means of simulation experiments that both these dimensions have a significant impact to the achieved efficiency of the peers. We also investigate experimentally the achievable efficiency of specific reputation-based policies for the case of short-lived peers of two different fixed-strategy types. Finally, we deal with the efficient computation of the reputation value by means of aggregation of the ratings' feedback provided by the peers. We propose that this can be accomplished by aggregating only a small randomly selected subset of this feedback. Simulation experiments indicate that this approach indeed leads to the fast and accurate calculation of the reputation values even if the peer-topeer population is renewed with a high rate.
Significant achievements have been made for automated allocation of cloud resources. However, the... more Significant achievements have been made for automated allocation of cloud resources. However, the performance of applications may be poor in peak load periods, unless their cloud resources are dynamically adjusted. Moreover, although cloud resources dedicated to different applications are virtually isolated, performance fluctuations do occur because of resource sharing, and software or hardware failures (e.g. unstable virtual machines, power outages, etc.). In this paper, we propose a decentralized economic approach for dynamically adapting the cloud resources of various applications, so as to statistically meet their SLA performance and availability goals in the presence of varying loads or failures. According to our approach, the dynamic economic fitness of a Web service determines whether it is replicated or migrated to another server, or deleted. The economic fitness of a Web service depends on its individual performance constraints, its load, and the utilization of the resources where it resides. Cascading performance objectives are dynamically calculated for individual tasks in the application workflow according to the user requirements. By fully implementing our framework, we experimentally proved that our adaptive approach statistically meets the performance objectives under peak load periods or failures, as opposed to static resource settings.
Optimal design of serious games for demand side management
Serious games are a promising approach for demand-side management that aims to higher user engage... more Serious games are a promising approach for demand-side management that aims to higher user engagement and active participation. In this paper1, we introduce the problem of optimal serious-game design for achieving specific energy-consumption reduction goals. We consider a serious game, where a game designer entity presents publicly to all consumers a list of top-K consumers and a list of bottom-M consumers according to their respective energy-consumption reduction at peak hours. The driving forces of this game are the user discomfort due to demand load shifting, the user desire for social approval and the user sensitivity to social outcasting. According to their private values to these parameters, users compete to enter the top-K list and be recognized for their achievement, or to avoid ending up in the bottom-M list and become pinpointed for not being energy-friendly. We formulate the problems of the game designer as an operational-cost minimization one for the utility company and that of each consumer as a utility-maximization one. The game-design problem is to decide on K, M and on the feedback provided to the consumers, while the consumer-side problem amounts to selecting the behavioral change to energy consumption that maximizes the expected user utility. By a series of simulations, we show how the choices of K, M affect the energy consumption reduction for different types of customers.
Future Generation Computer Systems, Oct 1, 2010
Hidden information is a critical issue for the successful delivery of services in grid systems. I... more Hidden information is a critical issue for the successful delivery of services in grid systems. It arises when the agents (hardware and software resources) employed to serve a task belong to multiple administrative domains, thus rendering monitoring of remote resource provision absent or unreliable. Therefore, the grid service broker can often observe only the outcome of the collective effort of groups of agents rather than their individual efforts, which makes it hard to identify cases of free-riding or low-performing agents. In this paper, we first identify cases of hidden information in grid systems and explain why they cannot be handled satisfactorily by the existing accounting systems. Second, we develop and evaluate a reputation-based mechanism enabling the grid service broker to deal effectively with hidden information. Our mechanism maintains a reputation metric for each agent; we propose and evaluate several approaches on how to update this metric based only on the observations of collective outcomes. We also provide recommendation on which such approach is preferable for a grid service broker in collaborative or competitive environments.
As the Online Social Networks (OSNs) amass unprecedented amounts of personal information, the pri... more As the Online Social Networks (OSNs) amass unprecedented amounts of personal information, the privacy concerns gain considerable attention from the community. Apart from privacy-enabling approaches for existing OSNs, a number of initiatives towards building decentralized OSN infrastructures have emerged. However, before this paradigm becomes a serious alternative to current centralized infrastructures, some key design challenges, often conflicting with each other, have to be addressed. In this paper, we explore such design objectives concerning various system properties, namely availability, replication degree, user online times, privacy, and experimentally study the tradeoffs among them based on real data sets from Facebook and Twitter. We introduce different mechanisms to model user online times in the OSN from their activity times. We demonstrate how different profile replica selection approaches significantly affect the system performance.
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Papers by Thanasis Papaioannou