Federations of Infrastructure Service Providers (InfSPs) have emerged as an effective practice to cater for demanding applications in cloud- and edge-computing environments, by overcoming the limitations of each such provider (in terms of available resources, geographic coverage etc.). In this paper, we develop innovative mechanisms and policies for the decentralized management of InfSP federations, considering the needs both for resource allocation meeting service composition requirements, and for performing a fair and efficient distribution of profits. Resource allocation is performed by means of a greedy algorithm that includes an opportunistic and an adjustment phase. To determine the payments of participating InfSPs, we introduce an innovative sealed-bid reverse auction that combines VCG and first-price auction mechanisms. This hybrid mechanism, coupled with a sophisticated federation wallet management mechanism, serves as a revenue sink for the federation. Notably, our approach exhibits several nice properties, including efficiency, long-term budget balance, truthfulness and individual rationality. Furthermore, we present how our solution can be implemented in a decentralized, privacy-preserving and trustworthy manner over a blockchain, achieving bid privacy, non-repudiation and public verifiability. Our approach is evaluated by means of extensive experiments, the results of which reveal that resource allocation is either optimal or very close thereto, while individual InfSP profits as well as total federation profits are maximized. The results underscore the inadequacy of relying solely on the VCG mechanism for budget balance, justifying the necessity of our sophisticated hybrid solution.

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.

Mobile-Edge Computing (MEC) is about offering application developers and service providers cloud-computing capabilities and an IT service environment at the edge of the mobile network. However, although cloud computing can be used to meet traditional challenges, like scalability concerns and provide for fast resource provisioning times, a multifaceted analysis is required when it comes in multi-operator environments with time-critical applications and services. In this work, we claim that the service importance must be at the epicenter when it comes to the scheduling and placement decision of whether to deploy the service at the edge network or not. Virtual machine (VM) scheduling decisions should avoid SLA violations for popular or time-critical services, and be fair between the service providers. A Lyapunov optimization framework is derived to solve this stochastic optimization problem that aims to maximize the revenue of the physical infrastructure owner in a multi-network operator-sharing environment with time-critical SLAs. A series of simulation experiments validate the high effectiveness of the proposed approach over benchmarking ones.

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 ofte n 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.

Infinite nature of sensor data poses a serious challenge for query processing even in a cloud infrastructure. Model-based sensor data approximation reduces the amount of data for query processing, but all modeled segments need to be scanned, in the worst case. In this paper, we propose an innovative index for modeled segments in key-value stores, namely KVI-index. KVI-index has an in-memory tree component and a secondary structure materialized in the key-value store that maps the tree nodes to the modeled data segments. Then, we introduce a KVI-index-Scan-MapReduce hybrid approach to perform efficient query processing. As proved by a series of experiments in a real private cloud infrastructure, our approach outperforms in query response time and index updating efficiency both Hadoop-based parallel processing of the raw sensor data and multiple alternative indexing approaches of model-view data.

Participatory sensing (PS) is becoming a popular data acquisition means for interesting emerging applications. However, as data queries from these applications increase, the sustainability of this platform for multiple concurrent applications is at stake. In this paper, we consider the problem of efficient data acquisition in PS when queries of different types come from different applications. We effectively deal with the issues related to resource constraints, user privacy, data reliability, and uncontrolled mobility. We formulate the problem as multi-query optimization and propose efficient heuristics for its effective solution for the various query types and mixes that enable sustainable sensing. Based on simulations with real and artificial data traces, we found that our heuristic algorithms outperform baseline approaches in a multitude of settings considered.

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 house-level 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 meters-when appropriately placed — can very accurately disaggregate the total real power consumption of a residential setting and verified the effectiveness of our heuristic approach.

An overwhelming and growing amount of data is available online. The problem of untrustworthy online information is augmented by its high economic potential and its dynamic nature, e.g. transient domain names, dynamic content, etc. In this paper, we address the problem of assessing the credibility of web pages by a decentralized social recommender system. Specifically, we concurrently employ i) item-based collaborative filtering (CF) based on specific web page features, ii) user-based CF based on friend ratings and iii) the ranking of the page in search results. These factors are appropriately combined into a single assessment based on adaptive weights that depend on their effectiveness for different topics and different fractions of malicious ratings. Simulation experiments with real traces of web page credibility evaluations suggest that our hybrid approach outperforms both its constituent components and classical content-based classification approaches.

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, 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.

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.

The increasing use of sensor technology for various monitoring applications (e.g. air-pollution, traffic, climate-change, etc.) has led to an unprecedented volume of streaming data that has to be efficiently aggregated, stored and retrieved. Real-time model-based data approximation and filtering is a common solution for reducing the storage (and communication) overhead. However, the selection of the most efficient model depends on the characteristics of the data stream, namely rate, burstiness, data range, etc., which cannot be always known a priori for (mobile) sensors and they can even dynamically change. In this paper, we investigate the innovative concept of efficiently combining multiple approximation models in real-time. Our approach dynamically adapts to the properties of the data stream and approximates each data segment with the most suitable model. As experimentally proved, our multi-model approximation approach always produces fewer or equal data segments than those of the best individual model, and thus provably achieves higher data compression ratio than individual linear models.

As the online social networks (OSNs), such as Facebook, witness explosive growth, the privacy challenges gain critical consideration from governmental and law agencies due to concentration of vast amount of personal information within a single administrative domain. In this paper, we demonstrate a privacy-aware decentralized OSN called My3, where users can exercise full access control on their data. Our system exploits trust relationships in the social network for providing the necessary decentralized storage infrastructure. By taking users' geographical locations and online time statistics into account, it also addresses availability and storage performance issues.

Service-oriented architecture (SOA) paradigm for orchestrating large-scale distributed applications offers significant cost savings by reusing existing services. However, the high irregularity of client requests and the distributed nature of the approach may deteriorate service response time and availability. Static replication of components in datacenters for accommodating load spikes requires proper resource planning and underutilizes the cloud infrastructure. Moreover, no service availability guarantees are offered in case of datacenter failures. In this paper, we propose a cost-efficient approach for dynamic and geographically-diverse replication of components in a cloud computing infrastructure that effectively adapts to load variations and offers service availability guarantees. In our virtual economy, components rent server resources and replicate, migrate or delete themselves according to self-optimizing strategies. We experimentally prove that such an approach outperforms in response time even full replication of the components in all servers, while offering service availability guarantees under failures.

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 cost-efficient 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.

Failures of any type are common in current datacenters. 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 cost-efficient 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 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. Comprehensive experimental evaluations suggest that our solution is highly scalable and adaptive to query rate variations and to resource upgrades/failures.

Hidden information is a critical issue for the successful delivery of SLAs 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.

The effectiveness of online feedback mechanisms for rating the performance of providers in electronic markets is vulnerable to the submission of dishonest ratings. In this paper, we deal with how to elicit honest such ratings in a competitive electronic market where each participant can occasionally act both as provider and as client. We assume that each service provision is rated by both parties involved; only upon agreement, this rating is included in the calculation of reputation for the provider's performance. We first study as a single-shot game the effectiveness of inducing, upon evidence of lying (i.e. disagreement of the submitted feedback), fixed fines to both transacted parties, yet different ones for the provider and the client. We prove that the submission of honest feedback can be a stable equilibrium for the whole market under certain initial system conditions. Then, we refine our game-model for repeated transactions and calculate proper different reputation-based fines for lying. These fines enable the submission of honest feedback as a stable Nash equilibrium of the repeated game and reduce the social losses due to unfair punishments. Finally, we argue that our model is appropriate for analyzing actual electronic markets, and we investigate the impact of employing our approach to the feedback schemes of such markets.

We propose a mechanism for providing the incentives for reporting truthful feedback in a peer-to-peer system for exchanging services. This mechanism is to complement reputation mechanisms that employ ratings' feedback on the various transactions in order to provide incentives to peers for offering better services to others. Under our approach, both transacting peers (rather than just the client) submit ratings on performance of their mutual transaction. If these are in disagreement, then both transacting peers are punished, since such an occasion is a sign that one of them is tying. The severity of each peer's punishment is determined by his corresponding non-credibility metric; this is maintained by the mechanism and evolves according to the peer's record. When under punishment, a peer is not allowed to transact with others. We present the results of a multitude of experiments of dynamically evolving peer-to-peer systems. The results show clearly that our mechanism detects and isolates effectively liar peers, while rendering lying costly. Also, our mechanism diminishes the efficiency losses induced to sincere peers by the presence of large subsets of the population of peers that provide their ratings either falsely or according to various unfair strategies. Finally, we explain how our approach can be implemented in practical cases of peer-to-peer systems.

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 reputation based 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-to-peer population is renewed with a high rate.