Whereas there are group strategyproof mechanisms for a variety of problems, to date, group strategyproof bargaining has not been studied. Although shill bidding is widely studied in auctions, there is currently no work that analyzes the effect of shills on bargaining mechanisms. This paper validates that Sim's agent-based fog bargaining (AFB) mechanism is both 1) strongly group strategyproof (i.e., it is more robust than existing group strategyproof mechanisms) and 2) shill resistant. Since Internet-based agents can coordinate themselves to shade (respectively, mark up) resource prices, bargaining mechanisms that are resistant to coordinated price shading (respectively, markup) by coalitions of agents are crucial in price bargaining between fog node owners and Internet-connected device owners. Mathematical proofs validate that the AFB mechanism is strongly group strategyproof because on top of satisfying the commonly adopted condition of group strategyproofness, i.e., coordinated price shading (respectively, markup) by coalitions of agents that results in the strict gain of some agent will also result in the strict loss of another agent, it also satisfies two additional stronger conditions that 1) there is no collusive surplus from coordinated price shading (respectively, markup) and 2) every agent cannot increase his/her utility by joining a coalition. Given the ease for agents to fake identities in the Internet, shill resistance is another critically important property of fog bargaining mechanisms. Mathematical evidence validates that coordinated price shading (respectively, markup) by coalitions with shills is not feasible in the AFB mechanism.

This work contributes an (approximately) incentive-compatible and computationally efficient bargaining mechanism for pricing fog computing resources. In network settings (e.g., fog computing), it is plausible to think that self-interested and incompletely informed players (represented by software agents) will attempt to maximize their own benefits at the expense of others. Hence, it is crucial that fog bargaining mechanisms give incentives to agents for behaving in a manner consistent with the desired outcome where every agent’s benefit is maximized. Equilibrium analyses validate that the fog bargaining mechanism in this work is approximately Bayesian incentive compatible because every agent can approximately maximize its expected utility by adhering to the strategy recommended by the bargaining mechanism given that all other agents also adhere to their equilibrium strategies. That is, if every agent in the market adheres to the strategy recommended by the bargaining mechanism, then the strategy profile of the agents forms an approximate Bayesian Nash equilibrium. Given that a fog resource market has a large number of buyers and a large number of sellers, computational efficiency is also imperative since every agent needs to process a huge number of trading alternatives. Computational complexity analyses validate that 1) the procedure for carrying out the bargaining strategy has a linear time complexity, and with every passing round, the search space dwindles but the solutions become progressively better, 2) the number of rounds for each agent to complete bargaining is logarithmic in the number of its opponents, and 3) each agent has a linear message complexity.

While fog resource discovery involves propagating resource requests to a huge number of fog nodes, fog commerce refers to the activity of buying and selling fog resources. This work devises: 1) the KM-gossip algorithm for bolstering fog discovery and 2) a bargaining mechanism for pricing fog resources. The KM-gossip algorithm is a generalization of the gossip algorithm . It uses $K$ broker agents ( BAs ) to cooperatively “gossip” requests among themselves, and in parallel , each BA relays the requests to $M$ fog nodes. While computational complexity analysis validates that the KM-gossip algorithm has logarithmic time complexity (i.e., it is computationally efficient ), empirical results show that it significantly outperforms the existing gossip and flooding algorithms . Supplementing and complementing previous empirical results, game-theoretic analysis in this work validates that the bargaining mechanism generates Pareto optimal ( economically efficient ) solutions. The solutions also satisfy the famous Nash’s axioms that prescribe highly desirable properties of bargaining solutions.

This article 1) constructs a cloud intelligence model that specifies the desirable intelligent properties of cloud agents, 2) provides a tutorial on agent-based cloud, fog, and edge resource management, and 3) paves the way for designing intelligent interclouds, fogs, and edges. An intelligent intercloud is a “cloud of clouds” populated by a society of agents that automates intercloud resource management activities. By contributing a tutorial on agent-based cloud resource management techniques, this article provides researchers with the foundational knowledge for devising intelligent intercloud resource management techniques. By contributing an architectural blueprint, and suggesting and comparing different intelligent resource management techniques, this article provides an overall design together with pointers to and guidelines for constructing the components of an intelligent intercloud. This article describes a proof-of-concept prototype of an intelligent intercloud and provides an application example of the prototype. The need of IoT devices for reduced response time triggered technological advancements towards fog and edge computing. By providing a tutorial on agent-based fog and edge resource management techniques, this article provides researchers with the foundational knowledge for devising intelligent fog and edge agents. Relevant techniques are also suggested for improving and optimizing the performances of future intercloud, fog, and edge agents.

Whereas an Intercloud is an interconnected global “cloud of clouds” that enables each cloud to tap into resources of other clouds, interactions among Intercloud stakeholders are complex because Intercloud resources are distributed and controlled by different clouds. “Agent-based cloud computing” involves the construction of agents for bolstering discovery, matching, selection, composition, negotiation, scheduling, workflow, and monitoring of Intercloud resources. An agent is a computer system that is capable of making decisions independently and interacting with other agents through cooperation, coordination, and negotiation. Using an agent-based approach, characteristics associated with intelligent behaviors of agents such as interacting socially through cooperation, coordination, and negotiation can be built into clouds. This survey 1) discusses the significance and advantages of using an agent paradigm for Intercloud resource allocation, 2) reviews representative models of agent-based Intercloud resource allocation and provides a comparison among these models, 3) compares agent-based and non-agent-based approaches for task executions in multiple clouds, and 4) provides pointers to future directions.

Fog commerce refers to the activity of buying and selling fog computing resources. To bolster fog commerce, this work devises a novel layered bargaining mechanism comprising 1) a bargaining protocol that specifies the rules that govern the bargaining activities, 2) a bargaining strategy that adjusts price proposals by making concessions and enables agents to conserve computational resources by selectively engaging their bargaining activities only with trading partners with price proposals that are relatively close to theirs, and 3) a transitive pricing formula for pricing vertically arranged fog and cloud resources. To simulate price bargaining of vertically arranged fog and cloud resources, this work devises an agent-based fog commerce testbed. Whereas mathematical proofs validate that the layered fog bargaining mechanism is computationally efficient and rapidly converging, empirical results show that it outperforms related bargaining mechanisms.

Fog computing is a layered model that consists of fog nodes residing between cloud data centers and Internet of Things ( IoT ) devices. With a large number of highly distributed fog nodes and IoT devices, decentralized and computationally efficient techniques for resource discovery and selection are necessities of fog computing. Since consumers are charged for using fog and cloud resources, economically efficient resource pricing mechanisms are crucial. This work devises: 1) an agent-based fog computing model , 2) a gossip-based resource discovery algorithm for propagating resource requests, 3) a reasoning technique for determining similarities between user requirements and resource profiles, and 4) a bargaining mechanism for dynamically pricing vertically arranged fog and cloud resources. Mathematical proofs validate that the gossip algorithm and reasoning technique are computationally efficient , and the bargaining mechanism is economically efficient . Since agents can determine resource similarities independently and propagate requests autonomously without centralized control, agent-based resource discovery and selection approaches are decentralized techniques .

Cloud computing has attracted great interest from both industrial and academic communities. However, only a few efforts have been devoted to building tools for supporting Cloud service discovery. Therefore, we present a four-stage, agent-based Cloud service discovery protocol. Additionally, two Cloud ontologies (CO-1 and CO-2) are designed to semantically define the relationship among Cloud services. Whereas CO-1 contains only Cloud concepts, CO-2 contains a set of Cloud concepts, individuals of those concepts, and the relationship among those individuals. The similarity among Cloud services is determined by concept, object property, and data type property similarity reasoning. In addition, two kinds of recommendation approaches (R1 and R2) based on attribute value prediction are presented. R1 is based on the maximum and R2 on the average similarity between the provided and the requested requirements. Empirical results show that our system achieved the best performance in finding the appropriate Cloud services with CO-2 and R2.

Since participants in a Cloud may be independent bodies, some mechanisms are necessary for resolving the different preferences to establish a service-level agreement (SLA) for Cloud service reservations. Whereas there are some mechanisms for supporting SLA negotiation, there is little or no negotiation support involving price, time slot, and QoS issues concurrently for a Cloud service reservation. For the concurrent price, timeslot, and QoS negotiation, a tradeoff algorithm to generate and evaluate a proposal which consists of price, timeslot, and QoS proposals is necessary. The contribution of this work is designing a multi-issue negotiation mechanism to facilitate 1) concurrent price, time slot, and QoS negotiations including the design of QoS utility functions and 2) adaptive and similarity-based trade-off proposals for price, time slots, and level of QoS issues. The tradeoff algorithm referred to as "adaptive burst mode" is especially designed to increase negotiation speed, total utility, and to reduce computational load for evaluating proposals by adaptively generating concurrent set of proposals. The empirical results obtained from simulations carried out using an agent-based testbed suggest that using the negotiation mechanism, (i) a consumer and a provider agent have a mutually satisfying agreement on price, time slot, and QoS issues in terms of the aggregated utility and (ii) the fastest negotiation speed with (iii) comparatively lower number of evaluated proposals in a negotiation.

An InterCloud is an interconnected global "cloud of clouds" that enables each cloud to tap into resources of other clouds. This is the earliest work to devise an agent-based InterCloud economic model for analyzing consumer-to-cloud and cloud-to-cloud interactions. While economic encounters between consumers and cloud providers are modeled as a many-to-many negotiation, economic encounters among clouds are modeled as a coalition game. To bolster many-to-many consumer-to-cloud negotiations, this work devises a novel interaction protocol and a novel negotiation strategy that is characterized by both 1) adaptive concession rate (ACR) and 2) minimally sufficient concession (MSC). Mathematical proofs show that agents adopting the ACR-MSC strategy negotiate optimally because they make minimum amounts of concession. By automatically controlling concession rates, empirical results show that the ACR-MSC strategy is efficient because it achieves significantly higher utilities than the fixed-concession-rate time-dependent strategy. To facilitate the formation of InterCloud coalitions, this work devises a novel four-stage cloud-to-cloud interaction protocol and a set of novel strategies for InterCloud agents. Mathematical proofs show that these InterCloud coalition formation strategies 1) converge to a subgame perfect equilibrium and 2) result in every cloud agent in an InterCloud coalition receiving a payoff that is equal to its Shapley value.

Bag-of-tasks (BoTs) applications are highly parallel, unconnected and unordered tasks. Since BoT executions often require costly investments in computing infrastructures, Clouds offer an economical solution to BoT executions. Cloud BoT executions involve (1) allocating and deallocating heterogeneous resources with possibly different price rates from multiple Cloud providers, (2) distributing BoT execution across multiple, distributed resources, and (3) coordinating self-interested Cloud participants. This paper proposes a novel agent-based Cloud BoT execution tool (CloudAgent) supported by a 4-stage agent-based protocol capable of dynamically coordinating autonomous Cloud participants to concurrently execute BoTs in multiple Clouds in a parallel manner. CloudAgent is endowed with an autonomous agent-based resource provisioning system supported by the contract net protocol to dynamically allocate resources based on hourly cost rates from multiple Cloud providers. In addition, CloudAgent is also equipped with an agent-based resource deallocation system that autonomously and dynamically deallocates resources assigned to BoT executions. Empirical results show that CloudAgent can efficiently handle concurrent BoT executions, bear low BoT execution costs, and effectively scale. (C) 2015 Elsevier Inc. All rights reserved.

Airships are becoming promising platforms for Earth observing in emergency (e.g., nature disaster surveillance). Dynamic scheduling plays a very critical role in dealing with emergent tasks. In this paper, we devise a novel agent-based scheduling mechanism. In contrast to the traditional contract net protocol, our mechanism has a bidirectional announcement mechanism, and the collaborative process consists of forward announcements from the perspective of tasks and backward announcements from the perspective of resources to jointly accomplish the scheduling. Additionally, we devise calculation rules of the bidding values in both forward and backward announcements and two heuristics for selecting contractors. Based on the bidirectional announcement mechanism, we propose an agent-based dynamic scheduling (ABDS) strategy for scheduling Earth-observing tasks on multiple airships. The ABDS scheme employs the fair competition principle of a roulette wheel and the dynamic adjustment principle of a buffer pool to solve the problem of local searching and to improve the load balancing of resources. Extensive experiments were carried out to evaluate the performance of ABDS by comparing it with a unidirectional announcement (UA) scheduling algorithm and a genetic algorithm. In addition, the sensitivity of the priority parameter to the system performance is evaluated. Experimental results show that ABDS significantly outperforms the scheduling quality of UA and that it is suitable for the Earth-observing task scheduling on multiple airships in emergency.

An increasing number of cloud services has been emerging in the recent years. Amazon, gogrid, rackspace, to name a few, are several most popular cloud service providers. Users need a tool to discover the available options and to suggest the most appropriate alternatives. CB-Cloudle, as a cloud service search engine, could satisfy such users’ need. In this work, a centroid-based search engine with the help of a k-means clustering algorithm is designed and developed as a software platform specialised in searching for cloud services, aiming to improve the search effectiveness and efficiency. The centroidbased approaches were applied to search the cloud services with instant response. The k-means clustering algorithm was introduced to discover the groups of similar cloud service entries using a new similarity matrix to calculate the defined distance between cloud service entries. The similarity matrix consists of a non-numeric similarity formula and a numeric similarity formula.

Cloud services emerge as one of the most important parts for a company. Amazon, Rackspace, Google, Microsoft, to name a few, all fight to gain a foothold as cloud services providers. CB-Cloudle, a search engine aiming to discover the available options of cloud services and to suggest the most appropriate alternatives, is presented here to meet with the end users' needs. In this work, this software platform CB-Cloudle specialised in searching for cloud services, and an automated cloud services crawler is also implemented. A k-means clustering algorithm with centroids was utilised to improve the search effectiveness and efficiency. This k-means clustering algorithm was introduced to discover the groups of similar cloud service entries, using a new similarity matrix to calculate the distance between cloud service entries.

Bargaining is a popular paradigm to solve the problem of resource allocation. Factors such as complexity of dynamic environment, bounded rationality of negotiators, time constraints and incomplete information, make the design of optimal automated bargaining strategies difficult. Currently, most bargaining strategies are designed under the assumption that opponents offer according to specific models. Therefore, most of them focus on modeling opponents or predict opponents' private information such as reservation price, deadline, or the probabilities of different behaviors. Without model opponents, this paper presents an adaptive prediction-regret driven negotiation strategy for bilateral one-shot price bargaining, which extends the existing heuristic method of "looking forward" into "looking forward and reviewing the past" pattern by the regret principle in psychology. Four sets of experiments are designed and implemented to verify the general performance of this strategy. Results show that this strategy outperforms the strategies that model opponents and existing adaptive strategy when bargaining with multifarious opponents who offer according to pure consecutive concession strategies, sit-and-wait strategy, fixed mixture strategies, random mixture strategies, or even intelligent strategies. (C) 2014 Elsevier Ltd. All rights reserved.

Bargaining is an effective paradigm to solve the problem of resource allocation. The consideration of factors such as bounded rationality of negotiators, time constraints, incomplete information, and complexity of dynamic environment make the design of optimal strategy for one-shot bargaining much tougher than the situation that all bargainers are assumed to be absolutely rational. Lots of prediction-based strategies have been explored either based on assuming a finite number of models for opponents, or focusing on the prediction of opponent’s reserve price, deadline, or the probabilities of different behaviors. Following the methods of estimating opponent’s private information, this paper gives a strategy which improves the BLGAN strategy to adapt to various possible bargaining situations and deal with multifarious opponents. In addition, this paper compares the improved BLGAN strategy with related work. Experimental results show that the improved BLGAN strategy can outperform related ones when faced with various opponents, especially the agents who frequently change their strategies for anti-learning.

 Abstract—Previous work demonstrated that by adopting a semi-recursive contract net protocol (SR-CNP) equipped with service capability tables (SCTs) for dynamically selecting recorded cloud agents, their services and states, Cloud agents can effectively integrate disparate Cloud resources into a unified Cloud service. However, the choice of SCT may result in large overheads with Cloud agents exchanging a considerably large number of messages to achieve high success rates in service composition. In this paper, a comprehensive set of mathematical analyses of message exchanges by Cloud agents (Broker agents, Consumer agents and Service provider agents) in cloud service composition are presented. Experiments were performed where cloud agents adopt Particle Swarm Optimization for evolving the best service composition outcomes with the aim of minimizing the average number of messages propagated while successfully composing cloud services using SR-CNP and SCTs. Empirical results obtained from an agent-based testbed reveal that agents successfully minimized the number of messages exchanged during cloud service composition.

Finding optimal strategies for negotiation with incomplete information is a challenging issue in agent-based automated negotiation research. Although there are some previous works on finding the strategies through coevolutionary learning using evolutionary algorithms ( EAs ), their coevolving strategies tend to converge to non-global optima (which bring about ineffective negotiation outcomes for participating agents) due to biased coevolution and failures in coevolution. To cope with these drawbacks, this work introduces and compares novel genetic algorithms ( GAs ) and estimation of distribution algorithms ( EDAs ) that have additional capability of dynamic diversity control: (1) the dynamic diversity controlling GA ( D 2 C-GA ), (2) the dynamic diversity controlling EDA ( D 2 C-EDA ), (3) the improved D 2 C-GA ( ID 2 C-GA ) and (4) the improved D 2 C-EDA ( ID 2 C-EDA ). While D 2 C-GA and D 2 C-EDA adopt the novel diversification and refinement ( DR ) procedure, ID 2 C-GA and ID 2 C-EDA adopt the modified and enhanced DR ( mDR ) procedure with two additional local heuristics population repair and local neighborhood search . An extensive series of experiments were carried out to compare and evaluate the performance of the simple GA ( S-GA ), the simple EDA ( S-EDA ), and the novel GAs and EDAs in coevolving effective negotiation strategies of two self-interested negotiation agents for their various deadline combinations. Favorable empirical results showed that (i) ID 2 C-EDA could coevolve (near-)optimal negotiation strategies for all the considered cases due to its good generalization performance and (ii) it also generally outperformed S-GA , S-EDA , D 2 C-GA , D 2 C-EDA and ID 2 C-GA in terms of solution accuracy, coevolutionary search capability and average coevolution restart ratio. Interestingly, it was also found that the coevolution performance of ID 2 C-GA and ID 2 C-EDA is complementary in that ID 2 C-GA and ID 2 C-EDA generally achieved better results in the cases of equal and different deadlines, respectively.

In this paper, we present an estimation of distribution algorithm (EDA) augmented with enhanced dynamic diversity controlling and local improvement methods to solve competitive coevolution problems for agent-based automated negotiations. Since optimal negotiation strategies ensure that interacting agents negotiate optimally, finding such strategies--particularly, for the agents having incomplete information about their opponents--is an important and challenging issue to support agent-based automated negotiation systems. To address this issue, we consider the problem of finding optimal negotiation strategies for a bilateral negotiation between self-interested agents with incomplete information through an EDA-based coevolution mechanism. Due to the competitive nature of the agents, EDAs should be able to deal with competitive coevolution based on two asymmetric populations each consisting of self-interested agents. However, finding optimal negotiation solutions via coevolutionary learning using conventional EDAs is difficult because the EDAs suffer from premature convergence and their search capability deteriorates during coevolution. To solve these problems, even though we have previously devised the dynamic diversity controlling EDA (D2C-EDA), which is mainly characterized by a diversification and refinement (DR) procedure, D2C-EDA suffers from the population reinitialization problem that leads to a computational overhead. To reduce the computational overhead and to achieve further improvements in terms of solution accuracy, we have devised an improved D2C-EDA (ID2C-EDA) by adopting an enhanced DR procedure and a local neighborhood search (LNS) method. Favorable empirical results support the effectiveness of the proposed ID2C-EDA compared to conventional and the other proposed EDAs. Furthermore, ID2C-EDA finds solutions very close to the optimum.

To many real-life games, the algorithm of Iterated Eliminating Regret-dominated Strategies (IERS) can find solutions that are consistent with experimental observations, which have been proved to be problematic for Nash Equilibrium concept. However, there are a serious problem in characterising the IERS epistemic procedure. That is, the rationality of choosing un-dominated strategies cannot be assumed as the common knowledge among all the players of a game, otherwise the outcome of the IERS cannot be implied. Nevertheless, the common knowledge of rationality among players is an essential premise in game theory. To address these issues, this paper develops a new epistemic logic model to interpret the IERS procedure as a process of dynamic information exchanging by setting the players’ rationality as a proper announcement assertion. Finally, we show that under the assumption of rationality common knowledge rather than lower probabilities, our model can successfully solve a well-known traveler dilemma.