Agent-based Interactions and Economic Encounters in an Intelligent Inter Cloud

AbstractAn 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.

Existing System:

In Many existing research they only consider the power consumption cost. As a major difference between their models and ours, the resource rental cost is considered in this paper as well, since it is a major part which affects the profit of service providers.

Proposed System:

Proposed a multi‐tier Cloud negotiation model consisting of: 1) user tier (comprising consumers and brokers represented by CAs and BAs, respectively), 2) service tier (comprising service providers represented by service provider agents (SAs)) and 3) resource tier (comprising resource providers represented by resource agents (RAs)). In negotiation activities were carried out between CAs and BAs, between BAs and SAs, and between SAs and RAs. Agents in adopted the time‐dependent strategy with fixed concession rates, but market‐oriented issues such as outside options and rivalry were not considered. Empirical results in section 3.4 show that the ACR‐MSC strategy in this work achieved significantly higher utilities than the time‐dependent strategy without sacrificing success rates in negotiation. Additionally, mathematical proofs show that agents adopting the ACR‐MSC strategy negotiate optimally. Moreover, in game‐theoretic issues such as InterCloud coalition formation, equilibrium strategies, and fair division of payoff were not considered. The agent‐based testbed in consists of PAs and CAs that act as intermediaries between Cloud resource providers and consumers, respectively, and a set of BAs that connects resource requests from consumers to advertisements from providers.

Problem Statement: A profit maximization function is defined to find an optimal combination of the server size R and the queue capacity K such that the profit is maximized. However, this strategy has further implications other than just losing the revenue from some services, because it also implies loss of reputation and therefore loss of future customers. In , Cao et al. treated a cloud service platform as an M/M/m model, and the problem of optimal multiserver configuration for profit maximization was formulated and solved. This work is the most relevant work to ours, but it adopts a single renting scheme to configure a multiserver system, which cannot adapt to the varying

market demand and leads to low service quality and great resource waste. To overcome this weakness, another resource management strategy is used in , which is cloud federation. Using federation, different providers running services that have complementary resource requirements over time can mutually collaborate to share their respective resources in order to fulfill each one’s demand . However, providers should make an intelligent decision about utilization of the federation (either as a contributor or as a consumer of resources) depending on different conditions that they might face, which is a complicated problem.

Scope: Contributing the game‐theoretic foundations for analyzing and specifying the interactions of a society of agents in an InterCloud, this work has only taken the first step towards designing an intelligent InterCloud. The author hopes that this work will inspire others to take up future challenges of realizing and implementing the ideas and solution concepts in the paper.

Architecture:

Implementation of modules:

(1)CONSUMER-TO-CLOUD INTERACTIONS:

Negotiation between each pair of CA and PA is carried out by making proposals in alternate rounds. Unlike Rubinstein’s alternating offers protocol [16], which is a bilateral (one‐to‐one) negotiation protocol, in the multilateral consumer‐to‐Cloud interaction protocol, multiple CA‐PA pairs can negotiate deals simultaneously. Each CA (respectively, PA) can negotiate with multiple PAs (respectively, CAs) at the same time. Making proposals: When an agent makes a proposal, it proposes a deal from their space of possible deals. These consist of the most desirable price, the least desirable (reserve) price, and prices in between. An agent proposes its most preferred deal initially. If no agreement is reached, negotiation proceeds to the next round.

(2) CLOUD-TO-CLOUD INTERACTIONS:

InterCloud is a federation of Clouds, economic encounters among Clouds can be modeled as a coalition game. In an InterCloud coalition game (definition 4.1), the players are the Clouds (represented by hCAs and fCAs) that cooperate with one another by drawing upon each other’s resources to satisfy consumers’ demands, collectively generate more profit for the coalition, and share their total profit. Two major issues in InterCloud interaction are: 1) How does each Cloud choose its coalition partners? and 2) How should the coalition divide its payoff among the players? Within an InterCloud, each self‐interested Cloud negotiates and establishes agreements with other Clouds to meet its own objectives and to optimize its own payoff. The agent‐based Cloud‐to‐Cloud interaction protocol specified in algorithm 4.1 consists of four stages: 1) announcement of the availability of resource capacities, 2) bidding for the priority right to acquire resource capacities of other Clouds, 3) making offers for sharing the payoff generated by the InterCloud coalition, and 4) acceptance or rejection of offers.

(3)Adaptive Concession Rate:

Consumers in a Cloud market compete for computing services and Cloud providers compete to provide services, a market‐oriented approach taking into account the demand for and supply of Cloud services is appropriate. Bargaining with deadlines: Since consumers generally have deadlines in acquiring computing resources to execute jobs and Clouds also have deadlines for scheduling their resources and executing jobs, both CAs and PAs are programmed to make concessions with respect to time. Both CAs and PAs are designed with three classes of time‐dependent concession making strategies: i) conservative (conceding slowly by maintaining the initial price until an agent’s deadline is almost reached), ii) conciliatory (conceding rapidly to the reserve price), and iii) linear (conceding linearly).

(4)Minimally Sufficient Concessions:

Even though making a large amount of concession may increase the probability of reaching an agreement, doing so is inefficient because an agent “wastes” some of its utility. Nevertheless, if an agent makes too small an amount of concession, it runs the risk of not reaching any agreement with its opponent eventually. Hence, making minimally sufficient concession [19] is a desirable property of negotiation agents. Using the ACR‐MSC strategy, an agent negotiates optimally by making an amount of concession that is minimally sufficient. The general idea is that for a given market situation, an agent adopting the ACR‐MSC strategy strives to attain the highest possible utility while maintaining a minimum probability of reaching an agreement.

(5)Bargaining theory:

Bargaining or haggling is a type of negotiation in which the buyer and seller of a good or service debate the price and exact nature of a transaction. If the bargaining produces agreement on terms, the transaction takes place. Bargaining is an alternative pricing strategy to fixed prices. Optimally, if it costs the retailer nothing to engage and allow bargaining, he/she can divine the buyer's willingness to spend. It allows for capturing more consumer surplus as it allows price discrimination, a process whereby a seller can charge a higher price to one buyer who is more eager (by being richer or more desperate). Haggling has largely disappeared in parts of the world where the cost to haggle exceeds the gain to retailers for most common retail items. However, for expensive goods sold to uninformed buyers such as automobiles, bargaining can remain commonplace.

(6)Consumer Module:

In this project consumer module should perform the following tasks

Cloud consumer:

.consumer registeration. consumer login. consumer send request to consumer agent for accessing cloud. view status of the request. after accepting the request consumer upload his files in to cloud.

(7)Cloud Agent Module: In this project consumer module should perform the following tasks

Consumer agent:

.consumer registeration. consumer agent login. consumeragent view the consumer requests. forward consumer requests to corresponding cloud brokers. view all the requests and responses of cloud brokers.

Conclusion

The significance and novelty of this work are that it is the earliest work to propose an agent‐based InterCloud economic model for analyzing two types of interactions in an intelligent InterCloud: 1) consumer‐to‐Cloud interactions and 2) Cloud‐to‐Cloud interactions. Being the first to devise 1) best response strategies for InterCloud coalition formation that converge to both a subgame perfect equilibrium and the Shapley value payoff and 2) an optimal multilateral consumer‐to‐Cloud negotiation strategy, this work provides game‐theoretic solutions that lay the essential mathematical foundations for InterCloud economics. On this account, this work advances the state of the art in many ways as follows. From the perspective of Cloud computing, this work contributes a new branch of knowledge for realizing the InterCloud vision. Being the first of its kind to provide both 1) bargaining game and coalition game solution concepts in an InterCloud and 2) agent‐based interaction protocols for automating consumer‐to‐Cloud and Cloud‐to‐Cloud interactions, this work is an important milestone in introducing agent‐based InterCloud economics and agent‐based InterCloud interactions as new frontiers in InterCloud research.