# Machine Learning Failure-Aware Scheme for Profit Maximization in the Cloud Market
###### Abstract  
A successful cloud trading system requires suitable financial incentives for all parties involved. Cloud providers in the cloud market provide computing services to clients in order to perform their tasks and earn extra money. Unfortunately, the applications in the cloud are prone to failure for several reasons. Cloud service providers are responsible for managing the availability of scheduled computing tasks in order to provide high-level quality of service for their customers. However, the cloud market is extremely heterogeneous and distributed, making resource management a challenging problem. Protecting tasks against failure is a challenging and non-trivial mission due to the dynamic, heterogeneous, and largely distributed structure of the cloud environment. The existing works in the literature focus on task failure prediction and neglect the remedial (post) actions. To address these challenges, this paper suggests a fault-tolerant resource management scheme for the cloud computing market in which the optimal amount of computing resources is extracted at each system epoch to replace failed machines. When a cloud service provider detects a malfunctioning machine, they transfer the associated work to new machinery. cloud market; fault tolerance; task failure +
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CP, it is mandatory to gain maximum profit, which can be obtained by serving the clients' requests within the agreed upon deadline. To prevent a penalty for a delay, a deadline constraint should be met. Consequently, CPs need to be ready for any CR failure that could cause a violation the deadline constraint. Furthermore, CPs should take steps to lower customer queue wait times. To address this issue, we proposed a novel method that ensures the QoS of all requests while drastically reducing resource waste. In addition, CPs need to take action to shorten the time that customers must wait in line. However, earlier research has not addressed the needs and applications for fault-tolerant computing, as well as fundamental subjects, components, and trading metrics in the context of fault tolerance in the cloud. This paper investigates fault tolerance in the cloud market in a straightforward manner. To that end, we study short-term trading in the cloud market, where trade takes place between the CP (a monopoly supplier) and multiple clients. In this market, each client comes to an arrangement, known as a contract, with the CP. The contract specifies the following key elements:  
* The client's demand (i.e., the number of CRs);
* Payment in a given period;
* Rental period. Clients in the cloud market rent CRs in real time and on demand through an auction. Clients compete for CRs, and through pre-defined contracts, CPs insure clients from the uncertainty of future supply. The main concern of this research is to boost CPs' profit. Cloud market data information is typically erratic. Information about the cloud market includes service demand, supply, and customer bids. This problem is formulated as a profit maximization problem. The following are the main contributions:  
* A new modeling and solution technique is proposed to handle task failure for clients. In this work, we tackle the problem of profit maximization in cloud markets under stochastic network information. Clients' satisfaction is guaranteed by providing the committed number of CRs to guarantee the QoS for clients. The CP reserves a few CRs to deal with machine failure, and new requests are served based on an auction policy that guarantees the availability of CRs. * Under stochastic network information, we extract the optimal number of CRs that can be used to replace failed CRs. * We study the CP's profit and QoS constraints for clients under stochastic cloud market information. The remainder of this article is organized as follows: First, the related work and our contributions to the paper are introduced in Section 2. Next, the cloud market is presented in Section 3. We describe the proposed fault-tolerant trading scheme in Section 4.