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Cloud Computing is a newly emerged technology. It is getting popularity day by day due to its amazing services. The applications and services based on the cloud are emerging day by day. Due to networked nature of the cloud, resources, data and applications are vulnerable to the attack in cloud environment. So Intrusion Detection Systems (IDS) are employed in the cloud to detect malicious behaviour in the network and in the host. IDS monitors network or host system activities by collecting network information, and analyzes this information for malicious activities and generate alarms, if intrusion takes place. In this paper we surveyed various types of Intrusion Detection Systems proposed over the years for Cloud Computing environment.

Cloud computing is a novel paradigm that aims to provision on-demand computing capacities as services. Virtualization is an important technology integrated in Cloud Computing. Mapping the virtual machines to the appropriate physical machines is called VM placement. The effectiveness and elasticity of virtual machine placement has become the main concern in cloud computing environments. Effective placement of virtual machines is important for optimization of computational resources and reduction of the probability of virtual machine reallocation. This paper provides a survey and brief analysis of some of the main VM Placement mechanism utilized in cloud computing.

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Cloud computing allocates virtual resources dynamically on user's demand. The sudden rise of data storage and computation in the cloud computing environment may cause an imbalanced workload distribution. As a result, job completion time will be higher in overloaded servers than the underloaded servers in the same environment.Distributing load fairly in the cloud is a crucial challenge. Traditionally, load balancing is used to distribute the workload among multiple servers to overcome the overloading and underloading of servers. This article presents a novel load balancing approach for cloud computing using improved gray wolf optimization algorithm. We compare our approach with harmony search algorithm, artificial bee colony algorithm, particle swarm optimization, and gray wolf optimization algorithms. Results of simulation are encouraging with improved system performance and fair utilization of resources.

Blockchain enables smart contract for secure data transfer by which fog offloading servers can have trustworthy access control to work with data execution. When cloud is used for handling requests from mobile users, the attacker may perform denial of service attack and the same is possible at fog nodes and the same can be handled with the help of blockchain technology. In this paper, smart city application is discussed a use case study for blockchain based fog computing architecture. We propose a novel offload chain architecture for blockchain-based offloading in internet of things (IoT) networks where mobile devices can offload their data to fog servers for computation by an access control mechanism. The offload chain model using deep reinforcement learning (DRL) is proposed to improve the efficiency of blockchain based fog offloading amongst existing models.