Summary As opposed to cloud servers, fog servers, and fog users may be malicious, so developing a mutual identity‐preserving authentication mechanism between them is a crucial and difficult problem in fog computing. Such a technique must conceal the user's true identity from the adversary; otherwise, the adversary will be able to determine which fog user and fog server are in communication. This article suggests a secure and reliable anonymous mutual authentication system for use at the network's edge between fog users and fog servers. With the aid of the registration authority (RA) in our system, they can verify one another and decide on a new session key that will be used to encrypt messages throughout the session. Fog users don't need to re‐register with RA to wander freely over the network and authenticate to any fog server that is within their range. The proposed technique only needs a small number of symmetric encryption/decryption and one‐way hash functions, making it easy to implement for fog‐user devices with limited resources. The new scheme's performance is evaluated in comparison to the existing one, showing that it is more resilient to various types of assaults (such as known plaintext attacks, man‐in‐the‐middle attacks, session hijacking, etc.). The widely used Automated Validation of Internet Security Protocols and Applications tool is used to verify the proposed system. The outcomes demonstrate that our approach can safely withstand different attacks and accomplish the desired outcomes. Additionally, the proposed method is tested in real‐world scenarios with the NS3 simulator.

Summary Internet of Things (IoT) devices have become part of our daily life. IoT applications are used in vast domains such as smart healthcare, smart cities, smart transportation, Industry 4.0, and so forth. However, many IoT applications come under the ultra‐reliable and low‐latency communications category; minimal execution time is crucial for such applications. Limitations such as network reliability and the cloud's multi‐hop distance to the IoT devices can affect providing efficient solutions for IoT applications. Fog computing has emerged as an important paradigm that extends cloud computing by delivering cloud‐like services nearer to the end‐users. Placement of IoT applications onto the appropriate fog nodes has an important influence on the overall execution time of applications and energy consumption of fog nodes. Efficiently deploying IoT applications to fog nodes is difficult due to two factors: fog nodes have varying processing capacity and are geographically located in different places from IoT devices. Hence, this article proposes a decentralized bi‐objective optimization application placement policy, that is, DMAP, to minimize IoT applications' overall execution time and energy consumption of fog nodes. The matching game methodology is used for mapping applications to fog nodes. The performance of DMAP is verified using large‐scale simulation experiments. Experimental results show significant improvement in overall execution time, energy consumption, and scalability compared to the existing solutions.

Memory is a crucial resource which limits the scalability and performance of the virtualized systems. It is evident from the existing literature that substantial memory savings can be achieved by obviating the redundant memory across virtual machines. Memory deduplication is one such approach that harnesses these memory redundancies through sharing of the duplicate memory. However, this approach incurs a significant overhead when shared pages are liable to frequent modification which results in early breaking of Copy-on-Write (CoW)\ mechanism. In worst case, this early breaking of CoW mechanism leads to the problem of thrashing which nullifies the benefits of memory deduplication. The existing memory deduplication techniques have not focused on the thrashing issue. The proposed approach, MDedup++, addresses this issue through a hinting mechanism, where hints can be inferred from the temporal analysis of page-sharing behaviors. With these hints, the memory deduplication scanner explores the sharing potential only on stable pages. In addition, MDedup++ also analyzes the spatial distribution of page-sharing behaviors to reduce the latency and search cost of the memory deduplication process. The proposed approach is implemented in Linux Kernel daemon (Kernel Samepage Merging, KSM), without guest operating system modification. The evaluation with several benchmark workloads shows that MDedup++ achieves a significant improvement in terms of memory savings, deduplication speed and CPU overhead over vanilla KSM memory deduplication scanner.