With the advent of the Internet of Things (IoT) paradigm, the cloud model is unable to offer satisfactory services for latency‐sensitive and real‐time applications due to high latency and scalability issues. Hence, an emerging computing paradigm named as fog/edge computing was evolved, to offer services close to the data source and optimize the quality of services (QoS) parameters such as latency, scalability, reliability, energy, privacy, and security of data. This article presents the evolution in the computing paradigm from the client‐server model to edge computing along with their objectives and limitations. A state‐of‐the‐art review of Cloud Computing and Cloud of Things (CoT) is presented that addressed the techniques, constraints, limitations, and research challenges. Further, we have discussed the role and mechanism of fog/edge computing and Fog of Things (FoT), along with necessitating amalgamation with CoT. We reviewed the several architecture, features, applications, and existing research challenges of fog/edge computing. The comprehensive survey of these computing paradigms offers the depth knowledge about the various aspects, trends, motivation, vision, and integrated architectures. In the end, experimental tools and future research directions are discussed with the hope that this study will work as a stepping‐stone in the field of emerging computing paradigms.

Journey from cloud of things to fog of things: Survey, new trends, and research directions

Infrastructure as a Service (IaaS) is a cloud computing service provided over the internet to facilitate the provisioning of various services such as storage, processes, etc The provider in the IaaS market may offer some purchasing plans including: reservation, on-demand, and spot plans for its resources As in real scenarios, demand volume for each plan is assumed to be a random variable with a given probability distribution The provider maximizes its average revenue in the long run by optimal allocation of its resources among the plans We formulate an Integer Linear Programming (ILP) model with a stochastic constraint, to determine the number of resources to be allocated for each plan in every time slot in the planning horizon First, fixed prices are considered for each plan, then two mechanisms of Continuous Double Auction and Second Price Sealed Bid Auction are considered for reservations and spot plans, respectively, to obtain market-driven prices of the services The Seasonal Weighted Moving Average method is used to predict the amount of demand in every slot Finally, the proposed mechanisms are evaluated through simulations and the results confirm the effectiveness of the methods in maximizing the revenue and overall utilization of the available IaaS capacity

Resource allocation mechanisms for maximizing provider’s revenue in infrastructure as a service (IaaS) cloud

For cloud computing, elaborately managing resources and workloads to optimize various metrics such as performance, cost and energy is of strategic importance, but by no means trivial. Traditional model- or heuristic-based solutions are highly knowledge- and labor-intensive, as well as problem-specific. Recently, with the booming of AI, researchers in cloud computing community are motivated to revisit cloud resource/workload management problem by applying the emerging deep reinforcement learning (DRL) method. In this paper, we first identify the motivations of applying DRL to the long-standing and challenging cloud management problems. Then we provide a selective survey of the recent advances with analysis of their design principles and benefits. Based on those pilot attempts, we summarize the general workflow and conduct a case study to illustrate how to apply DRL for intelligent cloud resource/workload management. The goal of this article is to provide a broad guideline on DRL-based intelligent cloud management to help stimulate researchers to develop innovative algorithms, frameworks and standards.

Deep Reinforcement Learning for Intelligent Cloud Resource Management

This paper proposes a data center business continuity risk grading model in a cloud environment, which determines the disaster recovery capability level of the target business system based on system attribute information, comprehensive basic level, system life cycle and return on investment, and determines the target business based on the disaster recovery capability level The business continuity protection mode of the system is to protect the target business system continuously according to the business continuity protection mode.

Research on business continuity rating model in cloud environment

A dynamic Stackelberg game based multi-objective approach for effective resource allocation in cloud computing

Cloud computing offers a vast number of processing opportunities and heterogeneous resources and meets the requirements of numerous applications at various levels. Thus, the allocation and management of resources are vital in cloud computing. Resource allocation is a technique in which the available resources such as central processing unit, random-access memory, storage, and network bandwidth in cloud data centres are divided among users in a way that facilitates resource utilisation, provider profit, and user satisfaction. Integration and interaction with other modules of the resource management system, security, privacy, fairness, non-fragmentation of resources, resource utilisation, provider profit, user satisfaction, reducing energy consumption, load balancing, flexibility, scalability, availability, improvement the number and time of virtual machine migrations, and the number of overloaded resources are considered as challenges for the resource allocation mechanism. A systematic resource allocation survey with innovations in resource management system architecture, categorising mechanisms, addressing the challenges, and issues is presented. In addition to introducing the existing resource allocation mechanisms, other similar survey papers have been reviewed. Finally, there are some suggested topics for future work.

Mostafa Vakili Fard

Papers

Resource allocation mechanisms in cloud computing: a systematic literature review