ENPP: Extended Non-preemptive PP-aware Scheduling for Real-time Cloud Services

By increasing the use of cloud services and the number of requests to processing tasks with minimum time and costs, the resource allocation and scheduling, especially in real-time applications become more challenging. The problem of resource scheduling, is one of the most important scheduling problems in the area of NP-hard problems. In this paper, we propose an efficient algorithm is proposed to schedule real-time cloud services by considering the resource constraints. The simulation results show that the proposed algorithm shorten the processing time of tasks and decrease the number of canceled tasks

An Effiecient Approach for Resource Auto-Scaling in Cloud Environments

Cloud services have become more popular among users these days. Automatic resource provisioning for cloud services is one of the important challenges in cloud environments. In the cloud computing environment, resource providers shall offer required resources to users automatically without any limitations. It means whenever a user needs more resources, the required resources should be dedicated to the users without any problems. On the other hand, if resources are more than user’s needs extra resources should be turn off temporarily and turn back on whenever they needed. In this paper, we propose an automatic resource provisioning approach based on reinforcement learning for auto-scaling resources according to Markov Decision Process (MDP). Simulation Results show that the rate of Service Level Agreement (SLA) violation and stability that the proposed approach better performance compared to the similar approaches

ControCity: An Autonomous Approach for Controlling Elasticity Using Buffer Management in Cloud Computing Environment

Cloud computing has been one of the most popular distributed computing paradigms. Elasticity is a crucial feature that distinguishes cloud computing from other distributed computing models. It considers the resource provisioning and allocation processes can be implemented automatically and dynamically. Elasticity feature allows cloud platforms to handle different loads efficiently without disrupting the normal behavior of the application. Therefore, providing a resource elasticity analytical model can play a significant role in cloud resource management. This paper presents Controlling Elasticity (ControCity) framework for controlling resources elasticity through using “buffer management” and “elasticity management”. In the proposed framework, there are two essential components called buffer manager and elasticity manager in the application layer and middleware layer, respectively. The buffer management controls the input queue of the user’s request and the elasticity management controls the elasticity of the cloud platform using learning automata technique. In the application layer, applications are received by cloud applications and, then, placed in the control of the buffer. Buffer manager controls the queue of requests, and elasticity manager of the middleware layer using the learning automata provides a solution for controlling the elasticity of the cloud platform. The experimental results indicate that ControCity reduces the response time by up to 3.7%, and increases the resource utilization and elasticity by up to 8.4% and 5.4%, respectively, compared with the other approaches

Context-aware multi-user offloading in mobile edge computing: A federated learning-based approach

Mobile edge computing (MEC) provides aneffective solution to help the Internet of Things (IoT)devices with delay-sensitive and computation-intensivetasks by offering computing capabilities in the proximityof mobile device users. Most of the existing studies ignorecontext information of the application, requests, sensors,resources, and network. However, in practice, contextinformation has a significant impact on offloading decisions.In this paper, we consider context-aware offloadingin MEC with multi-user. The contexts are collected usingautonomous management as the MAPE loop in alloffloading processes. Also, federated learning (FL)-basedoffloading is presented. Our learning method in mobiledevices (MDs) is deep reinforcement learning (DRL). FLhelps us to use distributed capabilities of MEC with updatedweights between MDs and edge devices (Eds). Thesimulation results indicate our method is superior to localcomputing, offload, and FL without considering contextawarealgorithms in terms of energy consumption, executioncost, network usage, delay, and fairness

An Evolutionary Multi-objective Optimization Technique to Deploy the IoT Services in Fog-enabled Networks: An Autonomous Approach

The Internet of Things (IoT) generates countless amounts of data, much of which is processed in cloud data centers. When data is transferred to the cloud over longer distances, there is a long latency in IoT services. Therefore, in order to increase the speed of service provision, resources should be placed close to the user (i.e., at the edge of the network). To address this challenge, a new paradigm called Fog Computing was introduced and added as a layer in the IoT architecture. Fog computing is a decentralized computing infrastructure in which provides storage and computing in the vicinity of IoT devices instead of sending to the cloud. Hence, fog computing can provide less latency and better Quality of Service (QoS) for real-time applications than cloud computing. In general, the theoretical foundations of fog computing have already been presented, but the problem of IoT services placement to fog nodes is still challenging and has attracted much attention from researchers. In this paper, a conceptual computing framework based on fog-cloud control middleware is proposed to optimally IoT services placement. Here, this problem is formulated as an automated planning model for managing service requests due to some limitations that take into account the heterogeneity of applications and resources. To solve the problem of IoT services placement, an automated evolutionary approach based on Particle Swarm Optimization (PSO) has been proposed with the aim of making maximize the utilization of fog resources and improving QoS. Experimental studies on a synthetic environment have been evaluated based on various metrics including services performed, waiting time, failed services, services cost, services remaining, and runtime. The results of the comparisons showed that the proposed framework based on PSO performs better than the state-of-the-art methods