Fog/edge computing enables IoT applications to improve the scalability and energy efficiency of IoT systems, exploit computational node resources to analyse the collected data, and meet latency requirements. Nevertheless, a series of challenging problems need to be addressed in order to fully utilize fog/edge computing for IoT. For instance, the majority of computational nodes in fog/edge computing are battery-operated, which means that fog/edge computing tends to be unreliable. Moreover, efficiently providing computing resources to latency-sensitive IoT applications (such as AR/VR, haptic technology, intelligent manufacturing, connected autonomous driving, and others), and seamless integration of fog/edge computing with cloud computing to provide scalable services needs to be further studied.

Traditional data centers are shifted toward the cloud computing paradigm. These data centers support the increasing demand for computational and data storage that consumes a massive amount of energy at a huge cost to the cloud service provider and the environment. Considerable energy is wasted to constantly operate idle virtual machines (VMs) on hosts during periods of low load. Dynamic consolidation of VMs from overloaded or underloaded hosts is an effective strategy for improving energy consumption and resource utilization in cloud data centers. The dynamic consolidation of VM from an overloaded host directly influences the service level agreements (SLAs), utilization of resources, and quality of service (QoS) delivered by the system. We proposed an algorithm, namely, GradCent, based on the Stochastic Gradient Descent technique. This algorithm is used to develop an upper CPU utilization threshold for detecting overloaded hosts by using a real CPU workload. Moreover, we proposed a dynamic VM selection algorithm called Minimum Size Utilization (MSU) for selecting the VMs from an overloaded host for VM consolidation. GradCent and MSU maintain the trade-off between energy consumption minimization and QoS maximization under specified SLA goal. We used the CloudSim simulations with real-world workload traces from more than a thousand PlanetLab VMs.

Vehicular Fog Computing (VFC) provides solutions to relieves overload cloudlet nodes, reduces service latency during peak times, and saves energy for battery-powered cloudlet nodes by offloading user tasks to a vehicle (vehicular node) by exploiting the under-utilized computation resources of nearby vehicular node. However, the wide deployment of VFC still confronts several critical challenges: lack of energy-latency tradeoff and efficient resource allocation mechanisms. In this paper, we address the challenges and provide an Energy-efficient dynamic Computation Offloading and resources allocation Scheme ( ECOS ) to minimize energy consumption and service latency. We first formulate the ECOS problem as a joint energy and latency cost minimization problem while satisfying vehicular node mobility and end-to-end latency deadline constraints. We then propose an ECOS scheme with three phases. In the first phase, we propose an overload cloudlet node detection policy based on resource utilization. In the second phase, we propose a computational offloading selection policy to select a task from an overloaded cloudlet node for offloading, which minimizes offloading cost and the risk of overload. Next, we propose a heuristic approach to solve the resource allocation problem between the vehicular node and selected user tasks for energy-latency tradeoff. Extensive simulations have been conducted under realistic highway and synthetic scenarios to examine the ECOS scheme’s performance. In comparison, our proposed scheme outperforms the existing schemes in terms of energy-saving, service latency, and joint energy-latency cost.

In this paper, we address the problems of massive amount of energy consumption and service level agreements (SLAs) violation in cloud environment. Although most of the existing work proposed solutions regarding energy consumption and SLA violation for cloud data centers (CDCs), while ignoring some important factor: (1) analysing the robustness of upper CPU utilization threshold which maximize utilization of resources; (2) CPU utilization prediction based VM selection from overloaded host which reduce performance degradation time and SLA violation.

The main aim of this paper is based on the cache performance test of the QoC: quality of experience framework for cloud computing on the server. QoC framework is based on the server-side design and implementation of the use of hierarchical architecture. Reverse proxy technology is used to build a server cluster, which is composed of front-end access layer to achieve the server for load balancing, improve the performance of the system and the use of built-in distributed cache server. The cluster consists of the cache acceleration layer, which reduces the load of the backend database. The second database server cluster, which is constructed by the database master and slave synchronization technology, forms the data storage layer, which realizes the database read and writes separation and data redundancy. The server-side hierarchical architecture improves the performance and stability of the entire system, and has a high degree of scalability, laying a solid foundation for future expansion of system business logic and increases user volume. This paper presents new cache replacement algorithm for inconsistent video file size and then analyses the specific needs for the multi-terminal type of QoC framework, and gives the client and server-side outline design; it describes the implementation details of the client and the server-side and finally the whole system of detailed functional and performance testing.