Smart mobile devices (SMDs) can meet users’ high expectations by executing computational intensive applications but they only have limited resources including CPU, memory, battery power and wireless medium. To tackle this limitation, partial computation offloading can be used as a promising method to schedule some tasks of applications from resource-limited SMDs to high-performance edge servers. However, it brings communication overhead issues caused by limited bandwidth, and inevitably increases latency of tasks offloaded to edge servers. Therefore, it is highly challenging to achieve the balance between high-resource consumption in SMDs and high communication cost for providing energy-efficient and latency-low services to users. This work proposes a partial computation offloading method to minimize the total energy consumed by SMDs and edge servers by jointly optimizing offloading ratio of tasks, CPU speeds of SMDs, allocated bandwidth of available channels and transmission power of each SMD in each time slot. It jointly considers execution time of tasks performed in SMDs and edge servers, and transmission time of data. It also jointly considers latency limits, CPU speeds, transmission power limits, available energy of SMDs, and maximum number of CPU cycles and memories in edge servers. Considering these factors, a nonlinear constrained optimization problem is formulated and solved by a novel hybrid meta-heuristic algorithm named Genetic Simulated-annealing-based Particle swarm optimization (GSP) to produce a close-to-optimal solution. GSP achieves joint optimization of computation offloading between a cloud data center and the edge, and resource allocation in the data center. Real-life data-based experimental results prove that it achieves lower energy consumption in less convergence time than its three typical peers.

Accurate and real-time prediction of network traffic can not only help system operators allocate resources rationally according to their actual business needs but also help them assess the performance of a network and analyze its health status. In recent years, neural networks have been proved suitable to predict time series data, represented by the model of a long short-term memory (LSTM) neural network and a temporal convolutional network (TCN). This article proposes a novel hybrid prediction method named SG and TCN-based LSTM (ST-LSTM) for such network traffic prediction, which synergistically combines the power of the Savitzky–Golay (SG) filter, the TCN, as well as the LSTM. ST-LSTM employs a three-phase end-to-end methodology serving time series prediction. It first eliminates noise in raw data using the SG filter, then extracts short-term features from sequences applying the TCN, and then captures the long-term dependence in the data exploiting the LSTM. Experimental results over real-world datasets demonstrate that the proposed ST-LSTM outperforms state-of-the-art algorithms in terms of prediction accuracy. Note to Practitioners —This work considers real-time and high-accuracy prediction of network traffic. It is highly important to well predict network traffic by capturing long-term dependence and effectively extracting high- and low-frequency information from time series data. Yet, it is a big challenge to achieve it because there are unstable characteristics and strong nonlinear features in the network traffic due to continuous expansion of network scale and fast emergence of new services. Current prediction methods usually have oversimplified theoretical assumptions, need significant time and memory, or suffer problems of gradient disappearance or early convergence. Thus, they fail to effectively capture the nonlinear characteristics of large-scale network sequences. This work proposes a hybrid prediction method named SG and TCN-based LSTM (ST-LSTM), which integrates the merits of the Savitzky–Golay filter, the temporal convolutional network (TCN), and the long short-term memory (LSTM), serving as smoothing time series, capturing short-term local features, and capturing long-term dependence, respectively. Experimental results based on the real-life dataset demonstrate that it achieves better prediction accuracy than its state-of-the-art peers, including the TCN and the LSTM. It can be readily implemented and deployed in many real-life industrial areas including smart city, edge computing, cloud computing, and data centers.

The artificial potential field approach is an efficient path planning method. However, to deal with the local-stable-point problem in complex environments, it needs to modify the potential field and increases the complexity of the algorithm. This study combines improved black-hole potential field and reinforcement learning to solve the problems which are scenarios of local-stable-points. The black-hole potential field is used as the environment in a reinforcement learning algorithm. Agents automatically adapt to the environment and learn how to utilize basic environmental information to find targets. Moreover, trained agents adopt variable environments with the curriculum learning method. Meanwhile, the visualization of the avoidance process demonstrates how agents avoid obstacles and reach the target. Our method is evaluated under static and dynamic experiments. The results show that agents automatically learn how to jump out of local stability points without prior knowledge.

Edge computing is a new architecture to provide computing, storage, and networking resources for achieving the Internet of Things. It brings computation to the network edge in close proximity to users. However, nodes in the edge have limited energy and resources. Completely running tasks in the edge may cause poor performance. Cloud data centers (CDCs) have rich resources for executing tasks, but they are located in places far away from users. CDCs lead to long transmission delays and large financial costs for utilizing resources. Therefore, it is essential to smartly offload users' tasks between a CDC layer and an edge computing layer. This work proposes a cloud and edge computing system, which has a terminal layer, edge computing layer, and CDC layer. Based on it, this work designs a profit-maximized collaborative computation offloading and resource allocation algorithm to maximize the profit of systems and guarantee that response time limits of tasks are strictly met. In each time slot, this work jointly considers CPU, memory, and bandwidth resources, load balance of all heterogeneous nodes in the edge layer, maximum amount of energy, maximum number of servers, and task queue stability in the CDC layer. Considering the abovementioned factors, a single-objective constrained optimization problem is formulated and solved by a proposed simulated-annealing-based migrating birds optimization procedure to obtain a close-to-optimal solution. The proposed method achieves joint optimization of computation offloading between CDC and edge, and resource allocation in CDC. Realistic data-based simulation results demonstrate that it realizes higher profit than its peers.

The industry of data centers is the fifth largest energy consumer in the world. Distributed green data centers (DGDCs) consume 300 billion kWh per year to provide different types of heterogeneous services to global users. Users around the world bring revenue to DGDC providers according to actual quality of service (QoS) of their tasks. Their tasks are delivered to DGDCs through multiple Internet service providers (ISPs) with different bandwidth capacities and unit bandwidth price. In addition, prices of power grid, wind, and solar energy in different GDCs vary with their geographical locations. Therefore, it is highly challenging to schedule tasks among DGDCs in a high-profit and high-QoS way. This work designs a multiobjective optimization method for DGDCs to maximize the profit of DGDC providers and minimize the average task loss possibility of all applications by jointly determining the split of tasks among multiple ISPs and task service rates of each GDC. A problem is formulated and solved with a simulated-annealing-based biobjective differential evolution (SBDE) algorithm to obtain an approximate Pareto-optimal set. The method of minimum Manhattan distance is adopted to select a knee solution that specifies the Pareto-optimal task service rates and task split among ISPs for DGDCs in each time slot. Real-life data-based experiments demonstrate that the proposed method achieves lower task loss of all applications and larger profit than several existing scheduling algorithms.

With their fast development and deployment, a large number of cloud services provided by distributed cloud data centers have become the most important part of Internet services. In spite of numerous benefits, their providers face some challenging issues, e.g., dynamic resource scaling and power consumption. Workload prediction plays a crucial role in addressing them. Accuracy and fast learning are the key performances. Its consistent efforts have been made for their improvement. This paper proposes an integrated prediction method that combines the Savitzky-Golay filter and wavelet decomposition with stochastic configuration networks to predict workload at the next time slot. In this approach, a task time series is first smoothed by the SG filter, and the smoothed one is then decomposed into multiple components via wavelet decomposition. Based on them, an integrated model is, for the first time, established and can well characterize the statistical features of both trend and detailed components. Experimental results demonstrate that it achieves better prediction results and faster learning speed than some representative prediction methods.