Serverless is an emerging cloud paradigm that offers fine-grained resource sharing through serverless functions. However, this resource sharing can cause interference, leading to performance degradation and QoS violations. Existing white box-based approaches for serverless resource provision often demand extensive expert knowledge, which is challenging to obtain due to the complexity of interference sources. This paper proposes Qora, a neural-enhanced interference-aware resource provisioning system for serverless computing. We model the resource provisioning of serverless functions as a novel combinatorial optimization problem, wherein the constraints on the queries per second are derived from neural network performance model. By leveraging neural networks to model the nonlinear performance fluctuations under various interference sources, our approach better captures the real-world behavior of serverless functions. To solve the formulated problem efficiently, rather than adopting commercial optimizer solvers like Gurobi, we propose a two-stage-VNS algorithm that searches discrete variables more efficiently and supports Sigmoid activations, avoiding introducing redundant discrete variables. Unlike pure machine learning methods lacking theoretical optimal guarantees, our approach is rigorously proven globally optimal based on optimization theory. We implement Qora on Kubernetes as a serverless system automating resource provisioning. Experimental results demonstrate that Qora reduces the QoS violation rate by 98% while reducing up to 35% resource costs compared with the state-of-the-arts. Note to Practitioners—From the perspective of cloud service providers, this paper considers the automatic resource provisioning for serverless functions. To improve hardware utilization, cloud providers tend to co-locate serverless functions on the same server. However, co-located functions compete for shared resources (memory bandwidth, L3 cache, etc.), which causes interference and leads to performance degradation and QoS violations. We use neural networks to build the performance models of interference-prone serverless functions and form the resource allocation optimization problem with neural network performance models as constraints. Compared to white box modeling methods, our neural network modeling adapts to complex and variable interference. Compared to deep reinforcement learning methods, our combinatorial optimization methods have stronger interpretability. In order to solve this optimization problem efficiently, we design the two-stage-VNS solution algorithm. We implement Qora on Kubernetes as a serverless system, which can automatically allocate computing resources. Experiments with small-scale real clusters and large-scale simulations demonstrate the effectiveness of Qora.

In recent years, the rapid development of the Internet of Things (IoT) has produced a large amount of data that needs to be processed in a timely manner. Traditional cloud computing systems can provide us with plentiful resources to process such data. However, the increasing requirements of IoT applications on data privacy, energy consumption savings and location-aware data processing pushes the emergence and the interplay of fog computing and cloud computing. This paper examines the resource scheduling issue under such a system to minimize makespan and energy consumption. A multi-objective estimation of distribution algorithm (EDA) as well as a partition operator is adopted to divide the graph and determine the task processing permutation and processor assignment. Single and multiple application simulation were both conducted. The comparative results show that the Pareto set produced by our proposed algorithm is able to dominate a large proportion of those solutions by the heuristic method and the simple EDA under single application simulation. When it comes to multi-application simulation, IoT devices can have a much longer lifetime with our proposed scheduling algorithm as well having similar performance to the other algorithms on fog node energy consumption and much better on makespan.

The Internet of Things (IoT) develops rapidly and has produced a large amount of data these years. A range of responsive IoT applications arise and are needed to be processed in a timely manner. Compared with traditional cloud computing system, fog computing is one of the promising solutions of processing the huge amount of local data and decreasing the end-to-end latency. Hard and soft deadlines are assigned to the tasks s. In this work, the resource allocation and task scheduling problem under fog system is considered to minimize total tardiness of the tasks and meet the hard deadlines. A deadline-aware estimation of distributed algorithm (dEDA) with a repair procedure and local search is adopted to determine the task processing order and computing node allocation. The comparative results show that the solution produced by our proposed algorithm performs better than the algorithm without repair procedure or knowledge driven local search. In addition, the performance of our algorithm exceeds significantly the heuristic method on both total tardiness and successful rate metrics. Compared with the existing fog computing resource management algorithm, our algorithm performs much better under most situations.

In this paper, an effective estimation of distributed algorithm (eEDA) is proposed to solve the uniform parallel machine scheduling problem with precedence constraints (prec-UFPMSP). In the eEDA, the permutation-based encoding scheme is adopted and the earliest finish time (EFT) method is used to decode the solutions to the detail schedules. A new effective probability model is designed to describe the relative positions of the jobs. Based on such a model, an incremental learning based updating method is developed and a sampling mechanism is proposed to generate feasible solutions with good diversity. In addition, the Taguchi method of design-of-experiment (DOE) method is used to investigate the effect of key parameters on the performance of the eEDA. Finally, numerical tests are carried out to demonstrate the superiority of the probability model, and the comparative results show that the eEDA outperforms the existing algorithm for most cases.