Edge computing is an effective complementary technology to cloud computing, allowing end devices to offload tasks onto edge base stations (BSs) to satisfy the quality of experience of consumers. Due to the limitation of storage and computing resources, a single BS cannot satisfy the heavy computing tasks. In this regard, multi-BS collaboration is an effective way to alleviate this issue. Moreover, service caching and cloud-edge collaboration computing also show attractive advantages in handling the surging data traffic. However, to the best of our knowledge, there is rarely work that consider all of the aforementioned scenarios simultaneously. To fill this research gap, this paper comprehensively considers the computing offloading problem in sustainable edge computing based on the above scenarios. Specifically, the computing offloading problem is first modeled as a multi-objective optimization problem with the purpose of minimizing the delay and energy consumption. Then, a two-stage hybrid multi-objective optimization evolutionary algorithm, called TH-MOEA, is designed to address the above formulated problem, which uses a novel competitive swarm optimizer to accelerate convergence in the early evolutionary stage and adopts a diversity-enhanced immune algorithm to improve diversity in the later evolutionary stage. Simulation results show that TH-MOEA outperforms several state-of-the-art peer methods.

Service providers tend to deploy application services to several different virtual machines (VMs) to improve the scalability and manageability of the cloud data center (CDC). Therefore, high frequency communication traffic is always involved among those VMs that are deployed the same application service. In order to reduce the communication cost (CC) of CDC, all VMs running the same service should be redeployed in the same subnet as much as possible by using live migration technology, because CC between VMs in different subnets is much higher than that within the same subnet. On the other hand, the migration time (MT) to complete all migration tasks is also crucial for providers and customers, because a prolonged MT will lead to the increased maintenance cost and the deterioration of quality of service (QoS). To address the aforementioned issues, this paper proposes an efficient service-aware VM scheduling approach (ES-VSA) based on a multi-objective evolutionary algorithm to minimize CC and MT, simultaneously. Finally, experiments are conducted on four different scenarios, and the simulation results demonstrate that our proposed algorithm is superior to several state-of-the-art algorithms in terms of both CC and MT.

Since the advent of cloud computing, Elastic Computing (EC) has become the standard architecture for resource allocation and scheduling. EC typically allocates computing resources based on predefined specifications, such as virtual machine or container flavors. However, these flavors are often constrained by fixed CPU-to-memory ratios, which frequently fail to match the actual resource needs of applications. As a result, cloud providers experience high resource allocation rates nearing saturation ( $\gt $ 80%) but with low utilization ( $\lt $ 25%). This study introduces Flexible Computing (FC), a novel approach to resource allocation and scheduling. Unlike EC, FC allocates resources based on an application resource usage profile, derived from the historical resource consumption of workloads, rather than relying on fixed specifications. Additionally, FC incorporates a real-time performance degradation detection mechanism to address performance issues caused by the noisy-neighbor effect when colocated workloads interfere with each other. FC dynamically adjusts resource allocation according to actual usage, ensuring that application performance meets Service Level Agreements (SLAs), while preventing resource waste and performance degradation from improper resource over-commitment. Large-scale experimental validations conducted on the FC architecture within Huawei Cloud data centers demonstrate that, compared to EC, FC can reduce computing resource consumption by over 33% while managing the same workloads. Furthermore, FC's real-time performance degradation detection model achieves a prediction error of less than 5% across various testing environments, highlighting its commercial viability.