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. Note to Practitioners—This work considers the joint optimization of computation offloading between Cloud data center (CDC) and edge computing layers, and resource allocation in CDC. It is important to maximize the profit of distributed cloud and edge computing systems by optimally scheduling all tasks between them given user-specific response time limits of tasks. It is challenging to execute them in nodes in the edge computing layer because their computation resources and battery capacities are often constrained and heterogeneous. Current offloading methods fail to jointly optimize computation offloading and resource allocation for nodes in the edge and servers in CDC. They are insufficient and coarse-grained to schedule arriving tasks. In this work, a novel algorithm is proposed to maximize the profit of distributed cloud and edge computing systems while meeting response time limits of tasks. It explicitly specifies the task service rate and the selected node for each task in each time slot by considering resource limits, load balance requirement, and processing capacities of nodes in the edge, and server and energy constraints in CDC. Real-life data-driven simulations show that the proposed method realizes a larger profit than several typical offloading strategies. It can be readily implemented and incorporated into large-scale industrial computing systems.

中文翻译：

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分布式云和边缘计算系统中利润最大化的协作计算卸载和资源分配


边缘计算是为实现物联网提供计算、存储和网络资源的新架构。它将计算带到靠近用户的网络边缘。然而，边缘节点的能量和资源有限。完全在边缘运行任务可能会导致性能不佳。云数据中心（CDC）拥有丰富的执行任务资源，但它们位于远离用户的地方。疾病预防控制中心会导致长时间的传输延迟和利用资源的巨大财务成本。因此，在 CDC 层和边缘计算层之间智能地卸载用户的任务至关重要。这项工作提出了一个云和边缘计算系统，该系统具有终端层、边缘计算层和CDC层。在此基础上，本文设计了一种利润最大化的协同计算卸载和资源分配算法，以最大化系统的利润并保证严格满足任务的响应时间限制。在每个时隙中，该工作共同考虑CPU、内存和带宽资源、边缘层所有异构节点的负载平衡、最大能量、最大服务器数量以及CDC层的任务队列稳定性。考虑到上述因素，通过提出的基于模拟退火的候鸟优化程序来制定和解决单目标约束优化问题，以获得接近最优的解决方案。该方法实现了CDC和边缘之间的计算卸载以及CDC中的资源分配的联合优化。基于真实数据的模拟结果表明，其实现的利润高于同行。 从业者须知——这项工作考虑了云数据中心（CDC）和边缘计算层之间的计算卸载以及CDC中的资源分配的联合优化。在给定用户特定的任务响应时间限制的情况下，通过优化调度分布式云和边缘计算系统之间的所有任务，最大化分布式云和边缘计算系统的利润非常重要。在边缘计算层的节点中执行它们具有挑战性，因为它们的计算资源和电池容量通常受到限制并且是异构的。当前的卸载方法无法联合优化边缘节点和 CDC 中服务器的计算卸载和资源分配。它们的粒度不足以安排到达的任务。在这项工作中，提出了一种新颖的算法，可以在满足任务响应时间限制的同时最大化分布式云和边缘计算系统的利润。它通过考虑资源限制、负载平衡要求、边缘节点的处理能力以及CDC中的服务器和能量限制，明确指定每个时隙中每个任务的任务服务速率和选择的节点。现实生活中的数据驱动模拟表明，所提出的方法比几种典型的卸载策略实现了更大的利润。它可以很容易地实现并合并到大规模工业计算系统中。