Complex Mobile Applications Research Articles

Multi-agent deep reinforcement learning for online request scheduling in edge cooperation networks

Edge computing as a complementary paradigm of cloud computing has gained more attention by providing mobile users with diversified services at the network edge. However, the increasingly complex mobile applications put a heavier load on edge networks. It is challenging to provide concurrency requests with high-quality service processing, especially when the edge networks are dynamically changing. To address the above issues, this paper investigates the online concurrent user requests scheduling optimization problem in edge cooperation networks. We model it as an online multi-stage decision-making problem, where requests are divided into a group of independent and logically related sub-tasks. We proposed a centralized training distributed execution based multi-agent deep reinforcement learning technique to realize the implicit cooperation scheduling decision-making policy learning among edge nodes. At the centralized training stage of the proposed mechanism, a value-decomposition-based policy learning technique is adopted to improve the long-term system performance, while at the distributed execution stage, only local environment status information is needed for each edge node to make the request scheduling decision. Extensive experiments are conducted, and simulation results demonstrate that the proposed mechanism outperforms other request scheduling mechanisms in reducing the long-term average system delay and energy consumption while improving the throughput rate of the system.

Security and Cost-Aware Computation Offloading via Deep Reinforcement Learning in Mobile Edge Computing

With the explosive growth of mobile applications, mobile devices need to be equipped with abundant resources to process massive and complex mobile applications. However, mobile devices are usually resource-constrained due to their physical size. Fortunately, mobile edge computing, which enables mobile devices to offload computation tasks to edge servers with abundant computing resources, can significantly meet the ever-increasing computation demands from mobile applications. Nevertheless, offloading tasks to the edge servers are liable to suffer from external security threats (e.g., snooping and alteration). Aiming at this problem, we propose a security and cost-aware computation offloading (SCACO) strategy for mobile users in mobile edge computing environment, the goal of which is to minimize the overall cost (including mobile device’s energy consumption, processing delay, and task loss probability) under the risk probability constraints. Specifically, we first formulate the computation offloading problem as a Markov decision process (MDP). Then, based on the popular deep reinforcement learning approach, deep Q-network (DQN), the optimal offloading policy for the proposed problem is derived. Finally, extensive experimental results demonstrate that SCACO can achieve the security and cost efficiency for the mobile user in the mobile edge computing environment.

모바일 클라우드 컴퓨팅을 위한 실용적인 오프로딩 기법 및 비용 모델

제한된 모바일 디바이스의 자원을 해결하기 위해, 클라우드에 있는 서비스 또는 자원을 활용하는 모바일 클라우드 컴퓨팅(Mobile Cloud Computing, MCC) 연구가 활발히 진행되고 있다. MCC에서는 주로 기능 컴포넌트를 다른 노드로 오프로딩 (Offloading) 시킴으로써, 모바일 노드의 자원 문제를 해결하는 접근법을 주로 사용한다. 그러나, 현재 진행되고 있는 MCC에 대한 연구는 사전에 결정된 노드로 오프로딩 시키는 기법들이 주로 진행되고 있으며, 개념적인 수준에서 기법이 제시되고 있다. 본 논문에서는 복잡도가 높은 모바일 애플리케이션의 성능을 보장하기 위한 4가지 종류의 오프로딩 기법을 제안한다. 제시된 기법은 구현이 가능하도록 실용적인 수준으로 설계되며, 비용 모델을 제시하여 오프로딩을 통한 성능향상이 있음을 정량적으로 증명한다. As a way of augmenting constrained resources of mobile devices such as CPU and memory, many works on mobile cloud computing (MCC), where mobile devices utilize remote resources of cloud services or PCs, /have been proposed. A typical approach to resolving resource problems of mobile nodes in MCC is to offload functional components to other resource-rich nodes. However, most of the current woks do not consider a characteristic of dynamically changed MCC environment and propose offloading mechanisms in a conceptual level. In this paper, in order to ensure performance of highly complex mobile applications, we propose four different types of offloading mechanisms which can be applied to diverse situations of MCC. And, the proposed offloading mechanisms are practically designed so that they can be implemented with current technologies. Moreover, we define cost models to derive the most sutilable situation of applying each offloading mechanism and prove the performance enhancement through offloadings in a quantitative manner.

Task allocation and scheduling in wireless distributed computing networks

Wireless distributed computing (WDC) is an enabling technology that allows radio nodes to cooperate in processing complex computational tasks of an application in a distributed manner. WDC research is being driven by the fact that mobile portable computing devices have limitations in executing complex mobile applications, mainly attributed to their limited resource and functionality. This article focuses on resource allocation in WDC networks, specifically on scheduling and task allocation. In WDC, it is important to schedule communications between the nodes in addition to the allocation of computational tasks to nodes. Communication scheduling and heterogeneity in the operating environment make the WDC resource allocation problem challenging to address. This article presents a task allocation and scheduling algorithm that optimizes both energy consumption and makespan in a heuristic manner. The proposed algorithm uses a comprehensive model of the energy consumption for the execution of tasks and communication between tasks assigned to different radio nodes. The algorithm is tested for three objectives, namely, minimization of makespan, minimization of energy consumption, and minimization of both makespan and energy consumption.