Workload allocation in mobile edge computing empowered internet of things

In the past few years, a tremendous number of smart devices and objects, such as smart phones, wearable devices, industrial and utility components, are equipped with sensors to sense the real-time physical information from the environment. Hence, Internet of Things (IoT) is introduced, where various smart devices are connected with each other via the internet and empowered with data analytics. Owing to the high volume and fast velocity of data streams generated by IoT devices, the cloud that can provision flexible and efficient computing resources is employed as a smart brain to process and store the big data generated from IoT devices. However, since the remote cloud is far from IoT users which send application requests and await the results generated by the data processing in the remote cloud, the response time of the requests may be too long, especially unbearable for delay sensitive IoT applications. Therefore, edge computing resources (e.g., cloudlets and fog nodes) which are close to IoT devices and IoT users can be employed to alleviate the traffic load in the core network and minimize the response time for IoT users. In edge computing, the communications latency critically affects the response time of IoT user requests. Owing to the dynamic distribution of IoT users (i.e., UEs), drone base station (DBS), which can be flexibly deployed for hotspot areas, can potentially improve the wireless latency of IoT users by mitigating the heavy traffic loads of macro BSs. Drone-based communications poses two major challenges: 1) the DBS should be deployed in suitable areas with heavy traffic demands to serve more UEs; 2) the traffic loads in the network should be allocated among macro BSs and DBSs to avoid instigating traffic congestions. Therefore, a TrAffic Load baLancing (TALL) scheme in such drone-assisted fog network is proposed to minimize the wireless latency of IoT users. In the scheme, the problem is decomposed into two sub-problems, two algorithms are designed to optimize the DBS placement and user association, respectively. Extensive simulations have been set up to validate the performance of the proposed scheme. Meanwhile, various IoT applications can be run in cloudlets to reduce the response time between IoT users (e.g., user equipments in mobile networks) and cloudlets. Considering the spatial and temporal dynamics of each application\u27s workloads among cloudlets, the workload allocation among cloudlets for each IoT application affects the response time of the application\u27s requests. To solve this problem, an Application awaRE workload Allocation (AREA) scheme for edge computing based IoT is designed to minimize the response time of IoT application requests by determining the destination cloudlets for each IoT user\u27s different types of requests and the amount of computing resources allocated for each application in each cloudlet. In this scheme, both the network delay and computing delay are taken into account, i.e., IoT users\u27 requests are more likely assigned to closer and lightly loaded cloudlets. The performance of the proposed scheme has been validated by extensive simulations. In addition, the latency of data flows in IoT devices consist of both the communications latency and computing latency. When some BSs and fog nodes are lightly loaded, other overloaded BSs and fog nodes may incur congestion. Thus, a workload balancing scheme in a fog network is proposed to minimize the latency of IoT data in the communications and processing procedures by associating IoT devices to suitable BSs. Furthermore, the convergence and the optimality of the proposed workload balancing scheme has been proved. Through extensive simulations, the performance of the proposed load balancing scheme is validated

Spatio-temporal Edge Service Placement: A Bandit Learning Approach

Shared edge computing platforms deployed at the radio access network are expected to significantly improve quality of service delivered by Application Service Providers (ASPs) in a flexible and economic way. However, placing edge service in every possible edge site by an ASP is practically infeasible due to the ASP's prohibitive budget requirement. In this paper, we investigate the edge service placement problem of an ASP under a limited budget, where the ASP dynamically rents computing/storage resources in edge sites to host its applications in close proximity to end users. Since the benefit of placing edge service in a specific site is usually unknown to the ASP a priori, optimal placement decisions must be made while learning this benefit. We pose this problem as a novel combinatorial contextual bandit learning problem. It is "combinatorial" because only a limited number of edge sites can be rented to provide the edge service given the ASP's budget. It is "contextual" because we utilize user context information to enable finer-grained learning and decision making. To solve this problem and optimize the edge computing performance, we propose SEEN, a Spatial-temporal Edge sErvice placemeNt algorithm. Furthermore, SEEN is extended to scenarios with overlapping service coverage by incorporating a disjunctively constrained knapsack problem. In both cases, we prove that our algorithm achieves a sublinear regret bound when it is compared to an oracle algorithm that knows the exact benefit information. Simulations are carried out on a real-world dataset, whose results show that SEEN significantly outperforms benchmark solutions