Mobile Edge Computing (MEC) is a promising technology in mobile communication networks. To improve the computing service efficiency of MEC, the service placement of base stations (BSs) and the request scheduling of user equipment (UEs) should be jointly optimized. This paper considers the Collaborative Service Placement and Request Scheduling (CSPRS) problem in mobile communication networks, considering that the BSs can cooperate with each other over both wireless links and backhaul network. This paper models the CSPRS problem as a nonlinear programming problem and proposes a two-step heuristic algorithm to solve it. Simulation results show that the algorithm proposed in this paper can improve the service efficiency of MEC and increase the total data traffic of service requests that served by the BSs. Thus, the data traffic of service requests that need to be forwarded to the cloud is reduced.

Edge servers are densely deployed in the future mobile edge networks to meet the rapid increasing demand of mobile users. Since the distribution and traffic demand of user equipment (UE) fluctuate in time and over space, a number of edge servers may be underutilized which causes a great deal of energy waste. Therefore, we intend to reduce the energy cost of mobile edge networks, by dynamically switching on/off edge servers according to the variation of UEs' distribution. We formulate the energy saving problem in mobile edge networks as the minimum energy consumption (MinEn) problem which involves two critical issues: (1) cooperative service caching and UE association of adjacent BSs; (2) switching on/off edge servers. To solve the MinEn problem, we propose a dynamic server switching algorithm along with a lightweight UE distribution prediction mechanism. Simulation results show that our algorithm can greatly reduce the energy consumption of mobile edge networks compared with existing methods.

With the growing demand for satellite network technology in military and commercial applications, a large number of LEO(Low Earth Orbit) satellites have been launched in various countries around the world. One of the most important tasks is to download the data collected by these satellites to the Earth Stations(ESs) for processing. Previous research efforts have focused on how to download satellite-collected data to ESs. They didn't consider the situation that the satellites continuously collect data when downloading data. In this paper, our goal is to optimize the data download from the satellites to the ES in the case of dynamic collection of satellite data. We use inter-satellite links (ISLs) to offload data from heavily loaded satellites to the light ones. We build a topology map based on the interaction time between satellites and the interaction time between the satellite and the ES, and allocate the download time to the satellite carrying the largest amount of data. Then, the time slice is dynamically divided and the inter-satellite scheduling is determined by a way of classifying the idle satellites. Finally, the maximum flow algorithm is applied to determine the specific inter-satellite transmission scheme. In this way, the ES idle time is minimized. Simulations results show that our solution can greatly improve the data download efficiency from the satellites to the ES.

Energy saving is critical for the cloud radio access networks (C-RANs), which are composed by massive radio access units (RAUs) and energy-intensive computing units (CUs) that host numerous virtual machines (VMs). We attempt to minimize the energy consumption of C-RANs, by leveraging the RAU sleep scheduling and VM consolidation strategies. We formulate the energy saving problem in C-RANs as a joint resource provisioning (JRP) problem of the RAUs and CUs. Since the active RAU selection is coupled with the VM consolidation, the JRP problem shares some similarities with a special bin-packing problem. In this problem, the number of items and the sizes of items are correlated and are both adjustable. No existing method can be used to solve this problem directly. Therefore, we propose an efficient low-complexity algorithm along with a context-aware strategy to dynamically select active RAUs and consolidate VMs to CUs. In this way, we can significantly reduce the energy consumption of C-RANs, while do not incur too much overhead due to VM migrations. Our proposed scheme is practical for a large-size network, and its effectiveness is demonstrated by the simulation results.

Mobile edge computing (MEC) can improve the quality of services and save the bandwidth of backhual networks, by placing application services in the base stations (BSs), which are endowed with computing resources and are in close proximity to user equipments (UEs). Since the capacity of an individual BS is limited, only a small number of service instances can be allowed for each BS at the same time. Meanwhile, in a densely deployed network, the coverage areas of adjacent BSs are overlapped. Therefore, these capacity-limited BSs can collaboratively optimize their service placements to improve the performance of MEC. In this paper, we investigate the collaborative service placement (CSP) problem in MEC, which aims to minimize the traffic load caused by service request forwarding. The CSP problem involves several difficult issues, including correlations of adjacent BSs' service placement decisions, joint service placement and UE association, and joint allocation of computing and radio resources. This makes the CSP problem be a complex combinatorial optimization problem. To solve the CSP problem, we propose an efficient decentralized algorithm based on the Matching Theory. It can optimize the decisions of service placement and BS-UE association for BSs, according to local interactions between BSs and UEs. Our proposed algorithm is practical for large-size networks, and its effectiveness is demonstrated by the simulation results.

Caching application services at the edge of mobile networks can both reduce the traffic load in core networks and improve the quality of services. Since the capacity of a single BS is constrained, only a small number of service can be executed simultaneously by each BS. However, when the BSs are densely deployed in the network, the BSs that are close to each other can cooperatively cache the services to improve the performance of the system. Moreover, we should avoid frequent service switching when the users' service requests always change. In this paper, we study the dynamic service caching (DSC) problem in mobile edge networks. Our objective is to minimize the traffic load that needs to be forwarded to the cloud, as well as considering service switching cost of BSs. This DSC problem involves two important issues, which include cooperative service caching of adjacent BSs and service switching in adjacent time slots. To solve the DSC problem, we propose a dynamic service caching algorithm for the BSs to cooperatively cache the services in an online manner. The simulation results show that our algorithm can greatly reduce the forwarded traffic load without frequently changing the service caching of BSs.

Computational resource allocation is a critical issue for the baseband unit (BBU) pool in a cloud radio access network (C-RAN). There are multiple resources in a BBU, including CPU, memory, disk, etc. The virtual machines (VMs) have diverse requirements along these resources to handle the baseband signal processing of corresponding remote radio units (RRUs). Consolidating VMs to BBUs based on single resource incurs over-allocation of the resources that are not explicitly allocated. Therefore, we study the multi-resource allocation problem in CRANs, which aims to minimize the number of active BBUs that are required to serve all users in the network. Since the RRU can be set to an idle state when its traffic is low, the number of VMs and their resource demands are all adjustable. We propose an efficient algorithm to solve this problem. This algorithm selects active RRUs and associates users with these RRUs in an iterative way. It adapts a heuristic for the multi-dimensional bin packing problem to assign VMs to BBUs. Our proposed method can significantly reduce the number of required active BBUs, while satisfying the VMs' demands for multiple computational resources. Simulation results demonstrate the effectiveness of our proposed algorithm.

In this paper, we consider the real-time pricing problem for a small scale local power supplier (LPS) in a smart energy community. The LPS supplies power to the residential users (RUs) in a local area and sells the remaining power to the main grid. Since the selling price to the main grid is relative low, LPS intends to sell more power to the RUs with an appropriate price. The LPS determines the price based on the proposed pricing scheme to maximize its revenue. The price is informed to RUs through the communication infrastructure. According to the announced price of LPS, each RU schedules its power consumption to maximize its utility. We model the interactions between the local power supplier and all users as a one-leader multi-followers Stackelberg game, where the LPS acts as the leader and RUs act as the followers. To address this problem, a distributed algorithm based on information exchange between the LPS and RUs is proposed. Simulation results show that the distributed algorithm converges to the Stackelberg equilibrium.

The most efficient way to save energy in cellular networks is to switch ON/OFF base stations (BSs) dynamically according to the distribution of user equipment (UE) at real time. When a BS is switched ON/OFF, there is a switching energy cost incurred, which is a significant amount and cannot be ignored. By considering this switching cost, we formulate the energy saving problem of BSs in cellular networks as the minimum energy cost problem (MECP). The objective of MECP is to choose the BSs to be active during a period of time and determine the levels of transmission power of the active BSs according to the UEs that are served by the BSs, such that the total energy cost of the BSs is minimized. We propose a scheme to solve the MECP in two steps. In the first step, we aim to minimize the energy cost of all BSs in a time unit independently, without considering the switching ON/OFF BSs across adjacent time units. In the second step, we consider the switching cost of state transitions of BSs by introducing a state transition graph a BS over an entire time period, and transform the MECP into a minimum energy cost flow problem. A minimum cost flow algorithm is developed to solve this problem. Simulation results show that our proposed scheme can achieve significant energy cost reduction of the cellular network, compared with the existing methods.

In this paper, we investigate the residential load scheduling problem within a smart energy community, which is powered by a primary utility along with a small scale local power supplier. As a premise, unit prices set by these two suppliers are different and both are time-varying. Therefore, users are motivated to control their household appliances' operation time and calculate appropriate portions of power purchased from these two suppliers to achieve bill curtailments. The capacity constraint of local power supplier, arising from the renewable energy source and the limited storage capability, also should not be violated. We formulate a residential load scheduling problem to address this situation. Distributed scheme based on information exchange among users is proposed, without over revealing individual user's load profile. Then we propose a distributed algorithm to solve this scheduling problem. Simulation results show that the proposed approach can reduce energy cost of the community and cut down electricity payments of users, and the peak-to-average ratio in load demand is also decreased.

This paper studies the energy efficient routing for data aggregation in wireless sensor networks. The data aggregation tree is a tree where the root of the tree is the data center called the sink node and the other nodes are sensor nodes. The sensor nodes sense the data and pass the data back to the data center along the data aggregation tree. We consider a real-time scenario where the data aggregation must be performed within a specified latency constraint. The objective is to minimize the overall energy cost of the sensor nodes for data aggregation subject to the latency constraint. The original contributions of the paper include: 1) Development of an analytic model for IEEE Standard 802.15.4 CSMA-CA to compute the worst case delay for a sensor node to aggregate the data from all its child nodes in the aggregation tree; 2) Proposal of a heuristic algorithm for constructing data aggregation trees that minimize total energy cost under the latency bound obtained from our analytical model. Extensive simulations have been conducted and the results verify the validity of the proposed analytical model and the superior performance of the proposed algorithm for constructing aggregation trees.