Mobile edge computing emerges to serve mobile users with low-latency computation offloading in edge networks, which are resource-constrained with massive users and workloads. However, existing communication and computing resource allocation schemes for offloaded tasks aren't efficient enough, where finished tasks still occupy resources, wasting constrained resources. Besides, the multi-user offloading is usually for scenarios of one task per user, ignoring real-worldmulti-taskoffloading scenarios where each user has multiple tasks, lack generality and flexibility. Meanwhile, local computing resource allocation schemes in multi-task scenarios ignore resource readjustment, causing low resource utilization. To solve these problems, we propose ECO-GAME, an efficient multi-task offloading scheme, which dynamically allocates bandwidth and computing resources to unfinished tasks, resulting in high resource utilization. We initially formulate the multi-task offloading problem as the game minimizing each user's cost, which is NP-hard. Thus we re-formulate the game utilizing potential games to optimize user's objective either locally or globally, and prove the existence of its Nash equilibrium. We then design an efficient multi-task offloading algorithm to obtain an approximate solution in polynomial time, together with computational complexity analysis. We further conduct performance evaluation on ECO-GAME utilizing price of anarchy. Numerical results demonstrate the efficiency of ECO-GAME, and show ECO-GAME reduces 49.2% cost over the state-of-the-art work, and scales well with the increasing number of tasks and users.

Flow scheduling and congestion control are two important techniques to reduce flow completion time in data center networks. While existing works largely treat them independently, the interactions between flow scheduling and congestion control are in general overlooked which leads to sub-optimal solutions, especially given that the link capacity is increasing faster than the switch port buffer size. In this paper, we presentFlash, a simple yet effective scheme that integrates scheduling and congestion control. Specifically,Flashputs forward a congestion-aware scheduling scheme to determine the priority of flows based on the latest network congestion extent and the flow’s bytes sent. Besides,Flashproposes a priority-based packet dropping scheme in switch port buffers and implements a priority-aware congestion control scheme. Experiment results show thatFlashhas superior performance: (1) it has 35.8% lower tail latency than PIAS and performs similar with pFabric in a 10G network without knowing the flow size, (2) in 100G networks with shallow buffers, the information agnosticFlashhas 6.8% lower average FCT than the information-aware pFabric, (3) it outperforms pFabric by 13.5% in FCT if flow size is also known toFlash.

Mobile edge computing is emerging to provide cloud-computing capabilities to mobile users, so that they can offload computation intensive tasks to close proximity for execution. However, most existing works imply that a transmission-finished task still occupies the channel until all users on the same channel finish the transmission, leading to severe channel resource waste. To solve this problem, we propose an efficient computation offloading mechanism which releases the channel resources of transmission-finished tasks for transmission-unfinished tasks, and aims to minimize the response time and energy consumption for each user. Specifically, we formulate the computation offloading problem as a game, analyze its structural properties and show how it possesses a Nash equilibrium and admits the finite improvement property, in the cases of elastic cloud and non-elastic cloud respectively. We then propose aDistributedMulti-channelComputationOffloading (DMCO) algorithm, which can converge to a Nash equilibrium, and find the upper bound of the convergence time. We further evaluate the performance of DMCO using the price of anarchy. Numerical results show that DMCO scales well with the number of users, and outperforms existing works, for example, benefits 13.3 percent more users and reduces cost by 23.7 percent than CO, one of the best existing works.

Task scheduling between edge devices and remote servers is a common application scenario in edge computing or cloud computing, also known as computational offloading. A reasonable scheduling strategy can effectively shorten task completion time, reduce energy consumption, and improve user experience. However, the traditional offline task scheduling algorithm is NP-hard, and the decision requires obtaining all the information of the task and the device (such as task computing amount, data amount, device computing resources, etc.), which is challenging to meet in practical applications. The semi-online algorithm describes the task scheduling method when the system cannot obtain all the information. In this paper, we propose an Efficient Semi-online algorithm for Multi-users task offloading (ESaM), which includes two specific implementations: ESaM-I as known server-side idle time, and ESaM-O for known task computing amount. Because ESaM-I has obtained server information, it is better than ESaM-O in performance for most of the scenarios. The experimental results show that ESaM-I and ESaM-O are superior to the well-known semi-online scheduling algorithm SPaC in task completion time. As the remote processor computing ability increases, the average makespan converges to 0.875, 0.742, 0.782 for SPaC-M, ESaM-O, and ESaM-I in the simulation.