Cloud offloading for multi-radio enabled mobile devices

Abstract

Abstract-The advent of 5G networking technologies has increased the expectations from mobile devices, in that, more sophisticated, computationally intense applications are expected to be delivered on the mobile device which are themselves getting smaller and sleeker. This predicates a need for offloading computationally intense parts of the applications to a resource strong cloud. Parallely, in the wireless networking world, the trend has shifted to multi-radio (as opposed to multi-channel) enabled communications. In this paper, we provide a comprehensive computation offloading solution that uses the multiple radio links available for associated data transfer, optimally. Our contributions include: a comprehensive model for the energy consumption from the perspective of the mobile device; the formulation of the joint optimization problem to minimize the energy consumed as well as allocating the associated data transfer optimally through the available radio links and an iterative algorithm that converges to a locally optimal solution. Simulations on an HTC phone, running a 14-component application and using the Amazon EC2 as the cloud, show that the solution obtained through the iterative algorithm consumes only 3% more energy than the optimal solution (obtained via exhaustive search).

A Survey on Mobile Edge Computing: The Communication Perspective

“…It was shown that the optimal task-scheduling policy significantly outperforms the greedy scheduling policy (i.e., tasks are scheduled to the local CPU/transmission unit whenever they are idle). To jointly optimize the computation latency and energy consumption, the problem of minimizing the longterm average execution cost was considered in [102] and [106], where the former only optimized the offloading data size based on the MDP theory while the latter jointly controlled the local CPU frequency, modulation scheme as well as data rates under a semi-MDP framework. In [107], the energylatency tradeoff in MEC systems with heterogeneous types of applications was investigated, including the non-offloadable workload, cloud-offloadable workload and network traffic.…”

“…While the preceding subsection aims at resource management policies for single-user MEC systems with a dedicated MEC server, this subsection considers the multiuser MEC systems comprising multiple mobile devices that share one edge server. Several new challenges are investigated in the sequel, including the multiuser joint radio-and-computational [81] Optimize local computing and offloading by controlling the CPU frequency and transmission rate [83] Propose a novel framework of wirelessly powered MEC and optimize both local computing and offloading [94] Propose general guidelines to make offloading decision for energy consumption minimization [96] Propose the optimal binary computation offloading decision using convex optimization Energy and latency [95] Propose general guidelines to make offloading decision for energyconsumption and computation-latency minimization Partial Offloading Energy [62] Propose a joint scheduling and computation offloading algorithm by parallel processing appropriate components in the mobile and cloud [99] Formulate a stochastic shortest-path problem and derive the one-climb optimal policy [101] Jointly optimize the program partitioning with the selection of transmit power and constellation size [102] Propose an iterative algorithm for the optimal offloading scheduling as well as the percentage of the data to be carried on each radio interface Latency [61] Propose a heuristic load-balancing program-partitioning algorithm [98] Propose a polynomial-time approximate solution with guaranteed performance Energy and latency [97] Jointly optimize the offloading ratio, transmission power and CPUcycle frequency using variable-substitution technique [100] Propose an algorithmic to leverage the structure of the call graphs by means of message passing under both serial and parallel implementations of processing and communication…”

Energy-Efficient Online Resource Management and Allocation Optimization in Multi-User Multi-Task Mobile-Edge Computing Systems with Hybrid Energy Harvesting

“…These policies mainly manage and distribute resources of local execution, comunication process, and edge servers. The scenario of resource management and allocation in MEC systems is divided into single-user MEC systems [ 14 , 15 , 16 , 17 , 18 , 19 ], multi-user MEC systems [ 20 , 21 , 22 , 23 , 24 , 25 , 26 ] and heterogeneous server MEC systems [ 27 , 28 ]. In [ 14 ], the offloading ratio, transmission power, and the CPU clock frequency are jointly optimized to minimize the latency subject to the energy consumption or minimize the energy consumption subject to the latency.…”

Green Cloud Computing for Multi Cell Networks

“…Also, in [13] a heuristic approach is adopted to minimize the energy consumption of all users while making decision on offloading and resource allocation for each task. The study in [14], models the decision offloading in a multi radio interface to figure out the optimal solution of the conflicting objectives, namely, computation costs and the execution time of the application. In [15], a game theoretic approach is used to design an offloading mechanism for mobile devices.…”