Optimal Joint Scheduling and Cloud Offloading for Mobile Applications

Mahmoodi, Seyed Eman; Uma, R. N.; Subbalakshmi, K. P.

doi:10.1109/tcc.2016.2560808

Cited by 197 publications

(103 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For this purpose, we have considered the test DAG4 reported in Fig. 15, that is typically considered in the literature for comparing task allocation policies under different service models [33]. Specifically, DAG4 details the workflow of a real-world video navigation program for mobile stream application.…”

Section: G Performance Sensitivity On the Adopted Service Modelmentioning

confidence: 99%

“…Hence, including in the afforded JOP formulation also the dynamic optimization of the task-execution ordering followed by the computing nodes may be a first research direction of potential interest. The main expected challenge stems from the fact that the dynamic optimization of the task scheduling discipline has been recently proved to be an NP-hard integer-valued problem, even in the basic case of fixed resource allocation [33].…”

Section: Conclusion and Hints For Future Researchmentioning

confidence: 99%

See 1 more Smart Citation

EcoMobiFog–Design and Dynamic Optimization of a 5G Mobile-Fog-Cloud Multi-Tier Ecosystem for the Real-Time Distributed Execution of Stream Applications

2019

View full text Add to dashboard Cite

The emerging 5G paradigm will enable multi-radio smartphones to run high-rate stream applications. However, since current smartphones remain resource and battery-limited, the 5G era opens new challenges on how to actually support these applications. In principle, the service orchestration capability of the Fog and Cloud Computing paradigms could be an effective means of dynamically providing resource-augmentation to smartphones. Motivated by these considerations, the peculiar focus of this paper is on the joint and adaptive optimization of the resource and task allocations of mobile stream applications in 5G-supported multi-tier Mobile-Fog-Cloud virtualized ecosystems. The objective is the minimization of the computing-plus-network energy of the overall ecosystem under hard constraints on the minimum streaming rate and the maximum computing-plus-networking resources. To this end, (i) we model the target ecosystem energy by explicitly accounting for the virtualized and multi-core nature of the Fog/Cloud servers; (ii) since the resulting problem is nonconvex and involves both continuous and discrete variables, we develop an optimality-preserving decomposition into the cascade of a (continuous) resource allocation sub-problem and a (discrete) task-allocation sub-problem; (iii) we numerically solve the first sub-problem through a suitably designed set of gradient-based adaptive iterations, while we approach the solution of the second sub-problem by resorting to an ad-hoc-developed elitary Genetic algorithm. Finally, we design the main blocks of EcoMobiFog, a technological virtualized platform for supporting the developed solver. Extensive numerical tests confirm that the energy-delay performance of the proposed solving framework is typically within a few per-cent the benchmark one of the exhaustive searchbased solution.

show abstract

Section: G Performance Sensitivity On the Adopted Service Modelmentioning

confidence: 99%

Section: Conclusion and Hints For Future Researchmentioning

confidence: 99%

EcoMobiFog–Design and Dynamic Optimization of a 5G Mobile-Fog-Cloud Multi-Tier Ecosystem for the Real-Time Distributed Execution of Stream Applications

2019

View full text Add to dashboard Cite

show abstract

“…and D a i being the auxiliary variables introduced corresponding to the uplink transmission time, downlink transmission time, and the CAP processing time, respectively. Auxiliary variables D u i and D d i are similarly defined as in (15) and (16), except that x ij in (15) and (16) is now replaced by (x a ij + x c ij ). Similar to these two constraints, the new auxiliary variable D a i for the CAP processing time also introduces a new constraint…”

Section: Multi-user Multi-task Offloading With Cap (Mumto-c) Algormentioning

confidence: 99%

Multi-User Multi-Task Offloading and Resource Allocation in Mobile Cloud Systems

Chen

Liang

Dong

2018

IEEE Trans. Wireless Commun.

128

View full text Add to dashboard Cite

We consider a general multi-user Mobile Cloud Computing (MCC) system where each mobile user has multiple independent tasks. These mobile users share the computation and communication resources while offloading tasks to the cloud. We study both the conventional MCC where tasks are offloaded to the cloud through a wireless access point, and MCC with a computing access point (CAP), where the CAP serves both as the network access gateway and a computation service provider to the mobile users. We aim to jointly optimize the offloading decisions of all users as well as the allocation of computation and communication resources, to minimize the overall cost of energy, computation, and delay for all users. The optimization problem is formulated as a non-convex quadratically constrained quadratic program, which is NP-hard in general. For the case without a CAP, an efficient approximate solution named MUMTO is proposed by using separable semidefinite relaxation (SDR), followed by recovery of the binary offloading decision and optimal allocation of the communication resource. To solve the more complicated problem with a CAP, we further propose an efficient three-step algorithm named MUMTO-C comprising of generalized MUMTO SDR with CAP, alternating optimization, and sequential tuning, which always computes a locally optimal solution. For performance benchmarking, we further present numerical lower bounds of the minimum system cost with and without the CAP. By comparison with this lower bound, our simulation results show that the proposed solutions for both scenarios give nearly optimal performance under various parameter settings, and the resultant efficient utilization of a CAP can bring substantial cost benefit.

show abstract

“…We consider that the mobiles and VMs have unbounded computation capacities and the monomial order m = 3, since it can fairly approximate the transmission-energy consumption in practice. 7 More importantly, it will lead to useful insights into the structure of the optimal policy as shown in the sequel that the optimal time-division policy admits a defined effective computing-power balancing structure.…”

Section: Optimal Resource Management With Identical Arrival-deadlimentioning

confidence: 99%

“…Specifically, for each epoch, the derived time-division policy only determines the offloading durations allocated for different mobiles, without specifying the scheduling order. In other words, if considering the scheduling order, one time-division policy resulted from the solution to Problem P1 can correspond to multiple 7 The results can be extended to derive the suboptimal policy for the case of m = 3 by using approximating techniques, although the corresponding optimal policy has no closed form which can be computed by iterative algorithms.…”

Section: Optimal Resource Management With Identical Arrival-deadlimentioning

confidence: 99%

Asynchronous Mobile-Edge Computation Offloading: Energy-Efficient Resource Management

You

Zeng

Zhang

et al. 2018

IEEE Trans. Wireless Commun.

108

View full text Add to dashboard Cite

Mobile-edge computation offloading (MECO) is an emerging technology for enhancing mobiles' computation capabilities and prolonging their battery lifetime, by offloading intensive computation from mobiles to nearby servers such as base stations. In this paper, we study the energy-efficient resourcemanagement policy for the asynchronous MECO system, where the mobiles have heterogeneous inputdata arrival time instants and computation deadlines. First, we consider the general case with arbitrary arrival-deadline orders. Based on the monomial energy-consumption model for data transmission, an optimization problem is formulated to minimize the total mobile-energy consumption under the time-sharing and computation-deadline constraints. The optimal resource-management policy for data partitioning (for offloading and local computing) and time division (for transmissions) is obtained in (semi-) closed-form expression by using the block coordinate decent method. To gain further insights, we study the optimal resource-management design for two special cases. First, consider the case of identical arrival-deadline orders, i.e., a mobile with input data arriving earlier also needs to complete computation earlier. The optimization problem is reduced to two sequential problems corresponding to the optimal scheduling order and joint data-partitioning and time-division given the optimal order. It is found that the optimal time-division policy tends to equalize the defined effective computing power among offloading mobiles via time sharing. Furthermore, this solution approach is extended to the case of reverse arrival-deadline orders. The corresponding time-division policy is derived by a proposed transformation-and-scheduling approach, which first determines the total offloading duration and data size for each mobile in the transformation phase and then specifies the offloading intervals for each mobile in the scheduling phase.

show abstract

Optimal Joint Scheduling and Cloud Offloading for Mobile Applications

Cited by 197 publications

References 24 publications

EcoMobiFog–Design and Dynamic Optimization of a 5G Mobile-Fog-Cloud Multi-Tier Ecosystem for the Real-Time Distributed Execution of Stream Applications

EcoMobiFog–Design and Dynamic Optimization of a 5G Mobile-Fog-Cloud Multi-Tier Ecosystem for the Real-Time Distributed Execution of Stream Applications

Multi-User Multi-Task Offloading and Resource Allocation in Mobile Cloud Systems

Asynchronous Mobile-Edge Computation Offloading: Energy-Efficient Resource Management

Contact Info

Product

Resources

About