SCIENCE CHINA Information Sciences, Volume 63 , Issue 4 : 140313(2020) https://doi.org/10.1007/s11432-019-2780-0

Joint time delay and energy optimization with intelligent overclocking in edge computing

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  • ReceivedOct 19, 2019
  • AcceptedFeb 4, 2020
  • PublishedMar 9, 2020



This work was supported by National Natural Science Foundation of China (Grant Nos. 61672395, 61972448, 61911540481), Fund of Hubei Key Laboratory of Inland Shipping Technology (Grant No. NHHY2019004), and National Research Foundation of Korea (NRF) Grant Funded by the Korea Government (MSIT) (Grant No. 2019K2A9A2A060-24389).


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  • Figure 1

    (Color online) System model.

  • Figure 2

    Proposed framework for solving the problem (12).

  • Figure 3

    (Color online) (a) Loss function $L(t)$ vs. time $t$; (b) task allocation resource $f^r_n$ vs. processing time $t^r_n$.

  • Figure 4

    (Color online) (a) Computation overhead vs. the number of iterations; (b) computational overhead vs. the number of UEs.

  • Figure 5

    (Color online) (a) Computational overhead in different states; (b) benefit difference between overclocking and non-overclocking.

  • Figure 6

    (Color online) (a) Processing time of MEC servers in two states; (b) computational overhead in different states.

  • Figure 7

    (Color online) (a) The number of UEs changes with time; (b) computational resource overhead vs. time.


    Algorithm 1 Joint optimization for offloading decision, overclocking decision, and computation resource allocation (JOOC)


    Make $\boldsymbol{x}=\{x_n=1~|~\forall~n~\in~\mathcal{N}$, $\mathcal{N}_{\mathrm{Off}}=\mathcal{N}$.


    for $a$ $\leftarrow$ 0 to 1

    Get $\boldsymbol{f}$ according to (16), (17) or (39).

    if $\exists~n~\in~\mathcal{N}_{\mathrm{Off}}$, $U^r_n~>~U^l_n$ then




    Update $\boldsymbol{x}$ by setting $x_{i^{\#}}~\leftarrow~0$;

    Update $\boldsymbol{f}$ according to (16), (17) or (39);

    Until $U^r_n~<~U^l_n$, $\forall~n~\in~\mathcal{N}_{\mathrm{Off}}$ or $\mathcal{N}_{\mathrm{Off}}~=~\phi$;

    end if

    if $\exists~n~\in~\mathcal{N}_{\mathrm{Off}}$, $(t^{p}_n+t^{r}_n)>~T^{\rm~max}_n$ then




    Update $\boldsymbol{x}$ by setting $x_{i^{\#}}~\leftarrow~0$;

    Update $\boldsymbol{f}$ according to (16), (17) or (39);

    Until $(t^{p}_n+t^{r}_n)~<~T^{\rm~max}_n$, $\forall~n~\in~\mathcal{N}_{\mathrm{Off}}$ or $\mathcal{N}_{\mathrm{Off}}~=~\phi~$;

    end if

    end for

    Update $a$ according to (40);

    Output: the optimal solution $\boldsymbol{x}$, $a$, $\boldsymbol{f}$.

  • Table 1   The simulation parameters
    Parameter name Parameter value Unit
    Bandwidth $W$ $2.~5~\times~10^7$ Hz
    Transmission power $P_n$ 20 dBm
    Nose power $N_0$ $-$85 dBm
    CPU frequency of UEs $f^l_n$ 0.5 GHz
    Total CPU frequency F 20 GHz
    Task CPU cycles $C_n$ [0.5,~0.7] Gigacycle
    Task date size $D_n$ [20,~350]$\times~10^3$ kB
    Task QoS $T^{\rm~max}$ [1.0,~1.1] s
    Time weighting factor $\lambda^t_n$ 0.5
    Energy weighting factor $\lambda^e_n$ 0.5
    Growth rate of loss function $\alpha$ 0.3