# Cross-Layer Energy Optimization for IoT Environments: Technical Advances and Opportunities
## 4 Energy Efficient Techniques in the IoT
### Energy Efficient Cloud Computing Techniques
#### 4.4.2 Energy Efficient Lyapunov Optimization Techniques  
Dynamic Resource and Task Allocation for Energy Minimization in Mobile Cloud Systems (DREAM) [52] has been proposed by invoking Lyapunov optimization to solve jointly three dynamic problems: first, determining the use of local CPU or cloud resources, second, task allocation to transmit across the network and processing in local CPU and third, CPU clock speed and network interface controls. DREAM minimizes the energy usage of mobile nodes while considering delay constraints under heavy traffic in an IoT cloud system. Lyapunov drift plus penalty function is defined as sum of expected network and CPU energy consumption during time slot s, and formulated as:  
\[\Delta(L(s))+VE\{E^{c}(r^{c}(s))+E^{n}(l(s))|Q(s)\} \tag{29}\]  
where \(V\) is energy-delay trade off parameter.  
Summaries of the related literatures on energy efficient cloud computing techniques are given in Tables 14 and 15, which comprise characteristics/protocol, issues, contributions, techniques, simulation tools used, metrics and research limitations of each paper reviewed.  
In Table 15, the characteristics of each paper reviewed are categorized into three groups, including protocol/author, approach, and metrics. The protocol/author column comprises abbreviations of techniques and references. The approach column is categorized into five classes, namely, SVM, LO, PO, QoS and CLT. Last column represents metrics including DL, EE, PD, EUS, AR, PC, CU and NoM. Considering Table 15, the approaches in column four demonstrate that more contributions have been made by QoS-based techniques, followed by SVM, LO, PO and CLT techniques. Further, the metrics column shows that EE is the most considered metric, followed by DL and CU then PD, EUS and AR for measuring performance of energy efficient cloud computing techniques.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Protocol/Author**} & \multicolumn{6}{c}{**Approaches**} & \multicolumn{6}{c}{**Metrics**} \\ \cline{2-11}  & **SVM** & **LO** & **PO** & **QoS** & **CLT** & **DL** & **EE** & **PD** & **EUS** & **AR** & **PC** & **CU** & **NoM** \\ \hline DREAM [52] & & \(\surd\) & & \(\surd\) & & \(\surd\) & \(\surd\) & \(\surd\) & & & & \(\surd\) & \(\surd\) \\ SVOP [53] & \(\surd\) & & & \(\surd\) & & \(\surd\) & & \(\surd\) & & \(\surd\) & & \(\surd\) & \(\surd\) \\ POE [54] & & & \(\surd\) & & \(\surd\) & \(\surd\) & \(\surd\) & & & & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table}
Table 15: Comparative assessment of energy efficient cloud computing techniques.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Protocols**} & \multirow{2}{*}{**Characteristics**} & \multirow{2}{*}{**Issues**} & **Contributions** & **Techniques** & **Simulators** & **Metrics** & **Limitations** \\ \hline \multirow{2}{*}{DREAM} & Increasing cpu and energy consumption in mobile device & Energy efficient and fairly scheduled solution with less delay & Lyapunov optimization technique & Android phone (Galaxy note2) & -DL & Recorded video streaming -EP & has not been evaluated \\ \hline \multirow{2}{*}{SVOP} & High cloud provider cost and energy consumption & Consolidation based energy efficient and robust solution & Smart virtual machine over provision technique & VaneMobiSim & -AR & Less acceptation rate \\ \hline \multirow{2}{*}{POE} & Challenge to balance system performance and power consumption & Energy efficient and adaptive solution & Predictive and convex optimization technique & NS-2 and SUMO & -PC & Limited to homogenous \\  & & Energy efficient and adaptive solution & Predictive and convex optimization technique & NS-2 and SUMO & -DL & -DL & environment and not suitable in real life \\ \multicolumn{1}{c}{} & & & & & & & -CU & scenarios \\ \hline \hline \end{tabular}
\end{table}
Table 14: Summary of energy efficient cloud computing techniques.