Data centers are increasingly adopting renewable energy sources to mitigate environmental impact and reduce operational costs. However, effectively optimizing energy costs remains challenging due to unpredictable workloads and fluctuating renewable energy availability. This paper introduces LOECM, a Lyapunov-driven online scheduling algorithm designed to minimize energy cost without relying on future information. LOECM is formulated as an online stochastic optimization problem and leverages Lyapunov optimization techniques to balance immediate cost minimization against task queue stability. By exploiting the inherent structure of the optimization problem, we design an efficient solver with constant time and space complexity ($\mathcal{O}(1)$). Additionally, we propose the LOECM scheduling policy to dynamically manage workloads and node activation based on a covering subset strategy. To evaluate the practicality and effectiveness of our approach, we implement a prototype system called LOECMHadoop, integrating the LOECM scheduler into Hadoop-a widely-used data processing platform-across a 10- node cluster. Experimental results show that our power model has an average deviation of approximately 5%, and LOECM achieves over 20% electricity cost savings compared without our policy on the prototype system.

Intel Software Guard extensions (SGX), a hardware-based Trusted Execution Environment (TEE), has become a promising solution to stopping critical threats such as insider attacks and remote exploits. SGX has recently drawn extensive research in two directions---using it to protect the confidentiality and integrity of sensitive data, and protecting itself from attacks. Both the applications and defense mechanisms of SGX have a fundamental need---flexible memory protection that updates memory-page permissions dynamically and enforces the least-privilege principle. Unfortunately, SGX does not provide such a memory-protection mechanism due to the lack of hardware support and the untrustedness of operating systems.