- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
With the proliferation of various big data applications and resource demand from Internet data centers (IDCs), the energy cost has been skyrocketing, and it attracts a great deal of attention and brings many energy optimization management issues. However, the security problem for a wide range of applications, which has been overlooked, is another critical concern and even ranked as the greatest challenge in IDC. In this paper, we propose an energy cost minimization (ECM) algorithm with job security guarantee for IDC in deregulated electricity markets. Randomly arriving jobs are routed to a FIFO queue, and a heuristic algorithm is devised to select security levels for guaranteeing job risk probability constraint. Then, the energy optimization problem is formulated by taking the temporal diversity of electricity price into account. Finally, an online energy cost minimization algorithm is designed to solve the problem by Lyapunov optimization framework which offers provable energy cost optimization and delay guarantee. This algorithm can aggressively and adaptively seize the timing of low electricity price to process workloads and defer delay-tolerant workloads execution when the price is high. Based on the real-life electricity price, simulation results prove the feasibility and effectiveness of proposed algorithm.
6. Conclusions and Future Work
In this paper, we devise the energy cost optimization architecture for IDC operator to minimize the energy cost under the job risk probability constraint. The jobs may be delay tolerant big data applications or data intensive MapReduce applications that demands large-scale infrastructures such as Internet data center to provide computing resources. Due to high time complexity of optimal security levels mapping scheme, a heuristic algorithm with polynomial time complexity is developed to select security levels for tasks. Then, we formulate the energy stochastic optimization problem and propose our ECM algorithm to schedule workloads taking the temporal diversity of electricity price into account. The ECM algorithm, which is based on Lyapunov optimization framework, offers provable energy cost and delay guarantees. It aggressively and adaptively seizes the timing of low electricity price to process tasks, and defers delay-tolerant tasks execution when the price is high.
Four reference algorithms are conducted in our experiments in comparison with our ECM algorithm in terms of energy cost and queuing delay. The experiments confirm the [O(1/V),O(V)] energy-delay tradeoff of ECM algorithm. However, the performance of ECM algorithm is close to the enumeration algorithm, but with lower time complexity. In a word, Extensive evaluation experiments based on the real-life electricity price demonstrate the effectiveness of our ECM algorithm.
As a future work, we plan to incorporate the big data scientific workflow scheduling method and delay guarantee into our energy cost optimization problem. Moreover, we are going to consider some new aspects in better usage of power in IDC, such as renewable energy, energy storage, battery and so on.