Improving the energy efficiency of cloud computing systems has become an important issue because the electric energy bill for 24/7 operation of these systems can be quite large. The focus of this paper is on the virtual machine consolidation in a cloud computing system as a way of lowering daily energy consumption of the system. In contrast to the existing works that assume resource demands of virtual machines are given as scalar variables, this paper treats these demands as random variables with known means and standard deviations because the demands are not deterministic in many situations. These random variables may be correlated with one another, and there are several types of resources which can be performance bottlenecks. Therefore, both correlations and resource type heterogeneity must be considered. The virtual machine consolidation problem is thus formulated as a multi-capacity stochastic bin packing problem. This problem is NP-hard, so we present a heuristic method to efficiently solve the problem. Simulation results show that, in spite of its simplicity and scalability, the proposed method produces high quality solutions.

Companies operating large datacenters are focusing on how to reduce the electrical energy costs of operating datacenters. A common way of cost reduction is to perform a dynamic voltage and frequency scaling (DVFS), thereby matching the CPU's performance and power level to incoming workloads. Another power saving technique is CPU consolidation, which uses the minimum number of CPUs necessary to meet the service request demands and turns OFF the remaining unused CPUs. DVFS has been already extensively studied and verified its effectiveness. On the other hand, it is necessary to study more about the effectiveness of CPU consolidation. Key questions that must be answered are how effectively the CPU consolidation improves the energy efficiency and how to maximize the improvement. These questions are addressed in this paper. After understanding modern power management techniques and developing an appropriate power model, this paper provides an extensive set of hardware-based experimental results and makes suggestions about how to maximize energy efficiency improvement through CPU consolidation. In addition, this paper also presents new online CPU consolidation algorithms, which reduce the energy-delay product up to 13% compared with the Linux default DVFS algorithm.

Improving the energy efficiency of cloud computing systems has become an important issue because the electric energy bill for 24/7 operation of these systems can be quite large. The focus of this paper is on the virtual machine (VM) consolidation in a cloud computing system as a way of lowering daily energy consumption of the system. In contrast to the existing works that assume resource demands of VMs are known and given as scalar variables, this paper treats these demands as random variables with known means and standard deviations. These random variables may be correlated with one another, and there are several kinds of resources which can be performance bottlenecks. Therefore, both the correlation and multiple resource type should be considered. The VM consolidation problem is then formulated as a multi-capacity stochastic bin packing problem. This problem is NP-hard, so we propose a heuristic method to solve the problem efficiently. The simulation results show that, in spite of its simplicity and scalability, the proposed method produces high quality solutions.

The focus of this paper is on energy-aware resource management in a cloud computing system. Much of the existing work assumes that the resource requirements for various applications are known and given as scalar values. However, it is very difficult to know the exact resource requirements, and thus, it is more appropriate to treat resource requirements for applications as random variables with known characteristics. For a desired quality of service, the required total resource amount can then be estimated as a function of the means and standard deviations of these random variables. Inspired by the modern portfolio theory, this paper presents algorithms that minimize the total amount of estimated resource in the system. A source of difficulty is that some of the aforesaid random variables may be correlated with each other. The proposed algorithms effectively deal with correlated applications. Experimental results show that, in spite of its simplicity and scalability, the proposed solution outperforms the well-known heuristics i.e., first fit decreasing (FFD) and best fit decreasing (BFD) by an average of 10% while having a low execution time.

The focus of this paper is on dynamic power management in virtualized multi-core server systems. The paper starts by analyzing the effect of virtualization and CPU consolidation on power dissipation and performance (latency) of such systems, and concludes by presenting two new CPU consolidation algorithms for multi-core servers. The paper also reports an extensive set of experimental results founded on a realistic multi-core server system setup and well-developed benchmarks, i.e., SPEC2K and SPECWeb2009 and obtained through hardware measurements.

With power efficiency and lifetime awareness becoming critical design concerns in wireless sensor networks (WSN), it has become essential to exploit the physical layer characteristics at the system deployment stage. This paper focuses on how certain physical layer attributes can affect both the lifetime and the end-to-end delay in a hierarchical WSN. We present a heterogeneous modulation scheme and report its impact on the spatial distribution of energy dissipation and the resulting network lifetime. Moreover, we study how this heterogeneous modulation scheme affects the end-to-end delay due to inherent trade-offs in power efficiency and bandwidth efficiency of the different modulation schemes.