Cloud computing is powered by an engine known as Internet data center (IDC). As cloud computing flourishes, the energy consumption and cost for IDCs are soaring. The energy cost minimization problem for IDCs in deregulated electricity markets has generated growing interest. In this paper we study how to leverage both the geographic and temporal variation of energy price to minimize energy cost for distributed IDCs. We propose a novel architecture and two algorithms for unified spatial and temporal load balancing. Rigorous analysis shows that our algorithms have a low computational complexity, require a relaxed accuracy in electricity price estimation, and guarantee a service completion time for user requests. Using real-life electricity price and workload traces, extensive evaluations demonstrate that compared to the schemes using either spatial load balancing or temporal load balancing alone, the proposed spatio-temporal load balancing method significantly reduces energy cost for distributed IDCs.

The past decade witnessed the tremendous growth of online services and applications. Together with the increase of cloud computing, more and more computation are hosted by Internet data centers (IDCs). Today's IDCs are achieving significant advances in communication and computation capabilities. However, along with the increasing demand from IDC clients, power consumption for powering up and cooling these IDCs has been skyrocketing. Most existing works optimize the power consumption of either servers or Computer Room Air Conditioners (CRACs), and overlook the correlation between the power consumption of these two types of equipment. In this paper, we propose an adaptive power control method which leverages the correlation between the power consumption of servers and CRACs. To capture the workload uncertainties and thermal dynamics, we exploit Recursive-Least Square based Model Predictive Control (MPC) to solve the power control problem. Performance evaluations shows the effective power peak reduction using our approach.

Content service is a type of Internet cloud service that provides end-users plentiful contents. To ensure high performance for content delivering, content service utilizes a technology known as content multihoming: contents are generated from multiple geographically distributed data centers and delivered by multiple distributed content distribution networks (CDNs). The electricity costs for data centers and the usage costs for CDNs are major contributors to the contents service cost. As electricity prices vary across data centers and usage costs vary across CDNs, scheduling data centers and CDNs has a tremendous consequence for optimizing content service cost. In this paper, we propose a novel framework named Cost Optimization for Internet Content Multihoming (COMIC). COMIC dynamically balances end-users' loads among data centers and CDNs so as to minimize the content service cost. Using real-life electricity prices and CDN traces, the experiments demonstrate that COMIC effectively reduces the content service cost by more than 20 percent.

Cloud computing services are becoming integral part of people's daily life. These services are supported by infrastructure known as Internet data center (IDC). As demand for cloud computing services soars, energy consumed by IDCs is skyrocketing. Both academia and industry have paid great attention to energy management of IDCs. This paper studies an important energy management problem-how to minimize energy cost for IDCs in deregulated electricity markets. We propose a novel two-stage design and the eco-IDC (Energy Cost Optimization-IDC) algorithm to exploit the temporal diversity of electricity price and dynamically schedule workload to execute on IDC servers through an input queue. Extensive evaluation experiments are performed using real-life electricity price and workload traces at an enterprise production data center. The evaluation results demonstrate that the proposed approach significantly reduces energy cost for IDCs, guarantees a service delay bound, and alleviates workload drop if the service delay bound is sufficiently large.

Cloud computing is supported by an infrastructure known as Internet data center (IDC). As cloud computing thrives, the energy consumption and cost for IDCs are exploding. There is growing interest in energy cost minimization for IDCs in deregulated electricity markets. In this paper we study how to leverage both geographic and temporal variation of energy price to minimize energy cost for distributed IDCs. To this end, we propose a novel spatio-temporal load balancing approach. Using reallife electricity price and workload traces, extensive evaluations demonstrate that the proposed spatio-temporal load balancing approach significantly reduces energy cost for distributed IDCs.

Cloud computing services are becoming integral part of people's daily life. These services are supported by Internet data centers (IDCs). As demand for cloud computing services soars, energy consumed by IDCs is skyrocketing. This paper studies an energy management problem - how to minimize energy cost for IDCs in deregulated electricity markets. While several existing works handle this problem by leveraging spatial diversity of electricity price, little has been done to address the temporal uncertainty in electricity price and arriving workload. This paper proposes a novel two-stage design and the eco-IDC (Energy Cost Optimization-IDC) algorithm to exploit temporal diversity of electricity price and dynamically schedule workload to execute on IDC servers through an input queue. Extensive evaluation experiments are performed to demonstrate that the proposed approach significantly reduces energy cost for IDCs, and guarantees a service delay bound for user requests.

This paper presents three techniques to reduce the power consumption in ternary content-addressable memories (TCAMs). The first technique is to use newly developed monolithically stacked 3D-IC technology for the implementation, because vertical stacking can drastically reduce interconnect length in both matchlines and searchlines, hence reducing signal path delay and power consumption. The second technique is to replace the conventional SRAM memory in a TCAM with an array of programmable vias (or electrolyte non-volatile memory). Special programming circuitry is designed to read/write memory bits from/to the programmable via array because they do not simply store data in the form of low and high voltage levels. We also devised a new TCAM cell design to further reduce power consumption in TCAMs by taking full advantage of 3D-IC technology. A 1024 times 144-bit TCAM using the proposed schemes is implemented with 1.0-V 65 nm CMOS technology. Our analysis and simulations have shown that the proposed monolithically stacked 3D-TCAM can reduce the total dynamic power consumption by almost 3.5 times and increase TCAM cell density by about 4 times in comparison with a conventional 2D-TCAM chip of the same capacity.

This paper presents Ethane, a new network architecture for the enterprise. Ethane allows managers to define a single network-wide fine-grain policy, and then enforces it directly. Ethane couples extremely simple flow-based Ethernet switches with a centralized controller that manages the admittance and routing of flows. While radical, this design is backwards-compatible with existing hosts and switches.