Lei Rao

Minimizing Electricity Cost: Optimization of Distributed Internet Data Centers in a Multi-Electricity-Market Environment

The study of Cyber-Physical System (CPS) has been an active area of research. Internet Data Center (IDC) is an important emerging Cyber-Physical System. As the demand on Internet services drastically increases in recent years, the power used by IDCs has been skyrocketing. While most existing research focuses on reducing power consumptions of IDCs, the power management problem for minimizing the total electricity cost has been overlooked. This is an important problem faced by service providers, especially in the current multi-electricity market, where the price of electricity may exhibit time and location diversities. Further, for these service providers, guaranteeing quality of service (i.e. service level objectives-SLO) such as service delay guarantees to the end users is of paramount importance. This paper studies the problem of minimizing the total electricity cost under multiple electricity markets environment while guaranteeing quality of service geared to the location diversity and time diversity of electricity price. We model the problem as a constrained mixed-integer programming and propose an efficient solution method. Extensive evaluations based on real-life electricity price data for multiple IDC locations illustrate the efficiency and efficacy of our approach.

Performance and Reliability Analysis of IEEE 802.11p Safety Communication in a Highway Environment

Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications are gaining increasing importance in vehicular applications. Dedicated short-range communication (DSRC) is a fundamental set of short-to-medium-range communication channels and a set of protocols and standards that are specifically designed for V2V and V2I communications. IEEE 802.11p is a protocol that has been standardized as the medium access control (MAC) layer of the DSRC standard. Due to the highly dynamic topology and low delay constraints in vehicular ad hoc networks (VANETs), direct (or one-hop) broadcast on the control channel (CCH) is an effective approach to inform the neighborhood of safety-related messages. The 802.11p enhanced distributed channel access (EDCA) mechanism allows four access categories (ACs) in a station for applications with different priorities according to their criticalities for the vehicles safety. This paper focuses on the analysis of the 802.11p safety-critical broadcast on the CCH in a VANET environment and improves the existing work by taking several aspects into design consideration. Extensive performance evaluations based on the NS-2 simulator help to validate the accuracy of the proposed model and analyze the capabilities and limitations of the standard 802.11p broadcast on the CCH.

Distributed Coordination of Internet Data Centers Under Multiregional Electricity Markets

This paper addresses the problem of electricity cost management for Internet service providers with a collection of spatially distributed data centers. As the demand on Internet services and cloud computing has kept increasing in recent years, the power usage associated with IDC operations has been uprising significantly. The cyber and physical aspects of IDCs interact with each other, and bring unprecedented challenges in power management. While most existing research focuses on reducing power consumptions of IDCs at one specific location, the problem of reducing the total electricity cost has been overlooked. This is an important problem faced by service providers, especially in the present multielectricity-market environment, where the price of electricity may exhibit temporal and spatial diversities. Further, for these service providers, guaranteeing the quality of service (QoS; i.e., service level objectives) such as service delay guarantees to the end users is of critical importance. This paper studies the problem of minimizing the total electricity cost geared to QoS constraint as well as the location diversity and time diversity of electricity price under multiregional electricity markets. We jointly consider both the cyber and physical management capabilities of IDCs, and exploit both the center-level load balancing and the server-level power control in a unified scheme. We model the problem as a constrained mixed integer programming based on generalized benders decomposition (GBD) technique. Extensive evaluations based on real-life electricity price data for multiple IDC locations demonstrate the effectiveness of our scheme.

Coordinated Energy Cost Management of Distributed Internet Data Centers in Smart Grid

This paper addresses the problem of electricity cost management for Internet service providers with a collection of spatially distributed data centers. As the demand on Internet services drastically increases in recent years, the electricity consumed by Internet data centers (IDCs) has been skyrocketing. While most existing research focuses on reducing electric energy consumption of IDCs at one specific location, the problem of reducing the total electricity cost has been overlooked. This is an important problem faced by service providers, especially in the present multi-electricity-market environment, where the price of electricity may exhibit temporal and spatial diversities. Further, for these service providers, guaranteeing the quality of service (i.e., service level objectives) such as service delay guarantees to the end users is of critical importance. This paper studies the problem of minimizing the total electricity cost under multiple electricity markets environment while guaranteeing the quality of service geared to the location diversity and time diversity of electricity price. The problem is modeled as a constrained mixed-integer programming and an efficient solution algorithm is proposed. Extensive evaluations based on real-world electricity price data for multiple IDC locations illustrate the efficiency and efficacy of our approach.

Temporal Load Balancing with Service Delay Guarantees for Data Center Energy Cost Optimization

Cloud computing services are becoming integral part of peoples 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.

D-Pro: Dynamic Data Center Operations With Demand-Responsive Electricity Prices in Smart Grid

The study of todays cyber-physical system (CPS) has been an important research area. Internet data centers (IDCs) are energy consuming CPSs that support the reliable operations of many important online services. Along with the increasing Internet services and cloud computing in recent years, the power usage associated with IDC operations had been surging significantly. Such mass power consumption has brought extremely heavy burden on IDC operators. While most previous work only consider about dynamical optimization of IDC under electricity markets, the reaction of IDC toward electricity market has been overlooked. Due to the fact that IDCs are usually large-volume users in the electricity market, they might have market power to affect the electricity price. In this paper, we study how to address the challenge of interactions between IDC operation and electricity market price. To this end, we propose a supply function to model the market power of IDC and formulate a total electricity cost minimization problem as a non-linear programming. Then we present CMC algorithm inspired by the economics concept. CMC algorithm not only solves the optimization problem efficiently, but also uncovers the impetus of the work load distribution. Extensive performance evaluations demonstrate that the proposed method can effectively minimize the total electricity cost of IDCs by adaptively handling the interaction between IDCs and smart grid.

Delay analysis and study of IEEE 802.11p based DSRC safety communication in a highway environment

As a key enabling technology for the next generation inter-vehicle safety communications, The IEEE 802.11p protocol is currently attracting much attention. Many inter-vehicle safety communications have stringent real-time requirements on broadcast messages to ensure drivers have enough reaction time toward emergencies. Most existing studies only focus on the average delay performance of IEEE 802.11p, which only contains very limited information of the real capacity for inter-vehicle communication. In this paper, we propose an analytical model, showing the performance of broadcast under IEEE 802.11p in terms of the mean, deviation and probability distribution of the MAC access delay. Comparison with the NS-2 simulations validates the accuracy of the proposed analytical model. In addition, we show that the exponential distribution is a good approximation to the MAC access delay distribution. Numerical analysis indicates that the QoS support in IEEE 802.11p can provide relatively good performance guarantee for higher priority messages while fails to meet the real-time requirements of the lower priority messages.

Hedging Against Uncertainty: A Tale of Internet Data Center Operations Under Smart Grid Environment

Internet Data Center (IDC) supports the reliable operations of many important online services. As the demand of Internet services and cloud computing keep increasing in recent years, the power usage associated with IDC operations had been surging significantly. Such mass power consumption has brought heavy burden on IDC operators. Recently there are extensive research on power management for IDCs. However, one important challenge faced by IDC operators has been overlooked. How to handle the uncertainties in IDC operations is a challenging task. The uncertainties come from both the dynamic workload and time-varying electricity prices. In this paper, we systematically investigate the problem of minimizing the operation risk of IDCs against those uncertainties at the same time guaranteeing quality of service under deregulated electricity market environment. We propose a novel hedging scheme and model the operation risk minimization problem as a bilevel programming. We also design an optimal hedging algorithm. We conduct extensive evaluations based on real-life workload data from Google and electricity price data from deregulated electricity market for multiple IDC locations. Results show that our scheme can significantly reduce the operation risk by countering the uncertainties.

MEC-IDC: joint load balancing and power control for distributed Internet Data Centers

Internet Data Center (IDC) supports the reliable operations of many important Internet on-line services. As the demand on Internet services and cloud computing keep increasing in recent years, the power usage associated with IDC operations has been uprising significantly. The cyber and physical aspects of IDCs interact with each other, and brings unprecedented challenges in power management. While most existing research focuses on reducing power consumptions of IDCs, this paper studies the problem of minimizing the total electricity cost geared to quality of service constraint as well as the location diversity and time diversity of electricity price under multiple electricity markets. We jointly consider both the cyber and physical management capabilities of IDCs, and exploit both the center-level load balancing, and the server-level power control in a unified scheme. We model the problem as a constrained mixed integer programming based on Generalized Benders Decomposition (GBD) technique. Extensive evaluations based on real-life electricity price data for multiple IDC locations demonstrates the effectiveness of our scheme.

Exploiting Concurrency for Efficient Dissemination in Wireless Sensor Networks

Wireless sensor networks (WSNs) can be successfully applied in a wide range of applications. Efficient data dissemination is a fundamental service which enables many useful high-level functions such as parameter reconfiguration, network reprogramming, etc. Many current data dissemination protocols employ network coding techniques to deal with packet losses. The coding overhead, however, becomes a bottleneck in terms of dissemination delay. We exploit the concurrency potential of sensor nodes and propose MT-Deluge, a multithreaded design of a coding-based data dissemination protocol. By separating the coding and radio operations into two threads and carefully scheduling their executions, MT-Deluge shortens the dissemination delay effectively. An incremental decoding algorithm is employed to further improve MT-Deluges performance. Experiments with 24 TelosB motes on four representative topologies show that MT-Deluge shortens the dissemination delay by 25.5-48.6 percent compared to a typical data dissemination protocol while keeping the merits of loss resilience.