There exists an interference due to colocating applications which depends on the applications' workload types, degrades the performance and affects the energy consumption of applications. We hypothesize the interference energy consumption and model it as “interference coefficient” and use it to develop an application-aware colocation management policy to colocate the applications in data centers. Including the interference effect in simulations using synthetic workload in the colocation management policy results energy savings of up to 8%.

Dynamic Application Hosting Management (DAHM) is proposed for geographically distributed data centers, which decides on the number of active servers and on the workload share of each data center. DAHM achieves cost-efficient application hosting by taking into account: (i) the spatio-temporal variation of energy cost, (ii) the data center computing and cooling energy efficiency, (iii) the live migration cost, and (iv) any SLA violations due to migration overhead or network delay. DAHM is modeled as fixed-charge min-cost flow and mixed integer programming for stateless and stateful applications, respectively, and it is shown NP-hard. We also develop heuristic algorithms and prove, when applications are stateless and servers have an identical power consumption model, that the approximation ratio on the minimum total cost is bounded by the number of data centers. Further, the heuristics are evaluated in a simulation study using realistic parameter data; compared to a performance-oriented application assignment, that is, hosting at the data center with the least delay, the potential cost savings of DAHM reaches 33%. The savings come from reducing the total number of active servers as well as leveraging the cost efficiency of data centers. Through the simulation study, the article further explores how relaxing the delay requirement for a small fraction of users can increase the cost savings of DAHM.

A two-tier Internet data center management scheme, TACOMA, with thermal-aware server provisioning (TASP) in one tier, and thermal-aware workload distribution (TAWD) in the other is proposed. TASP and TAWD coordinate to maximize the energy savings by leveraging the workload dynamics, at coarse and fine time scale, respectively. TACOMA is aware of the QoS constraints, the energy proportionality of servers, and the potential trade-off between cooling and computing power. The obtained energy savings are a combination of suspending idle servers, using servers at their peak efficiency, and avoiding heat recirculation.

A distributed cyber-physical system (DCPS) may receive and induce energy-based interference to and from its environment. This article presents a model and an associated methodology that can be used to (i) schedule tasks in DCPSs to ensure that the thermal effects of the task execution are within acceptable levels, and (ii) verify that a given schedule meets the constraints. The model uses coarse discretization of space and linearity of interference. The methodology involves characterizing the interference of the task execution and fitting it into the model, then using the fitted model to verify a solution or explore the solution space.

Dynamic Application Hosting Management (DAHM) allows clouds to dynamically host applications in data centers at different locations based on: (i) spatio-temporal variation of energy price, (ii) data center computing and cooling energy efficiency, (iii) Virtual Machine (VM) migration cost for the applications, and (iv) any SLA violations due to migration overhead or network delay. DAHM is complementary to dynamic workload distribution problem and is modeled as mixed integer programming; online algorithms are developed to solve the problem. The algorithms are evaluated in a simulation study using realistic data and compared with performance-oriented application assignment, i.e., hosting the application at a data center whose delay is the least. Our simulations results indicate that DAHM can potentially save up to 20% cost while incurring only a nominal increase in SLA violations. The savings are obtained by exploiting the cost efficiency variation as well as reducing the total number of VMs employed to host applications.

Cloud is the state-of-the-art back-end infrastructure for most large-scale web services. This paper studies what effect energy proportionality has on the energy savings of cloud data center management, under various equipment compositions and power densities. Our findings show that although it is a common expectation that improved energy proportionality should diminish the benefits of power management's server provisioning, this is not true in all cases. Results show that equipping server provisioning with thermal awareness can keep it as a useful technique when the data center exhibits power consumption heterogeneity and non-uniform heat recirculation phenomena.

With the increasing popularity of Internet-based information retrieval and cloud computing, saving energy in Internet data centers (a.k.a. hosting centers, server farms) is of increasing importance. Current research approaches are based on dynamically adjusting the active server set in order to turn off a portion of the servers and save energy without compromising the quality of service; the workload is then distributed, conventionally equally (i.e. balanced), across the active servers. Although there is ample work that demonstrates energy savings through dynamic server provisioning, there is little work on thermal-aware server provisioning. This paper provides a formulation of the thermal aware active server set provisioning (TASP), in a nonlinear minimax binary integer programming form, and a series of heuristic approaches to solving them, namely MiniMax, bb-sLRH, CP-sLRH and sLRH. Furthermore, it introduces thermal-aware workload distribution (TAWD) among the active servers. The proposed heuristics are evaluated using a thermal model of the ASU HPCI data center, while the request traffic is based on real web traces of the 1998 FIFA World Cup as well as the SPECweb2009 suite. The TASP heuristics are found to outperform a power-aware-only server set selection scheme (CPSP), by up to 9.3% for the simulated scenario. The order of achieved energy efficiency is: MiniMax (9.3% savings), CP-sLRH (9.2%), bb-sLRH (8.6%), sLRH (5.8%), compared to CPSP.

High-performance computing data centers have been rapidly growing, both in number and size. Thermal management of data centers can address dominant problems associated with cooling such as the recirculation of hot air from the equipment outlets to their inlets and the appearance of hot spots. In this paper, we show through formalization that minimizing the peak inlet temperature allows for the lowest cooling power needs. Using a low-complexity linear heat recirculation model, we define the problem of minimizing the peak inlet temperature within a data center through task assignment (MPIT-TA), consequently leading to minimal cooling-requirement. We also provide two methods to solve the formulation: Xlnt-GA, which uses a genetic algorithm, and Xlnt-SQP, which uses sequential quadratic programming. Results from small-scale data center simulations show that solving the formulation leads to an inlet temperature distribution that, compared to other approaches, is 2 degC to 5 degC lower and achieves about 20 to 30 percent cooling energy savings at common data center utilization rates. Moreover, our algorithms consistently outperform the minimize heat recirculation algorithm, a recirculation-reducing task placement algorithm in the literature.

Ongoing research has demonstrated the potential benefits of thermal-aware load placement in data centers to both reduce cooling costs and component failure rates. However, thermal-aware load placement techniques have not been widely deployed in existing data centers. This is mainly because they rely on a thermal map or profile of the data center, the derivation of which is an interruptive process to the data center operation. We propose a noninvasive solution of producing a thermal map; it consists of training a neural network with observed data from actual data center operation. Our results show that gathering the data and selecting a training set is a fast process, while the neural network with no hidden layers achieves the lowest mean squared error.

Power-aware and thermal-aware techniques such as power-throttling and workload manipulation have been developed to counter the increasing power density in the current data centers. The basis for any such power-aware and/or thermal-aware technique, however, depends heavily on the equipment's power consumption model assumed. The goal of this paper is to perform power-profiling of different systems-namely, the Dell PowerEdge 1855 and 1955-based on actual power measurements. Gamut (Generic Application eMUlaTor) benchmark, double-precision matrix multiplication, and convolution of two vectors are used for varying the CPU utilization and Disk I/O.

The thermal environment of data centers plays a significant role in affecting the energy efficiency and the reliability of data center operation. A dominant problem associated with cooling data centers is the recirculation of hot air from the equipment outlets to their inlets, causing the appearance of hot spots and an uneven inlet temperature distribution. Heat is generated due to the execution of tasks, and it varies according to the power profile of a task. We are looking into the prospect of assigning the incoming tasks around the data center in such a way so as to make the inlet temperatures as even as possible; this will allow for considerable cooling power savings. Based on our previous research work on characterizing the heat recirculation in terms of cross-interference coefficients, we propose a task scheduling algorithm for homogeneous data centers, called XInt, that minimizes the inlet temperatures, and leads to minimal heat recirculation and minimal cooling energy cost for data center operation. We verify, through both theoretical formalization and simulation, that minimizing heat recirculation will result in the best cooling energy efficiency. XInt leads to an inlet temperature distribution that is 2degC to 5degC lower than other approaches, and achieves about 20%-30% energy savings at moderate data center utilization rates. XInt also consistently achieves the best energy efficiency compared to another recirculation minimized algorithm, MinHR.

Many applications, such as disaster response and military applications, call for proactive maintenance of links and routes in Mobile Ad hoc NETworks (MANETs) to ensure low latency during data delivery. The goal of this paper is to minimize the wastage of energy in the network due to high control traffic, which restricts the scalability and applicability of such protocols, without trading-off the low latency. We categorize the proactive protocols based on the periodic route and link maintenance operations performed; and analytically derive the optimum periods for these operations in different protocol classes. The analysis takes into account data traffic intensity, link dynamics, application reliability requirements, and the size of the network. The proposed optimization significantly reduces the control traffic for low data traffic intensity in the network and increases protocol scalability for large networks without compromising the low latency of proactive protocols.

Personal communication services (PCS) standards such as the IS-41 and the GSM use a location management scheme, which is based on registration areas (RA). Overlapping of registration areas has been proposed to reduce the overhead of location updates in such systems. Overlapping provides multiple choices for the selection of a registration area, a feature that does not appear in environments of nonoverlapping RAs. In this paper, we demonstrate how the choice of a registration area affects the number of subsequent registrations and define the problem of optimal choice based on minimization of subsequent registrations. We study two versions of the problem: 1) the deterministic (or offline) version, in which the entire trajectory of the mobile unit is known a priori, and 2) the stochastic (or online) version, in which the trajectory of the mobile unit is not known a priori, but the system has the knowledge of the mobility pattern of the mobile. The mobility pattern is modeled as a random walk across coverage area. In the deterministic case, the method presented computes an optimal solution to the registration problem. In the stochastic case, we present a method that determines the registration that minimizes the expected number of registrations by looking ahead in the probable future paths of the mobile unit. The algorithm's time complexity has exponential dependence on the length of the look-ahead. The cost of search is independent of the choice, therefore the algorithm does not increase the search cost. We also consider an alternative cost model for the stochastic case and propose a preemptive and a nonpreemptive approach each of which incurs different numbers of hard and soft registration operations. Simulation results show a reduction of 25-30 percent in number of registrations over a random-choice scheme. This can be translated to 5-20 percent effective location management load reduction for most cases, subject to the call-to-mobility ratio (CMR) of a mobile unit and search-cost-to-update-cost ratio (CCR) in the network.

In this paper, we propose an extension to the personal communication services (PCS) location management protocol which uses dynamically overlapped registration areas. The scheme is based on monitoring the aggregate mobility and call pattern of the users during each reconfiguration period and adapting to the mobility and call patterns by either expanding or shrinking registration areas at the end of each reconfiguration period. We analytically characterize the trade-off resulting from the inclusion or exclusion of a cell in a registration area in terms of expected change in aggregate database access cost and signaling overhead. This characterization is used to guide the registration area adaption in a manner in which the signaling and database access load on any given location register (LR) does not exceed a specified limit. Our simulation results show that it is useful to dynamically adapt the registration areas to the aggregate mobility and call patterns of the mobile units when the mobility pattern exhibits locality. For such mobility and call patterns, the proposed scheme can greatly reduce the average signaling and database access load on LRs. Further, the cost of adapting the registration areas is shown to be low in terms of memory and communication requirements.