BACKGROUND: Wet-lab experiments for identification of interactions between drugs and target proteins are time-consuming, costly and labor-intensive. The use of computational prediction of drug-target interactions (DTIs), which is one of the significant points in drug discovery, has been considered by many researchers in recent years. It also reduces the search space of interactions by proposing potential interaction candidates. RESULTS: In this paper, a new approach based on unifying matrix factorization and nuclear norm minimization is proposed to find a low-rank interaction. In this combined method, to solve the low-rank matrix approximation, the terms in the DTI problem are used in such a way that the nuclear norm regularized problem is optimized by a bilinear factorization based on Rank-Restricted Soft Singular Value Decomposition (RRSSVD). In the proposed method, adjacencies between drugs and targets are encoded by graphs. Drug-target interaction, drug-drug similarity, target-target, and combination of similarities have also been used as input. CONCLUSIONS: The proposed method is evaluated on four benchmark datasets known as Enzymes (E), Ion channels (ICs), G protein-coupled receptors (GPCRs) and nuclear receptors (NRs) based on AUC, AUPR, and time measure. The results show an improvement in the performance of the proposed method compared to the state-of-the-art techniques.

This paper designs a holistic global workload management solution which explores diversities of a set of geo-distributed data centers and energy buffering in order to minimize the electricity cost, reduce the peak power drawn from utilities while maintaining the carbon capping requirement of the data centers. The prior work often designed solutions to address each of the aforementioned energy and cost optimization separately, disregarding the possible conflicts between the solutions' objectives. We propose a holistic solution to concurrently optimize the aforementioned potentially competing objectives. The proposed solution combines the techniques from Lyapunov optimization and predictive solution in order to manage the tradeoffs of electricity cost and carbon footprint reduction, and electricity cost and peak power cost reduction, respectively. The predicted data center parameters, being a significant aid to near optimally manage energy buffering and smoothing data centers' peak power draw, adversely affect the peak power cost due to the parameters' prediction error. The proposed holistic solution adapts stochastic programing to take the predicted parameters' randomness into consideration for minimizing the harmful impact of the prediction error. Our trace-based study confirms our analytical result that our holistic solution balances all the tradeoffs towards achieving energy and cost sustainability. Also our solution removes up to 66% of the prediction error impact in increasing the cost.

Internet data centers, typically distributed across the world in order to provide timely and reliable Internet service, have been increasingly pressurized to reduce their carbon footprint and electricity cost. Particularly, data centers will soon be required to abide by carbon capping polices which impose carbon footprint limits to encourage brown energy conservation. We propose an online algorithm, called OnlineCC, for minimizing the operational cost while satisfying the carbon footprint reduction target of a set of geo-distributed data centers. OnlineCC makes use of Lyapunov optimization technique while operating without long-term future information, making it attractive in the presence of uncertainties associated with data center information e.g., input workload. We prove that OnlineCC achieves a near optimal operational cost (electricity cost) compared to the optimal algorithm with future information, while bounding the potential violation of carbon footprint target, depending on the Lyapunov control parameter, namely V. We also give a heuristic for finding V which significantly shortens the search space to adjust its value. Finally, we perform a trace-based simulation study and a small scale experiment to complement the analysis. The results show that OnlineCC reduces cost by more than 18% compared to a prediction-based online solution while resulting in equal or smaller carbon footprint.

Energy buffering, has been proposed to store renewable energy and low cost electricity in Energy Storage Devices (ESDs) and use it judiciously to reduce electricity bill in Internet data centers. Recent research have considered long term variation in electricity price, renewable power and workload and have shown the efficiency of energy buffering in reducing electricity bill. However, these aspects of data centers exhibit both long and short term variation. Further, there is inherent heterogeneity in ESD physical characteristics (e.g., charging and discharging rates). We hypothesize that a multi-tier energy buffering management can leverage the heterogeneity in ESD characteristics and better optimize utilization of renewable energy and low-cost power in presence of both short and long term variabilities in a data center. This paper proposes an analytical study of multi-tier workload and energy buffering management technique that frames each tier as an optimization problem and solves them in an online and proactive way using Receding Horizon Control (RHC). Our study shows that multi-tier energy buffering management increases the utilization of the renewables by upto two times compared to one-tier management.

Phase imbalance has been considered as a source of inefficiency in the data center that causes energy loss due to line impedance and increases reactive power. Strategies assume high loss due to phase imbalance and propose sophisticated energy management algorithms including phase balance aware workload scheduling algorithm and dynamic power distribution unit assignment to servers. However, such attempts do not utilize an objective measure of the inefficiencies due to phase imbalance to evaluate the significance of their contributions. Excessive imbalance in a three phase load has various undesirable effects. This paper, first objectively characterizes the inefficiencies due to phase imbalance and then provides numerical measures of the losses in realistic data center deployments. Phase imbalanced load in a delta configuration results in reduced power factor, which is undesirable for several reasons. Also, an imbalanced load (both in delta or star configuration), results in higher line currents, leading to higher line loss. However, this increase in loss is a fraction of a percentage of the energy consumed. The paper also discusses effects of work load scheduling on phase imbalance, and how to minimize the same.

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.

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.