Proactive Resource Research Articles

Stochastic Pre-hurricane Restoration Planning for Electric Power Systems Infrastructure

Proactive preparedness to cope with emergencies, especially those of nature origins, significantly improves the resilience and minimizes the restoration cost of electric power systems. In this paper, a proactive resource allocation model for repair and restoration of potential damages to the power system infrastructure located on the path of an upcoming hurricane is proposed. The objective is to develop an efficient framework for system operators to minimize potential damages to power system components in a cost-effective manner. The problem is modeled as a stochastic integer program with complete recourse. The large-scale mixed-integer equivalence of the original model is solved by the Benders' decomposition method to handle computation burden. The standard IEEE 118-bus system is employed to demonstrate the effectiveness of the proposed model and further discuss its merits.

CONDESA: A Framework for Controlling Data Distribution on Elastic Server Architectures

Applications running in today's data centers show high workload variability. While seasonal patterns, trends and expected events may help building proactive resource allocation policies, this approach has to be complemented with adaptive strategies which should address unexpected events such as flash crowds and volume spikes. Additionally, the limitations of current I/O infrastructures in the face of dramatic increase of data generation require, the ability to build novel abstractions and models for robust decision making regarding data layout and data locality. In this work, we present CONDESA (CONtrolling Data distribution on Elastic Server Architectures), a framework for exploring adaptive data distribution strategies for elastic server architectures. To the best of our knowledge CONDESA is the first platform that permits to systematically study the interplay between five data related strategies: workload prediction, adaptive control of data distribution and server provisioning, adaptive data grouping, adaptive data placement, and adaptive system sizing. We demonstrate how CONDESA can be used for browsing the design space of adaptive data distribution policies. We show how prediction models can be compared in terms of overhead and accuracy. We evaluate the impact of change detection on prediction accuracy and how CONDESA can be used for choosing an adequate prediction horizon. We demonstrate how adaptive prediction can be used for sizing a server system. Finally, we show how prediction models, change detection strategies, and data placement policies can be combined and compared based on server utilization, load balance, data locality, over- and underprovisioning.

Multiperiod robust optimization for proactive resource provisioning in virtualized data centers

Energy management has become a significant concern in data centers for reducing operational costs. Using virtualization allows server consolidation, which increases server utilization and reduces energy consumption by turning off idle servers. This needs to consider the power state change overhead. In this paper, we investigate proactive resource provisioning in short-term planning for performance and energy management. To implement short-term planning based on workload prediction, this requires dealing with high fluctuations that are inaccurately predictable by using single value prediction. Unlike long-term planning, short-term planning can not depend on periodical patterns. Thus, we propose an adaptive range-based prediction algorithm instead of a single value. We implement and extensively evaluate the proposed range-based prediction algorithm with different days of real workload. Then, we exploit the range prediction for implementing proactive provisioning using robust optimization taking into consideration uncertainty of the demand. We formulate proactive VM provisioning as a multiperiod robust optimization problem. To evaluate the proposed approach, we use several experimental setups and different days of real workload. We use two metrics: energy savings and robustness for ranking the efficiency of different scenarios. Our approach mitigates undesirable changes in the power state of servers. This enhances servers' availability for accommodating new VMs, its robustness against uncertainty in workload change, and its reliability against a system failure due to frequent power state changes.

Matrix based proactive resource provisioning in mobile cloud environment

Mobile cloud computing is a dynamic, virtually scalable and network based computing environment where mobile device acts as a thin client and applications run on remote cloud servers. Mobile cloud computing resources required by different users depend on their respective personalized applications. Therefore, efficient resource provisioning in mobile clouds is an important aspect that needs special attention in order to make the mobile cloud computing a highly optimized entity. This paper proposes an adaptive model for efficient resource provisioning in mobile clouds by predicting and storing resource usages in a two dimensional matrix termed as resource provisioning matrix. These resource provisioning matrices are further used by an independent authority to predict future required resources using artificial neural network. Independent authority also checks and verifies resource usage bill computed by cloud service provider using resource provisioning matrices. It provides cost computation reliability for mobile customers in mobile cloud environment. Proposed model is implemented on Hadoop using three different applications. Results indicate that proposed model provides better mobile cloud resources utilization as well as maintains quality of service for mobile customer. Proposed model increases battery life of mobile device and decreases data usage cost for mobile customer.

Decreasing Impact of SLA Violations:A Proactive Resource Allocation Approachfor Cloud Computing Environments

User satisfaction as a significant antecedent to user loyalty has been highlighted by many researchers in market based literatures. SLA violation as an important factor can decrease users’ satisfaction level. The amount of this decrease depends on user's characteristics. Some of these characteristics are related to QoS requirements and announced to service provider through SLAs. But some of them are unknown for service provider and selfish users are not interested to reveal them truly. Most the works in literature ignore considering such characteristics and treat users just based on SLA parameters. So, two users with different characteristics but similar SLAs have equal importance for the service provider. In this paper, we use two user's hidden characteristics, named willingness to pay for service and willingness to pay for certainty, to present a new proactive resource allocation approach with aim of decreasing impact of SLA violations. New methods based on learning automaton for estimation of these characteristics are provided as well. To validate our approach we conducted some numerical simulations in critical situations. The results confirm that our approach has ability to improve users’ satisfaction level that cause to gain in profitability.

Self‐adaptive workload classification and forecasting for proactive resource provisioning

SUMMARYAs modern enterprise software systems become increasingly dynamic, workload forecasting techniques are gaining an importance as a foundation for online capacity planning and resource management. Time series analysis offers a broad spectrum of methods to calculate workload forecasts based on history monitoring data. Related work in the field of workload forecasting mostly concentrates on evaluating specific methods and their individual optimisation potential or on predicting QoS metrics directly. As a basis, we present a survey on established forecasting methods of the time series analysis concerning their benefits and drawbacks and group them according to their computational overheads. In this paper, we propose a novel self‐adaptive approach that selects suitable forecasting methods for a given context based on a decision tree and direct feedback cycles together with a corresponding implementation. The user needs to provide only his general forecasting objectives. In several experiments and case studies based on real‐world workload traces, we show that our implementation of the approach provides continuous and reliable forecast results at run‐time. The results of this extensive evaluation show that the relative error of the individual forecast points is significantly reduced compared with statically applied forecasting methods, for example, in an exemplary scenario on average by 37%. In a case study, between 55 and 75% of the violations of a given service level objective can be prevented by applying proactive resource provisioning based on the forecast results of our implementation. Copyright © 2014 John Wiley & Sons, Ltd.

A Study of the Best Labor Practices in Selected Tourism-Oriented Establishments in the National Capital Region, Philippines

The philosophy that human resource is a company’s most valuable asset holds true in a service-oriented industry like tourism. However, this resource cannot be fully maximized towards the company’s growth when labor-related issues that deter a good work performance are mishandled.This descriptive and exploratory study primarily aims to provide information on how selected Metro Manila-based companies from five tourism-oriented sectors handle or manage labor issues through expert interviews with purposively sampled human resource managers and other human resources personnel handling supervisory positions in the following sectors: hotel, restaurant, transport, travel trade, and tourist sites.The research paradigm of this study shall be illustrated as follows:A labor problem starts from the relationship existing between management and employees/workers. It is given that there always exist gaps between management and labor. These identified gaps give rise to a lot of labor-related problems. Hence, there is a need for management to provide creative, proactive and effective intervention measures to minimize gaps, increase productivity and eventually promote industrial peace. These intervention measures may be considered as the best labor practices being utilized by selected companies within the tourism-oriented sector. Possible outcomes by reason of these intervention measures may yield to higher productivity, employee morale, and high savings.Data reveals the following as the leading problems encountered by the selected tourism-oriented establishments: absenteeism/tardiness, illegal dismissal/illegal suspension, pilferage/theft, dishonesty. The top critical factors that give rise to the above labor problems are as follows: Personal/Family/Health problems, Poverty/Economic Reasons, Lack of discipline, Weak Human Resources Department/Lack of support of management with the Human Resources Department. In summary, the following are the schemes employed by the selected tourism-oriented establishments in handling labor problems: Labor Management Council; Unions; Written reports/Incident Report/Committee Investigation; Grievance Machinery; Counseling/Coaching; Constant Dialogues with the Human Resources Department; General Assemblies/Departmental Meetings/Meetings with the President or General Manager; Employee councils to handle cases.In conclusion, ninety percent of the key informants agreed on the imperativeness of having a proactive and supportive Human Resource Department and an open-door policy with the company’s General Manager or President. A comprehensive background check before employment, a two-week orientation, training and immersion, regular performance evaluation, incentives based on merits, due process in cases of termination, strict adherence to civil service laws, rules and regulations, are among the other “best labor practices”.

Abnormal proactive and reactive cognitive control during conflict processing in major depression.

According to the Dual Mechanisms of Control framework, cognitive control consists of two complementary components: proactive control refers to anticipatory maintenance of goal-relevant information, whereas reactive control acts as a correction mechanism that is activated when a conflict occurs. Possibly, the well-known diminished inhibitory control in response to negative stimuli in Major Depressive Disorder (MDD) patients stems from a breakdown in proactive control, and/or anomalies in reactive cognitive control. In our study, MDD patients specifically showed increased response latencies when actively inhibiting a dominant response to a sad compared with a happy face. This condition was associated with a longer duration of a dominant ERP topography (800-900 ms poststimulus onset) and a stronger activity in the bilateral dorsal anterior cingulate cortex, reflecting abnormal reactive control when inhibiting attention to a negative stimulus. Moreover, MDD patients showed abnormalities in proactive cognitive control when preparing for the upcoming imperative stimulus (abnormal modulation of the contingent negative variation component), accompanied by more activity in brain regions belonging to the default mode network. All together, deficits to inhibit attention to negative information in MDD might originate from an abnormal use of both proactive resources and reactive control processes.

HRD Practices and Initiatives for Managing Talent in Public Sector Banks (PSBs)

In public sector banks, the HR department is more administrative in nature and less developmental and professional in its approach. As the paradigm is shifting the PSBs are undergoing a transition phase and struggling to cope up with the challenges of hiring, retaining, capacity building, motivation and developing leadership in pipeline. Human resource management department requires a systematic approach for acquisition, development and retention of human resources in order to achieve organisational objectives. A proactive human resource development department is the essence for the growth of the organisation and for the development of its employees. In a world that is moving towards a knowledge-based economy, HR systems are becoming an important source of competitive advantage in both manufacturing and service industries, thus demanding the adoption of correct practices in areas such as human resource (HR) selection, job design, performance appraisal, compensation, training and development and retaining and motivating them to deliver the result beyond their capability.

Proactive resource allocation optimization in LTE with inter-cell interference coordination

This paper presents a distributed, dynamic self-organizing network (SON) solution for downlink resources in an LTE network, triggered by support vector regression instances predicting various traffic loads on the nodes in the network. The proposed SON algorithm pro-actively allocates resources to nodes which are expected to experience traffic spikes before the higher traffic load occurs, as opposed to overloaded nodes reacting to a resource-exhaustion condition. In addition, the solution ensures inter-cell interference coordination is maintained across the LTE cells/sectors.

Proactive Resource Allocation: Harnessing the Diversity and Multicast Gains

This paper introduces the novel concept of proactive resource allocation for wireless networks, through which the predictability of user behavior is exploited to balance the wireless traffic over time, and significantly reduces the bandwidth required to achieve a given blocking/outage probability. We start with a simple model in which smart wireless devices are assumed to predict the arrival of new requests and submit them to the network <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$T$</tex></formula> time slots in advance. Using tools from large deviation theory, we quantify the resulting prediction diversity gain to establish that the decay rate of the outage event probabilities increases with the prediction duration <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$T$</tex> </formula> . Remarkably, we also show that, in the cognitive networking scenario, the appropriate use of proactive resource allocation by primary users improves the diversity gain of the secondary network at no cost in the primary network diversity. We also shed light on multicasting with predictable demands and show that proactive multicast networks can achieve a significantly higher diversity gain that scales superlinearly with <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$T$</tex></formula> . Finally, we conclude by a discussion of the new research questions posed under the umbrella of the proposed proactive wireless resource framework.

Categorisation of fire safety management: Results of a Delphi Panel

This paper exploits the concept of the Delphi Method where a panel of experts is drawn together to deliberate and decide on the subjective issue of ‘fire safety management’. The paper describes the process of setting up the Panel, presenting their credentials, results and conclusions. As their starting point, the Panel were presented with an existing model of fire safety management containing ten categories of fire safety management. They were asked to consider these categories to confirm or repudiate their relevance and their definition. The Panel's conclusions represent the collective subjective perception of individuals with relevant experience in the field of fire safety management.The research carries the potential to reduce the cost of fire by addressing the root cause of most structural fires; inadequate fire safety management. Linking statistical evidence gathered from fire incidents to each category regarding the characteristics of ignition and the type of property, would enable the creation of property profiles. Analysis of the profiles would enable predicting, forecasting and targeting of the most damaging profiles. This could result in efficiency savings, benefits to the community and the environment and allow the fire and rescue service to re-balance its reactive and proactive resources.

Network Virtualization for QoS-Aware Resource Management in Cloud Data Centers: A Survey

The increasing popularity of Cloud Computing is leading to the emergence of large virtualized data centers hosting increasingly complex and dynamic IT systems and services. Over the past decade, the efficient sharing of computational resources through virtualization has been subject to intensive research, while network management in cloud data centers has received less attention. A variety of network-intensive applications require QoS (Quality-of-Service) provisioning, performance isolation and support for flexible and efficient migration of virtual machines. In this paper, we survey existing network virtualization approaches and evaluate the extent to which they can be used as a basis for realizing the mentioned requirements in a cloud data center. More specifically, we identify generic network virtualization techniques, characterize them according to their features related to QoS management and performance isolation, and show how they can be composed together and used as building blocks for complex network virtualization solutions. We then present an overview of selected representative cloud platforms and show how they leverage the generic techniques as a basis for network resource management. Finally, we outline open issues and research challenges in the area of performance modeling and proactive resource management of virtualized data center infrastructures.

Moving towards Proactive Resource Provisioning in the Cloud Environment

IJCA is a computer science and electronics journal related with Theoretical Informatics, Quantum Computing, Software Testing, Computer Vision, Digital Systems, Pervasive Computing, Computational Topology etc.

Characterizing the psychological distress response before and after a cancer diagnosis

Psychological distress among cancer survivors is common. It is unknown if symptoms predate diagnosis or differ from patients without cancer because studies are limited to patient follow-up. Linked cohort (Wisconsin Longitudinal Study) and tumor registry records were used to assess the psychological distress response pre- to post-cancer diagnosis. Adjusted predicted probabilities of being in one of five categories of change for three psychological distress measures (depression, anxiety, well-being) were compared for participants diagnosed with cancer between 1993-1994 and 2004-2005 and participants without cancer (N=5,162). Cancer survivors were more likely to experience clinically significant increases (≥0.8 standard deviation) in depression (15, 95% CI=12-18%) and anxiety (19%, CI=16-22%) compared to their no-cancer counterparts (10%, CI=10-11%; 11%, CI=11-12%). Cancer survivors<5years from diagnosis were more likely to experience worsening depression. Survivors≥5years were more likely to experience worsening anxiety. No significant results were found for well-being. Characterizing the psychological distress response is a prerequisite for identifying at-risk patients and communicating expected symptoms, allowing for proactive resource provision.

Decentralized proactive resource allocation for maximizing throughput of P2P Grid

Peer-to-peer Desktop Grids provide integrated computational resources by leveraging autonomous desktop computers located at the edge of the Internet to offer high computing power. The arbitrary arrival and serving rates of tasks on peers impedes the high throughput in large-scale P2P Grids. We propose a novel autonomous resource allocation scheme, which can maximize the throughput of self-organizing P2P Grid systems. Our design possesses three key features: (1) high adaptability to dynamic environment by proactive and convex-optimal estimation of nodes’ volatile states; (2) minimized task migration conflict probability (upper bound can be limited to 2%) of over-utilized nodes individually shifting surplus loads; (3) a load-status conscious gossip protocol for optimizing distributed resource discovery effect. Based on a real-life user’s workload and capacity distribution, the simulation results show that our approach could get significantly improved throughput with 23.6–47.1% reduction on unprocessed workload compared to other methods. We also observe high scalability of our solution under dynamic peer-churning situations.

Donor-driven resource procurement for post-disaster reconstruction: Constraints and actions

Post-disaster reconstruction suffers bottlenecks and challenges due to the inadequacies of resource procurement. In the aftermath of the 2004 Indian Ocean tsunami, difficulties in acquiring resources compromised donors’ efforts in achieving a successful recovery. By drawing on in-field observations and surveys in Banda Aceh, Indonesia, this paper identifies the key factors that obstructed the process for NGOs to procure building materials and labour. The result demonstrates that donor-driven resource procurement was primarily impeded by (1) NGO-related factors including: NGOs competency of resource procurement and competition for resources among aid agencies; (2) external hurdles in NGOs implementing environment including: low local transportation and supply capacity, incompetence of contractor, and insufficient government support; and (3) community-related factors including: local housing culture and lack of community influence and participation. Continuous capacity building in NGOs, proactive resource assessment and planning, strengthening relationships with local community and institutions, together with a collaborative resourcing approach are likely to address resourcing constraints faced by NGOs when rebuilding communities following a disaster.

NursesforTomorrow: A Proactive Approach to Nursing Resource Analysis

This paper describes the background, development, implementation and utilization of NursesforTomorrow (N4T), a practical and comprehensive nursing human resources analysis method to capture regional, institutional and patient care unit-specific actual and predicted nurse vacancies, nurse staff characteristics and nurse staffing changes. Reports generated from the process include forecasted shortfalls or surpluses of nurses, percentage of novice nurses, occupancy, sick time, overtime, agency use and other metrics. Readers will benefit from a description of the ways in which the data generated from the nursing resource analysis process are utilized at senior leadership, program and unit levels to support proactive hiring and resource allocation decisions and to predict unit-specific recruitment and retention patterns across multiple healthcare organizations and regions.

EFFECTIVE CONSTRUCTION METHOD STATEMENTS AND TREE RETENTION

Summary Construction activity places pressure on retained trees. This pressure generally comes in the form of modifications of the soil environment. This can result in the direct loss of roots, or changes to the soil profile resulting in a decline in the quality of the root environment. BS5837:2005 recommendations for Trees in Relation to Construction, provides a framework for the integration of trees into the built environment. Circular 11/95 allows for Local Planning Authorities (LPA) to secure additional information by use of appropriate planning conditions. Increasingly, tree officers are requesting detailed Method Statements where they believe pressures may be placed on trees. A review has identified that there is little consistency to the content of these documents. There is often a lack of consistency from LPAs as to what circumstances will stimulate a requirement for a Method Statement. There is also a lack of consistency as to when they are required within the planning timeline. Effective Method Statements will primarily cover appropriate health & safety issues, placing planned operations within the context of measures to limit risks to employees and any impacts to retained trees. For larger construction projects, early consideration allows inclusion within the build contract. LPAs generally do not deploy proactive enforcement resources for ensuring on site adherence of approved Method Statements.

Investor Confidence and Organizational Downsizing in Financial Institutions

This paper aims innovative perspectives on strategy development in financial institutions to overcome the current global financial recession. Organizational restructuring is argued in reference to proactive and reactive financial and human resource strategies. It is observed that growth of many financial institutions is jeopardized due to primary market downfall, falling investor confidence, liquidity crisis, misjudged lending, high operational costs and blurring corporate image among markets. Authors suggest proactive and reactive measures which may be employed in responding to the current financial crisis. Among many proactive roll-on strategies, banks need to urge government to provide a guarantee that bonds and securities created out of mortgages or out of loans to companies do not become irrecoverable and seize yielding. Financial institutions may manage human resource during the period of economic crisis in a financial institution is to pursue defensive downsizing rather than strategic restructuring, as a result of both internal and external constraints on restructuring strategies.