Inefficient Resource Utilization Research Articles

Energy-efficient fuzzy-based approach for dynamic virtual machine consolidation

In cloud environments, the overload leads to performance degradation and Service Level Agreement (SLA) violation while underload results in inefficient utilisation of resources and needless energy consumption. Dynamic Virtual Machine (VM) consolidation is considered as an effective solution to deal with both overload and underload problems. However, dynamic VM consolidation is not a trivial solution as it can also lead to violation of negotiated SLA due to runtime overheads in VM migration. Further, dynamic VM consolidation approaches need to answer many questions such as (i) when to migrate a VM? (ii) which VM is to be migrated? and (iii) where to migrate the selected VM? In this work, efforts are made to develop a comprehensive approach to achieve better solution to above discussed problems. In the proposed approach, future forecasting methods for host overload detection are explored; a fuzzy logic based VM selection approach that enhances the performance of VM selection strategy is developed; and a VM placement algorithm based on destination CPU utilisation is also developed. The performance evaluation of the proposed approaches is carried out on CloudSim toolkit using PlanetLab data set. The simulation results have exhibited significant improvement in the number of VM migrations, energy consumption, and SLA violations.

A chemical tanker scheduling problem: Port of Houston case study

The objective of this study was to develop scheduling solutions for chemical vessels in a port. Chemical tankers carry multiple liquid cargos and have to visit multiple terminals to load/unload these cargos. The practice to date in scheduling these loading/unloading activities for the Port of Houston (PoH) has primarily involved manual methods, which result in several inefficiencies such as long waiting times and inefficient resource utilisation. This paper proposes three methods for tanker-scheduling problems, formulated as an open-shop scheduling problem with sequence-dependent post-set-up times: a mixed-integer programming (MIP); a constraint programming (CP); and a dispatching rule, longest processing time (LPT) scheduling heuristic. Overall, the results showed the MIP method to be a more suitable tool for hourly and daily scheduling, whereas the CP and the LPT heuristic proved suitable for longer-horizon scheduling problems (weekly, monthly, or semi-annual) and for use as real-time scheduling tools.

Horizontal Relation Identification Method to Handle Misalignment of Goals and Strategies Across Organizational Units

GQM+Strategies aligns business goals and strategies to help organizations achieve business objectives. Using the initial set of goals and strategies, GQM+Strategies creates a grid, through which the goals and strategies are linked throughout an organization by iteratively determining the lower-level goals and strategies. The GQM+Strategies creates consistency within a vertical refinement tree but cannot handle horizontal relationships from different branches. Hence, horizontal relationships lead to problems, such as redundant investments, inefficient resource utilization, and failure. Horizontal relation identification method (HoRIM) is proposed to handle horizontal relationships. The HoRIM is a grid modification approach that identifies differences between the initial GQM+Strategies grid and a model by interpretive structural modeling (ISM). Herein, we experimentally demonstrate that HoRIM finds about 1.5 times more horizontal relationships than an ad hoc review. Additionally, we show its practicality via a case study involving a real-world application.

Edge Computing Assisted Joint Quality Adaptation for Mobile Video Streaming

Most studies on adaptive streaming over Hypertext Transport Protocol (HTTP) have focused on improving the quality of experience (QoE) of clients by running the rate adaptation algorithm on the client side. In a cellular environment, this leads to inefficient resource utilization because of the lack of coordination between the competing clients. In cellular networks, the key challenge for HTTP adaptive streaming (HAS) is to optimize the conflicting video quality objectives. Edge cloud-assisted adaptive streaming presents an opportunity to optimize the quality of experience in cellular networks by moving the adaptation intelligence from the client to the edge cloud. HAS algorithms select the video quality based on the estimated throughput and playback buffer level. In this paper, we first present a joint throughput estimation method for HAS by taking advantage of mobile edge computing. Next, we present an optimized solution for multi-access edge computing (MEC)-assisted HAS by using edge cloud capabilities. Due to the non-deterministic polynomial-time hardness of the problem, we design a heuristic rate adaptation algorithm to jointly enhance the quality metrics of the competing clients. Our extension simulation results show that the proposed edge cloud-assisted rate adaptation algorithm outperforms the existing strategies under different client-side and server-side settings. Furthermore, we show that the proposed algorithm is promising under slow-moving and fast-moving environments.

Efficient Groupcast Schemes for Vehicle Platooning in V2V Network

Recently, groupcast has taken a lot of attention for vehicle platooning in vehicle-to-vehicle (V2V) network. In general, a traditional groupcast scheme by retransmission where source vehicle user equipment (S-VE) repeatedly transmits the same packet to destination VEs (D-VEs) is implemented in two ways; repetitive transmission for a predetermined number of times without receiving hybrid automatic repeat request (HARQ)-ACK/NACK from the D-VEs, and retransmission based on the HARQ-ACK/NACK. Although the former scheme is very easy to implement, it may lead to inefficient resource utilization due to excessive retransmissions. For the latter, the same problem arises when link quality between S-VE and D-VE is constantly poor. Hence, in this paper, we first investigate a heuristic scheme that can yield the superior performance of groupcast success rate with low computational complexity. Then, a groupcast scheme with the joint control of retransmission VE (R-VE) selection and time domain resource allocation is investigated by formulating a Markov decision process (MDP). The goal of the second proposed scheme is to find an optimal joint strategy for R-VE selection and time domain resource allocation that can minimize total time consumption while satisfying desired performance in terms of groupcast success rate. Simulation results validate that the proposed schemes significantly improve the groupcast success rate and the efficiency of time domain resource utilization, and greatly reduce the completion time of groupcast as compared with the two traditional groupcast schemes.

Efficient Resource Allocation and Utilization: The Missing Link in Nigeria’s Quest for Sustainable Development

Abstract This study examines resource allocation and utilization in Nigeria as the missing link in the nation’s quest for sustainable development. The main objective of the research is to examine how inefficient allocation and utilization of material, human and financial resources has made the attainment of sustainable development goals (SDGs) elusive in Nigeria. Given the abundant resources at the disposal of successive regimes in the country, it is difficult to explain why the citizens remain desperately poor with 70 percent living below poverty line. The excruciating poverty faced by the rural majority and the urban poor has led some people into indulging in unwholesome practices such as armed robbery, pipeline vandalism, prostitution, risky migrations, advanced fee fraud and other forms of social vices. The paper utilizes a qualitative design based on the interpretivist philosophy while the ontological orientation is subjectivism. Findings from the study indicate that there has been gross inefficiency in resource allocation and utilization in Nigeria due to corruption, ethnic prejudices, bad governance practices, absence of accountability, lack of transparency and wasteful spending on frivolous activities. Conclusion drawn from the study indicates that if proper project planning, execution, monitoring and evaluation are carried out in a transparent manner, and resources are efficiently allocated and utilized, Nigeria would be on the path to sustainable development. The paper therefore recommends that economic and technical efficiencies should be embraced in resource allocation and utilization for Nigeria to attain sustainable development goals.

Regulatory Role of Amino Acids in Pigs Fed on Protein-restricted Diets.

The high inclusion of dietary protein and the imbalance of amino acid (AA) composition in animal husbandry result in inefficient utilization of protein resources and increased nitrogen excretion. Therefore, an efficient approach to alleviate the nitrogen excretion and increase the utilization of protein resources is to formulate the AA-balance protein-restricted diet with crystalline AA supplementation. Nowadays, it is essential to thoroughly understand the regulatory mechanisms of AAs on body metabolism and their vital roles in protein-restricted diets. Besides, an establishment of the amino acid balanced protein-restricted diet system is beneficial for the maintenance of healthy environment and sustainable animal husbandry. This review focused on the recent advances on functional roles of AAs and development of a protein-restricted diet system in animal husbandry.

RACH Preamble Repetition in NB-IoT Network

NarrowBand-Internet of Things (NB-IoT) is a radio access technology recently standardized by 3GPP. To provide reliable connections with extended coverage, a repetition transmission scheme is applied in both Random Access CHannel (RACH) procedure and data transmission. In this letter, we model RACH in the NB-IoT network taking into account the repeated preamble transmission and collision using stochastic geometry. We derive the exact expression of RACH success probability under time correlated interference, and validate the analysis with different repetition values via independent simulations. Numerical results have shown that the repetition scheme can efficiently improve the RACH success probability in a light traffic scenario, but only slightly improves that performance with very inefficient channel resource utilization in a heavy traffic scenario.

Bandwidth-Guaranteed Resource Allocation and Scheduling for Parallel Jobs in Cloud Data Center

Cloud Computing has emerged as a powerful and promising way for running high performance computing (HPC) jobs. Most HPC jobs are designed under multi-processes paradigm and involve frequent communication and synchronization among parallel processes. However, as the underlying resources of cloud data centers are always shared among multiple tenants, the competition of jobs for limited bandwidth resources lead to unpredictable completion times for jobs in the cloud, which may lead to QoS violation and inefficient utilization of resources when scheduling parallel jobs in the cloud. To tackle the issue, it is essential to provide bandwidth guarantees for parallel jobs running in the cloud. Offering a dedicated virtual cluster (VC) for running applications in the cloud is a popular way to guarantee bandwidth demands. Motivated by these problems, in this paper, we firstly design a time-aware virtual cluster (TVC) request model for parallel jobs and consider how to embed requested TVCs of jobs into cloud efficiently under parallel job scheduling framework. An adaptive bandwidth-aware heuristic algorithm, which is denoted as AdaBa, is proposed to improve the job accept rate by adjusting the priorities of servers to accommodate the VMs of TVC adaptively according to the relative size of requested bandwidth demand. Then, a bandwidth-guaranteed migration and backfilling scheduling algorithm, which is denoted as BgMBF, is designed to schedule parallel jobs and the bandwidth demands are guaranteed by AdaBa. To obtain high job responsiveness performance, a bandwidth-reserved job backfilling strategy is designed when the requested TVC for current scheduled job cannot be allocated in the cloud. The migration cost of BgMBF is also considered and an enhanced version BgMBFSDF is then proposed to minimize the number of migration when the execution time of jobs are known. Through extensive simulation experiments on popular parallel workloads, our proposed TVC embedding algorithm AdaBa achieves up to 15 percent of improvement on accept rate compared with existing algorithms such as Oktupus and greedy algorithm. Our proposed BgMBF and BgMBFSDF also significantly outperform other popular scheduling algorithms integrated with AdaBa on average response time and average bounded slow down.

Evaluating the impact of optimization algorithms for patient transits dispatching using discrete event simulation

The on-time completion of patient transits can be identified as a bottleneck for the efficiency of health care services in major New Zealand hospitals. Delayed transits of patients between wards and treatment or diagnostic facilities lead to increasing waiting times at clinics and inefficient resource utilization (e.g. surgery teams) as appointment times are not met. Patient transits are carried out by orderlies, but in some cases require the assistance of a nurse. Ad-hoc dispatching of staff members, nurses and orderlies, to transits has been identified as one major source for delays currently observed in the system. To address this issue we present automated, optimized dispatching algorithms for staff members performing those transits. To develop these algorithms, a network formulation of the problem is introduced that is strongly related to classical vehicle routing problem with semi-soft time windows. However, the need to synchronize the routes of staff members of different types (nurses and orderlies) adds a whole new layer of complexity to the problem, as routes cannot be assessed independently. We present a set of algorithms with varying complexity, ranging from simple heuristics to the use of critical path methods to combine mixed integer formulations for the separated orderly and nurse problems. To address a transit service’s stochasticity, volatility and the resulting need for constant re-optimization, we embed the optimization algorithms in a discrete event simulation to evaluate their performance under realistic circumstances.

Interdependency Analysis of Inter-Site-Distance on Configuration of Handover Control Parameter in LTE-A HetNets

Handover (HO) is the procedure performed to maintain the ongoing session of mobile User Equipment (UE). In Long Term Evolution-Advanced (LTE-A) network, the UE-assisted, network controlled hard HO procedure is suggested by 3GPP. The adoption of the traditional HO mechanism in Heterogeneous Network (HetNet) concept of LTE-A imposes several challenges such as increase in frequent and unnecessary handover rate. This leads to complexity in network planning and maintenance resulting in inefficient resource utilization. Further, the increase in radio link and HO failures are caused due to high-speed UEs. To overcome these challenges, the HO control parameters such as Hysteresis Margin, Cell Specific Offsets, Frequency-Specific Offsets, A3offset and Time-To-Trigger need to be properly configured in consideration with environmental, network and UE characteristics. Hence in this research, an attempt has been made to investigate the impact of these control parameters on HO performance in HetNet environment. Three types of analysis that includes (1) the impact of Inter-Site-Distance, (2) the impact of offloading and (3) the impact of velocity are carried out. The HO performance in terms of HO success rate has been analyzed for Macro---Pico and Pico---Macro scenarios. The results have confirmed that the considered control parameters have a high impact on HO performance.

Implementing Barcode Medication Administration Systems in Public Sector Medical Units

Medication errors in healthcare have a high cost since it is one of the main causes of harming a patient; it leads to inefficient utilization of healthcare organization resources. The barcode medication administration system helps in improving the patients' safety. The purpose of this article is to determine preparatory needs for introducing a Barcoding Medication Administration System (BCMA) in the medical units in one of the largest tertiary hospital in Abu Dhabi City, United Arab Emirates. Analytical Hierarchical Process (AHP) has been employed to describe systematic decision-making by prioritizing different factors that affect the implementation of BCMA and how technology plays a role in helping to reduce or prevent human errors by promoting safety in the health care sectors. Five major domains are identified: leadership, technology, process, education, quality and safety. Leadership was found to be the most important factor oppositely of technology was the least important.

RALBA: a computation-aware load balancing scheduler for cloud computing

Cloud computing serves as a platform for remote users to utilize the heterogeneous resources in data-centers to compute High-Performance Computing jobs. The physical resources are virtualized in Cloud to entertain user services employing Virtual Machines (VMs). Job scheduling is deemed as a quintessential part of Cloud and efficient utilization of VMs by Cloud Service Providers demands an optimal job scheduling heuristic. An ideal scheduling heuristic should be efficient, fair, and starvation-free to produce a reduced makespan with improved resource utilization. However, static heuristics often lead to inefficient and poor resource utilization in the Cloud. An idle and underutilized host machine in Cloud still consumes up to 70% of the energy required by an active machine (Ray, in Indian J Comput Sci Eng 1(4):333–339, 2012). Consequently, it demands a load-balanced distribution of workload to achieve optimal resource utilization in Cloud. Existing Cloud scheduling heuristics such as Min–Min, Max–Min, and Sufferage distribute workloads among VMs based on minimum job completion time that ultimately causes a load imbalance. In this paper, a novel Resource-Aware Load Balancing Algorithm (RALBA) is presented to ensure a balanced distribution of workload based on computation capabilities of VMs. The RABLA framework comprises of two phases: (1) scheduling based on computing capabilities of VMs, and (2) the VM with earliest finish time is selected for jobs mapping. The outcomes of the RALBA have revealed that it provides substantial improvement against traditional heuristics regarding makespan, resource utilization, and throughput.

Multi-task transportation scheduling model with backhauls based on hub and spoke in collaborative logistics network

Hub and spoke collaborative logistics network has recently received considerable attention in terms of resource scheduling problem. In view of that problem such as low scale merit, inefficient resource utilization and conflict of interest between the operator and customer in the process of network operation, the paper seeks to formulate the scheduling strategy for multiple logistics tasks oriented to multi-origin and multi-destination on the basis of multiple-allocation-hybrid H&S collaborative network (including two layers). Taking the backhaul resource, resource departure time, capability limitation, task delay penalty, etc. into account, the aim of the problem is to minimize both total cost and total time for the task set. According to the characteristics of the problem, the immune genetic algorithm based on three-layer encoding mechanism is designed to solve the model. Moreover, the models and algorithms are validated by extensive results of numerical examples.

Novel Node-Ranking Approach and Multiple Topology Attributes-Based Embedding Algorithm for Single-Domain Virtual Network Embedding

Network virtualization (NV) is a promising approach to remove the ossification of current Internet. Virtual network embedding (VNE) is the key issue in NV which efficiently and effectively maps various of virtual networks (VNs), with different node and link resource requests, onto the shared substrate network(s) with finite underlying resources. Previous VNE algorithms in the literature are mostly heuristic. Single network topology attribute and each node’s local resources are assisted to rank nodes in most heuristic algorithms, leading to inefficient resource utilization of substrate network in the long run. To deal with this issue, we propose the network topology attribute and network resource-considered algorithm (VNE-NTANRC). The VNE-NTANRC algorithm adopts a novel node-ranking approach to rank all substrate and virtual nodes before embedding each given VN. The novel node-ranking approach has two subapproaches and considers five important network topology attributes and global network resources altogether. One subapproach is able to calculate all node values (NoV) directly. The other subapproach, stimulating from the Google PageRank website algorithm, enables to calculate NoVs in a stable state. Simulation results reveal that VNE-NTANRC algorithm outperforms typical and latest heuristic algorithms, only considering single network topology attribute and local resources.

Data-Driven Deployment and Cooperative Self-Organization in Ultra-Dense Small Cell Networks

Ultra-dense small-cell network is widely acknowledged as a key enabler for high capacity wireless networks. Some of the key challenges that ultra-dense networks face are profitable deployment distribution under complex traffic loads and efficient radio resource management (RRM) in excessive interference environments. Poor small-cell deployment locations can lead to excessive interference without clear profit margins and inefficient resource utilization. As such, data-driven small-cell deployment and self-organizing RRM of small-cell clusters are regarded as the two main technologies that can improve ultra-dense small-cell services. This paper first reviews the latest research in data-driven small-cell deployment using structured and unstructured social media data. A combination of irregular clustering techniques is used to identify hotspots, and natural language processing algorithms are used to identify blackspots. This paper then reviews recent advances in self-organization of small-cell RRM and analyzes how data can improve self-organization performance. Moreover, the idea of cooperative self-organization is introduced to further promote the self-organization capability. Finally, two ultra-dense small-cell RRM case studies are presented to demonstrate the performance which improves of cooperative self-organization.

A Rigorous Evaluation of State-of-the-Art Scheduling Algorithms for Cloud Computing

Cloud computing has recently been evolved in terms of the dynamic provision of computing resources to the users based on payment for usage on a pay-as-you-go basis. This provides feasibility to gain access to the large-scale and high-speed resources without establishing their own computing infrastructure to execute high-performance computing (HPC) applications. However, for the past several years, the efficient utilization of resources on a compute cloud has become a prime interest of the scientific community. One of the major causes behind inefficient resource utilization is the imbalance distribution of workload in a distributed computing. This paper contemplates the scheduling objectives of contemporary state-of-the-art heuristics to investigate their behavior to map HPC jobs to resources. Furthermore, the status of workload distribution in cloud computing is also critically assessed. A set of nine scheduling heuristics is validated in the CloudSim simulation environment. The potential of all the heuristics in terms of resource utilization is assessed by combining the workload balancing and machine-level load imbalance using different instances of benchmark scientific datasets (i.e., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Heterogeneous Computing Scheduling Problems</i> instances and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Google Cloud Jobs</i> dataset). The empirical assessment shows that it is not only an optimal solution to schedule the independent jobs on machines solely based on the execution time, throughput, and average resource utilization ratio; instead, the machine-level load balancing must also be considered to effectuate the usage of full capacity of computing power in a cloud system. Among all the heuristics, Resource-aware load balancing algorithm (RALBA) heuristic has outperformed, and it seems an optimal choice in terms of the tradeoff between complexity and the performance in terms of resource utilization and machine-level load balancing.

Fair Resource Allocation for System Throughput Maximization in Mobile Edge Computing

Communication resource allocation is important for improving the performance of users in mobile edge computing (MEC) scenarios. In existing studies, the users in the MEC system typically suffer from unfair resource allocation, which results in the inefficient resource utilization and degraded user performance. To address this challenge, in this paper we propose a fair resource allocation approach to maximize the overall network throughput, under the constraint of each mobile user's minimum transmission rate. We formulate the problem as a fair Nash bargaining resource allocation game, and the existence and uniqueness of the solution to this game model are analyzed. By adopting the time-sharing variable, we obtain the near optimal bargaining resource allocation strategy for the mixed integer nonlinear programming optimization. The user's priority is further considered in the iterative implementation of the proposed algorithm by considering the time delay constraint of users. Simulation results show that the proposed scheme outperforms existing methods in terms of resource allocation fairness and overall system throughput.

Dedicated Protection With Signal Overlap in Elastic Optical Networks

In optical dedicated path protection (1+1), the same optical signal is transmitted along both working and backup paths. Thus, in 1+1, the transmission parameters need to be configured according to the most impaired path, e.g., the backup one. This implies that the working signal is typically served with higher than necessary quality of transmission and occupies larger than necessary spectrum resources, leading to inefficient network resource utilization. In this study, we propose to apply the recently introduced signal overlap technique to improve network efficiency of optical dedicated path protection. The signal overlap technique enables uncorrelated optical signals to be superimposed along the same spectrum resources. It relies on a cancellation detection strategy also exploited in wireless communications and recently applied on coherent optical receivers. In particular, this study summarizes the key aspect and transmission performance of the overlap technique and discusses its implementation complexity. Then, two signal overlap schemes, namely, working signal overlap and working and backup signal overlap are introduced for effective optical dedicated path protection. An integer linear programming (ILP) formulation based on the routing, modulation, and spectrum allocation problem and an efficient heuristic are then presented to effectively assess the performance of the proposed overlap-based 1+1 protection strategy under various topologies and traffic profiles. In the considered simulative scenarios (e.g., network diameters well below a thousand kilometers), results show that the more efficient working and backup signal overlap scheme significantly improves the accepted load of dedicated protection requests.

LTSS: Load-Adaptive Traffic Steering and Forwarding for Security Services in Multi-Tenant Cloud Datacenters

Currently, different kinds of security devices are deployed in the cloud datacenter environment and tenants may choose their desired security services such as firewall and IDS (intrusion detection system). At the same time, tenants in cloud computing datacenters are dynamic and have different requirements. Therefore, security device deployment in cloud datacenters is very complex and may lead to inefficient resource utilization. In this paper, we study this problem in a software-defined network (SDN) based multi-tenant cloud datacenter environment. We propose a load-adaptive traffic steering and packet forwarding scheme called LTSS to solve the problem. Our scheme combines SDN controller with TagOper plug-in to determine the traffic paths with the minimum load for tenants and allows tenants to get their desired security services in SDN-based datacenter networks. We also build a prototype system for LTSS to verify its functionality and evaluate performance of our design.