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Load unbalancing problem is a multi-variant, multi-constraint problem that degrades performance and efficiency of computing resources. Load balancing techniques cater the solution for load unbalancing situation for two undesirable facets- overloading and under-loading. In contempt of the importance of load balancing techniques to the best of our knowledge, there is no comprehensive, extensive, systematic and hierarchical classification about the existing load balancing techniques. Further, the factors that cause load unbalancing problem are neither studied nor considered in the literature. This paper presents a detailed encyclopedic review about the load balancing techniques. The advantages and limitations of existing methods are highlighted with crucial challenges being addressed so as to develop efficient load balancing algorithms in future. The paper also suggests new insights towards load balancing in cloud computing.

With the growing demand and commercial availability of cloud services, the need for comparison of their functionality against different prices and performance has arisen. A relevant and fair comparison is still challenging due to diverse deployment options and dissimilar features of different services. This paper addresses a hybrid multi-criteria decision-making model involving the selection of cloud services among the available alternatives. The proposed methodology assigns various ranks to cloud services based on the quantified quality-of-service parameters using a novel extended Grey Technique for Order of Preference by Similarity to Ideal Solution integrated with analytical hierarchical process. Further, we analyse the proposed cloud service selection method in terms of sensitivity analysis, adequacy under change in alternatives, adequacy to support group decision-making, and handling of uncertainty. This analysis helps both researchers and practitioners for analysing more fruitful approaches for cloud service selection.

Despite the increasing interest toward environmental issues in the freight transport and logistics service sector, a comprehensive and updated assessment of the existing literature is still missing. This paper aims to fill this gap by presenting the results of a systematic literature review of publications in the area of environmental sustainability in third-party logistics service providers (3PLs) between the years 2000 and 2016. The review offers insight into the critical dimensions of green matters in transport and logistics service companies using an analytical framework based on the following five topic areas: influencing factors, green actions and the impact on performance, Information and Communication Technology (ICT) tools supporting the green actions, energy efficiency in road freight transport and shipper’s perspective and collaboration. The results indicate that, despite the number of published papers having grown significantly from 2008 onward, some areas remain highly under-researched such as ICT and performance measurement. Several research gaps have been identified in each topic area, and a set of propositions forming an agenda for future research directions has been suggested.

This article explores the applicability of a model of migration from the human geography literature as unifying, theoretical framework for understanding consumers' service provider switching behaviors. Survey data from approximately 700 consumers are used to examine the usefulness of the push, pull, and moorings (PPM) migration model. The PPM migration model performs better than an alternative model; all three categories of antecedents to switching (migration) - push, pull, and mooring variables - have significant direct, and some moderating, effects on switching intentions. [PUBLICATION ABSTRACT]

Since 1990s, the world has seen a lot of advances in providing humanitarian aid through sophisticated logistics operations. The current consensus seems to be that humanitarian relief organizations (HROs) can improve their relief operations by collaborating with logistics service providers (CLSPs) in the commercial sector. The question remains: how can HROs select the most appropriate CLSP for disaster preparation? Despite its practical significance, no explicit effort has been done to identify the criteria/factors in prioritizing and selecting a CLSP for disaster relief. The present study aims to address this gap by consolidating the list of criteria from a socio-technical systems (STS) perspective. Then, to handle the interdependence among the criteria derived from the STS, we develop a hybrid multi-criteria decision making model for CLSP selection in the disaster preparedness stage. The proposed model is then evaluated by a real-life case study, providing insights into the decision-makers in both HROs and CLSPs.

Smart cities need energy-efficient and low-emission transportation for people and goods. Most studies focus on sustainable urban-transportation systems for passengers. Freight transportation in cities has increased significantly during the COVID-19 pandemic, leading to greenhouse gases emissions and negative externalities, such as traffic congestion. The purpose of this paper is to identify through a systematic literature review which innovations (hardware and software) applied by logistics service providers (LSPs) in sustainable urban freight (SUF) are suitable to support the transition to energy-efficient smart cities. We propose to classify the existing innovations in last-mile delivery for SUF into categories: (1) urban freight consolidation and/or trans-shipment; (2) the Consumer as a Service Provider (CaaSP); (3) choice of transportation modes. We introduce the concept of CaaSP as an innovative solution in last-mile delivery (LMD), where customers take over some transport operations with the use of smart technologies, and thus reduce the energy demand. We consider the modes of transportation, such as: drones, autonomous delivery robots, autonomous vehicles, cargo bikes (including e-cargo bikes, e-tricycles), electric vehicles (mainly vans), and combined passenger-and-cargo transportation rapid-transit systems. From the analyzed dataset, we find that energy-efficiency in smart cities can be improved by the consolidation of parcels in micro-depots, parcel lockers, and mobile depots. We analyze smart technologies (the Internet of things, big data, artificial intelligence, and digital twins), which enable energy efficiency by reducing the energy demand (fuel) of SUF, due to better operational planning and infrastructure sharing by logistics service providers. We propose a new IEE matrix as an actionable tool for the classification of innovations applied by LSPs in SUF, according to the level of their interconnectivity and energy efficiency. Additionally, this paper contributes to the theory by exploring possible future research directions for SUF in energy-efficient smart cities.

Cloud computing is a model for on-demand delivery of IT resources (e.g., servers, storage, databases, etc.) over the Internet with pay-as-you-go pricing. Although it provides numerous benefits to cloud service users (CSUs) such as flexibility, elasticity, scalability, and economies of scale, there is a large trust deficit between CSUs and cloud service providers (CSPs) that prevents the widespread adoption of this computing paradigm. While some businesses have slowly started adopting cloud computing with careful considerations, others are still reluctant to migrate toward it due to several data security and privacy issues. Therefore, the creation of a trust model that can evolve to reflect the true assessment of CSPs in terms of either a positive or a negative reputation as well as quantify trust level is of utmost importance to establish trust between CSUs and CSPs. In this paper, we propose a fuzzy-logic based approach that allows the CSUs to determine the most trustworthy CSPs. Specifically, we develop inference rules that will be applied in the fuzzy inference system (FIS) to provide a quantitative security index to the CSUs. One of the main advantages of the FIS is that it considers the uncertainties and ambiguities associated with measuring trust. Moreover, our proposed fuzzy-logic based trust model is not limited to the CSUs as it can be used by the CSPs to promote their services through self-evaluation. To demonstrate the effectiveness of our proposed fuzzy-based trust model, we present case studies where several CSPs are evaluated and ranked based on the security index.

Efficient resource identification and discovery is the primary requirements for cloud computing services, as it assists in scheduling and managing of cloud applications. Cloud computing is a revolution of the economic model rather than technological. It takes advantage of several technologies that were tested and modified by replacing the local use of computers with centralized shared resources that are managed and stored by Cloud Service Providers (CSPs) in a transparent manner for Cloud Consumers (CCs). With this new use, various cloud services have appeared and it is mainly classified into three broad categories i.e., Infrastructure as a service (IaaS), Software as a service (SaaS) and Platform as a service (PaaS). Each of these cloud services provides several benefits to the CCs through their respective Quality of Service (QoS) metric. Among the cloud service models, most of the QoS attribute and metric are identical and some are different. The vendors of cloud have focused their objectives on the development of scalability, resource consumption and performance, other characteristics of cloud have been ignored. While CSPs face challenging difficulties in publishing cloud services that displays their cloud resources, at the same time CCs do not have standard mechanism for cloud resource discovery, automated cloud services selection, and easy use of cloud services. In this frame, this paper puts forward a set of QoS metric for SaaS, IaaS, PaaS services and propose (i) An efficient algorithm for identifying the cloud services based on the QoS metric given by the cloud consumer using decision tree classification algorithm (ii) An efficient algorithm for Cloud service resource registry which aims to enable CSPs to register their services with its QoS attributes and (iii) A Cloud service resource discovery that search for the suitable cloud service and their attributes in the cloud service registry that meets the CCs application requirements using Split and Cache (SAC) algorithm. Our new approach makes the provisioning of cloud service possible by ease of resource identification, publication, discovery based on dynamic QoS attributes via web GUI interface backed by series of test that has validated and the proposed approach is feasible and sound. The recommended solution is important: instead of putting effort in locating, learning about the services and evaluating them, CCs can easily identify, discover the services, select and use the required cloud resources. The efficiency of our algorithms was assessed through experiments using CloudSim, which primarily decreases the response time, CPU utilization and memory consumption for identifying and searching the cloud services and increases the accuracy of the CSPs list retrieved along with their QoS attributes.

This article describes how nowadays, cloud computing is one of the advanced areas of Information Technology (IT) sector. Since there are many hackers and malicious users on the internet, it is very important to secure the confidentiality of data in the cloud environment. In recent years, access control has emerged as a challenging issue of cloud computing. Access control method allows data accessing of an authorized user. Existing access control schemes mainly focus on the confidentiality of the data storage. In this article, a novel access control scheme has been proposed for efficient data accessing. The proposed scheme allows reducing the searching cost and accessing time, while providing the data to the user. It also maintains the security of the user's confidential data.