With the growth in computing technologies, cloud computing has added a new paradigm to user services that allows accessing Information Technology services on the basis of pay-per-use at any time and any location. Owing to flexibility in cloud services, numerous organizations are shifting their business to the cloud and service providers are establishing more data centers to provide services to users. However, it is essential to provide cost-effective execution of tasks and proper utilization of resources. Several techniques have been reported in the literature to improve performance and resource use based on load balancing, task scheduling, resource management, quality of service, and workload management. Load balancing in the cloud allows data centers to avoid overloading/underloading in virtual machines, which itself is a challenge in the field of cloud computing. Therefore, it becomes a necessity for developers and researchers to design and implement a suitable load balancer for parallel and distributed cloud environments. This survey presents a state-of-the-art review of issues and challenges associated with existing load-balancing techniques for researchers to develop more effective algorithms.

Issues and Challenges of Load Balancing Techniques in Cloud Computing: A Survey

An uncertain Z-number multicriteria group decision-making method with cloud models

Multilabel feature selection using ML-ReliefF and neighborhood mutual information for multilabel neighborhood decision systems

Diabetes and cancer are two major life-threatening human chronic disorders that have a high rate of disability and mortality. These diseases have been diagnosed using different deterministic and nature inspired computing algorithms. Here, an effort is made to represent the role of five different insect-based nature inspired computing algorithms [ant colony optimization (ACO), artificial bee colony (ABC), glow-worm swarm optimization (GSO), firefly algorithm (FA) and antlion optimization (ALO)] used for the diagnosis of these two chronic disorders. Initially, the basic statistics of diabetes and cancer patients have been presented. The main intention of this study lies in exploring the usage and performance of ACO, ABC, GSO, FA and ALO in diagnosing different stage and types of diabetes and cancer. It has been revealed that most of the diabetes diagnosis work has been carried out using ACO followed by ABC. As far as cancer is concerned, the three insect-based algorithms, i.e.
 ACO, ABC and FA, have been also effectively employed for detection of breast, lung, liver, prostate and ovarian cancer. In general, most of the disease diagnostic work has been carried out using ACO, whereas GSO found to be least explored. The rate of predictive accuracy achieved using the hybridization of ACO and neural network is found to be more promising as compared to other individual or hybrid approaches. Likewise, for breast cancer, the amalgamated use of ABC and neural network is more productive. Similarly, the hybrid approach of ACO and neural network is also found useful for early prognosis of lung and gastric cancer. In general, the diagnostic results obtained using hybrid approaches are more promising than their individual use. However, several hybrid combinations are still needed to be explored for the diagnosis of diabetes and different types of cancer, viz. liver, gastric, ovarian, leukaemia as well as a brain tumour. Finally, there is also a scope to use and explore the efficiency of binary and chaotic variants of ACO, ABC, GSO, FA and ALO for the diagnosis of these two critical human disorders.

A comprehensive review on nature inspired computing algorithms for the diagnosis of chronic disorders in human beings

Today’s modern decision-making problem is designated by not being the most effective fuzziness; however, additionally partial reliability also plays a crucial role. The incomplete and unreliable information may also affect the selection maker to earn inaccurate decisions, ensuing in monetary losses and wastes of resources. Thus, it is vital to describe the reliability of the facts. To cope with it entirely, a notion of Z-number, i.e., a pair of fuzzy sets modeling a probability-qualified fuzzy statement, is the most suitable medium to access it. In this paper, we spout a new technique to measure the uncertainty of discrete Z-numbers based on Shannon entropy. In the given approach, by using characteristics of Z-number, all the potential probability distributions are estimated by the maximum entropy method. Then, a new fuzzy subset of the Z-number is formed based on the probability distributions and the membership functions of the fuzzy number. Finally, the centroid of the formulated set is determined to rank the degree of the uncertainty of Z-number. The applicability of the delivered approach is read with some numerical examples related to the decision-making process.

Haihuan Jiang

Papers

On computing generalized Hukuhara differences of Z-numbers

Hierarchical multi-UAVs task assignment based on dominance rough sets