Rohit Sachan

In current cyber age, almost all the services are being provided to users over the cloud. Many of these services need to be highly available. Since these services require databases, the databases themselves must be made highly available. Redundancy is the technique of doing this. For this purpose Remus and its adaptation RemusDB use complete virtual machine migration to a backup host which is completely transparent to DBMS. This approach incurs a lot of overhead as the amount of data transferred to the backup host is large and this transfer is done very frequently. Another system Postgres-R works only with PostgreSQL databases. This system replicates data without taking into account the number of requests for a particular table and the load on the servers. In this paper we proposed a solution which is based on another existing system Threshold Based File Replication (TBFR) and uses the current system load to achieve load distribution.

Software Effort Estimation (SEE) is an important activity during development and production of software projects. The estimated effort is directly associated with the various planning and financial activities. It is also directly associated with business success. Constructive Cost Model (COCOMO) is a widely accepted SEE model. But in the current development scenario, existing parameters of COCOMO don't give realistic results. In the recent past, many researchers improved the performance of COCOMO by optimizing the parameters with the help of various Nature-Inspired Algorithms (NIAs). In this paper, a recently proposed NIA which is based on the frog's anti-predator behavior is used for the optimizing the parameters of basic COCOMO for SEE of 18 software projects listed in NASA data set. The performance of the Anti-Predatory NIA (APNIA) based proposed approach is also evaluated on NASA18 software data set in terms of the Mean Absolute Error (MAE). The result obtained shows 93....

Fast, efficient, timely delivery of goods and optimal transportation cost are the major challenges in a logistics industry. A well-planned transportation system overcomes these challenges and reduces the operational and investment costs of a logistics company. This transportation system is based on the Warehouse-and-Distribution Center (WD and then allocates the DCs to warehouses in order to reduce the total logistics cost. Experimental evaluations are conducted on a real-life Warehouse Setup Problem (WSP) of 10 locations of Kanpur city, India. It reveals that the proposed Truck Transportation Cost based Model (TTCM) gives approximate 2%–10% more accurate overall logistics cost from the perspective of a logistics company.

Nature-inspired algorithms are commonly used for solving the various optimization problems. In past few decades, various researchers have proposed a large number of nature-inspired algorithms. Some of these algorithms have proved to be very efficient as compared to other classical optimization methods. A young researcher attempting to undertake or solve a problem using nature-inspired algorithms is bogged down by a plethora of proposals that exist today. Not every algorithm is suited for all kinds of problem. Some score over others. In this paper, an attempt has been made to summarize various leading research proposals that shall pave way for any new entrant to easily understand the journey so far. Here, we classify the nature-inspired algorithms as natural evolution based, swarm intelligence based, biological based, science based and others. In this survey, widely acknowledged nature-inspired algorithms namely- ACO, ABC, EAM, FA, FPA, GA, GSA, JAYA, PSO, SFLA, TLBO and WCA, have been studied. The purpose of this review is to present an exhaustive analysis of various nature-inspired algorithms based on its source of inspiration, basic operators, control parameters, features, variants and area of application where these algorithms have been successfully applied. It shall also assist in identifying and short listing the methodologies that are best suited for the problem.

In current cyber age, almost all the services are being provided to users over the cloud. Many of these services need to be highly available. Since these services require databases, the databases themselves must be made highly available. Redundancy is the technique of doing this. For this purpose Remus and its adaptation RemusDB use complete virtual machine migration to a backup host which is completely transparent to DBMS. This approach incurs a lot of overhead as the amount of data transferred to the backup host is large and this transfer is done very frequently. Another system Postgres-R works only with PostgreSQL databases. This system replicates data without taking into account the number of requests for a particular table and the load on the servers. In this paper we proposed a solution which is based on another existing system Threshold Based File Replication (TBFR) and uses the current system load to achieve load distribution.

The rise in the adoption of blockchain technology has led to increased illegal activities by cyber-criminals costing billions of dollars. Many machine learning algorithms are applied to detect such illegal behavior. These algorithms are often trained on the transaction behavior and, in some cases, trained on the vulnerabilities that exist in the system. In our approach, we study the feasibility of using metadata such as Domain Name (DN) associated with the account in the blockchain and identify whether an account should be tagged malicious or not. Here, we leverage the temporal aspects attached to the DNs. Our results identify 144930 DNs that show malicious behavior, and out of these, 54114 DNs show persistent malicious behavior over time. Nonetheless, none of these identified malicious DNs were reported in new officially tagged malicious blockchain DNs. Keywords-Blockchain, ML, Suspect Identification, Domain Name, Temporal properties

Nature-inspired algorithms (NIAs) have established their promising performance to solve both single-objective optimization problems (SOOPs) and multi-objective optimization problems (MOOPs). Anti-predatory NIA (APNIA) is one of the recently introduced single-objective algorithm based on the self-defense behavior of frogs. This paper extends APNIA as multi-objective algorithm and presents the first proposal of APNIA to solve MOOPs. The proposed algorithm is a posteriori version of APNIA, which is named as multi-objective anti-predatory NIA (MO-APNIA). It uses the concept of Pareto dominance to determine the non-dominated solutions. The performance of the MO-APNIA is established through the experimental evaluation and statistically verified using the Friedman rank test and Holm-Sidak test. MO-APNIA is also employed to solve a multi-objective variant of hub location problem (HLP) from the perspective of the e-commerce logistics. Results indicate that the MO-APNIA is also capable to fin...

Background: Hub Location Problem (HLP) deals with the long-term strategic decision planning in various application domains with the aim of reducing overall transportation cost. It deals with identifying hubs and allocating spokes to hubs in order to route the flow of goods between origin-destination locations. Due to the complex nature of the problem, meta-heuristic algorithms are best suited to solve HLPs. The existing algorithms face the accuracy and consistency related issues for solving the HLPs. Objective: This paper attempts to solve a variant of HLP, which is known as Uncapacitated Single Allocation p-Hub Location Problem with Fixed Cost (USApHLP-FC), using Anti-Predatory Nature-Inspired Algorithm (APNIA)to improve accuracy and consistency in results. Method: APNIA is a recently proposed meta-heuristic nature-inspired algorithm, which is based on the anti-predatory behavior of frogs .For solving the HLP, APNIA is used for both identifying the hubs and allocating the spokes to...

Background: Nature-Inspired Algorithms (NIAs) are the most efficient way to solve advanced engineering and real-world optimization problems. Since the last few decades, various researchers have proposed an immense number of NIAs. These NIAs get inspiration from natural phenomenon. A young researcher attempting to undertake or solve a problem using NIAs is bogged down by a plethora of proposals that exist today. Not every algorithm is suited for all kinds of problem. Some scores over others. Objective: This paper presents a comprehensive study of seven NIAs, which have new and unique inspirations. This study shall useful to easily understand the fundamentals of NIAs for any new entrant. Conclusion: Here, we classify the NIAs as natural evolution based, swarm intelligence based, biological based, science based and others. In this survey, well-establish and relatively new NIAs, namely- Shuffled Frog Leaping Algorithm (SFLA), Firefly Algorithm (FA), Gravitational Search Algorithm (GSA),...

Nature-Inspired Algorithms (NIAs) are one of the most efficient methods to solve the optimization problems. A recently proposed NIA is the anti-predatory NIA, which is based on the anti-predatory behavior of frogs. This algorithm uses five different types of self-defense mechanisms in order to improve its anti-predatory strength. This paper demonstrates the computation steps of anti-predatory for solving the Rastrigin function and attempts to solve 20 unconstrained minimization problems using anti-predatory NIA. The performance of anti-predatory NIA is compared with the six competing meta-heuristic algorithms. A comparative study reveals that the anti-predatory NIA is a more promising than the other algorithms. To quantify the performance comparison between the algorithms, Friedman rank test and Holm-Sidak test are used as statistical analysis methods. Anti-predatory NIA ranks first in both cases of “Mean Result” and “Standard Deviation.” Result measures the robustness and correctne...