Faculty Publications
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Publications by NITK Faculty
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Item Optimized Distributed Job Shop Scheduling Using Balanced Job Allocation and Modified Ant Colony Optimization(Springer Science and Business Media Deutschland GmbH, 2022) Vivek, S.; Rakesh, K.; Mohan, B.R.Many challenges are being faced by the manufacturing industry: ensuring profitable growth, reducing costs, increasing productivity, and giving quick responses to customers. To become more productive, reduce transportation costs, and reduce bottleneck on a single factory, industrial companies are shifting from single to distributed systems. Scheduling problems like distributed job shop, distributed flow shop, and distributed process planning are becoming a popular field to study. We try to solve the distributed job shop scheduling problem (DJSP) where the allocation of jobs to different factories needs to be done and additionally, the determination of good operation schedules for each factory. The goal of DJSP is to minimize the makespan over all the factories. To solve this problem, we first use a method of allocating jobs to factories to evenly distribute the workloads among all the factories. Later, we use a bio-inspired algorithm on each factory after the allocations, namely ant colony optimization to get a solution that is close to the most optimal solution. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Survey on meta heuristic optimization techniques in cloud computing(Institute of Electrical and Electronics Engineers Inc., 2016) Shishira, S.R.; Kandasamy, A.; Chandrasekaran, K.Cloud computing has become a buzzword in the area of high performance distributed computing as it provides on demand access to shared resources over the Internet in a self-service, dynamically scalable and metered manner. To reap its full benefits, much research is required across a broad array of topics. One of the important research issues which need to be focused for its efficient performance is scheduling. The goal of scheduling is to map the job to resources that optimize more than one objectives. Scheduling in cloud computing belongs to a category of problems known as NP-hard problem due to large solution space and thus it takes long time to find an optimal solution. In cloud environment, it is best to find suboptimal solution, but in short period of time. Metaheuristic based techniques have been proved to achieve near optimal solutions within reasonable time for such problems. In this paper, we provide an extensive survey on optimization algorithms for cloud environments based on three popular metaheuristic techniques: Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and a novel technique: League Championship Algorithm (LCA). © 2016 IEEE.Item Combined approach based on ACO with MTSP for optimal internal electrical system design of large offshore wind farm(Institute of Electrical and Electronics Engineers Inc., 2018) Srikakulapu, R.; Vinatha Urundady, U.The wind turbine (WT) layout and electrical system layout plays a vital role in offshore wind farm (OWF) design. The wake effect has a significant impact on the power production of the OWF. WTs in wake region will not experience healthy wind; hence it affects the power production. The proper placement of WTs can reduce the wake effect in OWF. The optimal design of large OWF is based on combined approach of Ant colony optimization (ACO) with multiple travelling salesmen problem (MTSP) is presented. The objectives of the approach are to improve power production, minimize the length of cable and cable cost. By considering (a) placement of WT with consideration of wake effect, (b) placement of substation, (c) selection of submarine cables with higher reliability and minimal power loss, and (d) minimum length of WT cable routing with zero cross connection. ACO-MTSP approach is applied on large OWF connected with 280 WTs and results are compared with the outcome of reference OWF. © 2018 IEEE.Item An efficient cost optimized scheduling for spot instances in heterogeneous cloud environment(Elsevier B.V., 2018) Domanal, S.; Guddeti, G.In this paper, we propose a novel efficient and cost optimized scheduling algorithm for a Bag of Tasks (BoT) on Virtual Machines (VMs). Further, in this paper, we use artificial Neural Network to predict the future values of Spot instances and then validate these predicted values with respect to the current (actual) values of Spot instances. On-Demand and Spot are the key instances which are procured by the cloud customers and hence, in this paper, we use these instances for the cost optimization. The key idea of our proposed algorithm is to efficiently utilize the cloud resources (mainly VMs instances, Central Processing Unit (CPU) and Memory) and also to optimize the cost of executing the BoT in the heterogeneous Infrastructure as a Service (IaaS) based cloud environment. Experimental results demonstrate that our proposed scheduling algorithm outperforms state-of-the-art benchmark algorithms (Round Robin, First Come First Serve, Ant Colony Optimization, Genetic Algorithm, etc.) in terms of Quality of Service (QoS) parameters (Reliability, Time and Cost) while executing the BoT in the heterogeneous cloud environment. Since the obtained results are in the form of ordinal, hence we carried out the statistical analysis on both predicted and actual Spot instances using the Spearman's Rho Test. © 2018 Elsevier B.V.Item Optimized design of collector topology for offshore wind farm based on ant colony optimization with multiple travelling salesman problem(Springer Heidelberg, 2018) Srikakulapu, R.; Vinatha Urundady, U.A layout of the offshore wind farm (OSWF) plays a vital role in its capital cost of installation. One of the major contributions in the installation cost is electrical collector system (ECS). ECS includes: submarine cables, number of wind turbines (WTs), offshore platforms etc. By considering the above mentioned problem having an optimized design of OSWF provides the better feasibility in terms of economic considerations. This paper explains the methodology for optimized designing of ECS. The proposed methodology is based on combined elitist ant colony optimization and multiple travelling salesman problem. The objective is to minimize the length of submarine cable connected between WTs and to minimize the wake loss in the wind farm in order to reduce the cost of cable and cable power loss. The methodology is applied on North Hoyle and Horns Rev OSWFs connected with 30 and 80 WTs respectively and the results are presented. © 2018, The Author(s).Item A Hybrid Bio-Inspired Algorithm for Scheduling and Resource Management in Cloud Environment(Institute of Electrical and Electronics Engineers, 2020) Domanal, S.G.; Guddeti, R.M.R.; Buyya, R.In this paper, we propose a novel HYBRID Bio-Inspired algorithm for task scheduling and resource management, since it plays an important role in the cloud computing environment. Conventional scheduling algorithms such as Round Robin, First Come First Serve, Ant Colony Optimization etc. have been widely used in many cloud computing systems. Cloud receives clients tasks in a rapid rate and allocation of resources to these tasks should be handled in an intelligent manner. In this proposed work, we allocate the tasks to the virtual machines in an efficient manner using Modified Particle Swarm Optimization algorithm and then allocation / management of resources (CPU and Memory), as demanded by the tasks, is handled by proposed HYBRID Bio-Inspired algorithm (Modified PSO + Modified CSO). Experimental results demonstrate that our proposed HYBRID algorithm outperforms peer research and benchmark algorithms (ACO, MPSO, CSO, RR and Exact algorithm based on branch-and-bound technique) in terms of efficient utilization of the cloud resources, improved reliability and reduced average response time. © 2008-2012 IEEE.Item CoMCLOUD: Virtual Machine Coalition for Multi-Tier Applications over Multi-Cloud Environments(Institute of Electrical and Electronics Engineers Inc., 2023) Addya, S.K.; Satpathy, A.; Ghosh, B.C.; Chakraborty, S.; Ghosh, S.K.; Das, S.K.Applications hosted in commercial clouds are typically multi-tier and comprise multiple tightly coupled virtual machines (VMs). Service providers (SPs) cater to the users using VM instances with different configurations and pricing depending on the location of the data center (DC) hosting the VMs. However, selecting VMs to host multi-tier applications is challenging due to the trade-off between cost and quality of service (QoS) depending on the placement of VMs. This paper proposes a multi-cloud broker model called CoMCLOUD to select a sub-optimal VM coalition for multi-tier applications from an SP with minimum coalition pricing and maximum QoS. To strike a trade-off between the cost and QoS, we use an ant-colony-based optimization technique. The overall service selection game is modeled as a first-price sealed-bid auction aimed at maximizing the overall revenue of SPs. Further, as the hosted VMs often face demand spikes, we present a parallel migration strategy to migrate VMs with minimum disruption time. Detailed experiments show that our approach can improve the federation profit up to 23% at the expense of increased latency of approximately 15%, compared to the baselines. © 2013 IEEE.Item Geo-Distributed Multi-Tier Workload Migration Over Multi-Timescale Electricity Markets(Institute of Electrical and Electronics Engineers Inc., 2023) Addya, S.K.; Satpathy, A.; Ghosh, B.C.; Chakraborty, S.; Ghosh, S.K.; Das, S.K.Virtual machine (VM) migration enables cloud service providers (CSPs) to balance workload, perform zero-downtime maintenance, and reduce applications' power consumption and response time. Migrating a VM consumes energy at the source, destination, and backbone networks, i.e., intermediate routers and switches, especially in a Geo-distributed setting. In this context, we propose a VM migration model called Low Energy Application Workload Migration (LEAWM) aimed at reducing the per-bit migration cost in migrating VMs over Geo-distributed clouds. With a Geo-distributed cloud connected through multiple Internet Service Providers (ISPs), we develop an approach to find out the migration path across ISPs leading to the most feasible destination. For this, we use the variation in the electricity price at the ISPs to decide the migration paths. However, reduced power consumption at the expense of higher migration time is intolerable for real-time applications. As finding an optimal relocation is $\mathcal {NP}$NP-Hard, we propose an Ant Colony Optimization (ACO) based bi-objective optimization technique to strike a balance between migration delay and migration power. A thorough simulation analysis of the proposed approach shows that the proposed model can reduce the migration time by 25%-30% and electricity cost by approximately 25% compared to the baseline. © 2008-2012 IEEE.