Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Achieving operational efficiency with cloud based services(2011) Bellur, K.V.; Krupal, M.; Jain, P.; Raghavendra, P.S.Cloud Computing is the evolution of a variety of technologies that have come together to alter an organization's approach to building IT infrastructure. It borrows from several computing techniques - grid computing, cluster computing, software-as-a-service, utility computing, autonomic computing and many more. It provides a whole new deployment model for enterprise web-applications. The cloud proposes significant cost cuts when compared to using an internal IT infrastructure. The "pay for what you use" model of cloud computing is significantly cheaper for a company than the "pay for everything up front" model of internal IT. Hardware Virtualization is the enabling technology behind many of the cloud infrastructure vendor offerings. Through virtualization, a physical server can be partitioned into any number of virtual servers running their own operating systems, in their allocated memory, CPU and disk footprints. From the perspective of the user or application on the virtual server, no indication exists to suggest that the server is not a real, physical server. In this paper, we make an attempt to enhance dynamic cloud based services using efficient load balancing techniques. We describe various steps involved in developing and utilizing cloud based infrastructure in such a way that cloud based services can be offered to users in an efficient manner. In the design of load balancing algorithms for an application offering cloud based services, the various details described in this paper offer useful insight, while the actual implementation may be based on the exact requirements at hand. © 2011 IEEE.Item A novel proposal to effectively combine multipath data forwarding for data center networks with congestion control and load balancing using Software-Defined Networking Approach(Institute of Electrical and Electronics Engineers Inc., 2014) Mallik, A.; Hegde, S.Modern data center networks (DCNs) often use multi-rooted topologies, which offer multipath capability, for increased bandwidth and fault tolerance. However, traditional routing algorithms for the Internet have no or limited support for multipath routing, and cannot fully utilize available bandwidth in such DCNs. As a result, they route all the traffic through a single path, and thus form congestion. Multipath (MP) routing might be a good alternative, but is not sufficient alone to handle congestion that comes from the contention of end stations. Dynamic load balancing, on the other hand, protects the network from sudden congestions which could be caused by load spikes or link failures. However, little work has been done to incorporate all these features in a single and comprehensive solution for Data Center Ethernet (DCE). In this paper, we propose a novel method that attempts to integrate dynamic load balancing, multi-path scheme with congestion control (CC), with the use of pure Software-Defined-Networking (SDN) approach. SDN decouples control plane from the data forwarding plane, which reduces the overheads of the network switches. The major objectives that our solution attempts to achieve are, efficient utilization of network resources, high throughput and minimal frame loss. © 2014 IEEE.Item A novel bio-inspired load balancing of virtualmachines in cloud environment(Institute of Electrical and Electronics Engineers Inc., 2015) Ashwin, T.S.; Domanal, S.G.; Guddeti, G.R.M.Load Balancing plays an important role in managing the software and the hardware components of cloud. In this present scenario the load balancing algorithm should be efficient in allocating the requested resource and also in the usage of the resources so that the over/underutilization of the resources will not occur in the cloud environment. In the present work, the allocation of all the available Virtual Machines is done in an efficient manner by Particle Swarm Optimization load balancing algorithm. Further, we have used cloudsim simulator to compare and analyze the performance of our algorithm. Simulation results demonstrate that the proposed algorithm distributes the load on all the available virtual machines uniformly i.e, without any under/over utilization and also the average response time is better compared to all existing scheduling algorithms. © 2014 IEEE.Item Load Balancing in Cloud Environment Using a Novel Hybrid Scheduling Algorithm(Institute of Electrical and Electronics Engineers Inc., 2016) Domanal, S.; Guddeti, G.R.We propose a hybrid scheduling algorithm for load balancing in a distributed environment by combining the methodology of Divide-And-Conquer and Throttled algorithms referred to as DCBT. Our algorithm plays an important role in distributing the incoming load in an efficient manner so that it maximizes resource utilization in a cloud environment. Further, load balancer plays an important role in cloud environment by assigning incoming tasks to Virtual Machines (VM) intelligently. The main aim of the proposed DCBT is to reduce the total execution time of the tasks and thereby maximizing the resource utilization. Further, the proposed DCBT algorithm is analyzed using Cloud Sim simulator and also in customized distributed environment using python. Experimental results demonstrate that the proposed algorithm gives better efficiency in both Cloud Sim and customized environments. The proposed DCBT utilizes the Virtual Machines more efficiently while reducing the execution time of the tasks allocated to Request Handlers (RH) by 9.972% in comparison to the Modified Throttled algorithm. © 2015 IEEE.Item Distributed Adaptive Video Streaming using Inter-Server Data Distribution and Agent-based Adaptive Load Balancing(Institute of Electrical and Electronics Engineers Inc., 2020) Bhowmik, M.; Raghunandan, A.; Rudra, B.As the number and hours of videos available within an organisation increases, as well as it's demand, the need for fast video streaming applications arises. Cloud based services are not cost effective and are not an ideal choice for storing the ever-increasing video data that is usually stored and used only within a particular organisation, like a University. Hence, this paper proposes a web based system design to store and stream videos at a small-scale within an organisation. To improve the video viewing experience for the user, the system is flexible to handle sudden changes, like increase in number of requests. The system requires the use of a cluster of servers to deliver the content as a single server cannot handle the load as number of requests increases. This requires effective load distribution among the servers. This paper proposes a way to design this system for adaptive video streaming. This system is highly scalable and can handle high loads, i.e. a higher number of users connecting to the application simultaneously. This paper proposes an algorithm called inter-server load balancing algorithm with Adaptive Agent-based load balancing to solve this problem. The algorithms also incorporates dynamic video resolution delivery techniques to ensure smooth viewing experience in the whole user experience irrespective of the network speed and bandwidth. © 2020 IEEE.Item Dynamic Checkpointing: Fault Tolerance in High-Performance Computing(Institute of Electrical and Electronics Engineers Inc., 2024) Bhowmik, B.; Verma, T.; Dineshbhai, N.D.; Reddy, M.R.V.; Girish, K.K.Parallel computing has become a cornerstone of modern computational systems, enabling the rapid processing of complex tasks by utilizing multiple processors simultaneously. However, the efficiency and reliability of these systems can be significantly compromised by inherent challenges such as hardware failures, communication delays, and uneven workload distribution. These issues not only slow down computations but also threaten the dependability of applications reliant on parallel processing. To address these challenges, researchers have developed strategies like dynamic checkpointing and load balancing, which are crucial for enhancing fault tolerance and optimizing performance. Dynamic checkpointing periodically saves the computational state, allowing for recovery from failures without significant data loss, while load balancing ensures that tasks are evenly distributed across processors, preventing bottlenecks and underutilization of resources. By integrating these mechanisms, this paper proposes a robust framework that improves the reliability and efficiency of parallel systems, particularly in high-performance computing environments where the ability to handle large-scale data processing with minimal downtime is critical. © 2024 IEEE.Item Optimization of Resource and Energy in Distributed Systems Using Unified Genetic Algorithm(Institute of Electrical and Electronics Engineers Inc., 2025) Dhruthi, G.; Sinchana, N.M.; Annappa, B.; Kumar, N.M.R.Cloud and large distributed systems must ensure resource scheduling, energy management, and resource allocation. However, there exist complex and dynamic workloads, which may cause inefficient resource distribution, increased energy consumption, cost of operation and time delays which ultimately lead to reduced Quality of Experience (QoE). To address these issues the Unified Genetic Algorithm (UGA) is proposed, a proactive approach in optimization which helps achieve relatively better balance between CPU and memory usage across multiple nodes in a distributed system. UGA, was tested using the Materna workload trace and subjected to comparison with other existing load balancing algorithms such as Firefly, Coral Reef Optimization and Novel Family. It is found that UGA is superior with regard to efficiency in scheduling as it has revealed an improvement of 6.72% in average when compared to state of the art algorithms and proved to be beneficial in optimal resource allocation and improvement in system performance. © 2025 IEEE.Item Adaptive Workload Management for Enhanced Function Performance in Serverless Computing(Association for Computing Machinery, Inc, 2025) Birajdar, P.A.; Harsha, V.; Satpathy, A.; Addya, S.K.Serverless computing streamlines application deployment by removing the need for infrastructure management, but fluctuating workloads make resource allocation challenging. To solve this, we propose an adaptive workload manager that intelligently balances workloads, optimizes resource use, and adapts to changes with auto-scaling, ensuring efficient and reliable serverless performance. Preliminary experiments demonstrate an ≈ 0.6X% and 2X% improvement in execution time and resource utilization compared to the First-Come-First-Serve (FCFS) scheduling algorithm. © 2025 Copyright held by the owner/author(s).