Cloud Storage Level Service Offering in Virtualized Load Balancer using AWS

Distributed computing epitomizes an approach perfectly suited to the realm of IT commitments, leveraging the aggregation of information and resources through electronic cloud service providers utilizing interconnected hardware and software primarily based online, all at a reasonable cost. However, resource sharing can lead to challenges in their accessibility, potentially causing system crashes. To counter this, the technique of distributing network traffic across multiple servers, known as load balancing, plays a pivotal role. This paper ensures that no single server is overwhelmed, thereby preventing overloads and enhancing user responsiveness by equitably distributing tasks. Moreover, it significantly enhances the accessibility of tasks and websites to users. The fundamental objective of this concept is to comprehend load regulation, which operates in tandem with associated frameworks within communication structures like the Web. Load balancing stands as a critical domain within distributed computing, designed to prevent overburdening and to provide equally significant support. Various algorithms are employed to assess the system's complexity. In our proposed strategy, a process is outlined to determine optimal storage space utilization in real-time, utilizing 100 virtual computers, achieving an impressive 92% accuracy rate in its computations. This innovative approach promises efficient resource allocation within the distributed computing framework, thereby optimizing performance and accessibility for end-users

Empirical analysis of dynamic load balancing techniques in cloud computing

Virtualization, dispersed registration, systems administration, programming, and web administrations are all examples of distributed computing. Customers, datacenters, and scattered servers are just a few of the components that make up a cloud. It includes things like internal failure adaption, high accessibility, flexibility, adaptability, lower client overhead, lower ownership costs, on-demand advantages, and so on. The basis of a feasible load adjusting computation is key to resolving these challenges. CPU load, memory limit, deferral, and system load are all examples of heaps. Burden adjustment is a method for distributing the load across the many hubs of a conveyance framework in order to optimize asset utilization and employment response time while avoiding a situation where some hubs are heavily loaded while others are idle or performing little work. Burden adjustment ensures that at any one time, each processor in the framework or each hub in the system does about the same amount of work. This method may be initiated by the sender, the collector, or the symmetric sort (the blend of sender-started and recipient started types). With some example data center loads, the goal is to create several dynamic load balancing techniques such as Round Robin, Throttled, Equally Spread Current Execution Load, and Shortest Job First algorithms

Major Cloud Computing Security Challenges with Innovative Approaches

Cloud computing is the most favored contemporary era as it has the cap potential to decrease its costs related to computing which improves its pliability and extensibility for further computer processes. Among the past some years, cloud computing has been well superior as a hopeful idea for business organization to one of the additives of the IT organization it really is fastest growing. There are important troubles like protection which the IT agencies take into difficulty that misplaced with the heavy insertion of cloud computing Technology. Though records may be saved in any vicinity. The important reality is Storage of records is saved best with inside the vicinity of the clients that\u27s been the numerous varieties of worries created. In cloud computing region the maximum argued hassle is especially Security. This study mainly intends to analyze and address the major cloud computing security challenges with innovative approaches. To meet that aim, a range of scientific methods, including descriptive, analytical, observation and comparison are taken into account. Given the results, the major key to a success cloud computing projects is reaching stability among the commercial enterprise blessings and the hidden capacity dangers that can affect efficacy

Optimizing Cloud Computing Applications with a Data Center Load Balancing Algorithm

Delivering scalable and on-demand computing resources to users through the usage of the cloud has become a common paradigm. The issues of effective resource utilisation and application performance optimisation, however, become more pressing as the demand for cloud services rises. In order to ensure efficient resource allocation and improve application performance, load balancing techniques are essential in dispersing incoming network traffic over several servers. The workload balancing in the context of cloud computing, particularly in the Infrastructure as a Service (IaaS) model, continues to be difficult. Due to available virtual machines and the limited resources, efficient job allocation is essential. To prevent prolonged execution delays or machine breakdowns, cloud service providers must maintain excellent performance and avoid overloading or underloading hosts. The importance of task scheduling in load balancing necessitates compliance with Service Level Agreement (SLA) standards established by cloud developers for consumers. The suggested technique takes into account Quality of Service (QoS) job parameters, VM priorities, and resource allocation in order to maximise resource utilisation and improve load balancing. The proposed load balancing method is in line with the results in the body of existing literature by resolving these problems and the current research gap. According to experimental findings, the Dynamic LBA algorithm currently in use is outperformed by an average resource utilisation of 78%. The suggested algorithm also exhibits excellent performance in terms of accelerated Makespan and decreased execution time

Simulation and performance assessment of a modified throttled load balancing algorithm in cloud computing environment

Load balancing is crucial to ensure scalability, reliability, minimize response time, and processing time and maximize resource utilization in cloud computing. However, the load fluctuation accompanied with the distribution of a huge number of requests among a set of virtual machines (VMs) is challenging and needs effective and practical load balancers. In this work, a two listed throttled load balancer (TLT-LB) algorithm is proposed and further simulated using the CloudAnalyst simulator. The TLT-LB algorithm is based on the modification of the conventional TLB algorithm to improve the distribution of the tasks between different VMs. The performance of the TLT-LB algorithm compared to the TLB, round robin (RR), and active monitoring load balancer (AMLB) algorithms has been evaluated using two different configurations. Interestingly, the TLT-LB significantly balances the load between the VMs by reducing the loading gap between the heaviest loaded and the lightest loaded VMs to be 6.45% compared to 68.55% for the TLB and AMLB algorithms. Furthermore, the TLT-LB algorithm considerably reduces the average response time and processing time compared to the TLB, RR, and AMLB algorithms

An efficient resource sharing technique for multi-tenant databases

Multi-tenancy is one of the key components of cloud computing environment. Multi-tenant database system in SaaS (Software as a Service) has gained a lot of attention in academics, research and business arena. These database systems provide scalability and economic benefits for both cloud service providers and customers(organizations/companies referred as tenants) by sharing same resources and infrastructure in isolation of shared databases, network and computing resources with Service level agreement (SLA) compliances. In a multitenant scenario, active tenants compete for resources in order to access the database. If one tenant blocks up the resources, the performance of all the other tenants may be restricted and a fair sharing of the resources may be compromised. The performance of tenants must not be affected by resource-intensive activities and volatile workloads of other tenants. Moreover, the prime goal of providers is to accomplish low cost of operation, satisfying specific schemas/SLAs of each tenant. Consequently, there is a need to design and develop effective and dynamic resource sharing algorithms which can handle above mentioned issues. This work presents a model embracing a query classification and worker sorting technique to efficiently share I/O, CPU and Memory thus enhancing dynamic resource sharing and improvising the utilization of idle instances proficiently. The model is referred as Multi-Tenant Dynamic Resource Scheduling Model (MTDRSM) .The MTDRSM support workload execution of different benchmark such as TPC-C(Transaction Processing Performance Council), YCSB(The Yahoo! Cloud Serving Benchmark)etc. and on different database such as MySQL, Oracle, H2 database etc. Experiments are conducted for different benchmarks with and without SLA compliances to evaluate the performance of MTDRSM in terms of latency and throughput achieved. The experiments show significant performance improvement over existing Mute Bench model in terms of latency and throughput

Storage Solutions for Big Data Systems: A Qualitative Study and Comparison

Big data systems development is full of challenges in view of the variety of application areas and domains that this technology promises to serve. Typically, fundamental design decisions involved in big data systems design include choosing appropriate storage and computing infrastructures. In this age of heterogeneous systems that integrate different technologies for optimized solution to a specific real world problem, big data system are not an exception to any such rule. As far as the storage aspect of any big data system is concerned, the primary facet in this regard is a storage infrastructure and NoSQL seems to be the right technology that fulfills its requirements. However, every big data application has variable data characteristics and thus, the corresponding data fits into a different data model. This paper presents feature and use case analysis and comparison of the four main data models namely document oriented, key value, graph and wide column. Moreover, a feature analysis of 80 NoSQL solutions has been provided, elaborating on the criteria and points that a developer must consider while making a possible choice. Typically, big data storage needs to communicate with the execution engine and other processing and visualization technologies to create a comprehensive solution. This brings forth second facet of big data storage, big data file formats, into picture. The second half of the research paper compares the advantages, shortcomings and possible use cases of available big data file formats for Hadoop, which is the foundation for most big data computing technologies. Decentralized storage and blockchain are seen as the next generation of big data storage and its challenges and future prospects have also been discussed

Autonomic Rejuvenation of Cloud Applications as a Countermeasure to Software Anomalies

Failures in computer systems can be often tracked down to software anomalies of various kinds. In many scenarios, it could be difficult, unfeasible, or unprofitable to carry out extensive debugging activity to spot the causes of anomalies and remove them. In other cases, taking corrective actions may led to undesirable service downtime. In this article we propose an alternative approach to cope with the problem of software anomalies in cloud-based applications, and we present the design of a distributed autonomic framework that implements our approach. It exploits the elastic capabilities of cloud infrastructures, and relies on machine learning models, proactive rejuvenation techniques and a new load balancing approach. By putting together all these elements, we show that it is possible to improve both availability and performance of applications deployed over heterogeneous cloud regions and subject to frequent failures. Overall, our study demonstrates the viability of our approach, thus opening the way towards it adoption, and encouraging further studies and practical experiences to evaluate and improve it

Perbandingan Performa Fitur Connection Pooling dan Load Balancing pada Database PostgreSQL

Berdasarkan kebutuhan environment database PostgreSQL Perusahaan X mengenai penampungan koneksi, manajemen dan pemutusan koneksi idle, serta penggunaan sumber daya berlebih pada server database tereplikasi. Masalah tersebut dijawab oleh connection pooling dan load balancing. Connection pooling, menggunakan PGBOUNCER atau PGPOOL-II. Pengujian efektivitas connection pooling dan load balancing, akan menggunakan data Transaction Per Second (TPS) dan connection latency berdasarkan skenario kombinasi PGPOOL-II dan PGBOUNCER. Untuk memberikan implementasi connection pooling dan load balancing terbaik dari kombinasi PGPOOL-II dan PGBOUNCER, dibentuk environment database PostgreSQL tereplikasi secara Asynchronous dan diuji 3 skenario yang melibatkan PGPOOL-II dan PGBOUNCER. Tiga skenario ini dilakukan testing untuk 3 jumlah client yang berbeda dengan menggunakan tools pgbench yaitu (900, 500 dan 100). Dengan catatan load yang dibagi hanyalah query select saja. Didapatkan skenario yang terbaik adalah penggunaan PGBOUNCER sebagai connection pooling dan PGPOOL-II sebagai load balancing saja tanpa mengaktifkan fitur connection pooling dari PGPOOL-II. Skenario ini memiliki nilai latency yang paling rendah dan nilai TPS tertinggi untuk setiap jumlah clientnya. Nilai latency dari jumlah client yang berbeda-beda memiliki persentase 14% lebih rendah dibanding skenario lainnya dan memiliki nilai TPS 15% lebih tinggi dibanding skenario lainnya.Sehingga disarankan untuk environment database Perusahaan X digunakan kombinasi PGBOUNCER sebagai connection pooling dan PGPOOL-II sebagai load balancing