Cloud computing—effect of evolutionary algorithm on load balancing

© Springer International Publishing Switzerland 2015 In cloud computing due to the multi-tenancy of the resources, there is an essential need for effective load management to ensure an efficient load sharing. Depends on the structure of the tasks, different algorithms could be applied to distribute the load. Workflow scheduling as one of those load distribution algorithms, is specifically designed to schedule the dependent tasks on available resources. Considering a job as an elastic network of dependent tasks, this paper describes how evolutionary algorithm, with its mathematical apparatus, could be applied as workflow scheduling in cloud computing. In this research, the impact of Generalized Spring Tensor Model on workflow load balancing, in context of mathematical patterns have been studied. This research can establish patterns in cloud computing which can be applied in designing the heuristic workflow load balancing algorithms to identify the load patterns of the cloud network. Furthermore, the outcome of this research can help the end users to recognize the threats of tasks failure in processing the e-business and e-since data in cloud environment

A combined computing framework for load balancing in multi-tenant cloud eco-system

Since the world is getting digitalized, cloud computing has become a core part of it. Massive data on a daily basis is processed, stored, and transferred over the internet. Cloud computing has become quite popular because of its superlative quality and enhanced capability to improvise data management, offering better computing resources and data to its user bases (UBs). However, there are many issues in the existing cloud traffic management approaches and how to manage data during service execution. The study introduces two distinct research models: data center virtualization framework under multi-tenant cloud-ecosystem (DCVF-MT) and collaborative workflow of multi-tenant load balancing (CW-MTLB) with analytical research modeling. The sequence of execution flow considers a set of algorithms for both models that address the core problem of load balancing and resource allocation in the cloud computing (CC) ecosystem. The research outcome illustrates that DCVF-MT, outperforms the one-to-one approach by approximately 24.778% performance improvement in traffic scheduling. It also yields a 40.33% performance improvement in managing cloudlet handling time. Moreover, it attains an overall 8.5133% performance improvement in resource cost optimization, which is significant to ensure the adaptability of the frameworks into futuristic cloud applications where adequate virtualization and resource mapping will be required

Optimizing Cloud-Service Performance: Efficient Resource Provisioning Via Optimal Workload Allocation

Cloud computing is being widely accepted and utilized in the business world. From the perspective of businesses utilizing the cloud, it is critical to meet their customers\u27 requirements by achieving service-level-objectives. Hence, the ability to accurately characterize and optimize cloud-service performance is of great importance. In this dissertation, a stochastic multi-tenant framework is proposed to model the service of customer requests in a cloud infrastructure composed of heterogeneous virtual machines (VMs). The proposed framework addresses the critical concepts and characteristics in the cloud, including virtualization, multi-tenancy, heterogeneity of VMs, VM isolation for the purpose of security and/or performance guarantee and the stochastic response time of a customer request. Two cloud-service performance metrics are mathematically characterized, namely the percentile of the stochastic response time and the mean of the stochastic response time of a customer request. Based upon the proposed multi-tenant framework, a workload-allocation algorithm, termed max-min-cloud algorithm, is then devised to optimize the performance of the cloud service. A rigorous optimality proof of the max-min-cloud algorithm is given when the stochastic response time of a customer request assumed exponentially distributed. Furthermore, extensive Monte-Carlo simulations are conducted to validate the optimality of the max-min-cloud algorithm by comparing with other two workload-allocation algorithms under various scenarios. Next, the resource provisioning problem in the cloud is studied in light of the max-min-cloud algorithm. In particular, an efficient resource-provisioning strategy, termed the MPC strategy, is proposed for serving dynamically arriving customer requests. The efficacy of the MPC strategy is verified through two practical cases when the arrival of the customer requests is predictable and unpredictable, respectively. As an extension of the max-min-cloud algorithm, we further devise the max-load-first algorithm to deal with the VM placement problem in the cloud. MC simulation results show that the max-load-first VM-placement algorithm outperforms the other two heuristic algorithms in terms of reducing the mean of stochastic completion time of a group of arbitrary customers\u27 requests. Simulation results also provide insight on how the initial loads of servers affect the performance of the cloud system. In summary, the findings in this dissertation work can be of great benefit to both service providers (namely business owners) and cloud providers. For business owners, the max-min-cloud workload-allocation algorithm and the MPC resource-provisioning strategy together can be used help them build a better understanding of how much virtual resources in the cloud they may need to meet customers\u27 expectations subject to cost constraints. For cloud providers, the max-load-first VM-placement algorithm can be used to optimize the computational performance of the service by appropriately utilizing the physical machines and efficiently placing the VMs in their cloud infrastructures

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

Toward Customizable Multi-tenant SaaS Applications

abstract: Nowadays, Computing is so pervasive that it has become indeed the 5th utility (after water, electricity, gas, telephony) as Leonard Kleinrock once envisioned. Evolved from utility computing, cloud computing has emerged as a computing infrastructure that enables rapid delivery of computing resources as a utility in a dynamically scalable, virtualized manner. However, the current industrial cloud computing implementations promote segregation among different cloud providers, which leads to user lockdown because of prohibitive migration cost. On the other hand, Service-Orented Computing (SOC) including service-oriented architecture (SOA) and Web Services (WS) promote standardization and openness with its enabling standards and communication protocols. This thesis proposes a Service-Oriented Cloud Computing Architecture by combining the best attributes of the two paradigms to promote an open, interoperable environment for cloud computing development. Mutil-tenancy SaaS applicantions built on top of SOCCA have more flexibility and are not locked down by a certain platform. Tenants residing on a multi-tenant application appear to be the sole owner of the application and not aware of the existence of others. A multi-tenant SaaS application accommodates each tenant’s unique requirements by allowing tenant-level customization. A complex SaaS application that supports hundreds, even thousands of tenants could have hundreds of customization points with each of them providing multiple options, and this could result in a huge number of ways to customize the application. This dissertation also proposes innovative customization approaches, which studies similar tenants’ customization choices and each individual users behaviors, then provides guided semi-automated customization process for the future tenants. A semi-automated customization process could enable tenants to quickly implement the customization that best suits their business needs.Dissertation/ThesisDoctoral Dissertation Computer Science 201

ENHANCEMENT FOR DATA SECURITY IN CLOUD COMPUTING ENVIRONMENT

Cloud computing, a rapidly developing information technology, has aroused the concern of the whole world. Cloud computing is Internet-based computing, whereby shared resources, software and information, are provided to computers and devices on-demand, like the electricity grid. Cloud computing is the product of the fusion of traditional computing technology and network technology like grid computing, distributed computing parallel computing and so on. It aims to construct a perfect system with powerful computing capability through a large number of relatively low-cost computing entity, and using the advanced business model like SaaS (Software as a Service) to distribute the powerful computing capacity to end users’ hands. To address this longstanding limitation by building a multi-tenant system. Our system provides the environment for the user to perform his tasks, but with very high security. By using further facilities provided in this system user fill secure about his data and his account