Athanasia Evangelinou

Enterprise applications cloud rightsizing through a joint benchmarking and optimization approach

Migrating an application to the cloud environment requires non-functional properties consideration such as cost, performance and Quality of Service (QoS). Given the variety and the plethora of cloud offerings in addition with the consumption-based pricing models currently available in the cloud market, it is extremely complex to find the optimal deployment that fits the application requirements and provides the best QoS and cost trade-offs. In many cases the performance of these service offerings may vary depending on the congestion level, provider policies and how the application types that are intended to be executed upon them use the computing resources. A key challenge for customers before moving to Cloud is to know application behavior on cloud platforms in order to select the best-suited environment to host their application components in terms of performance and cost. In this paper, we propose a combined methodology and a set of tools that support the design and migration of enterprise applications to Cloud. Our tool chain includes: (i) the performance assessment of cloud services based on cloud benchmark results, (ii) a profiler/classifier mechanism that identifies the computing footprint of an arbitrary application and provides the best matching with a cloud service solution in terms of performance and cost, (iii) and a design space exploration tool, which is effective in identifying the deployment of minimum costs taking into account workload changes and providing QoS guarantees. A methodology and tools that support the design and migration of applications to Cloud.The performance advertised by cloud providers is to be used carefully.The proposed benchmark procedure for migrated Cloud applications leads to reduced costs.

A Model Driven Approach for Supporting the Cloud Target Selection Process

Abstract The decision making process for the selection of one cloud target over another plays a major role during the migration to the Cloud, affecting not only the operational costs, functional characteristics and QoS, but also the development, monitoring and maintaining experience of the IT professionals. As the Cloud gains ground, a progressively growing number of cloud providers, services and technologies are exposed in the market rendering the research and selection upon them complex and time consuming. Proposed efforts for automatic support, fail to follow the quick paste of evolution, demanding, thus, even more effort for maintaining the supporting systems. In this paper the Cloud Target Selection (CTS) tool methodology and prototype implementation are presented introducing a novel approach: The CloudML@artist modeling language is exploited as a representation of real-world cloud environments becoming a source of information for an extensible decision making mechanism. The proposed work contributes in the direction towards the construction of an adaptive solution, which will follow the technological advances requiring the minimum of human intervention

A Joint Benchmark-Analytic Approach For Design-Time Assessment of Multi-Cloud Applications☆

Abstract Verifying that a software system shows certain non-functional properties is a primary concern for cloud applications. Given the heterogeneous technology offer and the pricing models currently available in the cloud market it is extremely complex to find the deployment that fits the application requirements and provides the best Quality of Service (QoS) and cost trade-offs. This task can be very challenging, even infeasible if performed manually, since the number of solutions may become extremely large depending on the number of possible providers and available technology stacks. Furthermore, with the increasing adoption of cloud computing, there is a need for fair evaluation of cloud systems. Todays cloud services differ among others by cost, performance, consistency guarantees, load-balancing, caching, fault tolerance, and SLAs. Moreover, cloud systems are inherently multi-tenant and their performance can vary over time, depending on the congestion level, provider policies, and the competition among running applications. System architects and developers are challenged with this variety of services and trade-offs. Hence, the purpose of a cloud benchmark should be to help developers when choosing the right architecture and services for their applications. In this paper we propose a joint benchmarking and optimization methodology to support the design and migration of legacy applications to Cloud. Our approach is effective in identifying the deployment of minimum costs, which provide also QoS guarantees.

Performance Prediction of Cloud-Based Big Data Applications

Data heterogeneity and irregularity are key characteristics of big data applications that often overwhelm the existing software and hardware infrastructures. In such context, the exibility and elasticity provided by the cloud computing paradigm over a natural approach to cost-effectively adapting the allocated resources to the applications current needs. Yet, the same characteristics impose extra challenges to predicting the performance of cloud-based big data applications, a central step in proper management and planning. This paper explores two modeling approaches for performance prediction of cloud-based big data applications. We evaluate a queuing-based analytical model and a novel fast ad-hoc simulator in various scenarios based on different applications and infrastructure setups. Our results show that our approaches can predict average application execution times with 26% relative error in the very worst case and about 12% on average. Moreover, our simulator provides performance estimates 70 times faster than state of the art simulation tools.

Experimenting with Application-Based Benchmarks on Different Cloud Providers via a Multi-cloud Execution and Modeling Framework

Cloud services are emerging today as an innovative IT provisioning model, offering benefits over the traditional approach of provisioning infrastructure. However, the occurrence of multi-tenancy, virtualization and resource sharing issues raise certain difficulties in providing performance estimation during application design or deployment time. In order to assess the performance of cloud services and compare cloud offerings, cloud benchmarks are required. The aim of this paper is to present a mechanism and a benchmarking process for measuring the performance of various cloud service delivery models, while describing this information in a machine understandable format. The suggested framework is responsible for organizing the execution and may support multiple cloud providers. In our work context, benchmarking measurement results are demonstrated from three large commercial cloud providers, Amazon EC2, Microsoft Azure and Flexiant in order to assist with provisioning decisions for cloud users. Furthermore, we present approaches for measuring service performance with the usage of specialized metrics for ranking the services according to a weighted combination of cost, performance and workload.

A Multi-Cloud Framework for Measuring and Describing Performance Aspects of Cloud Services Across Different Application Types

Cloud services have emerged as an innovative IT provisioning model in the recent years. However, after their usage severe considerations have emerged with regard to their varying performance due to multitenancy and resource sharing issues. These issues make it very difficult to provide any kind of performance estimation during application design or deployment time. The aim of this paper is to present a mechanism and process for measuring the performance of various cloud services and describing this information in machine understandable format. The framework is responsible for organizing the execution and can support multiple cloud providers. Furthermore we present approaches for measuring service performance with the usage of specialized metrics for ranking the services according to a weighted combination of cost, performance and workload.