Enterprise applications cloud rightsizing through a joint benchmarking and optimization approach

Highlights

• 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.

Abstract

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.

Introduction

Cloud computing is a disruptive phenomenon in ICT world, which has rapidly entered mainstream consciousness and gained a significant attention of various communities like researchers, business and government organization. It is obvious, in fact, that everyday services like Dropbox, Netflix or Instagram owe part of their success to the benefits of cloud such as the infinite number of resources, the ability to dynamically adapt (scale-up or scale down) accordingly to usage behaviors and the pay-as-you-go economical model.

With a rapidly increasing number of companies entering the cloud market and offering heterogeneous and constantly evolving technologies  [1] the process of software design and implementation experienced a deep change. On the one hand, the cloud has meant for developers providing advanced cloud-based tools and abstractions for development, collaboration and deployment. Dynamic systems capable to react to workload fluctuations by adapting themselves in order to keep the performance unchanged can easily built and run delegating to the cloud provider the intensive tasks of infrastructure management and maintenance. On the other hand, performance unpredictability and vendor lock-in are just some of the issues that developers have to face and prove significant barriers to widespread cloud adoption. In fact, the increasing size and complexity of software systems, combined with the wide range of services and prices available in the market, puts the designer before the necessity to evaluate a combinatorially growing number of design alternatives  [2] with the goal of finding a minimum-cost configuration that suits the application Quality of Service (QoS) requirements.

To carry out such a task, the system designer should consider a large number of alternatives and should be able to evaluate costs (that often depends on the application dynamics) and performance for each of them. This 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. What is more, in many cases the performance of these service offerings may vary depending on the application types that are intended to be executed upon them and their characteristics in terms of resource usage. Intensive RAM, CPU, disk or GPU usage may be toggled between applications that have different goals, such as scientific components, database instances or front ends.

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 many cases the application owner might not be aware of how the migrated application component uses the available computing resources; for many application components the runtime behavior and usage of resources may not be known, mistakenly considered or altered due to structural changes during the software migration. On the other side, cloud providers might also be interested in knowing the application types hosted within their infrastructures to avoid interference effects of concurrently running VMs, which significantly degrade applications performance. Evaluating a specific and arbitrary application component over the entire range of offerings becomes a daunting task, especially when the deployment of the former may be subject to provider-specific or component-specific actions or code in each case. However, finding a more abstracted and common way for identifying both application profile and performance aspects of cloud environments may significantly reduce the effort needed in this task.

Finally, the performance characteristics of a cloud may change over time dramatically, depending on the congestion level, policies implemented by the cloud provider, and competition among running applications. Assessing the performance of an application in the cloud is a complex process that requires unbiased data and specialized models often implying skills that go beyond those commonly exhibited by software engineers. This situation calls for analytical techniques, models, application profiling and benchmarks that simplify the process of performance evaluation at design-time in order to support the user in the decision making process.

This work aims at proposing a methodology and a tool chain that support the migration and pricing scheme of enterprise applications to the cloud. Our tool chain exploits cloud benchmarking results and includes a profiler/classifier, which identifies the computational nature of a software component in a black box manner and a design space exploration tool, which is able to identify the cloud configuration of minimum cost fulfilling QOS constraints taking into account also daily workload patterns.

The remainder of the paper is organized as follows. In Section  2 an overview of the design methodology and the implemented tools are presented, while in Section  3 the benchmarking results acquisition is described. Section  4 introduces the profiling and classification tools, while Section  5 is devoted to the design time exploration tool. In Section  5 the experimental results achieved on a case study by combining and integrating the implemented tool chain are presented. In Section  7 related work in the respective fields is described. Conclusions are finally drawn in Section  8.