Improving Management of Distributed Services Using Correlations and Predictions in SLA-Driven Cloud Computing Systems

Recent advancements in cloud computing have enabled the proliferation of distributed applications, which require management and control of multiple services. However, without an efficient mechanism for scaling services in response to changing environmental conditions and number of users, application performance might suffer, leading to Service Level Agreement (SLA) violations and inefficient use of hardware resources. We introduce a system for controlling the complexity of scaling applications composed of multiple services using mechanisms based on fulfillment of SLAs. We present how service monitoring information can be used in conjunction with service level objectives, predictions, and correlations between performance indicators for optimizing the allocation of services belonging to distributed applications. We validate our models using experiments and simulations involving a distributed enterprise information system. We show how discovering correlations between application performance indicators can be used as a basis for creating refined service level objectives, which can then be used for scaling the application and improving the overall application's performance under similar conditions.

Service Level Agreements-Driven Management of Distributed Applications in Cloud Computing Environments

Cloud Computing enables provisioning and distribution of highly scalable services in a reliable, on-demand and sustainable manner. However, objectives of managing enterprise distributed applications in cloud environments under Service Level Agreement (SLA) constraints lead to challenges for maintaining optimal resource control. Furthermore, conflicting objectives in management of cloud infrastructure and distributed applications might lead to violations of SLAs and inefficient use of hardware and software resources. This dissertation focusses on how SLAs can be used as an input to the cloud management system, increasing the efficiency of allocating resources, as well as that of infrastructure scaling. First, we present an extended SLA semantic model for modelling complex service-dependencies in distributed applications, and for enabling automated cloud infrastructure management operations. Second, we describe a multi-objective VM allocation algorithm for optimised resource allocation in infrastructure clouds. Third, we describe a method of discovering relations between the performance indicators of services belonging to distributed applications and then using these relations for building scaling rules that a CMS can use for automated management of VMs. Fourth, we introduce two novel VM-scaling algorithms, which optimally scale systems composed of VMs, based on given SLA performance constraints. All presented research works were implemented and tested using enterprise distributed applications.

Thematic thinking and individual performance in research and development

Individuals differ in their preference for processing information on the basis of taxonomic, feature-based similarity, or thematic, relation-based similarity. These differences, which have been investigated in a recently emerging research stream in cognitive psychology, affect innovative behavior and thus constitute an important antecedent of individual performance in research and development (R&D) that has been overlooked so far in the literature on innovation management. To fill this research gap, survey and test data from the employees of a multinational information technology services firm are used to examine the relationship between thematic thinking and R&D professionals' individual performance. A moderated mediation model is applied to investigate the proposed relationships of thematic thinking and individual-level performance indicators. Results show a positive relationship between thematic thinking and innovativeness, as well as individual job performance. While the results do not support the postulated moderation of the innovativeness–job performance relationship by employees' political skill, they show that the relationship between thematic thinking and job performance is fully mediated by R&D professionals' innovativeness. The present study is thus the first to reveal a positive relationship between thematic thinking and innovative performance.

SLA-Driven Simulation of Multi-Tenant Scalable Cloud-Distributed Enterprise Information Systems

Cloud Computing is an enabler for delivering large-scale, distributed enterprise applications with strict requirements in terms of performance. It is often the case that such applications have complex scaling and Service Level Agreement (SLA) management requirements. In this paper we present a simulation approach for validating and comparing SLA-aware scaling policies using the CloudSim simulator, using data from an actual Distributed Enterprise Information System (dEIS). We extend CloudSim with concurrent and multi-tenant task simulation capabilities. We then show how different scaling policies can be used for simulating multiple dEIS applications. We present multiple experiments depicting the impact of VM scaling on both datacenter energy consumption and dEIS performance indicators.

Public service motivation and performance: a critical perspective

Purpose – A growing body of literature points to the importance of public service motivation (PSM) for the performance of public organizations. The purpose of this paper is to assess the method predominantly used for studying this linkage by comparing the findings it yields without and with a correction suggested by Brewer (2006), which removes the common-method bias arising from employee-specific response tendencies. Design/methodology/approach – First, the authors conduct a systematic review of published empirical research on the effects of PSM on performance and show that all studies found have been conducted at the individual level. Performance indicators in all but three studies were obtained by surveying the same employees who were also asked about their PSM. Second, the authors conduct an empirical analysis. Using survey data from 240 organizational units within the Swiss federal government, the paper compares results from an individual-level analysis (comparable to existing research) to two analyses where the data are aggregated to the organizational level, one without and one with the correction for common-method bias suggested by Brewer (2006). Findings – Looking at the Attraction to Policy-Making dimension of PSM, there is an interesting contrast: While this variable is positively correlated with performance in both the individual-level analysis and the aggregated data analysis without the correction for common-method bias, it is not statistically associated with performance in the aggregated data analysis with the correction. Originality/value – The analysis is the first to assess the robustness of the performance-PSM linkage to a correction for common-method bias. The findings place the validity of at least one part of the individual-level linkage between PSM and performance into question.

Bilayer Blind Deconvolution with the Light Field Camera

In this paper we propose a solution to blind deconvolution of a scene with two layers (foreground/background). We show that the reconstruction of the support of these two layers from a single image of a conventional camera is not possible. As a solution we propose to use a light field camera. We demonstrate that a single light field image captured with a Lytro camera can be successfully deblurred. More specifically, we consider the case of space-varying motion blur, where the blur magnitude depends on the depth changes in the scene. Our method employs a layered model that handles occlusions and partial transparencies due to both motion blur and out of focus blur of the plenoptic camera. We reconstruct each layer support, the corresponding sharp textures, and motion blurs via an optimization scheme. The performance of our algorithm is demonstrated on synthetic as well as real light field images.

Dynamic SLA management with forecasting using multi-objective optimization

Cost-efficient operation while satisfying performance and availability guarantees in Service Level Agreements (SLAs) is a challenge for Cloud Computing, as these are potentially conflicting objectives. We present a framework for SLA management based on multi-objective optimization. The framework features a forecasting model for determining the best virtual machine-to-host allocation given the need to minimize SLA violations, energy consumption and resource wasting. A comprehensive SLA management solution is proposed that uses event processing for monitoring and enables dynamic provisioning of virtual machines onto the physical infrastructure. We validated our implementation against serveral standard heuristics and were able to show that our approach is significantly better.