Energy-efficient Benchmarking for Energy-efficient Software

With respect to the continuous growth of computing systems, the energy-efficiency requirement of their processes becomes even more important. Different configurations, implying different energy-efficiency of the system, could be used to perform the process. A configuration denotes the choice among different hard- and software settings (e.g., CPU frequency, number of threads, the concrete algorithm, etc.). The identification of the most energy-efficient configuration demands to benchmark all configurations. However, this benchmarking is time- and energy-consuming, too. This thesis explores (a) the effect of dynamic voltage and frequency scaling (DVFS) in combination with dynamic concurrency throttling (DCT) on the energy consumption of (de)compression, DBMS query executions, encryption/decryption and sorting; and (b) a generic approach to reduce the benchmarking efforts to determine the optimal configuration. Our findings show that the utilization of optimal configurations can save wavg. 15.14% of energy compared to the default configuration. Moreover, we propose a generic heuristic (fractional factorial design) that utilizes data mining (adaptive instance selection) together with machine learning techniques (multiple linear regression) to decrease benchmarking effort by building a regression model based on the smallest feasible subset of the benchmarked configurations. Our approach reduces the energy consumption required for benchmarking by 63.9% whilst impairing the energy-efficiency of performing the computational process by only 1.88 pp, due to not using the optimal but a near-optimal configuration

An approach to dynamic web service composition

Today, changeable requirements to modern web-oriented services demand their fast development and constant reengineering. This is realized via dynamic composition of services, allowing to estimate changes of both functional and nonfunctional service parameters. The last ones are considered using Web Services Agreement technique. Nevertheless, state-of-the-art SLA-aware methods are not able to consider all classes of non-functional parameters. They also don’t provide service run-time support and dynamic reconfiguration. The novel approach to dynamic Web Services Composition, extending SLA with QoS ontology, is described in the paper. It includes service selection agents that use the QoS ontology and WS-Agreements, allowing agents to choose the most appropriate service based on quality preferences exposed by service consumer. The proposed approach allows performing dynamic WS composition based on SLA, providing required values of QoS parameters, improving general QoS and decreasing service development and re-engineering time

Advanced approach to web service composition

This is a pre-print of an article published in "Soft Computing in Computer and Information Science". The final authenticated version is available online at: https://doi.org/10.1007/978-3-319-15147-2_29.Web Service Composition (WSC) is a process that helps to save much programming and cost effort by reusing existing components—Web services. This process consists of two major stages—Web Service Discovery and Selection (WSD, WSS). This paper presents an overview of the current state-of-the-art WSD and WSS methods. It also provides an analysis and highlights major problems like lack of support of the syntactical description in fuzzy logic algorithms in WSD and complex approach shortage in WSS problem. Moreover, WSC approach and Service-level agreement (SLA) aware WSC System are presented

A Software Product Line for Parameter Tuning

Optimization is omnipresent in our world. Its numerous applications spread from industrial cases, such as logistics, construction management or production planning; to the private sphere, filled with problems of selecting daycare or vacation planning. In this thesis, we concentrate on expensive black-box optimization (EBBO) problems, a subset of optimization problems (OPs), which are characterized by an expensive cost of evaluating an objective function. Such OPs are reoccurring in various domains, being known as: hyperpameter optimization in machine learning, performance configuration optimization or parameter tuning in search-based software engineering, simulation optimization in operations research, meta-optimization or parameter tuning in the optimization domain itself. High diversity of domains introduces a plethora of solving approaches, which adhere to a similar structure and workflow, but differ in details. The software frameworks stemming from different areas possess only partially intersecting manageability points, i.e., lack manageability. In this thesis, we argue that the lack of manageability in EBBO is a major problem, which leads to underachieving optimization quality. The goal of this thesis is to study the role of manageability in EBBO and to investigate whether improving the manageability of EBBO frameworks increases optimization quality. To reach this goal, we appeal to software product line engineering (SPLE), a methodology for developing highly-manageable software systems. Based on the foundations of SPLE, we introduce a novel framework for EBBO called BRISE. It offers: 1) a loosely-coupled software architecture, separating concerns of the experiment designer and the developer of EBBO strategies; 2) a full coverage of all EBBO problem types; and 3) a context-aware variability model, which captures the experiment-designer-defined OP with the content model; and manageability points including their variants and constraints with the cardinality-based feature model. High manageability of the introduced BRISE framework enables us: 1) to extend the framework with novel efficient strategies, such as adaptive repetition management; and 2) to introduce novel EBBO mechanisms, such as multi-objective compositional surrogate modeling, dynamic sampling and hierarchical surrogate modeling. The evaluation of the novel approaches with a set of case studies, including: the WFG benchmark for multi-objective optimization, combined selection and parameter control of meta-heuristics, and energy optimization; demonstrated their superiority over the state-of-the-art competitors. Thus, it supports the research hypothesis of this thesis: Improving manageability of an EBBO framework enables to increase optimization quality

A Software Product Line for Parameter Tuning

Optimization is omnipresent in our world. Its numerous applications spread from industrial cases, such as logistics, construction management or production planning; to the private sphere, filled with problems of selecting daycare or vacation planning. In this thesis, we concentrate on expensive black-box optimization (EBBO) problems, a subset of optimization problems (OPs), which are characterized by an expensive cost of evaluating an objective function. Such OPs are reoccurring in various domains, being known as: hyperpameter optimization in machine learning, performance configuration optimization or parameter tuning in search-based software engineering, simulation optimization in operations research, meta-optimization or parameter tuning in the optimization domain itself. High diversity of domains introduces a plethora of solving approaches, which adhere to a similar structure and workflow, but differ in details. The software frameworks stemming from different areas possess only partially intersecting manageability points, i.e., lack manageability. In this thesis, we argue that the lack of manageability in EBBO is a major problem, which leads to underachieving optimization quality. The goal of this thesis is to study the role of manageability in EBBO and to investigate whether improving the manageability of EBBO frameworks increases optimization quality. To reach this goal, we appeal to software product line engineering (SPLE), a methodology for developing highly-manageable software systems. Based on the foundations of SPLE, we introduce a novel framework for EBBO called BRISE. It offers: 1) a loosely-coupled software architecture, separating concerns of the experiment designer and the developer of EBBO strategies; 2) a full coverage of all EBBO problem types; and 3) a context-aware variability model, which captures the experiment-designer-defined OP with the content model; and manageability points including their variants and constraints with the cardinality-based feature model. High manageability of the introduced BRISE framework enables us: 1) to extend the framework with novel efficient strategies, such as adaptive repetition management; and 2) to introduce novel EBBO mechanisms, such as multi-objective compositional surrogate modeling, dynamic sampling and hierarchical surrogate modeling. The evaluation of the novel approaches with a set of case studies, including: the WFG benchmark for multi-objective optimization, combined selection and parameter control of meta-heuristics, and energy optimization; demonstrated their superiority over the state-of-the-art competitors. Thus, it supports the research hypothesis of this thesis: Improving manageability of an EBBO framework enables to increase optimization quality

Energy-efficient Benchmarking for Energy-efficient Software

With respect to the continuous growth of computing systems, the energy-efficiency requirement of their processes becomes even more important. Different configurations, implying different energy-efficiency of the system, could be used to perform the process. A configuration denotes the choice among different hard- and software settings (e.g., CPU frequency, number of threads, the concrete algorithm, etc.). The identification of the most energy-efficient configuration demands to benchmark all configurations. However, this benchmarking is time- and energy-consuming, too. This thesis explores (a) the effect of dynamic voltage and frequency scaling (DVFS) in combination with dynamic concurrency throttling (DCT) on the energy consumption of (de)compression, DBMS query executions, encryption/decryption and sorting; and (b) a generic approach to reduce the benchmarking efforts to determine the optimal configuration. Our findings show that the utilization of optimal configurations can save wavg. 15.14% of energy compared to the default configuration. Moreover, we propose a generic heuristic (fractional factorial design) that utilizes data mining (adaptive instance selection) together with machine learning techniques (multiple linear regression) to decrease benchmarking effort by building a regression model based on the smallest feasible subset of the benchmarked configurations. Our approach reduces the energy consumption required for benchmarking by 63.9% whilst impairing the energy-efficiency of performing the computational process by only 1.88 pp, due to not using the optimal but a near-optimal configuration

A Software Product Line for Parameter Tuning

Optimization is omnipresent in our world. Its numerous applications spread from industrial cases, such as logistics, construction management or production planning; to the private sphere, filled with problems of selecting daycare or vacation planning. In this thesis, we concentrate on expensive black-box optimization (EBBO) problems, a subset of optimization problems (OPs), which are characterized by an expensive cost of evaluating an objective function. Such OPs are reoccurring in various domains, being known as: hyperpameter optimization in machine learning, performance configuration optimization or parameter tuning in search-based software engineering, simulation optimization in operations research, meta-optimization or parameter tuning in the optimization domain itself. High diversity of domains introduces a plethora of solving approaches, which adhere to a similar structure and workflow, but differ in details. The software frameworks stemming from different areas possess only partially intersecting manageability points, i.e., lack manageability. In this thesis, we argue that the lack of manageability in EBBO is a major problem, which leads to underachieving optimization quality. The goal of this thesis is to study the role of manageability in EBBO and to investigate whether improving the manageability of EBBO frameworks increases optimization quality. To reach this goal, we appeal to software product line engineering (SPLE), a methodology for developing highly-manageable software systems. Based on the foundations of SPLE, we introduce a novel framework for EBBO called BRISE. It offers: 1) a loosely-coupled software architecture, separating concerns of the experiment designer and the developer of EBBO strategies; 2) a full coverage of all EBBO problem types; and 3) a context-aware variability model, which captures the experiment-designer-defined OP with the content model; and manageability points including their variants and constraints with the cardinality-based feature model. High manageability of the introduced BRISE framework enables us: 1) to extend the framework with novel efficient strategies, such as adaptive repetition management; and 2) to introduce novel EBBO mechanisms, such as multi-objective compositional surrogate modeling, dynamic sampling and hierarchical surrogate modeling. The evaluation of the novel approaches with a set of case studies, including: the WFG benchmark for multi-objective optimization, combined selection and parameter control of meta-heuristics, and energy optimization; demonstrated their superiority over the state-of-the-art competitors. Thus, it supports the research hypothesis of this thesis: Improving manageability of an EBBO framework enables to increase optimization quality

Energy-efficient Benchmarking for Energy-efficient Software

With respect to the continuous growth of computing systems, the energy-efficiency requirement of their processes becomes even more important. Different configurations, implying different energy-efficiency of the system, could be used to perform the process. A configuration denotes the choice among different hard- and software settings (e.g., CPU frequency, number of threads, the concrete algorithm, etc.). The identification of the most energy-efficient configuration demands to benchmark all configurations. However, this benchmarking is time- and energy-consuming, too. This thesis explores (a) the effect of dynamic voltage and frequency scaling (DVFS) in combination with dynamic concurrency throttling (DCT) on the energy consumption of (de)compression, DBMS query executions, encryption/decryption and sorting; and (b) a generic approach to reduce the benchmarking efforts to determine the optimal configuration. Our findings show that the utilization of optimal configurations can save wavg. 15.14% of energy compared to the default configuration. Moreover, we propose a generic heuristic (fractional factorial design) that utilizes data mining (adaptive instance selection) together with machine learning techniques (multiple linear regression) to decrease benchmarking effort by building a regression model based on the smallest feasible subset of the benchmarked configurations. Our approach reduces the energy consumption required for benchmarking by 63.9% whilst impairing the energy-efficiency of performing the computational process by only 1.88 pp, due to not using the optimal but a near-optimal configuration

EUROCON 2013, International Conference on Computer as a Tool

This is a pre-print of an article published in “EUROCON 2013”. The final authenticated version is available online at: https://doi.org/10.1109/EUROCON.2013.6625004.Changeable requirements for modern services, provided in global environment, and their constant reengineering demand dynamic composition of services which is able to estimate changes of various parameters, both functional and non-functional ones. Web Services Agreement is a convenient way to contain QoS parameters, but state-of-the-art SLA-aware methods are not able to support all classes of nonfunctional parameters and provide runtime support and dynamic reconfiguration at the same time. This paper provides a novel SLA-aware dynamic Web Services Composition Approach that allows performing such composition