OptiMoCS 2018 - 2018 International Workshop on Optimization in Modern Computing Systems

Modern computing paradigms (e.g., service-oriented computing, cloud computing, edge and fog computing, mobile computing, network function virtualization) allow the construction, deployment, and operation of systems of ever larger scale and complexity. Such systems must satisfy several important, often contradicting, quantifiable goals. Metrics of interest include performance, consumption of computing and network resources, energy consumption, and financial costs. Optimization of these goals is an important concern in the design and operation of many different systems, leading to challenging optimization problems. Improved algorithms for these problems directly translate to more effective and more efficient systems.
Objectives
The workshop aims to provide a platform for discussing recent work on optimization in modern computing systems. By focusing on optimization as a domain- and technology-independent concern, synergies between different domains and technologies can be identified and used. In particular, the optimization of different systems gives rise to similar challenges, e.g., with respect to search space explosion, handling uncertainties, multiple objectives, or soft constraints. The workshop will foster the discussion on possible approaches to such challenges and the transfer of ideas between different technical domains.
Topics of Interest
The workshop solicits papers that address either general optimization techniques that are relevant to the optimization of modern computing systems, or specific optimization problems and approaches in the optimization of modern computing systems. Topics of interest include, but are not limited to:
Optimization problems in
Service-oriented computing
Cloud computing
Edge and fog computing
Mobile computing
Internet of Things
Network function virtualization
Business process management
Optimization techniques
Exact algorithms
Heuristics, meta-heuristics
Approximation algorithms
Mathematical programming methods
Matheuristics
Evaluation of optimization approaches
Formal quality guarantees
Systematic empirical studies
Probabilistic and statistical approaches
Case studies of real-world applications of optimization
Comparison of different algorithms
Further concerns relating to optimization
Multi-objective optimization
Handling uncertainty
Quantifying quality attributes like security, privacy, or dependability
Quality assurance, verification and validation of optimization algorithms