Identification of Selected Resource-aware Problems Across Scientific Disciplines and Applications Pawel Czarnul, Mariusz Matuszek Gdansk University of Technology, 11/12 Narutowicza St., 80-233, Gdansk, Poland Abstract In this work we perform preliminary identification by formulations of resource-aware problems across various disciplines considered in scientific literature. Formulations considered are: integer linear pro- gramming (ILP), greedy algorithms, dynamic programming and genetic algorithms (GA). We outline scientific disciplines (associated with profiles of journals the works appear in) and practical applica- tions. We were able to identify selected more universal resources considered in many problems, such as financial cost, time, energy, ecological value, security, apart from problem specific resources. We also identified to what degree certain resources appear in various problem formulations, as well as which problem formulations are prevalent in various disciplines. Keywords resource-aware problems, identification of resources, cross discipline problem analysis 1. Introduction In computer science, resources typically considered include: execution time (performance), energy, memory/storage, ease of programming/development time. Problem formulations in these cases are typically associated with trade-offs, for example: performance vs energy [1, 2], performance vs security of a system [3], performance vs storage [4], performance/time vs memory [5, 6], performance vs ease of programming/development effort [7], as well as optimization/portability. Problem domains considered in this analysis include, among others: allocating resources for fighting forest fires [8], emission minimization, fossil resource usage minimization, employ- ment maximization [9], allocation of health care resources [10], reconfiguration and resource optimization in power distribution networks [11], site selection of a wind power plant [12], operation of a hospital emergency department, studying the impact staffing policies have on such key quality measures as patient length of stay (LoS), number of handoffs, staff utilization levels, and cost [13], decision-CPM network in order to obtain an overall optimum including time, cost, quality and safety in a road building project [14], resource allocation in communi- cation [15, 16], clouds [17, 18], high performance computing systems [19, 1], management of natural resources [20], education [21] etc. CERCIRAS WS01: 1st Workshop on Connecting Education and Research Communities for an Innovative Resource Aware Society pczarnul@eti.pg.edu.pl (P. Czarnul); Mariusz.Matuszek@pg.edu.pl (M. Matuszek) { http://pg.edu.pl/pawel.czarnul (P. Czarnul)  0000-0002-4918-9196 (P. Czarnul); 0000-0001-7551-256X (M. Matuszek) © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings http://ceur-ws.org ISSN 1613-0073 CEUR Workshop Proceedings (CEUR-WS.org) In terms of resources considered in this cross-discipline preliminary review, these can be divided into two groups: problem specific resources – we consider resources specific to the given domain, e.g. water in water research, natural resources in environmental protection, computing resources in cloud computing etc. general resources applicable to many domains and applications, specifically optimization i.e. mainly: time (determined by system/process performance) – execution time, cost – monetary, energy (used within an optimization process), ecological/environmental value (respected by a society which it concerns), security – prevention of a crime, break-in. Outcome of this analysis allows to further outline problem formulations from the identified works and link analogous synthetic formulations and approaches used to solve the latter from the algorithmic point of view. This potentially allows to reuse approaches to take up problems already used in other disciplines and correspondingly identify base algorithms that form algorithmic foundations for resource-aware computing. 2. Resource-aware problems across disciplines by formulations Works considered in this analysis include selection (scientific papers) out of approximately 100 results returned by the Google search engine for queries involving particular problem formulations and resource, resource-aware problems. The search had been extended by selected results obtained from the Bing search engine, queried about resource aware computing an resource aware computing problems. Classification of these is included in Tables 1,2,3,4, versus: resources: both problem specific as well as more general ones like time, financial cost, security, formulation: ILP, dynamic programming, greedy approach, GA as an example of evolutionary approaches, discipline – a broader category of applications considered in the given work. Table 1: Selected resource-aware problems from various disciplines by resources and discipline, using ILP formulation problem description resources formulation discipline bib allocating resources for human resources; ILP wildfire sup- [8] fighting forest fires time; financial cost pression, simulation Mixed-Integer Linear Pro- jobs belong- ILP general cross [22] gramming for Resource ing to projects; domain appli- Constrained Project time; renewable, cable Scheduling Problem non-renewable resources for executing jobs Continued on next page Table 1 – continued from previous page problem description resources formulation discipline bib total electricity cost min- energy resources multi- energy sector [9] imization, CO2 emission (solar, wind, coal, objective minimization, energy im- natural gas, hydro- mixed port minimization, fossil electric, nuclear integer resource usage minimiza- etc.) linear pro- tion, employment max- gramming imization, social accep- (MOMILP) tance maximization allocation of health care health care re- ILP healthcare [10] resources (treatments, sources , financial domain, max- population, healthcare cost imization of programs) benefit finding the minimum power distribution ILP reconfiguration [11] power loss configuration network resources and resource of the network, definition optimization of the most efficient oper- in power ating condition of voltage distribution control apparatus and networks, reactive power resources losses opti- mization site selection of a wind energy ILP energy sector [12] power plant single and multiple-type wind tur- bine models for a selected site decision-CPM network in time; cost; quality; ILP road construc- [14] order to obtain an overall safety tion domain optimum including time, cost, quality and safety in a road building project operation of a hospital staff; time; re- ILP, simula- hospital [13] emergency department, sources assigned tion resource studying the impact by staff management staffing policies have on such key quality measures as patient length of stay (LoS), number of handoffs, staff utilization levels, and cost Continued on next page Table 1 – continued from previous page problem description resources formulation discipline bib data assignment for par- time ILP high per- [19] allel processing in a hy- formance brid heterogeneous envi- computing ronment considering com- using a cluster munication costs with multi- core/manycore CPUs and GPUs cloudlet selection in computing, stor- ILP cloud comput- [18] the multi-cloudlet en- age and network ing vironment, selection of resources cloudlet(s), selection of VMs for cloudlets Data-center power-aware data-center re- ILP HPC [23] management, efficient uti- sources, power, [24] lization of available re- time sources scheduling of satellite ob- observation capa- ILP satellite Earth [25] servations bilities of satellites, observations mission time constraints Table 2: Selected resource-aware problems from various disciplines by resources and discipline, using greedy formulation problem description resources formulation discipline bib dynamic multi-user re- communication greedy algo- resource [15] source allocation in the medium (channels); rithm allocation in downlink of OFDMA sys- power consump- communica- tem, power consumption tion tion minimization scheduling of flows from throughput; loss; greedy resource [16] various applications in time (delay) knapsack allocation in overload states, downlink algorithm communica- scheduling tion preparation of educa- school resources: greedy ap- education [21] tional schedule in the human; classes; proach with higher education courses local optimal steps Continued on next page Table 2 – continued from previous page problem description resources formulation discipline bib allocating resources in shared physical re- greedy algo- Virtual [26] Virtual Sensor Networks, sources (processing rithm Sensor maximizing revenue of power, bandwidth, Networks multiple concurrent appli- storage); time; en- cations’ schedule ergy Set Covering Problem as problem specific weighted resource [27] a template for resource resources; time greedy manage- management, examples of (algorithm running algorithm ment applications given for: op- time) erational research, ma- chine learning, planning, data mining, information retrieval Maximizing utility and problem specific re- greedy algo- datacenter [28] revenue of hardware re- sources rithm provisioning [29] sources in virtual machine allocation Reducing task duplication distributed compu- greedy algo- distributed [30] in task scheduling on tational resources rithm computing heterogeneous distributed systems Task offloading and computational and greedy algo- power [31] resource allocation in communication re- rithm network power network monitor- sources monitoring ing (PIoT) Flexible co-scheduling of problem specific re- greedy algo- physics mod- [32] computational and com- sources rithm eling munication resources in fluid dynamics calcula- tions task scheduling in a cloud energy consump- greedy algo- cloud com- [33] computing environment, tion, time rithm puting with time and energy con- straints Table 3: Selected resource-aware problems from various disciplines by resources and discipline, using dynamic formulation problem description resources formulation discipline bib agriculture and natural natural resources dynamic pro- agriculture, [20] resources management: gramming manage- buffer stocks policy; farm ment of machinery replacement; natural crop irrigation; fertilizer resources and pest management; livestock feeding and marketing; mining; pollu- tion control; irreversible development; forestry management and fisheries management dynamic programming water resources; dynamic pro- power [34] for scheduling water re- cost gramming systems sources; minimization of expected cost of running a hydroelectric system stochastic resource alloca- problem specific re- dynamic pro- general [35] tion sources; financial gramming resource cost; time allocation, decision making stochastic resource alloca- problem specific dynamic pro- military [36] tion resources; time; gramming naval op- security (stem- erations ming from the – setting application) resources to maximum efficiency in real-time on a ship Continued on next page Table 3 – continued from previous page problem description resources formulation discipline bib HPC compute nodes allo- application specific dynamic pro- high per- [37] cation resources; accelera- gramming formance tors, storage computing, dynamic allocation of resources, X10 pro- gramming language Dynamic code loading grid resources, dynamic pro- dynamic [38] power consump- gramming reconfigu- tion ration of internet servers, agent sys- tems Balancing resources in computational dynamic pro- obtaining [39] robotic vision power, bandwidth, gramming balanced responsiveness utilization of available computing resources between operating tasks of humanoid robots Edge computing, integra- energy, bandwidth, dynamic pro- healthcare, [40] tion of low cost wearable processing power, gramming clinical-level sensors, processing of sen- measurement qual- continuous sors’ data at the cloud ity patient edge monitoring Seamless image manipula- still images dynamic pro- image [41] tion gramming processing Task scheduling and allo- distributed com- dynamic pro- distributed [42] cation of resources in dis- puting resources, gramming processing [43] tributed systems incl. grids, cloud, [44] supercomputers, cost credits Continued on next page Table 3 – continued from previous page problem description resources formulation discipline bib planning water resources water resources dual inter- water re- [45] management systems un- val robust sources der uncertainty stochastic manage- dynamic pro- ment gramming (DIRSDP) method hydraulics and water re- water resources dynamic agricultural [46] sources simulating and op- program- consump- timizing water transfer ming and tion, envi- system integrated ronmental solution needs of water resource and hydraulic models stochastic dynamic pro- military resources; dynamic pro- military [47] gramming for military ap- financial cost gramming applications, plications determining soldiers/ medical support location, planning policies vs opponent’s behavior data center resource dy- energy; time; dynamic pro- data center [48] namic scheduling for en- computational gramming optimization ergy optimization, emis- resources: servers, sion reduction storage, routers; physical resources: cooling equip- ment, lighting equipment, power supply, distribution facilities Table 4: Selected resource-aware problems from various disciplines by resources and discipline, using genetic formulation problem description resources formulation discipline bib resource provisioning and financial cost; genetic algo- cloud comput- [17] scheduling in uncertain time (deadlines rithm ing cloud environments imposed) solving resource- problem specific re- genetic algo- cross disci- [49] constrained project sources; time rithm, trans- pline applica- scheduling problem with fer times for ble problem transfer times activities at formulation various loca- tions consid- ered solving resource con- problem specific re- genetic algo- cross disci- [50] strained multi-project sources; time rithm pline applica- scheduling problem ble problem (many projects, time de- formulation pendencies, constrained resources) solving resource con- problem specific re- genetic cross disci- [51] strained project schedul- sources; time algorithm, pline applica- [52] ing problem (RCPSP) compari- ble problem [53] son of GA formulation algorithms GA parame- [54] ter tuning decomposition [55] based GA quantum in- [56] spired GA Elitist GA [57] construction schedul- problem specific re- genetic algo- general prob- [58] ing/resource scheduling sources; time rithm lem formula- problem tion, bridge construction example considered troops-to-tasks problem military resources, genetic algo- military field/ [59, (generalized RCPSP, addi- time rithm applications 60] tional constraints) Continued on next page Table 4 – continued from previous page problem description resources formulation discipline bib grid resource allocation grid resources: genetic algo- grid comput- [61] computational rithm ing systems, stor- age servers, and network servers; time regional drinking water water resources; fi- genetic algo- water resource [62] supply nancial cost (pump- rithm research ing, purification, transport); ecolog- ical/environment value (vs potential damage, ground- water drawdown); energy groundwater manage- water resources; genetic algo- water resource [63] ment financial cost; en- rithm research vironmental value (risk of drawdown); time (pumping rate) surgery scheduling, max- hospital resources; genetic algo- healthcare sec- [64] imizing the use of operat- time (runtime of al- rithm tor ing rooms gorithm and indi- rectly because of re- source usage) scheduling problems on resource types: genetic manufacturing [65] flexible manufacturing machines (M), stor- algorithm, system systems (FMS) age buffers (SB), also other material handling approaches devices (HD), tool- like PSO, changing devices (TD), fixtures (FX) and pallets (PL); time protection of marine envi- cost; time; environ- genetic algo- environmental [66] ronment and allocation of mental burden rithm protection response vessels to mini- mize costs of oil spill at sea Continued on next page Table 4 – continued from previous page problem description resources formulation discipline bib Power aware resource re- resources, power genetic algo- cloud comput- [67] configuration consumption rithm ing processing of time- resources, power genetic algo- mobile edge [68] constrained workflows in limitation rithm computing mobile edge computing power-aware allocation energy, power con- genetic algo- cloud comput- [69] of virtual machines in a sumption rithm ing, virtualiza- cloud tion Solving resource con- problem specific re- genetic algo- Fog-cloud [70] straints in fog computing sources rithm computing, Internet of Things virtual network embed- problem specific re- genetic algo- network virtu- [71] ding onto underlying sources rithm alization physical infrastructure Additionally, during research we have encountered works that consider various formulations. Selected examples of these are shown in Table 5, described in terms of the same features as works in the previous tables. Table 5: Selected resource-aware problems from various disciplines by resources, mixed formu- lations problem description resources formulation discipline bib investigation of the qual- time; (financial) ILP, genetic applicable [72] ity and execution times cost algorithm, to scientific, of several algorithms divide-and- business for scheduling service conquer, and mixed based workflow applica- heuris- workflow tions with changeable tic GAIN applications service availability and approach parameters performance and energy execution time; en- (Halton high perfor- [1] trade-off analysis for run- ergy number) mance com- ning parallel applications sampling puting on heterogeneous multi of config- processing systems uration space for Pareto front generation Continued on next page Table 5 – continued from previous page problem description resources formulation discipline bib investigation of execution time; energy (regular, high perfor- [73, time vs energy consump- linear) con- mance com- 74, 75] tion trade-offs for parallel figuration puting applications using power (stemming capping, both using multi- from power core CPUs and GPUs limits) space exploration tugboat allocation opti- vessels; tugboats; combined marine [76] mization in container ter- time genetic al- research minals gorithm and ant colony optimization approximate dynamic cloud resources; approximate cloud re- [77] programming approach time (mapping dynamic source to resource management pre-purchased and program- manage- in multi-cloud envi- online requests to ming, rein- ment ronments, multi-cloud resources) forcement resource allocation learning algorithm to manage requests to the cloud with maximization of a cloud broker revenue 3. Conclusions – problem formulations and resources vs disciplines Preliminary identification of resource-aware problems by querying of Google and Bing search engines allows us to identify: 1. to what degree certain resources appear in various problem formulations, 2. which problem formulations are prevalent in various disciplines. Resources typically considered in various domains can be domain specific or more universal, such as time and financial cost. The aforementioned factors can be, based on the aforementioned analysis, summarized as follows. Resources often considered in various problem formulations are shown in Table 6. Table 6: Resources identified in various problem formulations dynamic programming greedy algorithms ILP GA resource time X X X X cost X X X energy X X X human resources X X computing and storage X X X natural resources X X resources in general problem formulations X X Furthermore, applications that are prevalent in various problem formulations are listed in Table 7. Table 7: Applications for which selected problem formulations are used dynamic programming greedy algorithms ILP GA application power/energy X X X general/specific resource management X HPC X grid/cloud computing X X resource allocation in communication X education X natural resources management X X military applications X X Additionally, we can identify common resources used in various applications/disciplines, apart from problem specific resources. The former can be identified as shown in Table 8. Table 8: General resources identified in various applications/disciplines HPC, grid/cloud power/energy nat res mgmt healthcare military resource time X X X cost X X X X energy X X data quality X ecological value X security X Finalizing this research, we can say that, apart from details shown in the aforementioned tables, we can generalize links between resources and problem formulations, resources and applications as well as applications and formulations among a relatively small number of these entities, which hints that some applications/disciplines can be linked by selected problem formulations. This, however, needs further analysis and identification of concrete variables and formulation mappings between these disciplines. Additionally, we can see that formulations such as dynamic programming and GA appear in research works in general problem formulations that are abstracted from particular applications but can be potentially mapped onto several application areas. 4. Future work Future work, extending the results presented in this paper, will involve the following: 1. involving other problem formulations such as other evolutionary approaches etc. 2. extending research in-depth by querying scientific databases, including Web of Science, Scopus and publisher’s like IEEE, Springer, Elsevier etc., 3. identifying other possible papers giving a broader-scope generalized approach to the subject, 4. finding actual links and generalizations between problem formulations that describe particular use cases. Some of the works, as noted above, refer to generalized problem formulations, while others have introduced problem specific constraints and specifics. It is possible to build an inheritance tree of resource-aware problem formulations by prior finding core problem descriptions. Acknowledgements This work is partially supported by CERCIRAS COST Action CA19135 funded by COST. References [1] A. M. Coutinho Demetrios, D. De Sensi, A. F. Lorenzon, K. 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