An Iterative Optimization Framework for Adaptive Workflow Management in Computational Clouds

Due to enormous data generation at rapid rate, processing of large volumes of data becomes a challenging assignment. This leads to many issues in computing environment since it requires complex data analysis. Issues related to performance and cost may certainly arise due to heterogeneity of the systems which are connected in the distributed environment. Various consumers submit their request to the dynamic cloud environment depending on their demand and requirement. To manage these volatile workloads, an efficient workflow management system is proposed in this paper. In order to deal with these vital issues, a novel design of adaptive workflow management system is described to support on demand service management in the cloud. This mechanism includes workflow scheduler and iteration controller to optimize the data processing through iterative workflow task scheduling mechanism. Upgrade fit—is an optimization technique that dynamically and continuously reallocates multiple types of cloud resources to fulfill the performance and cost requirements of cloud resources. Using this algorithm, iterative workflow tasks can be executed repetitively for data processing and workload execution. The performance of the algorithm is evaluated by using weather forecast workflow. Since the performance of the algorithm is showing considerable efficiency levels, the same algorithm can be used in real time large volume data processing. The evaluation results indicate that the system can effectively handle multiple types of cloud resources and optimize the performance iteratively. The present paper has implemented the algorithm in cloud computing environment using CloudSim that has proved that the completion time is being minimized with maximization of resource allocation.

The nature and peculiarities of formation of risks of economic activity of enterprise are researched. Expediency of application of instruments of adaptive risk management of economic activity of enterprise on the basis of use of the situational approach and study of the risk management system in dynamics is substantiated. A model of adaptive risk management of economic activity of enterprise on the basis of precedents has been formed, which includes subsystems of active and reactive risk management. Important components of influence on the adaptive management system are the current risk situation caused by the existing risk factors, the dynamics of risk factors development and the strategy of risk management of economic activity adopted by the enterprise, which defines the targets, priorities and prerequisites for making adaptive management decisions at the enterprise in the face of risk. The accumulation of information about the object of management within this model allows developing optimal management decisions, accumulating information about adaptive decisions in the Bank of precedents of management decisions and using them as a template for making management decisions in similar (typical) situations. It is substantiated that an important feature of the development of the system of adaptive management of economic risks at enterprise is the presence of a certain direction of transformation - the vector of development of the adaptive management system. A vector model of adaptive transformation of the risk management system at enterprise has been proposed, which contributes to a comprehensive understanding of the process of development of the system of adaptive risk management of economic activity of enterprise. Building a model of risk management at enterprise on the basis of precedents, taking into account the vector nature of its development, will contribute to a holistic understanding of the patterns of formation and transformation of the system of adaptive risk management of economic activity at enterprise. The study substantiates the feasibility of using an adaptive approach in the system of enterprise economic risk management, which is considered as a flexible open system capable of self-learning and progressive development. The use of precedents for making adaptive management decisions increases the system's ability to self-develop and more effectively adapt to the changing conditions of the external and internal environment of the enterprise.

In order to improve the reserve adequacy of high proportion new energy power systems and realize the efficient consumption of large-scale new energy, the source of reserve resources is expanded to the source network and load storage, and the operating reserve level that can be provided by the source network and load storage flexible regulating resources is quantified in terms of the direction of regulation and the mode of participation. Then, according to the regulation idea of “synchronous units bear the responsibility during regular hours and multiple types of resources share the responsibility during special hours”, a coordinated and participatory operating reserve capacity allocation strategy of source, network, load, and storage is proposed, and a reserve distribution optimization model incorporating multiple types of heterogeneous resources is constructed to achieve the optimization of reserve distribution of each type of reserve resource in the whole network. The optimization model of standby distribution is constructed to incorporate multiple types of heterogeneous resources and realize the step-by-step refinement of standby resources distribution among the whole grid - resources of the same type - units/stations. A case study is carried out based on a provincial power grid, and the effectiveness of the proposed strategy is verified by comparing it with the existing operational reserve allocation guidelines.

The aim of the study was to investigate the application and importance of Learning and Adaptive Management by the Ministry of Women Affairs, Community, Small and Medium Enterprises Development (MWACSMED) and beneficiaries when implementing development programs in Zaka district. The study investigated the objectives of the programs implemented by MWACSMED in Zaka district, the extent to which learning, risk management and adaptive management mechanisms are used by MWACSMED Zaka district are effective. The study also examined the challenges faced MWACSMED and beneficiaries with development programs in Zaka district. The study also aimed at developing a model for learning, risk management, adaptive management, monitoring, and evaluation for the efficient and effective implementation of development programs and the Monitoring, Evaluation, Learning, Adaptive and Risk Management model (MELARM) was developed. The researcher chose to conduct his research in this area because Zaka district is one of the districts in the country that is heavily reliant on subsistence farming, with farmers lacking adequate rain and inputs. Furthermore, there is no other industry or activity in the district that contributes to people's well-being. According to Chingarande and Matondi (2020:3), Zaka district is in a province that is reliant on activities such as livestock production, irrigated farming, wildlife conservation and smallholder agricultural production to support its population. Zimbabwe, where Zaka district is in the province of Masvingo used to be one of Southern Africa’s most vibrant, productive, and resilient countries, but the country has been facing economic and political challenges for close to two decades linked to decades of poor governance and corruption. Approximately 63% of Zimbabwean households are living in poverty and 16% in extreme poverty (USAID Country Development Cooperation Strategy 2016-2021:4). Given the above challenges in Zimbabwe, it is important to investigate whether decisionmakers are investing in long-term management approaches such as Adaptive Management. Decision-makers tend to be reluctant to invest in long-term management approaches which cover the concept of Adaptive Management and choose small-scale experiments instead. Moreover, they do not utilize opportunities to adjust and make improvements in their approach to management. Managers have often been making decisions based on funding opportunities that are short-term and political requests, rather than using the actual objectives of a program. The study used the quantitative approach in the form of a questionnaire that was designed using Google forms and shared through a link that was send to research participants through emails and WhatsApp. The sample population for the research questionnaire was 10 officials from MWACSMED and 7 beneficiaries of the programs managed by the Ministry in Zaka district. As shown by the study, the Ministry has not yet fully recognized the use of Learning and Adaptive Management to address the complexity and uncertainty of development programs. The Ministry has been managing programs through traditional monitoring and evaluation systems. The results of the study show that the Ministry was not using Adaptive Management approaches or learning thorough monitoring and evaluation systems, which may be because of socio-economic and political reasons. The introduction of adaptive programming and management calls for using information and learning to inform adjustments during implementation, which requires monitoring and evaluation systems that go beyond reporting on pre-specified indicators The study made recommendations for Learning and Adaptive Management mechanisms that are essential for development projects. The study also drafted a model known as the Monitoring, Evaluation, Learning, Adaptive and Risk Management Framework (MELARM). These recommendations are all based on the findings of the study. In general, government departments should avoid traditional thinking about program cycles, and they should merge program design, implementation, and evaluation through Learning and Adaptive Management. Government departments/agencies should also identify and document how procurement, contracting, work planning, reporting and risk management can foster adaptive programming and management to achieve intended development goals.

The article presents a systematic approach to the development and implementation of a monitoring and adjustment mechanism for the adaptive financial management system of e-commerce enterprises. It is substantiated that the effectiveness of financial flow management in the context of digital transformation largely depends on the enterprise's ability to integrate internal and external data sources, apply modern data collection and processing tools, and implement intelligent analysis technologies. A dual-component structure of the data base is described: the internal one includes CRM, ERP, CMS systems, logistics and transactional modules, while the external one covers price aggregators, social media, analytical services, legislative changes, and macroeconomic indicators. Data collection is ensured through API integrations, web scraping, user behavior tracking, and processing of calls and inquiries from contact centers. For data processing and analysis, stream platforms (Kafka, Spark), machine learning algorithms (ARIMA, LSTM), clustering methods (K-Means, DBSCAN), optimization models, and BI analytics tools (Power BI, Tableau) are used. The article outlines the software architecture requirements, which must include modularity, scalability, load resistance, and real-time data processing capabilities. The role of cloud services (AWS, Azure, Google Cloud) as an infrastructure foundation is emphasized. Special attention is given to the competencies of specialists involved in monitoring systems: data analysts who form primary reports; data scientists who build predictive models and automate analytical scenarios; and business analysts who translate analytics into strategic managerial decisions. It is proved that the harmonious interaction between data sources, technological solutions, and human capital is a critically important condition for creating an adaptive, risk-resilient, and efficient management system. An integrated approach to monitoring implementation is proposed, enabling transparency of financial processes, timely response to market fluctuations, accurate forecasting, and prompt decision-making. As a result, enterprises gain the ability to reduce financial losses, enhance flexibility, improve competitiveness, and lay the foundation for long-term sustainable development in a rapidly changing digital environment.

Cloud computing is essential for executing scientific workflows, offering scalable resources to process large volumes of data. However, efficient resource allocation remains a challenge due to fluctuations in demand and inadequate planning, which can increase makespan and thus energy consumption. In this context, infrastructure as a service is primarily used, allowing the rental of virtual machines with different characteristics. This study focuses on identifying the factors that influence the improvement of makespan for executing workflows from different disciplines and characteristics. The factors evaluated in this study are workfloF size, structure, and the number of virtual machines (VMs) required. The algorithms in this study consist of four heuristics and one metaheuristic, specifically a genetic algorithm (GA). Regarding the factors, the most relevant in CC is the number of VMs, where increasing the number of VMs reduces makespan to a certain extent, following the behavior described by the law of diminishing returns.

The rapid deployment of modern technologies such as iPhone, laptops, digital watch, social network, internet are resulted in handling of enormous amount of data in our day-to-day life. Since the data storage is increased abundantly and techniques to retrieve the relevant information and protection of data related issues to be focused efficiently to protect this big data. However various organisations and IT industries dealing with big data are faced with many security risks in storage and processing of data. Hence the protecting the big data is challenging task for the researchers. MapReduce is an efficient tool for processing large volume of data in a distributed environment. Security and secrecy of data are vital concerns while processing data carried out in MapReduce. In this article, the proposed secure data processing on big data using visual cryptography (SDPBDVC) technique ensures the protection of sensitive information by applying the visual cryptography technique and the processing done only by authorised entities.

Data analysis techniques need to be improved to allow the processing of data. One of the most commonly used techniques is the Association Rule Mining (ARM). These rules are used to detect facts that often occur together within a dataset. Though several methods have been suggested for the extraction of association rules, problems arise when data becomes large. To overcome such issue, we propose, in this paper, an efficient approach for ARM based on MapReduce framework, adapted for processing large volumes of data. Furthermore, because real-life databases lead to huge number of rules including many redundant rules, our algorithm propose to mine a compact set of rules with no loss of information. The results of experiments tested on large real world datasets highlight the relevance of mined data.

In grid systems, users compete for different types of resources such that they may execute their applications. Traditional grid systems are formed of organisations that join together for the purpose of collaborative projects. Resources of each of the participating organisation are pooled such that members of individual organisations may access the shared infrastructure. In general, each participant is both a provider and a consumer of resources. Whilst such systems address large organisations, in this paper we address democratic grid systems to satisfy needs of small organisations and even individuals, where on-demand grids may be formed by drawing idling resources available on the Internet. Whilst traditional grid systems resort to allocations that satisfy system specific objectives such as maximization of the resource utilisation, market mechanisms try to obtain allocations that are efficient economically. Economic mechanisms permit to achieve equilibrium between supply and demand and furthermore provide incentives for providers. Combinatorial auction has been argued as an effective mechanism to address the problem of resource allocation within grid systems. Auctions within which multiple types of resources in varying quantities may be traded eliminate the exposure problem by addressing co-allocation. In this paper, we describe a combinatorial exchange where multiple providers and multiple consumers may participate. We describe the winner determination problem that incorporates the time dimension, i.e. resource bundles may be requested for different time ranges, and describe a set of heuristics that have been designed to be fast. We show that these achieve a high level of efficiency as compared to exact solutions. The second part focusses on the pricing problem. The objective is to compute prices that represent the state of the market and bring trustworthy feedback to participants. Drawing on the approach taken by Kwasnica et al. (Manage Sci 51(3):419–434, 2005), we propose a pricing model that computes per-item pricing. Per-item pricing allows users to deduce the price of bundles that they require by linear summation. Furthermore, we propose a model that computes prices as a function of time, thus permitting users, in particular consumers to adjust their demand trading off price and time of execution.

This study assessed secondborn adolescents' perceptions of changes in the allocation of family resources following their firstborn siblings' departure from home after high school, and whether perceived changes were related to changes over 1year in secondborns' academic functioning. Participants were secondborn siblings (mean age=16.58, SD=0.91) from 115 families in which the older sibling had left the family home in the previous year. Allocation of resources was measured via coded qualitative interviews. Most (77%) secondborns reported increases in at least one type of family resource (i.e., parental companionship, attention, material goods), and many reported an increase in multiple types of resources in the year following their older sibling's departure. Consistent with resource dilution theory, perceptions of increases in fathers' companionship, fathers' attention, and mothers' companionship were related to improvements over time in secondborns' academic functioning.

Resource allocation strategy has been a hot anddifficult research topic in the field of cloud computing. We address the problem of resource fairness allocation in heterogeneous cloud computing where the multiple types of resource are considered, which is computationally intractable. There is a significant gap between the solutions obtained by existing heuristic algorithmsand the optimal solutions, leading to lower resource utilization and unfair resource allocation. We propose a hybrid algorithm based on ant colony optimization (ACO) and Tabu Search (TS) to maximize the minimum global dominant share in heterogeneous servers. In order to balance the exploitation and exploration of the algorithm, the new self-adaptive parameter settings are introducedas uniformly random numbers to enhance the diversityof the population. Furthermore, we propose a revising operation to change infeasible solutions into feasible solutions. Compared with some algorithms from the literature, the experimental results indicate that our proposed algorithm can maximize the globaldominant share fairly and increase the resource utilization, and it is highly adaptable to different situations.

The future of the automobile industry will be defined by the capacity to process large volumes of data originating from a multitude of interfaces in a timely manner. Presently, high-end vehicles have more than 100 electronic control units (ECU) performing multiple functions in parallel such as powertrain, chassis & safety, comfort, and infotainment. The components of this complex system communicate with each other via multiple network interfaces. As the trends toward advanced driver assisted systems (ADAS) and from in-vehicle communication to vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, exponential growth will be seen in the number of electronic control units used in an automobile. This growth will lead to a rise in fast, time-critical, and real-time data processing demand from multiple interfaces such as camera interfaces, sensors, other vehicles, and roadside infrastructure. In the automotive domain, data processing is time-critical and must be reliable. This system will demand high level of parallel, time-critical and fault-tolerant computing. Current multichip or software-based solutions may not be able to satisfy this demand. FPGAs are suitable for such applications as they are programmable and customizable, and can process high volumes of data in parallel on a single chip. Simultaneously, an FPGA can have sophisticated error correction algorithms providing reliability at the hardware level to provide the throughput required for automotive applications.

Forms control and error detection procedures used at the coordinating center of the Multiple Risk Factor Intervention Trial (MRFIT)

The Georgia Tech Research Institute (GTRI), under contract to the U.S. Air Force 46 Test Group, Radar Target ScatteringDivision (RATSCAT), at Holloman AFB, NM, has designed and developed a fttlly polarimetric, bistatic coherent radarmeasurement system (BICOMS). It will be used to measure both the monostatic and bistatic radar cross section (RCS) cftargets, as well as create two-dimensional, extremely high-resolution images of monostatic and bistatic signature data.BICOMS consists of a fixed radar unit (FRU) and a mobile radar unit (MRU), each of which is capable of independent monostatic operation as well as simultaneous coherent monostatic and bistatic operation. The two radar systems are coherently locked via a microwave fiber optic link (FOL). This paper discusses the system design requirements and keysystem features ofthe BICOMS and its major radio frequency (RF) subsystems.Keywords: bistatic, RCS, imaging, polarimetric, low observable. 1. INTRODUCTION AND BACKGROUND The use of low-observable technology has significantly reduced the monostatic RCS of aircraft, making their detection byconventional surveillance systems extremely challenging. The principal signature reduction technique employed in low-observable design is vehicle shaping. This technique relies on redirecting the incident radar energy in a direction away fromthe transmitting source, thereby reducing the monostatic signature ofthe vehicle. The laws of physics maintain that energymust be conserved. Hence, if the monostatic RCS is reduced by shaping, the incident energy must be distributed elsewheresuch that the signature is increased at some or all bistatic angles. It is therefore conceivable that a bistatic radar will havebetter performance than a monostatic radar for detection of low-observable targets where shaping is the principal method usedin RCS reduction.' While bistatic surveillance techniques have been proposed for several decades, only recently has thenecessary technology been available to perform both the difficult phase locking needed between the transmitter and receiverand the large volume of data processing required for the coordinate translation and matching between the transmit and receiveradar beams. These new advances in phase locking and data processing have made the development of bistatic surveillancesystems possible. In addition, a number of radar-directed anti-aircraft missiles in use today operate in a bistatic mode duringsome or all oftheir engagement envelope. Thus, there must be concern for the bistatic signatures of even non-low-observableair vehicles. The Department ofDefense (DoD) has therefore undertaken a program to upgrade and expand its bistatic RCSmeasurement capability at RATSCAT. BICOMS will provide the DoD with the capability to conduct monostatic andbistatic exploitation measurements of threat targets and evaluation of the radar signatures of blue targets.

Cloud computing and big data: Technologies and applications