Storage Constraints Research Articles

Interactive & Multiscale Thematic Maps: A Preliminary Study

Abstract. Problem: Interactive or “slippy” web maps have revolutionized cartography. Slippy maps present a single, coherently-designed reference map that can be panned to numerous geographic locations and zoomed across multiple scales. Further, they apply scale-dependent style rules to detailed geographic datasets, with the resulting designs rendered as a large set of interlocking tiles. To account for constraints in data bandwidth, processing, and storage, only those tiles relevant to the user’s location and past interactions are served into the web browser or other application, resulting in a seamless, real-time user experience of “a map of everywhere”. These slippy tilesets often are used as basemaps for advanced cartographic web and mobile applications, overlaying thematic information and other linework. Arguably, such slippy map mashups are the most common map seen and used today (and perhaps of all time). Yet, most of the cartographic design canon was developed long before slippy maps were possible. Do any of our time-tested design traditions in thematic cartography apply in today’s interactive and multiscale mapping context? In this presentation, we discuss preliminary insights from an online map study about the design of interactive and multiscale thematic maps.

BlockSecIoTNet: Blockchain-based decentralized security architecture for IoT network

The exponential growth of the use of insecure stationary and portable devices in the Internet of Things (IoT) network of the smart city has made the security of the smart city against cyber-attacks a vital issue. Various mechanisms for detecting security attacks that rely on centralized and distributed architectures have already been proposed, but they tend to be inefficient due to such problems as storage constraints, the high cost of computation, high latency, and a single point of failure. Moreover, existing security mechanisms are faced with the issue of monitoring and collecting historic data throughout the entire IoT network of the smart city in order to deliver optimal security and defense against cyberattacks. To address the current challenges, this paper proposes a decentralized security architecture based on Software Defined Networking (SDN) coupled with a blockchain technology for IoT network in the smart city that relies on the three core technologies of SDN, Blockchain, and Fog and mobile edge computing in order to detect attacks in the IoT network more effectively. Thus, in the proposed architecture, SDN is liable to continuous monitoring and analysis of traffic data in the entire IoT network in order to provide an optimal attack detection model; Blockchain delivers decentralized attack detection to mitigate the “single point of failure” problem inherent to the existing architecture; and Fog and mobile edge computing supports attack detection at the fog node and, subsequently, attack mitigation at the edge node, thus enabling early detection and mitigation with lesser storage constraints, cheaper computation, and low latency. To validate the performance of the proposed architecture, it was subjected to an experimental evaluation, the results of which show that it outperforms both centralized and distributed architectures in terms of accuracy and detection time.

Multi-Objective Materialized View Selection Using Improved Strength Pareto Evolutionary Algorithm

A data warehouse system uses materialized views extensively in order to speedily tackle analytical queries. Considering that all possible views cannot be materialized due to maintenance cost and storage constraints, the selection of an appropriate set of views to materialize that achieve an optimal trade-off among query response time, maintenance cost, and the storage constraint becomes an essential necessity. The selection of such an appropriate set of views for materialization is referred to as the materialized views selection problem, which is an NP-Complete problem. In the last two decades, several new selection approaches, based on heuristics, have been proposed. Most of these have used a single objective or weighted sum approach to address the various constraints. In this article, an attempt has been made to address the bi-objective materialized view selection problem, where the objective is to minimize the view evaluation cost of materialized views and the view evaluation cost of the non-materialized views, using the Improved Strength Pareto Evolutionary Algorithm. The experimental results show that the proposed multi-objective view selection algorithm is able to select the Top-K views that achieves a reasonable trade-off between the two objectives. Materializing these selected views would reduce the query response times for analytical queries and thereby facilitates the decision-making process.

Farmers knowledge, perceptions and management of Kersting\u2019s groundnut (Macrotyloma geocarpum Harms) insect pests in Benin

BackgroundMacrotyloma geocarpum Harms is a neglected and underutilized crop in Benin subject to several constraints including storage insect attacks, which contribute to the decrease in its production. An ethnobotanical survey using a semi-structured questionnaire was conducted in 15 villages in southern and central Benin to document farmers’ perceptions of M. geocarpum storage insect pests and their traditional management practices.ResultsThe results showed that insect pest were the most important storage constraint of M. geocarpum. To overcome this constraint, the promotion of resistant landraces has been proposed by farmers. Six vernaculars names of storage insect pests of M. geocarpum were identified throughout the study area and all corresponding to Callosobruchus maculatus (F.) which proved to be the most abundant insect in the stocks. Palatability and fragility of seeds coat have been identified by farmers as the main factors favoring the infestation by this pest. Various storage containers of M. geocarpum have been inventoried. Our study revealed that education, storage containers, and Kersting’s groundnut landraces significantly influenced farmers’ perceptions of severity of insect pest damages. The use of chemicals and repellent plants were the main control methods used by farmers to protect stocks. Different levels of resistance of M. geocarpum landraces to storage insect pests were reported.ConclusionsThis study provides baseline information for development of integrated management approaches against storage insect pests of M. geocarpum. The perceived level of resistance to insect damage on Kersting’s groundnut landraces needs to be investigated.

Optimal production delivery policies for supplier and manufacturer in a constrained closed-loop supply chain for returnable transport packaging through metaheuristic approach

Recent advances in the product packaging materials have enabled the supply chain management systems to adopt returnable transport packaging policies to achieve economic and environmental sustainability. The application of the advanced self-healing polymers in packaging material has enabled the packaging to withstand fatigue associated failures with the increased mechanical strength. In this perspective, this paper develops a multi-attribute closed-loop supply chain model for self-healing polymers based returnable transport packaging with single supplier, single manufacturer, and multi-retailers under budget and storage constraints. A single-setup-multi-delivery (SSMD) policy is recommended for the centralized decision making of the supplier and manufacturer in a proposed supply chain management to improve the economic sustainability. To depict the real world situations for environmental protection, the effect of the variable aspects of transportation and carbon emissions are minimized through the optimal production delivery strategies. Multi-objectives of the proposed supply chain model include profit maximization and carbon emissions minimization of the system. A weighted goal programming technique along with three distinct metaheuristic approaches are applied to obtain the efficient trade-off among model objectives. The experimental analysis is carried out to illustrate the practical implication of the proposed supply chain management model and numerical results are analyzed for their robustness. The experimental outcomes for the application of SSMD policy are compared with single-setup-single-delivery (SSSD) policy, which proves that the SSMD policy improves the total profit of the whole system by devising the optimal number of shipments. The sensitivity analysis is carried out to study the behavior of the key parameters involved in the proposed supply chain management for varying decision maker preferences and significant managerial insights are obtained.

DeepCachNet: A Proactive Caching Framework Based on Deep Learning in Cellular Networks

Content caching at the network edge is considered to be a suitable technique for enhancing the efficacy of content delivery in cellular networks. Caching the strategic content at the SBS is critical due to storage constraints; however, it requires information about popularity dissemination that is not known in advance. Furthermore, the popularity of content is varied due to the fact that every mobile user connected to the SBSs has a different preference for content. Thus, the nature of the content a user prefers depends on the features of both user and content. This article proposes a novel deep learning-based proactive caching framework in cellular networks, called DeepCachNet, in which a vast amount of data is collected from the mobile devices of users connected to SBSs. The deep-learning methods called auto-encoder and stacked denoising autoencoders are applied to the collected data to extract the features of users and content, respectively. The extracted features are then used to estimate the content popularity at the core network. Based on the estimated content popularity, the strategic content is cached at SBSs to obtain higher backhaul offloading and user satisfaction. To validate the effectiveness of the proposed framework, a case study is carried out in which mobile data are gathered from connected mobile devices by using a developed android mobile application, and a simulation of the proposed framework is performed on the collected data. The results of the simulation show that the framework resolves the cold-start and data sparsity problem, and yields significant improvements in terms of backhaul offloading and the user satisfaction ratio. It achieves gains of up to 6.2 percent and 30 percent for backhaul offloading and user satisfaction, respectively.

Hybrid collaborative caching in mobile edge networks: An analytical approach

With caching at the base stations (BSs) in a cooperative manner, mobile edge caching (MEC) can alleviate the heavy backhaul burden and reduce the duplicated transmissions of content downloads, which has recently been considered a promising solution to cope with the exponentially increasing data traffic. However, how to maximize the storage utilization while reduce service latency and improve energy savings is still a critical issue in the large-scale mobile edge networks (MENs), since the growth of MENs in size as well as uneven users’ distribution make it difficult to determine which MEC should cache which content. To address this problem, we propose a hybrid collaborative caching (Hy-CoCa) design that jointly leverages local independent, intra-group collaborative and intra-network collaborative caching manners. MECs are clustered into disjoint groups, and then each MEC’s storage is partitioned into local, intra-group and intra-network portions. Local storage is reserved for storing the most popular contents to each MEC locally, so that users can directly fetch them from their associated MEC; Intra-group storage of different MECs inside the same group are regarded as an entity, which is used for collaboratively storing the middle popular contents, so as to reduce the probability for requesting contents from distant MECs; Intra-network storage of all MECs are leveraged for collaboratively storing less popular contents to different MECs in the entire MENs, as a means to improve the overall content diversity. Specifically, we first develop the Hy-CoCa’s framework to support users’ requests locally and conduct the construction of logical groups, considering users’ distribution and MECs’ proximity. Moreover, under the storage and popularity constraints, we formulate the storage allocation optimization problem to minimize average service latency and derive the optimal storage allocation. Furthermore, given an optimal storage allocation, we also formulate the request-aware content placement problem into an integer linear programming problem to maximize the overall energy savings. We prove the submodularity property of the objective function and propose a greedy algorithm with linear computational complexity, which can achieve (1−1/e)-optimality. Simulation results with real-world YouTube trace data demonstrate that our caching strategy can achieve 6% to 28% latency reduction and 9% to 75% energy savings improvement compared with other existing caching strategies.

Impact of JPEG 2000 compression on deep convolutional neural networks for metastatic cancer detection in histopathological images.

The availability of massive amounts of data in histopathological whole-slide images (WSIs) has enabled the application of deep learning models and especially convolutional neural networks (CNNs), which have shown a high potential for improvement in cancer diagnosis. However, storage and transmission of large amounts of data such as gigapixel histopathological WSIs are challenging. Exploiting lossy compression algorithms for medical images is controversial but, as long as the clinical diagnosis is not affected, is acceptable. We study the impact of JPEG 2000 compression on our proposed CNN-based algorithm, which has produced performance comparable to that of pathologists and which was ranked second place in the CAMELYON17 challenge. Detecting tumor metastases in hematoxylin and eosin-stained tissue sections of breast lymph nodes is evaluated and compared with the pathologists' diagnoses in three different experimental setups. Our experiments show that the CNN model is robust against compression ratios up to 24:1 when it is trained on uncompressed high-quality images. We demonstrate that a model trained on lower quality images-i.e., lossy compressed images-depicts a classification performance that is significantly improved for the corresponding compression ratio. Moreover, it is also observed that the model performs equally well on all higher-quality images. These properties will help to design cloud-based computer-aided diagnosis (CAD) systems, e.g., telemedicine that employ deep CNN models that are more robust to image quality variations due to compression required to address data storage and transmission constraints. However, the results presented are specific to the CAD system and application described, and further work is needed to examine whether they generalize to other systems and applications.

Two-machine flow shops with an optimal permutation schedule under a storage constraint

We consider two-machine flow shop scheduling problems with storage to minimize the makespan. Under a storage constraint, each job has its own size, and the total size of the job(s) being processed on the machine(s) and the job(s) waiting for the second operation cannot exceed the given capacity. It is known that the computational complexity is strongly NP-hard and in general an optimal schedule does not exist in the set of schedules with the same job sequences on two machines, referred to as a permutation set. Thus, we consider two cases such that an optimal schedule exists in the permutation set. These two cases have the special structure of processing times. In the first case, the processing times of two machines for each job are identical, while in the second case, machine 2 dominates machine 1, that is, the smallest processing time of the second group of operations is larger than or equal to the largest one of the first group of operations. We show that the first case is strongly NP-hard and admits a polynomial-time approximation scheme (PTAS), while the second case is solved in polynomial time.

Graph partition–based data and task co‐scheduling of scientific workflow in geo‐distributed datacenters

SummaryMost large‐scale scientific workflows take place in multiple collaborative datacenters for access to community‐wide resources, while adhering to each datacenter's non‐uniform resource limits. However, moving both initial input datasets with predetermined locations and intermediate datasets needing placement decisions across geo‐distributed datacenters hinders efficient execution of large‐scale data‐intensive scientific workflows. Thus, scientific workflow's data and task co‐scheduling deal with situations such as pre‐placed initial input datasets, placement of intermediate datasets and each datacenter's non‐uniform computation and storage constraint, while minimizing the cross‐datacenter data transfer. Since this scheduling problem is known to be NP‐hard, here, we propose a novel approach, based on the multilevel graph coarsening and uncoarsening framework, together with a specialized hybrid genetic algorithm having distinctive graph partition driven features of repair and local improvement, for scheduling data‐intensive scientific workflows in geo‐distributed datacenters and optimizing the cross‐datacenter data transfer volume. Extensive simulations, based on four real‐world workflow traces, show that our algorithm significantly reduces the overall geo‐distributed data transfer and demonstrate its effectiveness.

Deploying Data-intensive Applications with Multiple Services Components on Edge

In the information age, the amount of data is huge which shows an exponential growth. In addition, most services of application need to be interdependent with data, cause that they can be executed under the driven data. In fact, such a data-intensive service deployment requires a good coordination among different edge servers. It is not easy to handle such issues while data transmission and load balancing conditions change constantly between edge servers and data-intensive services. Based on the above description, this paper proposes a Data-intensive Service Edge deployment scheme based on Genetic Algorithm (DSEGA). Firstly, a data-intensive edge service composition and an edge server model will be generated based on a graph theory algorithm, then five algorithms of Genetic Algorithm (GA), Simulated Annealing Algorithm (SA), Ant Colony Algorithm (ACO), Optimized Ant Colony Algorithm (ACO_v) and Hill Climbing will be respectively used to obtain an optimal deployment scheme, so that the response time of the data-intensive edge service deployment reaches a minimum under storage constraints and load balancing conditions. The experimental results show that the DSEGA algorithm can get the shortest response time among the service, data components and edge servers.

RANCANG BANGUN OWNCLOUD DAN MELINDUNGINYA TERHADAP SERANGAN DDOS

Layanan penyimpanan menggunakan Google Drive ataupun DropBox merupakan layanan yang digunakan secara cuma-cuma di internet, sebagai besar menggunakan teknologi Cloud Computing. Hal ini berarti layanan pada Cloud Computing dapat disediakan dengan cepat dan meminimalisir interaksi dengan penyedia layanan vendor/provider Cloud Computing. Tempat penyimpanan data yang sering menjadi kendala yang bersifat pribadi ataupun pekerjaan, seringkali menyulitkan dalam mengakses. Gangguan keamanan dari aktifitas jaringan seperti Denial of Service dan infeksi virus yang cukup beragam. Kesulitan berbagi data dengan inipun menjadi kendala terutama menyeting batas waktu dokumen yang dibagikan, dengan siapa saja dokumen dibagikan dan hak akses apasaja yang diberikan. Kendala seperti inilah yang dihadapi sehari-harinya dan cukup mengganggu fleksibelitas terutama dalam rutinitas pekerjaan, maka dibutuhkan konfigurasi firewall untuk meminimalisir gangguan tersebut sehingga layanan tetap bisa diakses

Dynamic Spatial Equilibrium Models: an Application to the Natural Gas Spot Markets

European gas markets have gone through profound restructuring processes in the last 20 years. Directive 98/30/EC represented the first attempt to liberalize this market through the unbundling of gas supply and generation from the operation of transmission networks. The goal of this Directive was the creation of an internal market for gas by breaking up vertically integrated national companies. This commitment to integrate the European gas market was strengthened by Directive 2003/55/EC few years later and then confirmed by the Gas Target Model in 2011. This progressively increasing competition of the European gas market implies significant changes in the pricing system. In particular, it has been envisaged that long-term contracts, traditionally used to trade gas in Europe, could be partially substituted by short-term transactions operated in (liquid) spot markets, possibly organized as implicit auctions. Considering this framework, we investigate the problem of managing a dynamic system of spatially distributed spot markets where gas is traded on an auction basis. These markets are cleared taking into account transmissions, balance, and storage constraints. We propose a set of equilibrium type conditions for this system of markets and show that it is equivalent to a single-level variational inequality problem. In order to find its solution, which yields an equilibrium trajectory, we apply a dual type method. Numerical experiments are conducted on the gas spot markets developed in the Netherlands and in the United Kingdom.

Collaborative joint caching and transcoding in mobile edge networks

Video streaming has become a leading consumer of network resources in the last decade. Despite considerable developments, video content providers still face major challenges, which include minimizing data transfer from Content Delivery Network (CDN) or origin servers, CDN cost, and video startup delays. Recent edge computing technologies, such as Mobile Edge Computing (MEC) introduces new opportunities for Radio Access Networks (RANs) by providing computing and storage resources at the Mobile Base Stations (MBSs). Caching and processing videos at the edge networks relieve excessive data transfers over the backhaul links and minimize the viewers perceived delay. Collaborative caching and processing strategies have been proposed to efficiently utilize the edge resources, where neighboring MEC servers share the cached videos. However, such strategies introduce new challenges due to excessive backhaul links utilization for video sharing and limited resources. We propose a collaborative joint caching and processing strategy using the X2 network interface for sharing video data among multiple caches. Our design aims to minimize: (a) backhaul links usage for sharing video data, (b) network usage in transferring data from the CDN, (c) the viewer perceived delay, and (d) CDN cost. We also propose to fetch the higher bitrate version video from the origin/CDN servers and transcode it to the requested version on the fly to effectively use the Adaptive Bit Rate (ABR) streaming and online transcoding. This joint caching and processing approach is formulated as a minimization problem, subject to storage, processing, and bandwidth constraints. We also propose an online greedy algorithm that controls video transcoding, sharing using the X2 or backhaul links, and manages video caching and removing at the edge caches. Simulation results prove a better performance of our proposed algorithm compared to the recent edge caching approaches in terms of cost, average delay, cache removal, and cache hit ratio for different configurations.

Optimal economic dispatch for multi heat-electric energy source power system

An optimal economic dispatch model to balancing power and heat demand in a power system incorporating combined heat and power (CHP) units, intermittent energy sources such as wind and solar photovoltaic (PV), and battery energy storage system are proposed. The model is structured to include heat storage tanks and district heating network (DHN) with a goal of increasing the flexibility of CHP units, and satisfying better integration of the energy sources. A bi-level optimization model and its representative mathematical relations are formulated with taking into account the stochastic nature of wind and PV outputs and the storage constraints as well. Furthermore, the formulated relations are optimally solved. So, hybrid particle swarm optimisation (PSO) technique and sequential quadratic programming (SQP) method are applied to provide the optimal power delivered from each energy source. The objective function is formulated in a manner that the social welfare (SW) is maximized. The proposed model is applied to both of modified IEEE 24-bus and IEEE 118-bus test systems and simulated to show to a large extent that the model is real and robust as well as it provides a powerful solution to obtain the global optimal power dispatch. A comparative study is implemented for validation.

Static traffic assignment with residual queues and spillback

In this paper we propose a novel static traffic assignment model where the network loading component accounts for non-stationary residual queues as well as spillback effects by explicitly adding capacity and storage constraints. This is achieved by deriving a static version of the dynamic general link transmission model that describes average link inflow and outflow rates during a given time period under simplified temporal assumptions. The resulting model adopts the same concave fundamental diagram and first order node model used in state-of-the-art macroscopic dynamic network loading without the need to explicitly describe time. We show that our mathematical problem formulation is an extension of several other models described in the literature, including the traditional capacity restrained static modelling paradigm. The equilibrium problem is formulated as a variational inequality problem while the network loading problem can be formulated as a fixed point problem. We prove that a solution exists to each problem as long as all traffic is loaded onto the network (i.e., no queue spillback into an origin). The model is illustrated via numerical examples on several hypothetical transport networks.

Energy-efficient Virtual Machine Allocation Technique Using Flower Pollination Algorithm in Cloud Datacenter: A Panacea to Green Computing

Cloud computing has attracted significant interest due to the increasing service demands from organizations offloading computationally intensive tasks to datacenters. Meanwhile, datacenter infrastructure comprises hardware resources that consume high amount of energy and give out carbon emissions at hazardous levels. In cloud datacenter, Virtual Machines (VMs) need to be allocated on various Physical Machines (PMs) in order to minimize resource wastage and increase energy efficiency. Resource allocation problem is NP-hard. Hence finding an exact solution is complicated especially for large-scale datacenters. In this context, this paper proposes an Energy-oriented Flower Pollination Algorithm (E-FPA) for VM allocation in cloud datacenter environments. A system framework for the scheme was developed to enable energy-oriented allocation of various VMs on a PM. The allocation uses a strategy called Dynamic Switching Probability (DSP). The framework finds a near optimal solution quickly and balances the exploration of the global search and exploitation of the local search. It considers a processor, storage, and memory constraints of a PM while prioritizing energy-oriented allocation for a set of VMs. Simulations performed on MultiRecCloudSim utilizing planet workload show that the E-FPA outperforms the Genetic Algorithm for Power-Aware (GAPA) by 21.8%, Order of Exchange Migration (OEM) ant colony system by 21.5%, and First Fit Decreasing (FFD) by 24.9%. Therefore, E-FPA significantly improves datacenter performance and thus, enhances environmental sustainability.

Smart and safe self‐adaption of connected devices based on discrete controllers

The Internet-of-things, which designates the interconnection of physical objects, is a growing research area with many challenges. One of these challenges is the management of failures and unforeseen situations when using connected devices in real world applications. To this end, the authors propose a self-adaptation framework to deal with changes and take into account storage, computational and communication constraints. Their self-adaptation framework relies on constraints expressed in terms of quality of service (QoS) and event-driven rules to specify control objectives. Internally, the framework generates labelled transitions systems and builds on the fly synchronous controllers to guarantee QoS properties. Moreover, their framework has capabilities to concurrently deal with dynamic control objectives, monitoring and self-adaptation. To prove the practicality of their framework, they present a healthcare case-study to remotely monitor a patient at risk of myocardial infarction recurrence. The objective control rules easily specify how wearable devices should coordinate their behaviours to ensure safety, resilience and health-awareness factors. The implementation of their framework is fully distributed and scalable and include their development of a wearable, remotely configurable, QoS aware cardiac activity sensor.

Guest Editorial: Adaptive and Reconfigurable Service‐Oriented, Cloud and Virtualised Architectures

The concept of adaptive and reconfigurable Service Oriented Architecture (SOA) has been introduced in order to describe architectures that exhibit emerging functional and non-functional properties in changing contexts. An adaptive and reconfigurable can repair itself if any execution problem occurs, in order to successfully complete its own execution, while respecting functional and non-functional agreements. In the design of an adaptive and reconfigurable SOA, several aspects have to be considered. For instance, the architecture components should be able to predict and to detect degradations and failures as soon as possible and to enact suitable recovery actions such as scaling-up and scaling-out of virtual execution resources. Moreover, different non-functional service-level requirements might be considered in order to complete the execution in case of a predicted imminent or observed occurring failure. Contributions are devoted to the design and the implementation of adaptive and reconfigurable and related Cloud applications. The issue contains five papers related to the foundations of SOA and Cloud and the tools and applications for and Cyber-Physical environments. Service Oriented Architecture and Cloud Computing E2SM: A Security tool for Adaptive Cloud-based Service-Oriented Applications by Takoua Abdellatif proposes an End-to-end Security Model (E2SM) that aims to protect data confidentiality in adaptive cloud-based applications. Whereas Leah Mutanu and Gerald Kotonya study the key factors that influence runtime adaptation in service-oriented architectures, and examines how well they are addressed in 29 adaptation approaches intended to support service-oriented systems in the paper entitled What, Where, When, How and Right of Runtime Adaptation in Service-Oriented Systems. The paper entitled and Analyzing Elastic Strategies in Cloud by Khaled Khebbeb et al. proposes a Bigraphical Reactive Systems based approach to provide a formal modelling of Cloud systems' structure using bigraphs, and their elastic behaviours using bigraphical reaction rules. Smart and Cyber-Physical environments and Verifying Time-aware Processes for Cyber-Physical Environments by Imen Graja et al. proposes to extend Business Process Modeling Notation to support the various Cyber-physical system concepts and properties. The paper entitled and Safe Self-Adaption of Connected Devices Based on Discrete Controllers by Arthur Gatouillat et al. proposes a self-adaptation framework to deal with changes and takes into account storage, computational and communication constraints.

Optimal allocation strategy for temperature and humidity based on improved glowworm swarm optimization algorithm

Aiming at the problems that the factors such as strong temperature and humidity coupling, nonlinear time delay and other factors of flower vending machine which affects the optimal matching of the power of the machine. This paper proposed a temperature and humidity optimization allocation strategy based on a variable-step adaptive glowworm swarm optimization algorithm. By studying flower storage constraints, temperature and humidity coupling characteristics and analyzing the influence of temperature and humidity coupling coefficient on the operation cost of flower vending machine. This paper established a temperature and humidity power optimization matching model of the machine with the goal of the optimal economy, and used fuzzy control and improved glowworm swarm optimization algorithm to adjust compensation parameters and optimize the objective function respectively. Simulation results showed that the optimized allocation strategy proposed in this paper effectively reduced the energy consumption of the equipment and improved the economic efficiency of the whole machine