Research and Design for TPL-LIP based on Hybrid Cloud Architecture

Transcription

1 Research and Design for TPL-LIP based on Hybrid Cloud Architecture 1,4 Qiang Huang, 2 Xinyuan Lou, 3 Wei Wang, 1 Shaoquan Ni 1 College of Traffic and Transportation, Southwest Jiaotong University, SiChuan, , China 2 College of Information and Engineering, Southwest Jiaotong University, SiChuan, ,China *3, Corresponding Author Department of Electronic Business, Sichuan Finance and Economic Vocational College, SiChuan, , China, duanguobing@163.com 4 College of Information and Engineering, Sichuan Agricultural University, SiChuan, ,China Abstract Oriented at LIS, a logistics information platform (LIP) architecture for TPL based on hybrid cloud - hybrid logistics cloud (HLC) is proposed to solve disadvantages of isolated system structure and weak data processing ability on centralized information system. Through constructing order driven based core system on public cloud which owns powerful calculating abilities, whole logistics process can be controlled universally and isolated from business system. The proposed architecture can provide various ways of seamless accesses, data exchange interfaces and business system rental service for both general clients and enterprises. Finally, performance enhancement based on the proposed architecture for key node of logistics process flow is validated through order allocation experiment, and result shows that this architecture is not only of feasibility but also advantages on throughput performances compared with general centralized order processing model. In short, this architecture can provide effect suggestions for area logistics cloud construction. 1. Introduction Key words: Cloud Computing; HLC; LIP; Order Driven The development of e-commerce has brought new opportunities and challenges to logistics information systems. However, disadvantages of high costs, inadequate fund and distributed logistics areas according to government administration have made a number of small and medium enterprises not to be able to create and participate in co-operation with corresponding platforms. With trend of wide application of RFID / sensors and appearance of internet of things, various goods in the supply chain can be tracked more easily, and logistics information system can also provide business supports for internet of things. New logistics information systems will also bring developments in logistics industry. From view of district point, logistics information platform can be divided into types of regional platform, governmental platform and also TPL-owned platforms for their high business efficiency. Construction of a single enterprise information platform makes not only high costs of enterprise investment but also mess applications without uniform standards. Thus, a lot of resources have been wasted. Regional information platform is of little possibility to be developed and transformed to an centralized system, and a government administrated platform can only be developed under government administration which is of rather limited effects in logistics industry with a single district. With these International Journal of Advancements in Computing Technology(IJACT) Volume4, Number20,November 2012 doi: /ijact.vol4.issue

2 limitations, those platforms are not able to provide enough services to the whole society. In general, modern TPL-LIS has many disadvantages, such as narrow application, low efficiency and high cost. As a newly emerged field, percentage application of the TPL-LIS is all above 50% in Europe and other developed countries. At the same time, more and more clients choose to apply third-party logistics platform in daily business. A number of surveys show that there are still many problems remained in current third party logistics platforms in our country even though these platforms developed at a satisfactory speed. Lag of business networks and information systems have seriously affected the development of TPL. In addition, shortage of human resources, poor equipment and low management are also important reasons which lead to slow development in logistics platforms [1]. 2. Cloud computing and hybrid logistics architecture Passive acceptance of state is one of the serious problem existed in current architecture of logistics platforms. In this situation, when large amount of orders reached, only simple judgment can be made instead of passive related logistics information and opportunities to be offered to clients. In addition, single logistics platform is rather too weak in information source and service objects, at the other hand, logistics plan can t be chosen intelligently, so that no effect information can be offered to clients. As a kind of distributed computing, cloud computing distributes a single complex computing to multiple servers and returns results to clients, which are all processed through internet. It can calculate and analyze large amounts of data issued from clients in a short period of time, and also of both good calculation rate and stability. Cloud architecture which is based on cloud computing provides a large comprehensive platform for calculation and analysis of the data processing. Stable and efficient calculation made strong data sharing among clients to be possible. Advantages of cloud data sharing mainly reflect on SOA architecture which can combine loosely coupled services easily so as to provide higher quality and fast response. By application of common servers in existing SOA architecture based on hybrid cloud architecture, real-time data transfers and updates are achieved. Powerful cloud computing highly integrates and filters visitors information then provides information to those services providers. Cloud computing supports secure data transfer in secure sockets layer to prevent information interception and leakage; key data in cloud computing apply advanced data encryption and storage technology; CA digital checking and remote secure identity checking are also supported. Based on application of these technologies, all the information from clients can be published in public and SOA architecture based data sharing will be both fast and safe [2][3]. TPL companies can make full use of different features of hybrid cloud architecture when they apply cloud architecture in daily business. Since a large number of public clouds existing, larger companies are of capabilities of using private cloud platform module to analyze project data individually, which can not only improve business efficiency, but also increases data security. Data security is the premise of platform data sharing. Logistics information platform based on cloud computing provides services for data process by technologies of SaaS (Software-as-a-service), PaaS (Platform-as-a-Service), IaaS (infrastructure-as-a-service) and RF. Software engineers develop software according to clients requirements. Platform program operators maintain and manage data resources and terminal resources. In this paper, a logistics information platform architecture (hybrid logistics clouds) based on hybrid 422

3 clouds is proposed. Taking TPL companies information system as an example in the application section, and establishes a core system based on order-driven model on public cloud. This system can integrate whole logistics procedure by its powerful computing capability, and can be accessed easily and openly by logistics companies and users because of its cloud architecture [1][3]. 3. Design of TPL information platform architecture based on hybrid cloud 3.1 Platform Overall Architecture Different from the traditional framework of logistics information platform, in this paper, supply chain of logistics information platform is divided into four parts: process management platform, business platform, services module and public interfaces, and takes process management platform as core parts, which is to be deployed on hybrid clouds and formed as HLC. The overall architecture of HLC is designed as Figure 1 [1][3][4] : Figure 1. Overall architecture of HLC for TPLC As shown in Figure 1, logistics information platform of HLC is deployed on the public cloud, and is composed of three parts: process management platform, module Services and public interfaces. It was driven by order and is responsible for controlling of entire logistics process so as to achieve enterprise collaboration and data sharing between enterprises and clients. For Large logistics companies, customized business platforms are deployed on the private clouds can effectively use private clouds computing capacity to deal with internal business operations and reduce loads of public platforms. Business platform exchanges two-way data by MPI and Web Service with public platforms. SSL encryption is supported to confirm transportation and exchange securities among clouds. Public platform notifies service platform to process relevant business when one procedure instance reached a node. Business system only need to transmit state data agreed by platform actively to reflect the state of business processes status and not internal business data, so that enterprise key data security can be ensured effectively. Unified SOA based public platform interfaces are also provided, which can provide approaches for business systems in TPL within enterprises to access to the platform conveniently. For TPL companies without information systems, HLC will form a virtual private cloud logically above computing resources in public cloud and deploy default business systems on it. HLC provides 423

4 services in way of lease to those logistics companies. Business clients can apply and rental any business system modules freely according to their needs. Business system will establish an instance for each logistics company individually, and a separate virtual memory space is also created separately. Business processes and data among enterprises are independently and confidentially. As a public service system, HLC on information platform provides a unified logistics portal for various exchanges between clients and logistics companies. Logistics portal collects and processes information of different logistics companies, and convert it into unified format to clients. Clients can access to portal by means of PC or other mobile devices, so that they can track logistics status, query information of logistics companies. Portal will also provide information aids-push system to deliver information to target clients according to characteristics and orders of the clients, in this way, clients can find the most suitable logistics companies with less time. HLC first separates controlling of management system and business system, which are deployed in a hybrid cloud. In this way, both data processing and analysis capabilities can be improved effectively. At the same time, integration of data from multiple areas can also be handled simultaneously. By the way, public information platform can put their energies only on process control, better grasp of global procedure along information supply chain, and massive information data mining on logistics companies and clients. In general, more effective logistics solutions can be provided to clients [1][5]. 3.2 Cross hierarchy design and interaction model of HLC Cross hierarchy of HLC is designed as Figure 2 according to general architecture and TPL characteristics: Figure 2. Cross hierarchy of HLC computing model It can be seen from Figure 2, hierarchy of HLC was composed of two parts: cloud platform level and perceptual level. Cloud platform level is mainly responsible for LIP platforms construction, process controlling, data collection and processing, and also interactions with the clients. HLC Divides cloud platform into levels of SaaS, PaaS and IaaS for management individually. Infrastructure operator of IaaS layer is responsible for the daily management and maintenance of infrastructure and data. Pass 424

5 layer Provider is responsible for managing LIP core platforms and business processes. CCP (Cloud Computing Platform) Operator in SaaS layer focuses on management of business systems according clients requirements. RFID information systems support clients service according to their characters. Perceptual layer acts as a bridge between the cloud platform and goods through RFID middleware and reader. Data reading and storage by means of RFID can process and handling data in cloud platforms so that goods tracking and managing are realized [5][6]. The interaction module of HLC between public and private cloud is shown as below Fig 3: Figure 3. Interaction model of HLC between public and private cloud As shown in Figure 3, hybrid clouds offer us the advantage of data transportation and exchange between public and private clouds, in which the initial request raised by users is first handled by cloud proxy and forwarded to CIS (Cloud Information Server) model to get the authentication. User information is stored in CIS and they re given rights to handle their orders. As to raw requests, they would be sent to the PDF (Public Delegation Factory) and added to cloud node to be analyzed. SRF (Service Relay Function) has its importance that other clouds would be analyzed and handled here as well as the raw requests. Many nodes here are regarded as the bridge between PDF and Monitor. Data between two monitors are exchanged through Resource Server, because it is between two PDF and swapped by SRF (Service Relay Function). As for safety, hybrid cloud should have its own monitor to manage whole clouds operation. But resource server must monitor all its clouds in Internet to insure safety [7]. 3.3 Business flow reform of HLC Public logistics platform is key system in HLC and is deployed in public cloud with strongest capability of calculation, which is also responsible for managing whole logistics supply chain. An order driven information process model for business flow in HLC is proposed as below: 425

6 Figure 4. Order driven business flow of HLC From Figure 4 we can see, main task for public logistics platform is to reform logistics control process in key enterprises to order driven business flow management in whole supply chain. Business systems in enterprises are created as process nodes. With bus structure like this, public platform only need to control key processes and TPLC manage specific business their self, which can greatly improve and simplify efficiency of management in logistics process. In Figure 4, platform management system is on left side of order process system and business management & related interfaces are on its right side. Clients can send their orders and requirements to portal system (ULIP) after approve and authentications of CA. Portal system will filter entering of order process procedure when orders and requirements arrived. In order handling process, orders will firstly be preprocessed, filtered and feature vectors will be extracted; secondly, feature vector of orders will be standardized and sent to Order Allocation Process (OAP) system which is the core stage of whole business workflow. From the diagram we can see it contains to parts from above diagram, decision making process (DMP), combined with Order Influence Factors Analyzing (OIFA) system will generate order allocation plan, according to which orders will be assigned to TPL companies, and then order tracing process will start. The next stage is BPM of logistics which is an internal process of logistics companies and managed by rental system on public cloud or LIS on private cloud. In this stage, goods will be distributed according to the order and status of goods and will be sent to information platforms by related interfaces, so that clients can check their order status in real-time. After distribution, the stage is called as logistics transaction management stage. In this stage, business such as online payment, service feedback, conflicts arbitration and also service assessments will all be completed. Finally, related statistic data will be updated according to completion situation, which will be useful for later decision making. Business system is mainly composed of Storage Management, Distribution & Transportation Management, Operation Management, CRM and etc. Platform can support the business system in various ways, and the platform can offer a complete business system to logistics enterprises without LIS by means of lease. The complete business system is generally composed of business such as supply, storage, transportation and distribution. At the same time, an unified data exchange suite is also 426

7 supported for external system, which can help the data exchanges between business system on private cloud and other LIS [1][4][8][9]. 4. Performance testing for simulated DMP model under cloud environment In current operation pattern, as a part of ecommerce platform not logistics platform, order allocation is manually set by consumer, it can not take a global consideration of transportation resources, result in increased overall transportation cost and become the performance bottleneck of whole business workflow. On the contrary, considered as the most important link of ULIP (shown in Figure 4), OAP system proposed by this paper can concurrently receive/analyze massive order data, generate allocation plan by DMP and auto-assign orders to TPL companies instead of manual settings. The purpose of DMP is to achieve fast auto order allocation with the lowest overall logistics cost. DMP which determines the overall performance of OAP, is much suitable to adapt parallel computing environment (ex. Cloud computing), which can be easily extended to enhance performance. Here a simple simulated DMP model based on MapReduce is put forward, and then we mainly validate the effect of performance enhancement in cloud environment - Hadoop framework. In the experiment, the process of a batch order data is carried out on both single computer and the proposed architecture. Though the experiment, we can compare the performances of the two: in model based on cloud computing, an order handling process is divided into 4 stages: data input, map, reduce and data output. Process flow based on cloud is designed as Figure 5 [3] : Figure 5. Hadoop based order process and decision flow Input data structure of order model is defined as the following expression, suppose the input data as vector: v11 v1 n Vo (1) vm 1 v mn In this vector, row vector represents an order and column vector represents one parameter in the order, such as: type, quantity, origination, destination, conditions of transportation and etc. Data dictionary vector of TPLC is supposed to be as Ut: u11 u 1q Ut (2) uh 1 u hq 427

8 In vector Ut, row vector represents information of a TPLC; column vector represents one parameter of the TPLC, such as transportation type, area, transportation price and etc. Decision making model of order can be briefly expressed as following: Min(cos t) d i( SV ( U)) * ui( SV ( U)) k (3) Objective function to be chosen should be the TPLC with least cost which satisfied the order constraints [11]. In the above formula, S( V U) represents TPLC which satisfies the conditions for the transportation of order Vi, i(s( V U)) di( S V is the index of company Vi, ( U)) is the total transportation distance of company Vi for given order, and ui(s( V U)) k is the unit cost of company Vi [9][10][11]. Input data in this paper are all simulated data randomly generated by the program, include an order vector V(m= ) and a dictionary vector of TPLC U(h=100). Order vector is read by rows. In stage of Map, vector of order is matched with dictionary vector of TPLC and then analyzed. Those satisfied companies will be the candidates for calculation in following stages. In stage of Reduce, all the order vectors will be collected, and costs of the candidate companies will be calculated and decision should be made to choose the TPLC with least cost. Finally, in stage for output, allocation plan will be generated like an order-tplc mappings; the optimal TPLC will be listed at the same time with information of index, transportation cost and other fees statistics, Environment of hardware for experiment is listed in Table 1: Table 1. Hardware configuration of 10 servers for performance testing Server Name Server Role Configuration C1: DELL (Master Server) Intel(R) i5 CPU 4G Memory Datanode, Tasktracker, Jobtracker 64 Bit Redhat Linux C2 to C10: DELL (Slave Servers) Intel(R) i3 CPU 2G Memory 32 Bit Redhat Linux Namenode, Datanode, Secondarynode 428

9 Performances testing and analyzing are shown in Table 2 and Table 3: Table 2. Result of Performance Testing in Single Machine Environment Single server Data size Sequence number Time (ms) C cost Average time cost (ms) 2568 Table 3. Result of Performance Testing in Cloud Environment Cloud servers Data size Sequence number Time (ms) cost Average time cost (ms) C C1,C C1,C2,C3,C C1,C2,C3,C4 C5,C6,C7,C C1,C2,C3,C4 2 3 C5,C6,C7,C C9,C

10 From the result of experiment, compared with the two modes, calculation speed will be almost equal. With the increase of server quantity, performance will be improved rapidly. Because of the serial way of single server in data handling, it must be low in data process performance. In cloud computing environment, it has better service expansibility, more servers can be integrated seamlessly to offer their calculation capabilities so that parallel calculation speed can be improved and better capability can be achieved. From the experiment, the performance of order driven model based on third party logistics cloud can be verified and a screenshot of experiment result with 10 servers under cloud computing environment is shown in Figure 6: Figure 6. Screenshot of experiment result under 10 servers cloud environment 5. Conclusion and Perspectives With the rapid development of logistics industry, a modern logistics information system should be of characters of comprehensive, large-scale, intelligent capability of data process and analysis capability. As the development and population of cloud computing, the combination of logistics information system and cloud computing must be a trend. Taking TPLC as an example, a general solution of building logistics information system based on cloud computing is put forward and an experiment has also been carried out to verify the feasibility and advantage of the performance, which is an effective reference for logistics information system of large scale. 6. Acknowledgment This work was supported by the National level: National Science and technology support program, no. 2012BAH20F01;Municipal: Chengdu Major science and technology achievements transformation project, no.11zhzdo38jh References [1] Liu JJ, So SCK,Choy KL,Lau H, Kwok SK, "Performance improvement of third-party logistics providers - an integrated approach with a logistics information system", International Journal of Technology Management 2010, 42(3). 430

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