Intelligent Automation & Soft Computing
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Developing a framework and algorithm for scalability to evaluate the performance and throughput of CRM systems Abdulrahman H. Altalhi, Abdullah AL-Malaise AL-Ghamdi, Zahid Ullah & Farrukh Saleem To cite this article: Abdulrahman H. Altalhi, Abdullah AL-Malaise AL-Ghamdi, Zahid Ullah & Farrukh Saleem (2017) Developing a framework and algorithm for scalability to evaluate the performance and throughput of CRM systems, Intelligent Automation & Soft Computing, 23:1, 149-152, DOI: 10.1080/10798587.2016.1184830 To link to this article: http://dx.doi.org/10.1080/10798587.2016.1184830
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Date: 15 January 2017, At: 00:19
Intelligent Automation & Soft Computing, 2017 VOL. 23, NO. 1, 149–152 http://dx.doi.org/10.1080/10798587.2016.1184830
Developing a framework and algorithm for scalability to evaluate the performance and throughput of CRM systems Abdulrahman H. Altalhia, Abdullah AL-Malaise AL-Ghamdib, Zahid Ullahb and Farrukh Saleemb a
Faculty of Computing and Information Technology, Department of Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia; Faculty of Computing and Information Technology, Department of Information Systems, King Abdulaziz University, Jeddah, Saudi Arabia
b
ABSTRACT
Scalability in hardware and/or software is an important factor for enhancing the performance of running processes as well as the throughput of the system of business organizations. This paper explores the need for scalability and issues related to extending the resources in order to ensure an improved and scaled-up Customer Relationship Management (CRM) architecture. The main contribution discussed in this paper is the proposal of a conceptual framework for measuring the process performance and throughput of the system beyond the selection of the type of scalability. Furthermore, this paper concerns the CRM system, as customer requests, their online transactions, and responses need a fast and efficient system. Taking into consideration all these factors, ultimately this paper proposed a customer-friendly framework for measuring the process performance and throughput of the system. Finally, the proposed framework’s steps are shown in an algorithm calculating process performance and throughput of the system.
1. Introduction Scalability is one of the most important aspects for growing organizations, which requires emphasis on a timely basis. As a business increases its value, durability, robustness, and reliability, it ultimately increases the number of customers as well. Thus, organizations need to scale up their systems as customers and their requests increase in order to satisfy them efficiently. As mentioned by Smith & Williams (2002), an important key for ensuring that businesses can sustain their performance is scalability in order to increase throughput of processes and response time of running transactions. This paper mainly investigates increases in the efficiency and management of customer relationship management systems (CRM) for any organization by dealing with scalability issues. Conceptually, two thoughts of scalability are discussed by Williams & Smith (2004) hardware and software scalability. Scalability means adding up hardware resources without any amendment in software systems, which ultimately improves the performance of the overall system. It shows that the system will be scaled and capable of handling more requests, improving response time, and eventually increasing the throughput of an application (Williams & Smith, 2005). Further described that the two basic strategies of scaling the system, known as vertical scaling—increasing the resources (CPU, RAM, and hard disk) in a single server—and horizontal scaling—increasing the additional server to improve the performance and throughput (Williams & Smith, 2005). The main benefit of scalability is the ability to maintain the system’s performance by executing multiple processes at a given time. Multiprocessing requires many sources to be upgraded while measuring performance, described by Gunther (1996) to include transforming data between processors and main
CONTACT Zahid Ullah © 2016 TSI® Press
[email protected]
KEYWORDS
CRM; scalability; performance; efficiency; throughput
memory, writing and dealing of input and output requests, and exchanging data. Thus, scalability has many issues to deal with and improve; accordingly, it is important to determine which type of scalability is most suitable and applicable based on established business requirements, rules, and budget specifications. As performance measurement is a complex issue that depends on different circumstances and scenarios generating by an organization, it is based on both types of architecture— namely, software architecture (Becker, Greve, & Albers, 2009; Kazman, Abowd, Bass, & Clements, 1996; Tekinerdogan, 2004; Williams & Smith, 2002) and hardware architecture (Williams & Smith, 2004, 2005; Zhao & Raychaudhuri, 2009). Regarding performance measurements for software architecture, one method discovered by Smith & Williams (2002) is known as performance assessment of software architectures (PASA). This method includes identifying the number of risks affecting the performance of the software system; overhauling these risks using this method will provide a list of strategies to reduce and overcome identified risks. On other hand, Williams & Smith (2004) proposed a model, which deals especially with measuring the performance of hardware architecture after adding new resources to the system. This paper also investigates the issues related with vertical and horizontal scaling to improve the hardware architecture of a CRM system. The presented model has been improved by adding different perspectives in terms of vertical and horizontal scaling. Furthermore, mathematical expressions and algorithms for measuring performance and throughput are discussed in detail. Scalability issues are predominantly based on the system, which is dealing with many processes initiated from several online and offline clients, where the time and response time is critical to follow. As a scaled-up system provides better
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performance, systems need to be scaled up through hardware and software perspectives. Unfortunately, scalability has no formal definition to follow, and it has been imputed between hardware and software architecture (Hill, 1990). Scalability is purposefully used to speed up the processing time and improve the efficiency of the system (Amdahl, 1967; Eager, Zahorjan, & Lazowska, 1989; Zhang, Yan, & Ma, 1994). Gunther (1993) proposed the performance metrics for scalability of transaction processing system to calculate the throughput of the system. Different scenarios of scalability have been discussed to check the performance of the systems, including the optimistic capacity model and generalized scale-up. Sarukkai (1994) addressed the issue of scalability by experimenting with SPMD messages passing parallel programs for automatically defining scalability trends for a class. Scalability has been applied in different trendbased applications to improve the performance and availability of resources, even when a large number of requests arrive in the system simultaneously (Bellavista, Corradi, & Foschini, 2013; Li, Yu, Zheng, Ren, & Lou, 2013). Many other models have been presented to overcome scalability issues. One of the pioneer studies was presented by Amdahl (1967), who sought to speed up the system using comparisons between parallel processing and single serial processors. According to Amdahl’s law, the workload is fixed while the number of processors can be increased. Amdahl presented the capacity model, which is defined as:
( ) A p =
p 1 + 𝜎(p − 1)
(1)
where p is the number of processors and σ is the ratio of work or tasks that cannot be run parallel. Gustafson’s law (Gustafson, 1988) improved the concept of Amdahl’s law by describing the same situation in a fixed time ratio. The number of processes can sometimes be increased due to extra computational sub-tasks. Gustafson’s equation to provide scalability speed up by using a fixed time ratio is describes as: �
G(S) = p + 𝜎 (1 − p)
(2)
where p is the number of processors and σ’ is the ratio of work or tasks run to number of processors in a given fixed time. Amdahl’s and Gustafson’s work was summarized by Kazman et al. (1996) that Gustafson’s law describes fixed-time speed-up while Amdahl’s law describes fixed-size speed-up.
customer life cycle in an enterprise, validated by Santouridis & Tsachtani (2015). On contrary, Josiassen, Assaf, & Cvelbar (2014) discussed that more investment doesn’t guarantee that firm performance will also be improved, because the ideal situation depends on the capability of CRM to deal with customers. Therefore, investment on right resources can support CRM capabilities as in this paper we investigated the idea of adding up resources based on the examined requirements. This paper explores the issues related to the scalability of CRM systems in business organizations. Businesses are important entities in every country that must show robustness in online transactions, such as purchasing, selling, transferring credit, and providing other customer-related services to ensure the easiest and fastest transactions when dealing with customers both locally and globally. For these reasons, companies invest sufficient budgetary funds in securing customer information and transactions, thereby improving their organizational architecture and customer care for smooth and successful information flows. This paper discusses a scalable CRM system in which customers encounter a fast and robust system rather than waiting in long queue (Williams & Smith, 2004) for responses to their queries. The discussion further includes the presentation of a mathematical model of CRM scalability in terms of resource upgrades to come up with a well-structured and organized architecture for companies. The model is then refined using an algorithm discussed in a later section of this paper. 2.2. Mathematical model descriptions The presented model was built to scale-up the resources and reduce the response time from the system to the clients’ requests for any services. Customers need either a fast and abstract way to access online systems or they might simply switch to a competitor’s company. In an effort to retain loyal customers, in this paper we remove the related myth and mystery (Williams & Smith, 2004) of CRM systems in businesses by scaling them up with new resources to follow the customers’ queries. Our presented model is shown in Figure 1. The mathematical description of the model is explained in the following algorithm, and Table 1 summarizes the list of notations used in the algorithm.
2. Model building 2.1. Problem statement The facilitation of loyal customers through a CRM system is one of the important concerns of business companies. Thus, companies are investing enough budget every year to deploy new and advanced software packages (Chang, Park, & Chaiy, 2010; Kim & Kim, 2009). Whether companies adopt readymade software packages or develop their own, their main purpose is to provide customers with an easy channel of accessing online products and services offered by the companies. Some major benefits for the companies using such advanced and expensive software are the rush of new customers, experience of an advanced working environment, and extensive growth in the sales volume for the company’s products. This highlights the advanced organizational and technological CRM resources that have positive impact on CRM processes throughout the
Figure 1. A Conceptual Framework for Measuring Process Performance and Throughput.
Intelligent Automation & Soft Computing
Table 1. Algorithm Notations. Notation ti ∀pi ta ∀pi tj ∀pi TAM ta ti pi tj te PPM TP Accuracy Ratio
Description Entry timestamp ti for all processes pi Allocated time ta for all processes pi Exit timestamp tj for all processes pi Time allocation method; assigned time for processing Allotted time of processes Entry time of processes Number of processes Exit time of processes Actual execution time for each process Process performance measurement Throughput Depends on organization’s architecture and number of customers dealt with per day.
Begin Process Entry Timestamp ti ∀pi send processes pi for (i = n; i >= 1; i - -) DO Time Allocation Method (TAM) t𝛼 ∀pi received argument ( ) Process pi End for Process Exit Timestamp tj ∀pi te =tj - ti PPM = 0 TP = 0 PPM = te - ta for (pi = 1; pi∑ = 0 && TP < Accuracy Ratio( ) ) Then The system need scalability if (case is Vertical Scaling) Then {add resources; i.e. Processors RAM, Hard Drives}; else if (case is Horizontal Scaling) Then {add more server} else The system is Scaled up; End if End
Algorithm: Mathematical Representation of the Proposed Model The algorithm presented above is developed for the purpose to calculate the performance and throughput of the proposed conceptual framework for scalability of CRM systems. Accuracy ratio is the expected response of the system on which we can decide whether the system is upgraded or need to be upgraded with extra resources. In this algorithm, we focused on hardware scalability to enhance the capability of the current systems and increase the throughput in the given time span. As mentioned in the algorithm, if the throughput is less than the accuracy ratio so the system needs to be scaled up. In this framework the system will be accurate if both conditions are satisfied. The remaining steps will be done based on the nature of the systems or organization’s requirements either vertical scaling or horizontal scaling.
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representation of this scenario on the server will be (t1p1) + n � � �� ∑ (t2p2) + (t3p3)+…..+ (tnpn), which is represented as ti pi . i=1
All these processes will be scheduled based upon the time allocation method (TAM), including last in first out (LIFO), first in first out (FIFO), and round robin (RR) on the bottleneck of process scheduling, as shown in Figure 1. TAM will allocate the time to each process for execution in the system as ta. After the execution of the process, exit time tj will be assigned and ultimately execution time measured as te, which is the difference between exit time tj and entry time ti, as shown in the algorithm. All these variables explain whether the process has been executed in the same timeframe allocated by TAM. Our next step in this work is to evaluate the process performance measurement (PPM) and throughput (TP).Using the scenario presented in the model and the algorithm, our definition for PPM in this work is shown in Equation 3, which is the difference between the executed time and allocated time of all processes. PPM will be calculating on periodic basis.
PPM =
m ( ( )) n ∑ ∑ ( ( )) ta pi te p j − j=1
(3)
i=1
where ta is the allocated time and te is the execution time of all processes p. The results obtained from PPM show that, if the execution time was more than the time allocated by TAM for execution, the system is not scaled-up, and more resources need to be added for efficiency as customers post thousands of requests every day. To grasp such issues from the system, servers or other resources are needed to scale-up the system and reduce the response time. As shown in Figure 1, we mentioned two types of scalabilities. In this research, the company’s architecture determines whether they are practicing vertical or horizontal scalability. The difference between these two kinds of scalabilities was discussed in the introduction section of this paper. TP is one of the objectives of this paper, is defined in equation 4 (processes per unit execution time). The system TP will be calculating using the system’s response time to the clients’/ customers’ requests for each process.
Throughput =
n ∑ pi i=1
te
(4)
where te is execution time of each process.TP is inversely related to response time, as shown in equation 5: The lower the response time, the higher throughput.
Throughput ∝
1 Response Time
(5)
In this paper, we aim to increase the system TP, and we presented a model and an algorithm to improve the system TP, performance, and efficiency using the scale-up technique.
3. Conclusion and future work 2.3. Prototype of model and algorithm building Whenever a request is generated by the customer, the process pi thread(s) will be initiated in the system and entry time ti will be assigned. All processes will follow the same naming format: p1in time t1, p2 in time t2, and so on. The mathematical
This paper demonstrated that scalability is a major concern of every business organization in terms of hardware and software. Here, we focused on hardware scalability in online CRM systems to reduce the long wait for responses to customers’ asked queries/ transactions. We improved the process performance measurement
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and throughput of the system in mathematical expressions. A system is considered to be scaled-up if the process performance measurement is high and throughput matches the accuracy ratio of a particular organization. However, the system will need to be scaled up with the use of additional resources. We presented a mathematical model related to scalability that can be applied in business organizations. The model might need to be changed for different architectures, but the core concept will remain the same. Furthermore, based on the presented model, we developed an algorithm to provide additional understanding of the model. In future, the same concept will be implementing in the banking sector and other business organizations where customers need fast and efficient online systems. The model will be validated with empirical data from the selected case studies.
Disclosure statement No potential conflict of interest was reported by the authors.
Notes on contributors Abdulrahman H. Altalhi is an associate professor of Information Technology at the College of Computing and Information Technology at King Abdulaziz University. He has obtained his Ph.D. in Engineering and Applied Sciences (Computer Science) from the University of New Orleans on May of 2004. He served as the chairman of the IT Department for two years (2007–2008), the Vice Dean of the College for five years (2008–2014). Currently, he is serving as the dean of the college of Computing and Information Technology. His research interests include wireless networks, software engineering, and computing education. Abdullah AL-Malaise AL-Ghamdi received a Ph.D. from George Washington University of USA. He is an associate professor of IS and CS, Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University. He has worked as a Chairman of IS Department. Currently he is the Vice Dean for Graduate Studies and Scientific Research. He has overall 20 years of experience in education field, mainly in teaching and curriculum building of several courses. Zahid Ullah is a lecturer at Department of information systems, Faculty of Computing and Information Technology (FCIT), King Abdulaziz University, Jeddah Saudi Arabia. He received his master’s in Computer Science from University of Peshawar, Pakistan. His research interests are business-IT alignment, IT business values, customer relationship management (CRM) and enterprise resource planning (ERP). He has worked as a researcher in King Saud University, Riyadh, Saudi Arabia for 5 years and published many papers in his research areas. Farrukh Saleem received a Master’s Degree from University of Karachi, Pakistan. He has worked as a researcher in King Saud University, Kingdom of Saudi Arabia. Currently he is working as a Lecturer, Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University. He has overall 10 years of experience in education field, mainly in teaching and different management work.
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