Queueing Based Edge Server Selection in Content Delivery Network Using Haversine Distance

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1 International Journal of Eltronics ommunication and omputer Thnology (IJET) Queueing ased Edge Server Seltion in ontent Delivery Network Using Haversine Distance Debabrata Sarddar Department of omputer Science & Engineering University of Kalyani Kalyani, India Sandip Roy Department of Information Thnology rainware Group of Institutions Kolkata, India Rajesh ose Senior Projt Engineer Simplex Infrastructures Ltd. Kolkata, India bstract New generation of thnology is inclining towards the uphill cloud computing thnology. Many users and organizations could not able to embrace cloud services blindly bause of their uncovering surity issue for managing huge amount of data. Data is transmigrated between different s which are disseminated globally. In cloud computing large number of cloud user should be cognizant of the physical location of their data over the earth surface to guarantee whether their data localization should also be maintained different data privacy law. t any peak hour when large number of cloud users request send to the accordingly network congestion and the number of data loss are drastically increased, the network bomes unmanageable. ontent Delivery Network is solution to remove these network hazards. We build the queueing based edge seltion algorithm to handle huge number of requests made by giant web customers to overcome the intrinsic limitations of cloud network. Keywords- loud omputing; ontent Delivery Network; Edge Server; Haversine Distance; Network ; Theory I. INTRODUTION ontent Delivery Network (DN) replicates contents over several edge s placed at various geographical locations for handling the request of burst crowds over the internet [1]. DN improves the performance of the cloud network in which popular web services suffer network congestion due to the large demands made on their services by serving user s request through the nearest and this approach helps to reduce the response time of the user request. The replica of the end-users content are distributed among a set of edge s scattered over geographical regions using content replica placement algorithm. The request routing infrastructure is responsible for redirting client request to appropriate edge s when requests are generated from end-users for particular services for the original host and original host is far away from the client at that instant [2]. DN improves the network performance by maximizing bandwidth and maintaining corrtness by content replication. In this paper, we focus on searching an edge such that the latency time between client and selted edge must be minimized. Therefore the average access time of the network should be reinforced. II. PRIVIOUS WORK l-mukaddim Khan Pathan and Rajkumar uyya presented detail taxonomy of DNs with respt to four different factors as request-routing procedure, content replication mhanisms, load balancing methodology and cache management [2]. In this paper, they have further build up taxonomies for each of these paradigms to relegate the common trends in content networking. Here comparison of existing DNs are articulated in such a manner that helps one to get an insight into the thnology, services, strategies, and practices which are currently followed in this field. S. Saroiu, K. P. Gummadi, R. J. Dunn, St. D. Gribble, and H. M. Levy examined four content delivery systems such as web traffic, ontent Delivery Network of the kamai and Kazaa and Gnutella peer-to-peer file sharing traffic [4]. G. Pallis,. Vakali, K. Stamos,. Sidiropoulos, D. Katsaros and Y. Manolopoulos addressed the content replication problem. Their objtive is to minimize the average response time when clients fetch objts from the nearest edge [5]. Khai Hsiang Wong, Phooi Yee Lau, and Sungkwon Park focused on simulating the concepts of Soarin and their simulation thnique concentrate in the relationship between the placement strategy and throughput among different edge s [6]. In queueing theory based cloud computing, M/M/S queueing model is used for multiple s overcome M/M/1 queueing model for reducing the mean queue length and waiting time [7]. III. OUR LTENY SED PROPOSED SIMULTION TEHNIQUE Our latency based edge simulation thnique aims to calculate latency from sender to reiver by focusing on the area of calculating propagation, serialization and ISSN: IJET 749

2 International Journal of Eltronics ommunication and omputer Thnology (IJET) queueing. Propagation depends upon the distance between source and destination. We can calculate distance between two locations using haversine formula [8]. Serialization concentrates in the bandwidth available between sender and reiver [3]. Here we consider the M/M/S queueing model to calculate queueing [10]. When the bomes busy, queue length and average waiting time will be increased. Finally we compare our latency based proposed simulation thnique with the latency using M/M/1 queueing model to calculate queueing to improve quality of service (QoS) in cloud environment [9].. alculate distance between two locations on the earth using haversine formula The haversine formula is used for calculating distance between sender and reiver on the earth evaluated from their given longitudes and latitudes. The following algorithm is used for calculating distance between sender and reiver. 1. R 6371 // Radius of Earth in k.m. 2. Φ 1 lat1.toradians() // Latitude of sender 3. Φ 2 lat2.toradians() // Latitude of reiver 4. λ 1 lon1.toradians() // Longitude of sender 5. λ 2 lon2.toradians() // Longitude of reiver 6. ΔΦ (lat2-lat1).toradians() 7. Δλ (lon2-lon1).toradians() 8. a Math.sin(ΔΦ/2) * Math.sin(ΔΦ/2) + Math.cos(Φ 1 ) * Math.cos(Φ 2 ) * Math.sin(Δλ/2) * Math.sin(Δλ/2) 9. c 2 * Math.atan2(Math.sqrt(a), Math.sqrt(1-a)) 10. d R * c // Haversine distance between sender and reiver over the earth surface. alculate propagation between sender and reiver using haversine distance Propagation is the amount of time required for the head of the signal to travel from sender to reiver over the cloud environment. Satellite communication uses eltromagnetic waves to circulate information through the space. The transmitter converts the information from eltrical signals to radio signals. The radio signals travel approximately at the speed of light for free space. Propagation can be calculated by the following formula: Propagation = d / Speed of Light. alculate serialization between sender and reiver Serialization is the amount of time required to push all the packet s bits over the channel between sender and reiver in the cloud system. Larger bandwidth plays an important role for reducing the serialization. Serialization is calculated as follows: Serialization = Packet size in bit / Transmission rate in bits per sond D. alculate queueing between sender and reiver is the amount of time a job waits in a queue until it can be served by a particular. We can calculate queuing or waiting time for single and multiple s using queuing theory [11]. Let s consider arrival rate and service rate and mean waiting time for M/M/1 and M/M/S queueing model are calculated by following formulas. Mean waiting time for M/M/1 is given by W q = λ / µ (µ - λ) Mean waiting time for M/M/S is given by Where W q = L q / λ customers in the queue is given by and the probability that no E. alculate latency from sender to reiver is an expression of how much time it takes for a packet of data to get from one point to another. from sender to reiver can be calculated by the following formula. IV. = Propagation + Serialization + Queueing OUR PROPOSED LOGORITHM FOR SELETING EDGE SERVERS OF PRTIULR DN The earth is divided into several regions by the geographers. The land of the surface of the world can be logically partitioned into eight regions: sia, Europe, ntarctica, frica, ustralia, North and South merica and the water surface of the world can be subdivided into four major oceans from largest to smallest, are the Pacific Ocean, tlantic Ocean, Indian Ocean, and rctic Ocean [12]. In our algorithm we are placing the edge s of a particular ontent Delivery Network (DN) over the eight regions and all the edge s are periodically synchronized by the DN Provider which is monitoring a particular DN. Selting a ISSN: IJET 750

3 International Journal of Eltronics ommunication and omputer Thnology (IJET) spific edge based on least latency of a particular DN Provider is the primary goal of our algorithm [13, 15]. To access a particular content from the web cloud user sends corresponding request to the particular web. Subsequently web provides the basic index page to the corresponding cloud user [14]. Then the original web redirt the request to the spified DN Provider for delivering the content. DN Provider can update the information periodically. Our simulation thnique helps DN Provider to calculate latency using propagation time, serialization time and queuing time of each registered edge of the DN Provider. Then DN Provider creates a list of registered edge based on the minimum latency. It then finds out the suitable edge from the list to establish the conntion with cloud user. In this paper we have studied the fact of selting edge with minimum latency value and at busy status would lead to high average waiting time. We selt the edge M/M/S queuing model instead of M/M/1 queuing model. We explained our proposed edge seltion algorithm of a particular DN in the next stion. Figure 1. The earth can be logically partition into eight regions and four oceans. lgorithm for selting edge of a particular DN 1 set U = {e 1, e 2, e 3,, e n } of multiple cloud users from different location over the earth surface request to connt with the original host using Uniform resource locator (URL) or spific IP address. 2 Par For each i = 1 to n do 2.1 Original host returns the basic index page to the corresponding requested cloud user e i. 2.2 Redirts the request to the DN Provider. 2.3 DN Provider chks the geographic coordinate values (i.e. latitude or longitude) of e i and based upon its coordinate value DN Provider selts the set S of edge spific or neighbor to the geographic region of the requested cloud user e i. 2.4 alculate haversine_distance between requested cloud user e i and each edge s from set S. 2.5 ollt the available bandwidth of each edge belongs to the set S. 2.6 ased upon haversine distance and bandwidth calculate propagation and serialization resptively for each edge of set S. 2.7 alculate the queuing of each edge belonging in set S of the DN Provider based on M/M/S queueing model. 2.8 alculate the latency of each edge belonging to the set S. 2.9 Selts edge S L based upon least latency from the set of edge s S If selted least latency edge S L has the ability to serve the content requested by the cloud user e i Seltion of the least latency edge S F gets finalized with S L Else Eliminate S L from S Repeating the step 2.9 for choosing the next least latency edge. 3 End for 4 onntion is established between requested cloud user e i and finally selted suitable latency edge S F of user spific geographic region until requested cloud user e i wants to disconnt the facility from the original host. 5 End. Practical example with result analysis Step 1. t any instant suppose there are multiple users from different region are requesting for a particular host. For simplicity let us consider a single cloud user from Kolkata, who wants to request for a mail service for sending 1500 bytes data using the mail located at hicago and the request send to the original at hicago. Step 2. The haversine distance d from Kolkata to hicago is km and original redirts the request to the DN Provider. Step 3. The DN Provider stores the details information of the edge s. ll edge s, which are geographically clustered based upon seven geographical regions as shown in Table 1. The set S of edge s spific or neighbor to the geographic region of the requested cloud user is generated enlisting those placed at Singapore, olombo, New Delhi, nkara, Islamabad and London resptively. Step 4. In Table 2 we have stored the mean waiting time of every edge using M/M/1 and M/M/S queueing model. Step 5. The haversine distance and bandwidth of each edge from cloud user located at Kolkata is calculated and enlisted in Table 3. Step 6. Propagation, serialization and queuing are calculated and periodically updated by the DN Provider as like as Table 4 and Table 5. Step 7. DN Provider selts edge S L placed at olombo from six edge s belonging to the set S and is ISSN: IJET 751

4 International Journal of Eltronics ommunication and omputer Thnology (IJET) capable of serving the request of user placed at Kolkata. So edge placed at olombo is our S F. Step 8. S F is connted with the cloud user of Kolkata until the user disconnts from the service. TLE I. TLE II. Host NME OF THE PLES OF THE EDGE SERVER OF PRTIULR DN MEN WITING TIME OF EVERY EDGE SERVER USING M/M/1 Name of the region 1 sia rriv al rate usto ND M/M/S MODEL Servic e rate usto Mean waiting time M/M/1 Mean waiting time M/M/S hicago Edge rriv Servic al rate e rate usto usto Name of the place of edge Singapore olombo New Delhi nkara Islamabad 2 Europe London 3 North merica Mexico Ottawa hicago (Host ) Kingston 4 South merica Santiago uenos ires 5 frica Harare ape Town 6 ustralia anberra 7 ntarctica Not available Mean waiting time M/M/1 Mean waiting time M/M/S London Singapore olombo New Delhi nkara Islamabad Host Latitu de hicago N Edge Latitu de London N Singapore N olombo N New Delhi N nkara N Islamabad N TLE IV. Original host Longit ude W Longit ude W E E E E E Haversine andwidth distance (km) Gbps Haversine andwidth distance (km) Mbps Mbps Mbps Mbps Mbps Mbps LTENY OF HOST SERVER ND EH EDGE SERVER USING M/M/1 QUEUEING MODEL Propagat ion (ms ) Serializat ion (ms ) hicago Edge Propagat ion (ms ) Serializat ion London Singapore olombo New Delhi nkara Islamabad TLE V. Original host LTENY OF HOST SERVER ND EH EDGE SERVER USING M/M/S QUEUEING MODEL Propag ation Seriali zation hicago Edge Propag ation Seriali zation London Singapore olombo New Delhi nkara Islamabad TLE III. HVERSINE DISTNE ND NDWIDTH OF HOST SERVER ND EH EDGE SERVER In this paper, our proposed algorithm calculate the haversine distance between cloud user located at Kolkata to the original host at hicago and all the edge s located ISSN: IJET 752

5 International Journal of Eltronics ommunication and omputer Thnology (IJET) different places particular or neighbor to the user as shown in Table 3 [16]. It is found that the haversine distance of New Delhi from Kolkata is the smallest distance among the all edge s and the calculated propagation s are represented in the Figure 2 (a). Figure 2 (b) explains the serialization which is also dirtly proportional with available bandwidth between cloud user and edge that is calculated by our simulation process. ased upon periodically updated value of arrival rate and departure rate from the available in a particular DN, our proposed algorithm is calculated the queueing for M/M/S queueing model which is shown in Figure 3 (b) and also compare with the queueing for M/M/1 queueing model in Figure 3 (a). t last latency is calculated by our simulation algorithm and DN Provider selts the least latency available edge. From our data DN Provider selt the edge located at London using M/M/1 queueing mode land also choose the edge located at olombo using M/M/S queueing model as shown in Figure 4 and Figure 5 resptively and at last conntion is established between the cloud user and the aforesaid edge. In this paper we have studied that latency of selting edge at olombo using M/M/S queueing model is 47.1 ms faster than the latency of choosing edge located at London using M/M/1 queueing model.. omparison study using simulation graph (a) (b) Figure 3. M/M/1 queueing (a) and M/M/S queueing of different serevrs of a particular DN (a) (a) (b) Figure 4. of different s using M/M/1 queueing (a) and M/M/S queueing (b) of a particular DN (b) Figure 2. Propagation (a) and serialization (b) of different edge s of a particular DN Figure 5. ompare latency between M/M/1 and M/M/S queueing model of a particular DN ISSN: IJET 753

6 International Journal of Eltronics ommunication and omputer Thnology (IJET) V. ONLUSION In this paper, region based clustering and neighbor region seltion for a requested cloud user is challenging problem for selting set of multiple edge s of a particular ontent Delivery Network. Here we have shown latency based edge seltion algorithm based on propagation, serialization and queueing. Moreover we have also considered M/M/S queueing model for multiple s which grows the performance over using one reducing queue length and waiting time. Our simulation analysis and numerical result shows that latency based edge seltion guarantees the quality of service requirements of the cloud user s jobs and also makes maximum benefits for the DN Provider. REFERENES [1] M. rlitt and T. Jin, Workload haracterization Study of 1998 World up Web Site, IEEE Network, May/June 2000, page no(s) [2] l-m. Khan Pathan and R. uyya, Taxonomy and Survey of ontent Delivery Networks, Taxonomy.pdf [3] What is Network and Why Does It Matter? [4] S. Saroiu, K.P. Gummadi, R J. Dunn, S D. Gribble and H. M. Levy, n nalysis of Internet ontent Delivery Systems, NSF grants R , ITR and IIS [5] G. Pallis,. Vakali, K. Stamos,. Sidiropoulos, D. Katsaros, and Y. Manolopoulos, -based Objt Placement pproach in ontent Distribution Networks, national programs funded by the ΕΠΕΑΕΚ. [6] K. H. Wong, P. Y. Lau and S. Park, Using Surrogate Servers for ontent Delivery Network Infrastructure with Guaranteed QoS, JN, (Vol. 1: No. 1), March [7] T. Sai. Sowjanya, D. Praveen, K. Satish and. Rahiman, The Queueing Theory in loud omputing to Reduce the Waiting Time, IJSET, (Vol.1: Issue 3), pril 2011, page no(s) [8] Sinnott, R. W. Virtues of the Haversine, Sky and Telescope, (Vol.68: No. 2), 1984, page no(s) [9] J. bate and W. Whitt, "Transient behavior of the M/M/l queue: Starting at the origin", Queueing Systems, (Vol. 2: Issue. 1), March 1987, page no(s) [10] D. J. Daley and L. D. Servi, "Idle and busy periods in stable M / M / k queues," Journal of pplied Probability, (Vol.35: No. 4), 1998, page no(s) [11] Dr. J. Sztrik, asic Queueing Theory, University of Debren, Faculty of Informatics, Debren, 2012, page no(s). 17, 149. [12] Geography Student Reading Understanding Maps of Earth, NS ISS EarthKM [13] L. herkasova From Internet Data enters to Dataenters in the loud, HP Research Labs [14] D.. Verma, ontent Distribution Networks: n Engineering pproach, John Wiley & Sons, Inc., New York, US, [15] M. ndrews,. Shepherd,. Srinivasan, P. Winkler and F. Zane, lustering and seltion using passive monitoring, Proc. of IEEE INFOOM, NY, US, [16] D. Sarddar, S. Roy and R. ose, n Efficient Edge Servers Seltion in ontent Delivery Network Using Voronoi Diagram, IJRIT, (Vol. 2: No. 8), 2014 page no(s) ISSN: IJET 754