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Performance of integrated workload scheduling and prefetching in multimedia mobile cloud computing
Journal of Cloud Computing volume 7, Article number: 13 (2018)
Abstract
This paper focuses on an integrated workload scheduling and prefetching model in a multimedia mobile cloud computing environment to enhance the performance of response time and reduce the cost to process multimedia data. The response time and cost optimization problems are presented along with the computation resources such as virtual machines (VMs) allocation, workload conservation, queueing stability constraints, and to overcome the total response time and cost, a heuristic approach of workload scheduling method is proposed. The integrated workload scheduling at prefetcher and cloud are considered to study the effects of various parameters such as VM’s processing speed, prefetcher’s utilization, the user requests arrival rate. The performance analysis results reveal that the cost and transmission speed are directly relevant factors, meaning that, once the rate of data transmission is increasing, the cost is also increasing and vice versa. Hence, the time and cost efficient workload scheduling is essential to satisfy both delay and cost in prefetcher enabled multimedia cloud systems.
Introduction
Multimedia communication over the cellular network with minimum latency become critical with the ever increasing number of cellular users along with multimedia data traffic. The evolution of cloud computing technology, data transmission methods have changed. Since cellular devices (e.g., smart phones, tablets etc.) face some limitations such as power, memory and computing power, the cloud providers offer services such as scalable and reconfigurable computing, storage and network services [1, 2]. Moreover, the cloud computing is a technology with growing popularity for many organizations. The main advantages that lead to this popularity are unlimited storage, backup and recovery, cost efficiency, easy maintenance and quick deployment [1]. Even though cloud computing has made our life much easier, there are many challenges to face. Latency along with security is the most important factor to satisfy when it comes to delaysensitive transactions like banking, stock market and real time multimedia data transfer.
Another challenging issue is to process multimedia services in cellular network environment [3]. Cellular data transmission was so successful until battery lives could not keep up with the users’ need. The cellular users, mostly cell phone users do not have unlimited storage to store all the information they require. To overcome this challenge, mobile cloud computing was introduced [4, 5]. In mobile cloud computing, mobile users utilize cloud servers, known as virtual machines (VMs) to process high computational services such as real time multimedia data processing (e.g., video on demand (VoD), online gaming, etc). Utilizing cloud computing with mobile networks do not need to consider storage capacity and battery life. However, delay became a new challenge in mobile cloud computing. Prefetching was one of the techniques that was proposed to reduce the delay in multimedia delay sensitive mobile communication [6]. This technique can reduce the delay significantly if it is well designed and implemented.
This paper investigates the performance of response time and cost in the integrated workload scheduling and prefetching model in mobile cloud computing environment to process multimedia data requests. In this paper, we propose a heuristic based workload scheduling method which utilizes both cloud and prefetcher to distribute workload to minimize the total response service time and cost.
The remainder of this paper is organized as follows. In the section system model and assumptions, the prefetcher enabled multimedia cloud computing model, followed by the description of system model and problem formulation is presented. Response time and cost optimization problem sections present the analytical formulation of time and cost minimization problems along with a combined prefetcher in a cloud system. The solution of workload scheduling in multimedia cloud networks is discussed in Heuristic approach of workload scheduling section. Performance analysis section provides the results of our proposed model. Finally, conclusion section concludes the paper and provides some insights of future research in multimedia mobile cloud computing model.
Related work
The topic of cloud computing is undergoing a growing popularity among practitioners, and scientific researchers. The cloud provides services such as software, platform, infrastructure and these services are maintained by cloud providers (e.g., AWS, Google cloud platform, Microsoft azure etc.)[1, 2]. The cloud technologies help service providers to provide faster and more secure computing services to users. The vast computing resources led to the selection of cloud as the integrating stage for the mobile environment. The core of the cloud is data center, consisting of different type of the servers, e.g., master, computing, etc.
Authors in [7, 8] present a threetier architecture for multimedia data center which comprises three different types of servers; master, computing and transmission, as depicted in Fig 1. In the beginning phase, the master servers distribute the workload to the computing servers and the cloud provider utilizes different types of VM clusters to complete each service task. In the end phase, the transmission server allocates bandwidth resources to transfer the results to the users. One of the challenges considered in [7, 9] is to schedule the workload among the VM clusters in such a way that each service utilizes minimum number of computing resources (e.g., VMs) and simultaneously ensures the quality of experience (QoE) among users.
Authors in [10, 11] allocate VMs at both user level and task level. In [11], authors proposed a task based workload scheduling method where each multimedia service is decomposed into independent tasks. The authors proposed three types of workload scheduling methods (e.g., serial, parallel, and mixed) where one VM is allocated to one task. However, in [11] the authors did not consider the dependency among tasks, total independent paths and the scheduling weight among paths. The Bayesian based workload scheduling method, proposed in [12] resolves the aforementioned issue and utilizes both task and user level scheduling to minimize the mean service time.
Multimedia processing enforces new challenges to cloud computing, according to attributes such as being delay sensitive, high computation intensity and significant bandwidth demands [13]. For delay sensitive applications, the authors in [8] consider the total round trip time (RTT) (e.g., both service time and transmission time), whereas in [12] only service time is considered. Moreover, to minimize delay, authors in [6, 14] proposed cloudlets model to process multimedia service requests. The cloudlets represent a small scale data center which resides within the proximity to the users and minimize the transmission delay. On the other hand, authors in [15] consider data processing delay in the mobile edge computing model. Similar to the above concepts, we proposed the prefetcher enabled multimedia mobile cloud computing model, shown in Fig.1, where the prefetcher is located in close proximity to the users and contains high speed VMs and storage servers. The next section presents the prefetcher enabled multimedia cloud computing model.
System model and assumptions
Multimedia mobile cloud computing with prefetcher
We consider a prefetcher enabled multimedia cloud computing model, depicted in Fig. 1 to enhance the multimedia end user communication process. Introduced model consists of three main components: multimedia cloud provider, prefetcher and end users. Multimedia cloud provider follows the threetier architecture, presented in [7, 8, 10, 12]. The multimedia data center has a core which is called master server. Master server receives the portion of the workload sent to computing server. The workload scheduler and load balancer are responsible for distributing the workload over N server clusters for processing. In addition, we consider a prefetch updater which is the intelligent core of the transmission server which calculates the probability of the next call on the same request and sends a copy to prefetcher. Each server cluster receives a portion of the total workload and processes it with different processing speed until the entire workload is processed. Then prefetch updater gradually fills the storage clusters in it by sending a copy of the critical data for next call.
Prefetcher is located near to the users, contains storage clusters and limited number of high speed VM clusters compared to the cloud. Prefetcher performs two types of operations: caching and prefetching. The latter one is the main focus of this paper. For caching, we assume that the storage clusters store data which is more likely to be requested next. Once the memory is full, it will be overridden based on adaptive replacement algorithm (ARA) [16]. For prefetching, we develop an integrated model that utilizes workload scheduling and prefetching operation both in the prefetcher and in the cloud with minimum cost and time.
There are Z end users considered in the model, and the probability of end users reaching prefetcher is βλ versus the probability of reaching multimedia cloud which is (1−β)λ. β denotes the accessing probability of prefetcher and λ denotes the number of user requests arrival per second. All symbols and parameters in the optimization problem are listed in Abbreviation.
Response time optimization problem
We assume that the multimedia cloud data center supports Bayesian method based workload scheduling [12] where each multimedia service request is represented by directed acyclic graph (DAG) [11] and is decomposed into tasks. Hence, the service requests are modeled by parallel structure of tasks and interconnected by path. One VM is allocated to one task. Assume that M tasks are scheduled into each path and served by VM cluster i with scheduling weight w_{i}. Thus, \({\sum \nolimits }_{i=1}^{N}\theta _{ij}\) classi VMs work together as the classi virtual cluster with the mean service rate \({\sum \nolimits }_{i=1}^{N}{\sum \nolimits }_{j=1}^{M}\theta _{ij} \mu _{ij}\), and the service time of a request is assumed to be exponentially distributed with an average of \(\frac {1}{{\sum \nolimits }_{i=1}^{N}{\sum \nolimits }_{j=1}^{M}\theta _{ij} \mu _{ij}}\). Similar to [10], the service process of each path follows an M/M/1 queuing model. The average response time of path k can be calculated as:
The total mean response time is considered as the QoS requirement of the system and can be formulated as:
where β is the probability (or percentage of time) of using prefetcher in time slot t,
\(T_{Cloud} ={\sum \nolimits }_{k=1}^{K}T_{k}\) represents the total mean response time in the cloud and T_{PF} represents the total mean response time in the prefetcher. The prefetcher’s mean response time T_{PF} can be formulated as:
The response time optimization problem for workload scheduling in cloud system with prefetcher can be formulated as:
The objective of P1 is to minimize the total mean response time of the system. Constraints C1 and C3 illustrate that at time instance t, each VM serves only one task whereas constraints C2 and C4 indicate that one task needs more than one VM. Constraint C5 is the workload conservation constraint for cloud. Constraint C6 is the queuing stability constraint associated with cloud. Constraint C7 is the workload conservation constraint related to prefetcher, constraint C8 is prefetcher’s queuing stability constraint, and C9 shows the processing capacity relation between the VMs in multimediacloud and prefetcher. Note prefetcher is assumed to process the requests faster, γ times faster than the processor in multimediacloud.
Cost optimization problem
According to our system model in Fig. 1, there is delay associated with prefetcher’s functionality denoted by T_{$}(sec). The delay T_{$} was chosen in a way that it is exponentially increasing with γ and β in our model. This delay is a time frame that prefetcher requires to react towards the received request. The cost problem for workload scheduling presence of prefetcher can be formulated as follows in (5).
The cost optimization problem can be formulated as
The objective of P2 is to minimize the cost of the system. Constraint C10 is the cloud and prefetcher’s response time difference threshold constraint that ensures that the difference is always a positive number and less than a maximum.
Heuristic approach of workload scheduling
Since the response time (P1) and cost (P2) problem are known as a convex mixedinteger problem, it is not easy to solve with mathematical and theoretical solutions. Hence, we propose a heuristic (exhaustive search) approach to optimize the response time and cost problems. However, the access probability for prefetcher (β) in our cost optimization problem can be analytically found as follows:
In our assumption, initially, we consider a function of β to relate the cost problem to the probability of received workload in prefetcher, as well as replace the cloud’s and the prefetcher’s objective functions with A and A^{′} to simplify (6).
As it was discussed previously, heuristic approach for workload scheduling is proposed and algorithms are provided for prefetcher (Algorithm 1) and cloud (Algorithm 2). In every algorithm, there are several steps to be followed in order to achieve the optimized solution. The scheduling procedure of heuristic approach is shown in Fig. 2 and works as follows:
Prefetcher: The prefetcher executes Algorithm 1 to distribute the workload among high speed VMs to faster processing. In the integrated model, we assume that VMs in prefetcher are γ times faster than Cloud VMs. For workload scheduling, the prefetcher receives requests from users. At first, each multimedia service request is decomposed into tasks based on directed acyclic graph (DAG) method [11] and then the maximum number of path and Bayesian weight is estimated [12] for the allocation of VM clusters. In this process, if the prefetcher finds the requested results in the storage servers, the results are served to the users. Otherwise, the lowest cost VMs are selected with Bayesian scheduling weight until all the VMs are occupied. During the workload scheduling stage the access probability (β) and the remaining requests (λ_{i,cloud}) are estimated if any further processing needed for cloud.
Cloud: The cloud executes Algorithm 2 to distribute the workload among VMs in cloud. The cloud starts the workload scheduling when the requested results are not found in the prefetcher and all the prefetcher VMs are occupied with βλ_{i,PF} service requests. The remaining requests λ_{i,cloud} are scheduled at cloud with access probability (1−β). Similar to the prefetcher workload scheduling, the cloud decomposes each service into tasks based on DAG method, estimates the maximum number of path and schedules the workload among VMs using Bayesian weight.
Performance analysis
All simulations are written and done in Matlab scripts. The results are categorized based on whether the values of γ and β are fixed or changing. γ is the speed indicator in prefetcher’s processor and β is the distribution probability of received workload in prefetcher and accordingly (1−β) is the probability of workload reaching to multimedia cloud in our system model. The simulation variables and their value/range are depicted in Table 1.
Response time performance analysis
In this part of our study, the primary objective is the total response time minimization. Hence, we performed simulations while changing different variables to see how advantageous prefetching scheme is towards the goal. The main variables in our model are β, γ and λ. Accordingly, we discuss each scenario individually and analyse the results.
Response time study with fixed γ and changing β: We compare the proposed prefetching enabled cloud computing model with the former model [12] where prefetcher was not considered (β=0). The processing speed in prefetcher is assumed to be fixed (γ=4) which is four times faster than processing time in multimedia cloud.
As expected, the response time in the results is ascending, while using prefetcher benefits us by saving a significant amount of time in the long run. This means, instead of financially investing on data transmission, a portion of the investment can be done to improve and maintain the processor in prefetcher.
As presented in Fig. 3, the top curve represents the response time of multimedia cloud based on the arrival rate without considering prefetcher. According to Fig. 3, as the number of the requests/sec increases, there is a slight elevation in the curve which means requests will need slightly more time to process and send back the requests to the source. The curve with lower response time represents the solo use of prefetcher without considering any load being shared or sent to multimedia cloud. The results show that there is a remarkable performance enhancement in response time when only prefetcher is responding to requests. The reason behind the performance enhancement is because the requests already exist in the memory of the prefetcher and they just need to be located and sent back, versus in multimedia cloud, there is an extra step required to compute the request. Therefore, their response process in the prefetcher is significantly faster. In Fig. 4, metrics and increment units are considered to stay the same similar to the previous figure. The value of γ is also is fixed and deemed to be four while β is changing.
In Fig. 4, we increase the workload distribution probability from 0 to 1 for 0.2 units in every increase to show how it would affect the response time as we move toward pushing more workload to prefetcher and less to multimedia cloud. As expected, when the percentage of access probability of prefetcher increases, the response time drops, and it is minimum in the purple curve when β=1. All the results show that, with the assumption of γ=4, the processor in prefetcher is running four times faster than in multimedia cloud.
Response time study on fixed β and changing γ: It is also shown that Fig. 5, with fixed β regardless of whether we increase the prefetcher utilization factor, the increase is still linear and at all various utilization levels there is no difference between using cloud and prefetcher.
Even though, increasing processing speed in prefetcher decreases response time, it also increases the cost (processing delay) associated with processing time. The delay is also considered and optimized using proposed heuristic approach. Results are shown and compared later. Initially, we perform simulations on prefetcher while considering the impact of delay and later, after considering the impact of cost (processing delay), we compare the results and show how an integrated model using prefetcher and cloud computing is beneficial towards minimization of total response time. Note that in our model we only study the dynamic communication of multimedia cloud, prefetcher and end users. We will also find the optimal β for different situations, since it changes based on circumstances from time to time.
In this part, we study the response time behaviour of the arrival rate λ=[1000,3000,500] and, γ=[1,4,1] at the probability distribution of β=0.6. As presented in Fig. 6, we observe the speed enhancement while speed multiplier γ is increasing as expected, the shortest response time belongs to the bottom curve with γ=4. Another noticeable result in Fig. 6 is that the response time gap between each curve is significantly decreasing as a result of speed enhancement. Here all the results and the performance analysis are on using prefetcher without considering the delay associated with its processor.
Response time study while β is changing: Figure 7 could be one of the most interesting comparisons. In this figure we have total response time (sec) on Y axis versus workload distribution probability (β) changing on X axis from 0.2 to 1 with 0.1 increment unit. As can be interpreted, when the response time increases and the load is pushed toward the prefetcher, only the top line is constant value at γ=1, since is it running at the same speed as the cloud and the other curves are all descending. Descending curves show that the response time decreases as the processor’s processing time increases and it reaches its minimum on the bottom curve.
As the workload is leaning more towards prefetcher, the processing speed is highlighted more than before. As we can observe in Fig. 7, the bottom curve has the minimum total response time, and the total response time is minimized when entire workload is sent to prefetcher. Accordingly, a faster processor is needed which means higher cost, and we will study that in the next part.
Performance analysis of cost
In this section, we investigate the cost improvements based on the simulation results. We will change the values of β,γ and λ to observe the changes in total cost and total response time.
Total cost improvement while PF utilization is β=0.6 and λ is increasing: In Fig. 8, we observe the simulation result when only 0.6 (β=60%) of the total workload is sent to prefetcher and arrival rate is increasing. γ is also considered to be increasing from 0 to 50. As expected, the total cost is increasing while the number of requests/sec increases. This cost is associated with the delay that occurs when prefetcher is processing the requests.
As presented, the initial γ is set to be 1 in all curves, which means processor of the multimedia cloud and prefetcher are both running at the same speed.
Total cost improvement while λ=3000 and PF utilization is increasing: As presented in Fig. 9, in this simulation arrival rate is set at 3000 while the use of prefetcher is increasing. Total cost is linear and constant when there is no prefetcher. While prefetcher is operating, initially β=1 has the minimum total cost and, as γ is increasing β=0.6 takes its places and becomes the overall optimum β for this particular instance.
The value of optimal β varies as we change the simulation criteria and change the arrival rate or prefetcher’s processing speed. For example, if we take γ=50, the optimum value for β is 0.4 and similarly optimal β changes its place based on the condition applied in the simulation. In other words, the percentage of using prefetcher varies from time to time, and it is not always best to allocate the entire workload to prefetcher even though it operates faster than cloud.
Response time Vs. β while PF is processing at fixed speed γ=15: One of our goals is to find the optimal response time for the amount of workload sent to prefetcher. In order to study that, we performed a simulation where arrival rate is set to be 2000 requests/sec and γ=15. Values of mare considered to be 0.25 and m^{′} is considered as 1.
Based on the results, response time is optimum when (β=0.48) almost half of the workload is sent to prefetcher when γ=15. The theoretical optimum β is also calculated and marked on Fig. 10 to show that both simulation and theoretical results are aligned at the same point. Staring from β=0, response time (sec) is considerably high, and it decreases slowly while the use of prefetcher increase up to 0.5, and after that, it starts rising again until it hits β=1. It is understandable that if only prefetcher is used, it is not the optimum condition, since response time and accordingly costs are increasing.
In Figs. 11 and 12, we investigate the impact of changing γ and λ, on optimum β. As presented, in both figures theoretically calculated optimum β is marked as well. As we can observe from the trends as increase in the arrival rate of the response time slightly increases, the β optimum point moves to the right. In other words, if the amount of the requests/sec is increasing, it is more beneficial to push the load toward prefetcher.
As shown in Fig. 11, all the curves for response time are slowly decreasing to the optimal point, and then it increases. The gap between all curves is also shrinking as we move away from the optimal point using (6) and we allocate more load toward prefetcher to handle. The optimum point of β aligns with the theoretical value β in all the curves. The response time gap between curves is also maximized at the optimal point and minimized at β=1.
In Fig. 12, we study the changing trend of the curves according to the processing speed, while arrival rate is fixed at λ=2000. Unlike the previous figure, in this figure, the gap between curves is slowly increasing as we push more load toward prefetcher.
As we push more load to prefetcher, initial response time decreases to the optimal point and after that, increases to maximum where all the workload is allocated to prefetcher. All the curves start basically from the same starting point and initially, their response time decrease is linear to β=0.5. Using prefetcher is most advantageous at prefetcher’s utilization optimum point. Similar to the previous figure, the optimum point of β aligns with theoretical value β in all the curves. In both Figs. 11 and 12, we can observe the impact of varying γ and λ when β=[0,1].
Optimal and heuristic optimization performance analysis
Figure 13 compares optimal and heuristic optimization approaches with and without cost factor. We performed both optimal and heuristic approaches on the total response time. In this simulation, prefetcher processing speed, γ is at 3, prefetcher utilization factor, β, is set at 0.6 while arrival rate changes from 4000 requests/sec to 9000 requests/sec. The heuristic scheme has longer response time than the optimal scheme with a small difference. The optimal scheme weights vary with the change of λ, while weights in the heuristic scheme are constant since it is normalized by the service rate.
Conclusion and future works
Our previous work was done with the primary objective in convex workload scheduling problem for cloud based multimedia applications. In this paper, we propose path selection algorithm to reduce response time by adjusting weights among the paths of tasks both in prefetcher and cloud. The considered problem was more complex compared to previous similar work, and hence the heuristic algorithm was considered to be the solution. In this study, we used a prefetcher that adopts and learns new frequent requests to cache and store them prior to the actual request. Even though our goal was to design and develop an intelligent prefetcher, there are times that request does not exist within the storage of the prefetcher (missratio). The multimedia cloud communication method is becoming one of the major focuses of researchers nowadays, yet intelligent prefetching and caching learning schemes can be improved and reducing missratio of intelligent caching schemes would be an interesting topic to work on in future studies.
Abbreviations
 ARA:

Adaptive Replacement Algorithm
 DAG:

Directed Acyclic Graph
 MCC:

Mobile Cloud Computing
 PF:

Prefetcher
 RTT:

Round Trip Time
 RT:

Response Time
 VM:

Virtual Machine
 M :

number of given tasks
 N :

number of VM clusters
 K :

maximum number of paths
 i :

VM indexing indicator
 j :

task indexing indicator
 k :

path indexing indicator
 β :

accessing probability associated with prefetcher
 λ :

arrival rate (requests/sec)
 μ :

mean servicing rate
 μ _{ i } :

represents the mean servicing rate for VM i
 γ :

processor’s speed multiplier
 α :

allocation index
 α _{ ij } :

represents the allocation index between the VM i and the task j for prefetcherθ allocation index
 𝜃 _{ ij } :

represents the allocation index between the VM i and the task j for cloud
 ω :

workload weight
 ω _{ i } :

is the workload weight associate with i^{th} VM cluster
 g(γ):

cost function(delay) associated with prefetcher
 C :

constant delay value in cost function
 m :

prefetcher processor’s speed increment indicator in the cost function
 m ^{′} :

prefetcher usability increment indicator in the cost function
References
Amazon Elastic Compute Cloud. https://aws.amazon.com/ec2/. Online; accessed 1July2017.
Bartolini C, El Kateb D, Le Traon Y, Hagen D (2018) Cloud providers viability. Electronic Markets. https://doi.org/10.1007/s1252501802847.
Harnal S, Chauhan RK (2016) Multimedia support from cloud computing: A review In: 2016 International Conference on Microelectronics, Computing and Communications (MicroCom), 1–6. https://doi.org/10.1109/MicroCom.2016.7522440.
Zhu W, Luo C, Wang J, Li S (2011) Multimedia cloud computing. IEEE Signal Processing Magazine 28(3):59–69. https://doi.org/10.1109/MSP.2011.940269.
Fernando N, Loke W, WennyRahayu S (2013) Data processing delay optimization in mobile edge computing 29(1):84–106. https://doi.org/10.1016/j.future.2012.05.023.
He Y, Yu FR, Zhao N, Leung VCM, Yin H (2017) Softwaredefined networks with mobile edge computing and caching for smart cities: A big data deep reinforcement learning approach. IEEE Communications Magazine 55(12):31–37. https://doi.org/10.1109/MCOM.2017.1700246.
Nan X, He Y, Guan L (2012) Optimal allocation of virtual machines for cloudbased multimedia applications. IEEE 14th International Workshop on Multimedia Signal Processing (MMSP):175–180.
Nan X, He Y, Guan L (2014) Towards optimal resource allocation for differentiated multimedia services in cloud computing environment. International Conference on Acoustics, IEEE, Speech and Signal Processing (ICASSP):684–688.
Gong W, Chen Z, Yan J, Qianjun S (2014) An optimal VM resource allocation for nearclientdatacenter for multimedia cloud. IEEE International Conference on Ubiquitous and Future Networks (ICUFN)l:149–254.
Nan X, He Y, Guan L (2013) Optimization of workload scheduling for multimedia cloud computing. IEEE International Symposium on Circuits and Systems (ISCAS):2872–2875.
Nan X, He Y, Guan L (2013) Optimal tasklevel scheduling for cloud based multimedia applications. International Conference on Acoustics, IEEE, Speech and Signal Processing (ICASSP):3771–3775.
Ferdouse L, Li M, Guan L, Anpalagan A (2016) Bayesian workload scheduling in multimedia cloud networks:83–88. https://doi.org/10.1109/CAMAD.2016.7790335.
Zhu W, Luo C, Wang J, Li S (2011) Multimedia cloud computing. IEEE Signal Processing Magazine 23(3):59–69.
Zhijun H, Grande RED, Boukerche A (2017) Towards efficient data access in mobile cloud computing using prefetching and caching In: 2017 IEEE International Conference on Communications (ICC), 1–6. https://doi.org/10.1109/ICC.2017.7997078.
Guangshun Li, JW Jiping Wang, Song J (2018) Data processing delay optimization in mobile edge computing:83–88. https://doi.org/10.1155/2018/6897523.
Megiddo N, Modha DS (2004) Outperforming lru with an adaptive replacement cache algorithm. Computer 37(4):58–65.
Acknowledgements
The authors thank Dr. X. Nan for his initial contributions.
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The work presented in this paper is based on KK MASc research and thesis, which was supervised by Prof. Anpalagan and Prof. Guan. LF is a PhD candidate and she contributed to this work with the system model and problem formulation, and writing a part of this paper. All the authors read and approved the final manuscript.
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K. Khorramnejad was a MASc student (201517) and L. Ferdouse is a PhD candidate (2016) in Ryerson University. L. Guan and A. Anpalagan are professors in the Department of Electrical and Computer Engineering, Ryerson University, Toronto, Canada.
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Khorramnejad, K., Ferdouse, L., Guan, L. et al. Performance of integrated workload scheduling and prefetching in multimedia mobile cloud computing. J Cloud Comp 7, 13 (2018). https://doi.org/10.1186/s1367701801156
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DOI: https://doi.org/10.1186/s1367701801156