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In a traditional Mobile Cloud Computing (MCC), a stream of data produced by mobile users (MUs) is uploaded to the remote cloud for additional processing throughout the Internet. Though, due to long WAN distance it causes high End to End latency. With the intention of minimize the average response time and key constrained Service Delay (network and cloudlet Delay) for mobile users (MUs), offload their workloads to the geographically distributed cloudlets network, we propose the Multi-layer Latency Aware Workload Assignment Strategy (MLAWAS) to allocate MUs workloads into optimal cloudlets, Simulation results demonstrate that MLAWAS earns the minimum average response time as compared with two other existing strategies.

The design and portability of mobile devices make them minuscule, which is mandatory for them to be movable and accessible in order to carry them anywhere [

This is especially unsatisfactory in augmented reality applications, mobile multilayer gaming systems, social media and real time data processing, where a low response time is crucial to the MUs experience [

Recent works [

Although there has been a little research done in the deployment of cloudlets either at LAN (Local Area Network) or Metropolitan Area Network [

Despite the proposed MCC architecture, there are many challenges related to Service Delay such as, How to reach the optimal workload allocation in geo-distributed cloudlet network? Optimal cloudlet placement in geo-distributed network, dynamic partitioning and load balancing among cloudlets, dynamicity issues, related to the vulnerability to failures (e.g., hardware problems, opponent attacks), continuously movement of the users, or the fluctuation in the demands of the service model, QoS related heterogeneity possibly address for different applications becomes much more significant [

We cannot be formulated all the problems at the same time, in this paper, we have investigated that how to reach the optimal workload allocation in geo-distributed cloudlet network with lower Service Delay, this problem is very difficult and more interesting for mobile application in real practice. We have following contributions in this paper:

・ Proposed the cloudlet architecture with lower Service Delay.

・ Formulate an optimal workload allocation problem which suggests the optimal workload allocations geo-distributed cloudlet network toward the minimal response time with the constrained Service Delay.

・ Proposed Method MLAWAS to tackle the optimal workload allocation with lower Service Delay.

・ Simulation results demonstrate that our proposed method has better results than existing methods.

Application service can be seen in

Rest of the paper is planned as follows. Section 2, we discuss existing approaches related to Service Delay issue in ECC. Section 3 contains mathematical model, how to select optimal cloudlet for workload assignment regarding Service Delay in ECC, Section 4 clarifies how we utilize techniques chosen to lower the Service Delay in conjunction with mathematical model, Section 5 contains proposed method about how to minimize overall delay, Section 6 conducts simulation with results and Section 7 encloses the conclusion.

In the past few decades, the domain of mobile offloading task to clusters of computer cloudlet gained much attention due to its vital applications [

this area can be found in [

The Cuckoo framework that performs the offloading via application partitioning at runtime proposed by [

Cloudlet has been more effective techniques for power and energy consumption task offloading [

The Service Delay is the combination of Network Delay and Cloudlet Delay, its geometric representation is shown in

Sun and Ansari [

High profiles, mobile applications typically involve time consumption computation in the cloudlet and small input and output packets for transmission. Same time database driven mobile applications have short computation but long transmission, it means some mobile applications effect with Process Delay and some of them Network Delay [

Service Delay is the amalgam of Network Delay and Cloudlet Delay in order to minimize the average Service Delay; we propose the Mobile Cloud Computing architecture shown in

Let assume that, here we are taking M as mobile user, B base station and C cloudlet. The Network Delay, denoted as Tnet, When mobile user request for offload the task to cloudlet, it encompasses on 1: TTrans M!B, where as TTrans is Transmission Delay for uploading the application task offload request to related BS. 2: TTnet B!C, Network Delay from B related mobile user base station to associated mobile user C cloudlet of mobile user to serve the offloaded task. 3: TTnet B!C Network Delay for transmitting the results of task from C to B. 4: TTrans B!M, transmitting results from B to M, where total Network Delay. Whereas total Network Delay Tnet = TTrans M!B + TTrans B!M + Tnet B$C. Tnet B$C, Round Trip Time, Delay between related Base station to Cloudlet. In paper we are assuming our decision variables I, J, and K set of mobile users, base stations and cloudlets respectively. However Xik denotes as binary variable to designate whether the application workloads generate by the mobile user i are handled by associated cloudlet as following showing in Equation (1).

X = { 1 , if X i k = 1 0 , otherwise } (1)

Moreover t j k is Round Trip Time (RTT) between the base station j and the cloudlet k. Note the value of t j k (j6 = k) can be measured by SDN periodically [

Y = { 1 , if Y i j = 1 0 , otherwise } (2)

Denote Y i j is the binary identification to Mobile users i being in coverage of Base stations j, finally total average Network Delay is

T i n e t = ∑ i ∈ I ∑ j ∈ J Y i j t j k X i k . (3)

The cloudlet is the collection computer clusters, in simple words it is the macro data center. Proposed architecture is shown in

exponentially distributed with the average service time equal to 1 μ k where as

μ k is the overall average service rate of individual cloudlet k [

T i k c l o u d l e t = 1 μ k − ∑ i ∈ I λ i X i k (4)

Consequently, the average cloudlet delay of the MU i

T i k c l o u d l e t = ∑ k ∈ K T i k c l o u d l e t X i k = ∑ k ∈ K X i k μ k − ∑ i ∈ I λ i X i k (5)

・ The mobile user application workload to be executed is composed of a collection of independent tasks that have no dependency to each other, often called metatask Independent tasks that have no priorities, no deadline.

・ Approximations of execution time of the task (ETC) on individual machine in homogeneous cloudlets (HC) are known. The approximations must be supplied before a task submits for execution. The task mapping procedure is to be done in a batch mode manner.

・ The mapper runs on a separate machine and controls the execution of all jobs on all machines in the suite.

・ The task mapper is executed on an individual machine and manages the task execution on individual machine in heterogeneous computing suite.

・ Every individual machine is to assign one task at a time; therefore the order of assigning is First Come First Served.

・ Independent task or meta-tasks size, machine numbers in HC are known.

In proposed heuristic, the exact approximation of the task execution time on machine is known priori, and contained within execution time to compute matrix (ETC), however ETC (ti, mj) is the approximated execution task time on individual machine j. The main purpose of the task scheduling algorithm is to minimize average Cloudlet Delay by using ETC matrix replica [

τ i = ∑ k ∈ K ( ∑ j ∈ J Y i j t j k + 1 μ k − ∑ i ∈ I λ i X i k ) X i k (6)

The main purpose of the proposed architecture is to minimize Service Delay [

min z = X i k 1 [ I ] ∑ i ∈ I ∑ k ∈ K ( ∑ j ∈ J Y i j t j k + 1 μ k − ∑ i ∈ I λ i X i k ) X i k (7)

s . t . ∀ k ∈ K , μ k − ∑ i ∈ I λ i X i k > 0 (8)

∀ i ∈ I , ∑ k ∈ K X i k = 1 (9)

∀ i ∈ I , ∀ k ∈ K X i k ∈ [ 0 , 1 ] (10)

minimize the average response time of mobile users in the network, so Equation (7) ensures that average service of cloudlet to be less than the average rate of mobile user arrival rate for individual cloudlet so that system would be stable. Equation (8) is to guarantee that every mobile user is only served by one cloudlet whereas, Equation (9)-(10) are assignment of exactly one resource for each application and vice versa [

Theorem 1: τ i is an NP-hard problem; we can express that this is decision problem and well known as NP-complete. The decision problem τ i can be described as follows: given a positive value b, is it possible to find a feasible solution as follows: S = { X i , k | i ε I , k ε K } and this solution must less than the given variable b

τ i ∑ i ∈ I ∑ k ∈ K ( ∑ j ∈ J Y i j t j k + 1 μ k − ∑ i ∈ I λ i X i k ) < b (11)

and it is necessary the preceding condition must be satisfied the equation (7)-(10). Furthermore, we convert problem in the partition problem as we can manage application workload equally on multiple cloudlets so that the total response time of the application could be minimized. τ i is reduced and satisfy all constraints when the total execution ought to less than b.

In Algorithm 1 MLAWAS, we initialed the application workload into descending order. Proposed algorithm MLAWAS is an iterative in nature rather than sequential. Application workload is allocated based on optimal cloudlet or cloud sever thereby minimize the Service Delay while performing the application execution [

To cope with the end to end latency problem and optimal task assignment on homogeneous cloudlet servers we have proposed MLAWAS. Which determine the optimal cloudlets among all and try to allocate user maximum workload on the optimal server? Algorithm 1 starts with application submission that is workload i.e., coarse grained in the nature and follow Poisson process, we follow the same offloading mechanism as CloneCloud but with different with offloading decision. Before offloading we need to schedule optimal network based on given b value, it is noteworthy we know in advance the anticipation of network and cloud status before offloading. The fundamental objective of the Algorithm 2 is to optimize communication delay whereas, optimal task allocation occurs in Algorithm 3 with minimum process delay.

We have designed the Communication Delay sub Algorithm 2 based on Markov decision process theory. Where a is the mobile controller always take action, state could be targeted wireless network, whereas, each state via learning factor and policy algorithm try to produce best decision as optimize the score of the offloading. Further detail about basic markov decision theory can be detailed in [

In the Q * is the optimal solution for offloading with lower communication and improve the offloading performance.

In Algorithm 3 Process Delay we initialed the application workload into descending order. Proposed algorithm 3 is an iterative in nature rather than sequential. Application workload is allocated based on optimal cloudlet based on equation (12) and (13) or cloud sever thereby minimize the response time of an application [

min τ i = X i k 1 [ I ] ∑ i ∈ I ∑ k ∈ K ( ∑ j ∈ J Y i j t j k + 1 μ k − ∑ i ∈ I λ i X i k ) < b , (12)

min τ i = X i k 1 [ I ] ∑ i ∈ I ∑ k ∈ K ( ∑ j ∈ J Y i j t j k + 1 μ k − ∑ i ∈ I λ i X i k ) < λ i X i k , (13)

Our propose algorithm MALWAS is compared with baseline approaches, for example Full offloading (Full) [

The service time plays an important role in high performance offloading system, because it is initial time to start the application. The requirement of mobile application is started with short delay. Our proposed MLAWAS outperforms as compared to existing approach as shown in

We set the b value as a threshold value for process time. These values make sure the process delay of offloaded workload must be lower anticipated value (e.g., predefined value). We can observe from

The MLAWAS framework proposed in this paper we are optimizing the Service Delay. Whereas, Service Delay is the combination of cloudlet delay and network delay in geo-distributed network. Proposed algorithm MLAWAS optimizes the mobile user application workload to optimal cloudlet and cloud server in order to minimize the Service Delay.

The authors declare no conflicts of interest regarding the publication of this paper.

Sajnani, D.K., Mahesar, A.R., Lakhan, A. and Jamali, I.A. (2018) Latency Aware and Service Delay with Task Scheduling in Mobile Edge Computing. Communications and Network, 10, 127-141. https://doi.org/10.4236/cn.2018.104011